import{s as b5,o as M5,n as L}from"../chunks/scheduler.25b97de1.js";import{S as T5,i as F5,g as i,s as n,r as p,A as C5,h as l,f as d,c as a,j as v,u as h,x as f,k as w,y as r,a as T,v as u,d as _,t as b,w as M}from"../chunks/index.d9030fc9.js";import{T as Nw}from"../chunks/Tip.baa67368.js";import{D as x}from"../chunks/Docstring.e257edda.js";import{C as k}from"../chunks/CodeBlock.e6cd0d95.js";import{E as $}from"../chunks/ExampleCodeBlock.20db4b6e.js";import{H as B,E as y5}from"../chunks/EditOnGithub.91d95064.js";function v5(y){let t,C=`If your <code>NewModelConfig</code> is a subclass of <a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, make sure its
<code>model_type</code> attribute is set to the same key you use when registering the config (here <code>&quot;new-model&quot;</code>).`,m,s,c=`Likewise, if your <code>NewModel</code> is a subclass of <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>, make sure its
<code>config_class</code> attribute is set to the same class you use when registering the model (here
<code>NewModelConfig</code>).`;return{c(){t=i("p"),t.innerHTML=C,m=n(),s=i("p"),s.innerHTML=c},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-13jhndi"&&(t.innerHTML=C),m=a(e),s=l(e,"P",{"data-svelte-h":!0}),f(s)!=="svelte-1nhlrlw"&&(s.innerHTML=c)},m(e,F){T(e,t,F),T(e,m,F),T(e,s,F)},p:L,d(e){e&&(d(t),d(m),d(s))}}}function w5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co (user-uploaded) and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-base-german-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If configuration file is in a directory (e.g., was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*).</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./test/bert_saved_model/&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Load a specific configuration file.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./test/bert_saved_model/my_configuration.json&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Change some config attributes when loading a pretrained config.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-uncased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>config, unused_kwargs = AutoConfig.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;google-bert/bert-base-uncased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>, foo=<span class="hljs-literal">False</span>, return_unused_kwargs=<span class="hljs-literal">True</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>unused_kwargs
{<span class="hljs-string">&#x27;foo&#x27;</span>: <span class="hljs-literal">False</span>}`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function x5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co (user-uploaded) and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;dbmdz/bert-base-german-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If vocabulary files are in a directory (e.g. tokenizer was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># tokenizer = AutoTokenizer.from_pretrained(&quot;./test/bert_saved_model/&quot;)</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download vocabulary from huggingface.co and define model-specific arguments</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;FacebookAI/roberta-base&quot;</span>, add_prefix_space=<span class="hljs-literal">True</span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function L5(y){let t,C="Passing <code>token=True</code> is required when you want to use a private model.";return{c(){t=i("p"),t.innerHTML=C},l(m){t=l(m,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15auxyb"&&(t.innerHTML=C)},m(m,s){T(m,t,s)},p:L,d(m){m&&d(t)}}}function k5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoFeatureExtractor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download feature extractor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>feature_extractor = AutoFeatureExtractor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If feature extractor files are in a directory (e.g. feature extractor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># feature_extractor = AutoFeatureExtractor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function $5(y){let t,C="Passing <code>token=True</code> is required when you want to use a private model.";return{c(){t=i("p"),t.innerHTML=C},l(m){t=l(m,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15auxyb"&&(t.innerHTML=C)},m(m,s){T(m,t,s)},p:L,d(m){m&&d(t)}}}function B5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download image processor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;google/vit-base-patch16-224-in21k&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If image processor files are in a directory (e.g. image processor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># image_processor = AutoImageProcessor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function j5(y){let t,C="Passing <code>token=True</code> is required when you want to use a private model.";return{c(){t=i("p"),t.innerHTML=C},l(m){t=l(m,"P",{"data-svelte-h":!0}),f(t)!=="svelte-15auxyb"&&(t.innerHTML=C)},m(m,s){T(m,t,s)},p:L,d(m){m&&d(t)}}}function A5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Qcm9jZXNzb3IlMEElMEElMjMlMjBEb3dubG9hZCUyMHByb2Nlc3NvciUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQXByb2Nlc3NvciUyMCUzRCUyMEF1dG9Qcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMmZhY2Vib29rJTJGd2F2MnZlYzItYmFzZS05NjBoJTIyKSUwQSUwQSUyMyUyMElmJTIwcHJvY2Vzc29yJTIwZmlsZXMlMjBhcmUlMjBpbiUyMGElMjBkaXJlY3RvcnklMjAoZS5nLiUyMHByb2Nlc3NvciUyMHdhcyUyMHNhdmVkJTIwdXNpbmclMjAqc2F2ZV9wcmV0cmFpbmVkKCcuJTJGdGVzdCUyRnNhdmVkX21vZGVsJTJGJykqKSUwQSUyMyUyMHByb2Nlc3NvciUyMCUzRCUyMEF1dG9Qcm9jZXNzb3IuZnJvbV9wcmV0cmFpbmVkKCUyMi4lMkZ0ZXN0JTJGc2F2ZWRfbW9kZWwlMkYlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download processor from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;facebook/wav2vec2-base-960h&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># If processor files are in a directory (e.g. processor was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># processor = AutoProcessor.from_pretrained(&quot;./test/saved_model/&quot;)</span>`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Z5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbC5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function R5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function G5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbCUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWwuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function P5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function S5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QXV0b01vZGVsLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function W5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModel.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function J5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JQcmVUcmFpbmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yUHJlVHJhaW5pbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function V5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JQcmVUcmFpbmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JQcmVUcmFpbmluZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclByZVRyYWluaW5nLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBURiUyMGNoZWNrcG9pbnQlMjBmaWxlJTIwaW5zdGVhZCUyMG9mJTIwYSUyMFB5VG9yY2glMjBtb2RlbCUyMChzbG93ZXIpJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9tb2RlbF9jb25maWcuanNvbiUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclByZVRyYWluaW5nLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX2NoZWNrcG9pbnQuY2twdC5pbmRleCUyMiUyQyUyMGZyb21fdGYlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function E5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclByZVRyYWluaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclByZVRyYWluaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function I5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function U5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUHJlVHJhaW5pbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JQcmVUcmFpbmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function N5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForPreTraining.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function q5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function X5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function D5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckNhdXNhbExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvckNhdXNhbExNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Q5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function H5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yQ2F1c2FsTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Y5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForCausalLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function z5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTWFza2VkTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function O5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function K5(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck1hc2tlZExNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function eq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function oq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTWFza2VkTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JNYXNrZWRMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function rq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMaskedLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function tq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtdDUlMkZ0NS1iYXNlJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VxMlNlcUxNLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function nq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/t5_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/t5_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function aq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS10NSUyRnQ1LWJhc2UlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlcTJTZXFMTS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function sq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/t5_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/t5_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function iq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxMlNlcUxNJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLXQ1JTJGdDUtYmFzZSUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function lq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;google-t5/t5-base&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/t5_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSeq2SeqLM.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/t5_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function dq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function mq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function cq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function fq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function gq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclNlcXVlbmNlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function pq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function hq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function uq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function _q(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvck11bHRpcGxlQ2hvaWNlLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function bq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Mq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTXVsdGlwbGVDaG9pY2UlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JNdWx0aXBsZUNob2ljZS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Tq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForMultipleChoice.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Fq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JOZXh0U2VudGVuY2VQcmVkaWN0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Cq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function yq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function vq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function wq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yTmV4dFNlbnRlbmNlUHJlZGljdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvck5leHRTZW50ZW5jZVByZWRpY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function xq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForNextSentencePrediction

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForNextSentencePrediction.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Lq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function kq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function $q(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Bq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBQeVRvcmNoJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwVGVuc29yRmxvdyUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B0X21vZGVsX2NvbmZpZy5qc29uJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JUb2tlbkNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGcHRfbW9kZWwlMkZiZXJ0X3B5dG9yY2hfbW9kZWwuYmluJTIyJTJDJTIwZnJvbV9wdCUzRFRydWUlMkMlMjBjb25maWclM0Rjb25maWclMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function jq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVG9rZW5DbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclRva2VuQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Aq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForTokenClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Zq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Rq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Gq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Pq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Sq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEZsYXhBdXRvTW9kZWxGb3JRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Wq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Jq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEZXB0aEVzdGltYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckRlcHRoRXN0aW1hdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDepthEstimation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Vq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDepthEstimation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDepthEstimation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Eq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JJbWFnZUNsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Iq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Uq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Nq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function qq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9ySW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvckltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Xq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Dq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaWRlb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JWaWRlb0NsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVideoClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Qq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVideoClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVideoClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Hq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JNYXNrZWRJbWFnZU1vZGVsaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JNYXNrZWRJbWFnZU1vZGVsaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Yq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForMaskedImageModeling.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function zq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvck1hc2tlZEltYWdlTW9kZWxpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yTWFza2VkSW1hZ2VNb2RlbGluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Oq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForMaskedImageModeling

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForMaskedImageModeling.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Kq(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JPYmplY3REZXRlY3Rpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvck9iamVjdERldGVjdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function e4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForObjectDetection.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function o4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9ySW1hZ2VTZWdtZW50YXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function r4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbWFnZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwbW9kZWwlMjBhbmQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JJbWFnZVNlZ21lbnRhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBJTBBJTIzJTIwVXBkYXRlJTIwY29uZmlndXJhdGlvbiUyMGR1cmluZyUyMGxvYWRpbmclMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckltYWdlU2VnbWVudGF0aW9uLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBURiUyMGNoZWNrcG9pbnQlMjBmaWxlJTIwaW5zdGVhZCUyMG9mJTIwYSUyMFB5VG9yY2glMjBtb2RlbCUyMChzbG93ZXIpJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnRmX21vZGVsJTJGYmVydF90Zl9tb2RlbF9jb25maWcuanNvbiUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckltYWdlU2VnbWVudGF0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjIuJTJGdGZfbW9kZWwlMkZiZXJ0X3RmX2NoZWNrcG9pbnQuY2twdC5pbmRleCUyMiUyQyUyMGZyb21fdGYlM0RUcnVlJTJDJTIwY29uZmlnJTNEY29uZmlnJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForImageSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForImageSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function t4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTZW1hbnRpY1NlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yU2VtYW50aWNTZWdtZW50YXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function n4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSemanticSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function a4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclNlbWFudGljU2VnbWVudGF0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function s4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSemanticSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSemanticSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function i4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JJbnN0YW5jZVNlZ21lbnRhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9ySW5zdGFuY2VTZWdtZW50YXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForInstanceSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function l4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForInstanceSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForInstanceSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function d4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JVbml2ZXJzYWxTZWdtZW50YXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclVuaXZlcnNhbFNlZ21lbnRhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForUniversalSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function m4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForUniversalSegmentation

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForUniversalSegmentation.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function c4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function f4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function g4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclplcm9TaG90SW1hZ2VDbGFzc2lmaWNhdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JaZXJvU2hvdEltYWdlQ2xhc3NpZmljYXRpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function p4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForZeroShotImageClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForZeroShotImageClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function h4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JaZXJvU2hvdE9iamVjdERldGVjdGlvbiUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yWmVyb1Nob3RPYmplY3REZXRlY3Rpb24uZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function u4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForZeroShotObjectDetection

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForZeroShotObjectDetection.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function _4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0NsYXNzaWZpY2F0aW9uJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JBdWRpb0NsYXNzaWZpY2F0aW9uLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function b4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function M4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckF1ZGlvQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yQXVkaW9DbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function T4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForAudioClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForAudioClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function F4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb0ZyYW1lQ2xhc3NpZmljYXRpb24lMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckF1ZGlvRnJhbWVDbGFzc2lmaWNhdGlvbi5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioFrameClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function C4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioFrameClassification

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioFrameClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function y4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JDVEMlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckNUQy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCTC

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function v4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForCTC

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCTC.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function w4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTcGVlY2hTZXEyU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function x4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function L4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function k4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function $4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yU3BlZWNoU2VxMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclNwZWVjaFNlcTJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function B4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForSpeechSeq2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForSpeechSeq2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function j4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JBdWRpb1hWZWN0b3IlMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvckF1ZGlvWFZlY3Rvci5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioXVector

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function A4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForAudioXVector

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForAudioXVector.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Z4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JUYWJsZVF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGdGFwYXMtYmFzZS1maW5ldHVuZWQtd3RxJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yVGFibGVRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function R4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/tapas_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForTableQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/tapas_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function G4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclRhYmxlUXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZ0YXBhcy1iYXNlLWZpbmV0dW5lZC13dHElMjIpJTBBbW9kZWwlMjAlM0QlMjBURkF1dG9Nb2RlbEZvclRhYmxlUXVlc3Rpb25BbnN3ZXJpbmcuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function P4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForTableQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/tapas-base-finetuned-wtq&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/tapas_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForTableQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/tapas_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function S4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyaW1waXJhJTJGbGF5b3V0bG0tZG9jdW1lbnQtcWElMjIlMkMlMjByZXZpc2lvbiUzRCUyMjUyZTAxYjMlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function W4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/layoutlm_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForDocumentQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/layoutlm_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function J4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckRvY3VtZW50UXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJpbXBpcmElMkZsYXlvdXRsbS1kb2N1bWVudC1xYSUyMiUyQyUyMHJldmlzaW9uJTNEJTIyNTJlMDFiMyUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yRG9jdW1lbnRRdWVzdGlvbkFuc3dlcmluZy5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function V4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvckRvY3VtZW50UXVlc3Rpb25BbnN3ZXJpbmclMEElMEElMjMlMjBEb3dubG9hZCUyMG1vZGVsJTIwYW5kJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJpbXBpcmElMkZsYXlvdXRsbS1kb2N1bWVudC1xYSUyMiUyQyUyMHJldmlzaW9uJTNEJTIyNTJlMDFiMyUyMiklMEElMEElMjMlMjBVcGRhdGUlMjBjb25maWd1cmF0aW9uJTIwZHVyaW5nJTIwbG9hZGluZyUwQW1vZGVsJTIwJTNEJTIwVEZBdXRvTW9kZWxGb3JEb2N1bWVudFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJpbXBpcmElMkZsYXlvdXRsbS1kb2N1bWVudC1xYSUyMiUyQyUyMHJldmlzaW9uJTNEJTIyNTJlMDFiMyUyMiUyQyUyMG91dHB1dF9hdHRlbnRpb25zJTNEVHJ1ZSklMEFtb2RlbC5jb25maWcub3V0cHV0X2F0dGVudGlvbnMlMEElMEElMjMlMjBMb2FkaW5nJTIwZnJvbSUyMGElMjBQeVRvcmNoJTIwY2hlY2twb2ludCUyMGZpbGUlMjBpbnN0ZWFkJTIwb2YlMjBhJTIwVGVuc29yRmxvdyUyMG1vZGVsJTIwKHNsb3dlciklMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGcHRfbW9kZWwlMkZsYXlvdXRsbV9wdF9tb2RlbF9jb25maWcuanNvbiUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yRG9jdW1lbnRRdWVzdGlvbkFuc3dlcmluZy5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyLiUyRnB0X21vZGVsJTJGbGF5b3V0bG1fcHl0b3JjaF9tb2RlbC5iaW4lMjIlMkMlMjBmcm9tX3B0JTNEVHJ1ZSUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForDocumentQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;impira/layoutlm-document-qa&quot;</span>, revision=<span class="hljs-string">&quot;52e01b3&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/layoutlm_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForDocumentQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/layoutlm_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function E4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXN1YWxRdWVzdGlvbkFuc3dlcmluZyUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmRhbmRlbGluJTJGdmlsdC1iMzItZmluZXR1bmVkLXZxYSUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbEZvclZpc3VhbFF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVisualQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function I4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVisualQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;dandelin/vilt-b32-finetuned-vqa&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/vilt_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVisualQuestionAnswering.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/vilt_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function U4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWxGb3JWaXNpb24yU2VxJTBBJTBBJTIzJTIwRG93bmxvYWQlMjBjb25maWd1cmF0aW9uJTIwZnJvbSUyMGh1Z2dpbmdmYWNlLmNvJTIwYW5kJTIwY2FjaGUuJTBBY29uZmlnJTIwJTNEJTIwQXV0b0NvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWJhc2UtY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JWaXNpb24yU2VxLmZyb21fY29uZmlnKGNvbmZpZyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function N4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a TF checkpoint file instead of a PyTorch model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./tf_model/bert_tf_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./tf_model/bert_tf_checkpoint.ckpt.index&quot;</span>, from_tf=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function q4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBURkF1dG9Nb2RlbEZvclZpc2lvbjJTZXElMEElMEElMjMlMjBEb3dubG9hZCUyMGNvbmZpZ3VyYXRpb24lMjBmcm9tJTIwaHVnZ2luZ2ZhY2UuY28lMjBhbmQlMjBjYWNoZS4lMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUtYmVydCUyRmJlcnQtYmFzZS1jYXNlZCUyMiklMEFtb2RlbCUyMCUzRCUyMFRGQXV0b01vZGVsRm9yVmlzaW9uMlNlcS5mcm9tX2NvbmZpZyhjb25maWcp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function X4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, TFAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFAutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function D4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBGbGF4QXV0b01vZGVsRm9yVmlzaW9uMlNlcSUwQSUwQSUyMyUyMERvd25sb2FkJTIwY29uZmlndXJhdGlvbiUyMGZyb20lMjBodWdnaW5nZmFjZS5jbyUyMGFuZCUyMGNhY2hlLiUwQWNvbmZpZyUyMCUzRCUyMEF1dG9Db25maWcuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRmxheEF1dG9Nb2RlbEZvclZpc2lvbjJTZXEuZnJvbV9jb25maWcoY29uZmlnKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_config(config)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function Q4(y){let t,C="Examples:",m,s,c;return s=new k({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, FlaxAutoModelForVision2Seq

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Download model and configuration from huggingface.co and cache.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Update configuration during loading</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>, output_attentions=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.config.output_attentions
<span class="hljs-literal">True</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Loading from a PyTorch checkpoint file instead of a TensorFlow model (slower)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./pt_model/bert_pt_model_config.json&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxAutoModelForVision2Seq.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;./pt_model/bert_pytorch_model.bin&quot;</span>, from_pt=<span class="hljs-literal">True</span>, config=config
<span class="hljs-meta">... </span>)`,wrap:!1}}),{c(){t=i("p"),t.textContent=C,m=n(),p(s.$$.fragment)},l(e){t=l(e,"P",{"data-svelte-h":!0}),f(t)!=="svelte-kvfsh7"&&(t.textContent=C),m=a(e),h(s.$$.fragment,e)},m(e,F){T(e,t,F),T(e,m,F),u(s,e,F),c=!0},p:L,i(e){c||(_(s.$$.fragment,e),c=!0)},o(e){b(s.$$.fragment,e),c=!1},d(e){e&&(d(t),d(m)),M(s,e)}}}function H4(y){let t,C,m,s,c,e,F,HS=`In many cases, the architecture you want to use can be guessed from the name or the path of the pretrained model you
are supplying to the <code>from_pretrained()</code> method. AutoClasses are here to do this job for you so that you
automatically retrieve the relevant model given the name/path to the pretrained weights/config/vocabulary.`,Ay,yd,YS=`Instantiating one of <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoConfig">AutoConfig</a>, <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel">AutoModel</a>, and
<a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a> will directly create a class of the relevant architecture. For instance`,Zy,vd,Ry,wd,zS='will create a model that is an instance of <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertModel">BertModel</a>.',Gy,xd,OS="There is one class of <code>AutoModel</code> for each task, and for each backend (PyTorch, TensorFlow, or Flax).",Py,Ld,Sy,kd,KS=`Each of the auto classes has a method to be extended with your custom classes. For instance, if you have defined a
custom class of model <code>NewModel</code>, make sure you have a <code>NewModelConfig</code> then you can add those to the auto
classes like this:`,Wy,$d,Jy,Bd,eW="You will then be able to use the auto classes like you would usually do!",Vy,ys,Ey,jd,Iy,me,Ad,qw,bh,oW=`This is a generic configuration class that will be instantiated as one of the configuration classes of the library
when created with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoConfig.from_pretrained">from_pretrained()</a> class method.`,Xw,Mh,rW="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Dw,Eo,Zd,Qw,Th,tW="Instantiate one of the configuration classes of the library from a pretrained model configuration.",Hw,Fh,nW=`The configuration class to instantiate is selected based on the <code>model_type</code> property of the config object that
is loaded, or when it’s missing, by falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Yw,Ch,aW='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> (ALBERT model)</li> <li><strong>align</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> (ALIGN model)</li> <li><strong>altclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> (AltCLIP model)</li> <li><strong>audio-spectrogram-transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> (Audio Spectrogram Transformer model)</li> <li><strong>autoformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/autoformer#transformers.AutoformerConfig">AutoformerConfig</a> (Autoformer model)</li> <li><strong>bark</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bark#transformers.BarkConfig">BarkConfig</a> (Bark model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> (BART model)</li> <li><strong>beit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> (BEiT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> (BERT model)</li> <li><strong>bert-generation</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> (Bert Generation model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> (BioGpt model)</li> <li><strong>bit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitConfig">BitConfig</a> (BiT model)</li> <li><strong>blenderbot</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> (BlenderbotSmall model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> (BLIP-2 model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> (BLOOM model)</li> <li><strong>bridgetower</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bridgetower#transformers.BridgeTowerConfig">BridgeTowerConfig</a> (BridgeTower model)</li> <li><strong>bros</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bros#transformers.BrosConfig">BrosConfig</a> (BROS model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> (CamemBERT model)</li> <li><strong>canine</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> (CANINE model)</li> <li><strong>chinese_clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> (Chinese-CLIP model)</li> <li><strong>chinese_clip_vision_model</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPVisionConfig">ChineseCLIPVisionConfig</a> (ChineseCLIPVisionModel model)</li> <li><strong>clap</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clap#transformers.ClapConfig">ClapConfig</a> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> (CLIP model)</li> <li><strong>clip_vision_model</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPVisionConfig">CLIPVisionConfig</a> (CLIPVisionModel model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> (CLIPSeg model)</li> <li><strong>clvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clvp#transformers.ClvpConfig">ClvpConfig</a> (CLVP model)</li> <li><strong>code_llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> (CodeGen model)</li> <li><strong>cohere</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cohere#transformers.CohereConfig">CohereConfig</a> (Cohere model)</li> <li><strong>conditional_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> (Conditional DETR model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> (ConvBERT model)</li> <li><strong>convnext</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> (ConvNeXTV2 model)</li> <li><strong>cpmant</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> (CPM-Ant model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> (CTRL model)</li> <li><strong>cvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> (CvT model)</li> <li><strong>data2vec-audio</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> (Data2VecAudio model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> (Data2VecText model)</li> <li><strong>data2vec-vision</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> (Data2VecVision model)</li> <li><strong>dbrx</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dbrx#transformers.DbrxConfig">DbrxConfig</a> (DBRX model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> (DeBERTa-v2 model)</li> <li><strong>decision_transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> (Decision Transformer model)</li> <li><strong>deformable_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> (Deformable DETR model)</li> <li><strong>deit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> (DeiT model)</li> <li><strong>depth_anything</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/depth_anything#transformers.DepthAnythingConfig">DepthAnythingConfig</a> (Depth Anything model)</li> <li><strong>deta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> (DETA model)</li> <li><strong>detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> (DETR model)</li> <li><strong>dinat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> (DiNAT model)</li> <li><strong>dinov2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> (DINOv2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> (DistilBERT model)</li> <li><strong>donut-swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> (DonutSwin model)</li> <li><strong>dpr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> (DPR model)</li> <li><strong>dpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> (DPT model)</li> <li><strong>efficientformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> (EfficientFormer model)</li> <li><strong>efficientnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> (EfficientNet model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> (ELECTRA model)</li> <li><strong>encodec</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/encodec#transformers.EncodecConfig">EncodecConfig</a> (EnCodec model)</li> <li><strong>encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> (Encoder decoder model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> (ERNIE model)</li> <li><strong>ernie_m</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> (ESM model)</li> <li><strong>falcon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> (Falcon model)</li> <li><strong>fastspeech2_conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fastspeech2_conformer#transformers.FastSpeech2ConformerConfig">FastSpeech2ConformerConfig</a> (FastSpeech2Conformer model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> (FlauBERT model)</li> <li><strong>flava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> (FLAVA model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> (FNet model)</li> <li><strong>focalnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> (FocalNet model)</li> <li><strong>fsmt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> (FairSeq Machine-Translation model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> (Funnel Transformer model)</li> <li><strong>fuyu</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fuyu#transformers.FuyuConfig">FuyuConfig</a> (Fuyu model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> (Gemma model)</li> <li><strong>git</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitConfig">GitConfig</a> (GIT model)</li> <li><strong>glpn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> (GLPN model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> (GPT NeoX model)</li> <li><strong>gpt_neox_japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> (GPT NeoX Japanese model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> (GPT-J model)</li> <li><strong>gptsan-japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> (GPTSAN-japanese model)</li> <li><strong>graphormer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> (Graphormer model)</li> <li><strong>grounding-dino</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/grounding-dino#transformers.GroundingDinoConfig">GroundingDinoConfig</a> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> (GroupViT model)</li> <li><strong>hubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> (Hubert model)</li> <li><strong>ibert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> (I-BERT model)</li> <li><strong>idefics</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> (IDEFICS model)</li> <li><strong>idefics2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2Config">Idefics2Config</a> (Idefics2 model)</li> <li><strong>imagegpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> (ImageGPT model)</li> <li><strong>informer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/informer#transformers.InformerConfig">InformerConfig</a> (Informer model)</li> <li><strong>instructblip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/instructblip#transformers.InstructBlipConfig">InstructBlipConfig</a> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/instructblipvideo#transformers.InstructBlipVideoConfig">InstructBlipVideoConfig</a> (InstructBlipVideo model)</li> <li><strong>jamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaConfig">JambaConfig</a> (Jamba model)</li> <li><strong>jetmoe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeConfig">JetMoeConfig</a> (JetMoe model)</li> <li><strong>jukebox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jukebox#transformers.JukeboxConfig">JukeboxConfig</a> (Jukebox model)</li> <li><strong>kosmos-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/kosmos-2#transformers.Kosmos2Config">Kosmos2Config</a> (KOSMOS-2 model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> (LayoutLMv3 model)</li> <li><strong>led</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> (LED model)</li> <li><strong>levit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> (LeViT model)</li> <li><strong>lilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> (LLaMA model)</li> <li><strong>llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava#transformers.LlavaConfig">LlavaConfig</a> (LLaVa model)</li> <li><strong>llava_next</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava_next#transformers.LlavaNextConfig">LlavaNextConfig</a> (LLaVA-NeXT model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> (Longformer model)</li> <li><strong>longt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> (LongT5 model)</li> <li><strong>luke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> (LUKE model)</li> <li><strong>lxmert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> (LXMERT model)</li> <li><strong>m2m_100</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> (M2M100 model)</li> <li><strong>mamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaConfig">MambaConfig</a> (Mamba model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> (Marian model)</li> <li><strong>markuplm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> (MarkupLM model)</li> <li><strong>mask2former</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> (Mask2Former model)</li> <li><strong>maskformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> (MaskFormer model)</li> <li><strong>maskformer-swin</strong> — <code>MaskFormerSwinConfig</code> (MaskFormerSwin model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> (mBART model)</li> <li><strong>mctct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> (M-CTC-T model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> (Megatron-BERT model)</li> <li><strong>mgp-str</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mgp-str#transformers.MgpstrConfig">MgpstrConfig</a> (MGP-STR model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> (Mistral model)</li> <li><strong>mixtral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralConfig">MixtralConfig</a> (Mixtral model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> (MobileBERT model)</li> <li><strong>mobilenet_v1</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> (MobileViTV2 model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> (MPNet model)</li> <li><strong>mpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> (MPT model)</li> <li><strong>mra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraConfig">MraConfig</a> (MRA model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> (MT5 model)</li> <li><strong>musicgen</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/musicgen#transformers.MusicgenConfig">MusicgenConfig</a> (MusicGen model)</li> <li><strong>musicgen_melody</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/musicgen_melody#transformers.MusicgenMelodyConfig">MusicgenMelodyConfig</a> (MusicGen Melody model)</li> <li><strong>mvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> (MVP model)</li> <li><strong>nat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nat#transformers.NatConfig">NatConfig</a> (NAT model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> (Nezha model)</li> <li><strong>nllb-moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> (NLLB-MOE model)</li> <li><strong>nougat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> (Nougat model)</li> <li><strong>nystromformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> (Nyströmformer model)</li> <li><strong>olmo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/olmo#transformers.OlmoConfig">OlmoConfig</a> (OLMo model)</li> <li><strong>oneformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> (OneFormer model)</li> <li><strong>open-llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> (OpenLlama model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> (OPT model)</li> <li><strong>owlv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlv2#transformers.Owlv2Config">Owlv2Config</a> (OWLv2 model)</li> <li><strong>owlvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> (OWL-ViT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/paligemma#transformers.PaliGemmaConfig">PaliGemmaConfig</a> (PaliGemma model)</li> <li><strong>patchtsmixer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/patchtsmixer#transformers.PatchTSMixerConfig">PatchTSMixerConfig</a> (PatchTSMixer model)</li> <li><strong>patchtst</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/patchtst#transformers.PatchTSTConfig">PatchTSTConfig</a> (PatchTST model)</li> <li><strong>pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> (Pegasus model)</li> <li><strong>pegasus_x</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> (PEGASUS-X model)</li> <li><strong>perceiver</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> (Perceiver model)</li> <li><strong>persimmon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonConfig">PersimmonConfig</a> (Persimmon model)</li> <li><strong>phi</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiConfig">PhiConfig</a> (Phi model)</li> <li><strong>phi3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3Config">Phi3Config</a> (Phi3 model)</li> <li><strong>pix2struct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pix2struct#transformers.Pix2StructConfig">Pix2StructConfig</a> (Pix2Struct model)</li> <li><strong>plbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> (PLBart model)</li> <li><strong>poolformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> (PoolFormer model)</li> <li><strong>pop2piano</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pop2piano#transformers.Pop2PianoConfig">Pop2PianoConfig</a> (Pop2Piano model)</li> <li><strong>prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> (ProphetNet model)</li> <li><strong>pvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> (PVT model)</li> <li><strong>pvt_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pvt_v2#transformers.PvtV2Config">PvtV2Config</a> (PVTv2 model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> (QDQBert model)</li> <li><strong>qwen2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2Config">Qwen2Config</a> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeConfig">Qwen2MoeConfig</a> (Qwen2MoE model)</li> <li><strong>rag</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rag#transformers.RagConfig">RagConfig</a> (RAG model)</li> <li><strong>realm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/realm#transformers.RealmConfig">RealmConfig</a> (REALM model)</li> <li><strong>recurrent_gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/recurrent_gemma#transformers.RecurrentGemmaConfig">RecurrentGemmaConfig</a> (RecurrentGemma model)</li> <li><strong>reformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> (Reformer model)</li> <li><strong>regnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> (RegNet model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> (RemBERT model)</li> <li><strong>resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> (ResNet model)</li> <li><strong>retribert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> (RetriBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> (RoCBert model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> (RoFormer model)</li> <li><strong>rt_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rt_detr#transformers.RTDetrConfig">RTDetrConfig</a> (RT-DETR model)</li> <li><strong>rt_detr_resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rt_detr#transformers.RTDetrResNetConfig">RTDetrResNetConfig</a> (RT-DETR-ResNet model)</li> <li><strong>rwkv</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> (RWKV model)</li> <li><strong>sam</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sam#transformers.SamConfig">SamConfig</a> (SAM model)</li> <li><strong>seamless_m4t</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TConfig">SeamlessM4TConfig</a> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2Config">SeamlessM4Tv2Config</a> (SeamlessM4Tv2 model)</li> <li><strong>segformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> (SegFormer model)</li> <li><strong>seggpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seggpt#transformers.SegGptConfig">SegGptConfig</a> (SegGPT model)</li> <li><strong>sew</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> (SEW model)</li> <li><strong>sew-d</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> (SEW-D model)</li> <li><strong>siglip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipConfig">SiglipConfig</a> (SigLIP model)</li> <li><strong>siglip_vision_model</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipVisionConfig">SiglipVisionConfig</a> (SiglipVisionModel model)</li> <li><strong>speech-encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> (Speech Encoder decoder model)</li> <li><strong>speech_to_text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> (Speech2Text model)</li> <li><strong>speech_to_text_2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> (Speech2Text2 model)</li> <li><strong>speecht5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> (SpeechT5 model)</li> <li><strong>splinter</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> (Splinter model)</li> <li><strong>squeezebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmConfig">StableLmConfig</a> (StableLm model)</li> <li><strong>starcoder2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2Config">Starcoder2Config</a> (Starcoder2 model)</li> <li><strong>superpoint</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/superpoint#transformers.SuperPointConfig">SuperPointConfig</a> (SuperPoint model)</li> <li><strong>swiftformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> (Swin Transformer model)</li> <li><strong>swin2sr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> (Swin2SR model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> (Swin Transformer V2 model)</li> <li><strong>switch_transformers</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> (SwitchTransformers model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> (T5 model)</li> <li><strong>table-transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> (Table Transformer model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> (TAPAS model)</li> <li><strong>time_series_transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> (Time Series Transformer model)</li> <li><strong>timesformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> (TimeSformer model)</li> <li><strong>timm_backbone</strong> — <a href="/docs/transformers/pr_31161/en/main_classes/backbones#transformers.TimmBackboneConfig">TimmBackboneConfig</a> (TimmBackbone model)</li> <li><strong>trajectory_transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> (Trajectory Transformer model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> (Transformer-XL model)</li> <li><strong>trocr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> (TrOCR model)</li> <li><strong>tvlt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> (TVLT model)</li> <li><strong>tvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvp#transformers.TvpConfig">TvpConfig</a> (TVP model)</li> <li><strong>udop</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/udop#transformers.UdopConfig">UdopConfig</a> (UDOP model)</li> <li><strong>umt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> (UMT5 model)</li> <li><strong>unispeech</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> (UniSpeechSat model)</li> <li><strong>univnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/univnet#transformers.UnivNetConfig">UnivNetConfig</a> (UnivNet model)</li> <li><strong>upernet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/upernet#transformers.UperNetConfig">UperNetConfig</a> (UPerNet model)</li> <li><strong>van</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/van#transformers.VanConfig">VanConfig</a> (VAN model)</li> <li><strong>video_llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/video_llava#transformers.VideoLlavaConfig">VideoLlavaConfig</a> (VideoLlava model)</li> <li><strong>videomae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> (VideoMAE model)</li> <li><strong>vilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> (ViLT model)</li> <li><strong>vipllava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vipllava#transformers.VipLlavaConfig">VipLlavaConfig</a> (VipLlava model)</li> <li><strong>vision-encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> (Vision Encoder decoder model)</li> <li><strong>vision-text-dual-encoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> (VisionTextDualEncoder model)</li> <li><strong>visual_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> (VisualBERT model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> (ViT model)</li> <li><strong>vit_hybrid</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> (ViT Hybrid model)</li> <li><strong>vit_mae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> (ViTMAE model)</li> <li><strong>vit_msn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> (ViTMSN model)</li> <li><strong>vitdet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vitdet#transformers.VitDetConfig">VitDetConfig</a> (VitDet model)</li> <li><strong>vitmatte</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vitmatte#transformers.VitMatteConfig">VitMatteConfig</a> (ViTMatte model)</li> <li><strong>vits</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vits#transformers.VitsConfig">VitsConfig</a> (VITS model)</li> <li><strong>vivit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> (ViViT model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertConfig">Wav2Vec2BertConfig</a> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> (Whisper model)</li> <li><strong>xclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> (X-CLIP model)</li> <li><strong>xglm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> (XGLM model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> (XLM model)</li> <li><strong>xlm-prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> (XLM-ProphetNet model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> (XLNet model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> (X-MOD model)</li> <li><strong>yolos</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> (YOLOS model)</li> <li><strong>yoso</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> (YOSO model)</li>',zw,vs,Ow,ws,Rd,Kw,yh,sW="Register a new configuration for this class.",Uy,Gd,Ny,ce,Pd,e3,vh,iW=`This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when
created with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoTokenizer.from_pretrained">AutoTokenizer.from_pretrained()</a> class method.`,o3,wh,lW="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",r3,Io,Sd,t3,xh,dW="Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary.",n3,Lh,mW=`The tokenizer class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,a3,kh,cW='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertTokenizer">AlbertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertTokenizerFast">AlbertTokenizerFast</a> (ALBERT model)</li> <li><strong>align</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (ALIGN model)</li> <li><strong>bark</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Bark model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartTokenizer">BartTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartTokenizerFast">BartTokenizerFast</a> (BART model)</li> <li><strong>barthez</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/barthez#transformers.BarthezTokenizer">BarthezTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/barthez#transformers.BarthezTokenizerFast">BarthezTokenizerFast</a> (BARThez model)</li> <li><strong>bartpho</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bartpho#transformers.BartphoTokenizer">BartphoTokenizer</a> (BARTpho model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (BERT model)</li> <li><strong>bert-generation</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert-generation#transformers.BertGenerationTokenizer">BertGenerationTokenizer</a> (Bert Generation model)</li> <li><strong>bert-japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert-japanese#transformers.BertJapaneseTokenizer">BertJapaneseTokenizer</a> (BertJapanese model)</li> <li><strong>bertweet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bertweet#transformers.BertweetTokenizer">BertweetTokenizer</a> (BERTweet model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdTokenizer">BigBirdTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdTokenizerFast">BigBirdTokenizerFast</a> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusTokenizer">PegasusTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusTokenizerFast">PegasusTokenizerFast</a> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptTokenizer">BioGptTokenizer</a> (BioGpt model)</li> <li><strong>blenderbot</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotTokenizer">BlenderbotTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotTokenizerFast">BlenderbotTokenizerFast</a> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallTokenizer">BlenderbotSmallTokenizer</a> (BlenderbotSmall model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (BLIP-2 model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomTokenizerFast">BloomTokenizerFast</a> (BLOOM model)</li> <li><strong>bridgetower</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizerFast">RobertaTokenizerFast</a> (BridgeTower model)</li> <li><strong>bros</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (BROS model)</li> <li><strong>byt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/byt5#transformers.ByT5Tokenizer">ByT5Tokenizer</a> (ByT5 model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertTokenizer">CamembertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertTokenizerFast">CamembertTokenizerFast</a> (CamemBERT model)</li> <li><strong>canine</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineTokenizer">CanineTokenizer</a> (CANINE model)</li> <li><strong>chinese_clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Chinese-CLIP model)</li> <li><strong>clap</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizerFast">RobertaTokenizerFast</a> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (CLIP model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (CLIPSeg model)</li> <li><strong>clvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clvp#transformers.ClvpTokenizer">ClvpTokenizer</a> (CLVP model)</li> <li><strong>code_llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/code_llama#transformers.CodeLlamaTokenizer">CodeLlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/code_llama#transformers.CodeLlamaTokenizerFast">CodeLlamaTokenizerFast</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenTokenizer">CodeGenTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenTokenizerFast">CodeGenTokenizerFast</a> (CodeGen model)</li> <li><strong>cohere</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cohere#transformers.CohereTokenizerFast">CohereTokenizerFast</a> (Cohere model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertTokenizer">ConvBertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertTokenizerFast">ConvBertTokenizerFast</a> (ConvBERT model)</li> <li><strong>cpm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cpm#transformers.CpmTokenizer">CpmTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/cpm#transformers.CpmTokenizerFast">CpmTokenizerFast</a> (CPM model)</li> <li><strong>cpmant</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cpmant#transformers.CpmAntTokenizer">CpmAntTokenizer</a> (CPM-Ant model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLTokenizer">CTRLTokenizer</a> (CTRL model)</li> <li><strong>data2vec-audio</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer">Wav2Vec2CTCTokenizer</a> (Data2VecAudio model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizerFast">RobertaTokenizerFast</a> (Data2VecText model)</li> <li><strong>dbrx</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (DBRX model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaTokenizer">DebertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaTokenizerFast">DebertaTokenizerFast</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Tokenizer">DebertaV2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2TokenizerFast">DebertaV2TokenizerFast</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertTokenizer">DistilBertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertTokenizerFast">DistilBertTokenizerFast</a> (DistilBERT model)</li> <li><strong>dpr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpr#transformers.DPRQuestionEncoderTokenizer">DPRQuestionEncoderTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/dpr#transformers.DPRQuestionEncoderTokenizerFast">DPRQuestionEncoderTokenizerFast</a> (DPR model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraTokenizer">ElectraTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraTokenizerFast">ElectraTokenizerFast</a> (ELECTRA model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (ERNIE model)</li> <li><strong>ernie_m</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMTokenizer">ErnieMTokenizer</a> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmTokenizer">EsmTokenizer</a> (ESM model)</li> <li><strong>falcon</strong> — <a href="/docs/transformers/pr_31161/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> (Falcon model)</li> <li><strong>fastspeech2_conformer</strong> —  (FastSpeech2Conformer model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertTokenizer">FlaubertTokenizer</a> (FlauBERT model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetTokenizer">FNetTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetTokenizerFast">FNetTokenizerFast</a> (FNet model)</li> <li><strong>fsmt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTTokenizer">FSMTTokenizer</a> (FairSeq Machine-Translation model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelTokenizer">FunnelTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelTokenizerFast">FunnelTokenizerFast</a> (Funnel Transformer model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaTokenizer">GemmaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaTokenizerFast">GemmaTokenizerFast</a> (Gemma model)</li> <li><strong>git</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (GIT model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt-sw3#transformers.GPTSw3Tokenizer">GPTSw3Tokenizer</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXTokenizerFast">GPTNeoXTokenizerFast</a> (GPT NeoX model)</li> <li><strong>gpt_neox_japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseTokenizer">GPTNeoXJapaneseTokenizer</a> (GPT NeoX Japanese model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (GPT-J model)</li> <li><strong>gptsan-japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseTokenizer">GPTSanJapaneseTokenizer</a> (GPTSAN-japanese model)</li> <li><strong>grounding-dino</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (GroupViT model)</li> <li><strong>herbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/herbert#transformers.HerbertTokenizer">HerbertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/herbert#transformers.HerbertTokenizerFast">HerbertTokenizerFast</a> (HerBERT model)</li> <li><strong>hubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer">Wav2Vec2CTCTokenizer</a> (Hubert model)</li> <li><strong>ibert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizerFast">RobertaTokenizerFast</a> (I-BERT model)</li> <li><strong>idefics</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (IDEFICS model)</li> <li><strong>idefics2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Idefics2 model)</li> <li><strong>instructblip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (InstructBlipVideo model)</li> <li><strong>jamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Jamba model)</li> <li><strong>jetmoe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (JetMoe model)</li> <li><strong>jukebox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jukebox#transformers.JukeboxTokenizer">JukeboxTokenizer</a> (Jukebox model)</li> <li><strong>kosmos-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizer">XLMRobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizerFast">XLMRobertaTokenizerFast</a> (KOSMOS-2 model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMTokenizer">LayoutLMTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMTokenizerFast">LayoutLMTokenizerFast</a> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Tokenizer">LayoutLMv2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2TokenizerFast">LayoutLMv2TokenizerFast</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Tokenizer">LayoutLMv3Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3TokenizerFast">LayoutLMv3TokenizerFast</a> (LayoutLMv3 model)</li> <li><strong>layoutxlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizer">LayoutXLMTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/layoutxlm#transformers.LayoutXLMTokenizerFast">LayoutXLMTokenizerFast</a> (LayoutXLM model)</li> <li><strong>led</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDTokenizer">LEDTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDTokenizerFast">LEDTokenizerFast</a> (LED model)</li> <li><strong>lilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Tokenizer">LayoutLMv3Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3TokenizerFast">LayoutLMv3TokenizerFast</a> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaMA model)</li> <li><strong>llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaVa model)</li> <li><strong>llava_next</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (LLaVA-NeXT model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerTokenizer">LongformerTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerTokenizerFast">LongformerTokenizerFast</a> (Longformer model)</li> <li><strong>longt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Tokenizer">T5Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5TokenizerFast">T5TokenizerFast</a> (LongT5 model)</li> <li><strong>luke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeTokenizer">LukeTokenizer</a> (LUKE model)</li> <li><strong>lxmert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertTokenizer">LxmertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertTokenizerFast">LxmertTokenizerFast</a> (LXMERT model)</li> <li><strong>m2m_100</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/m2m_100#transformers.M2M100Tokenizer">M2M100Tokenizer</a> (M2M100 model)</li> <li><strong>mamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXTokenizerFast">GPTNeoXTokenizerFast</a> (Mamba model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianTokenizer">MarianTokenizer</a> (Marian model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartTokenizer">MBartTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartTokenizerFast">MBartTokenizerFast</a> (mBART model)</li> <li><strong>mbart50</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBart50Tokenizer">MBart50Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBart50TokenizerFast">MBart50TokenizerFast</a> (mBART-50 model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizerFast">RobertaTokenizerFast</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Megatron-BERT model)</li> <li><strong>mgp-str</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mgp-str#transformers.MgpstrTokenizer">MgpstrTokenizer</a> (MGP-STR model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Mistral model)</li> <li><strong>mixtral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Mixtral model)</li> <li><strong>mluke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mluke#transformers.MLukeTokenizer">MLukeTokenizer</a> (mLUKE model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertTokenizer">MobileBertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertTokenizerFast">MobileBertTokenizerFast</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetTokenizer">MPNetTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetTokenizerFast">MPNetTokenizerFast</a> (MPNet model)</li> <li><strong>mpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXTokenizerFast">GPTNeoXTokenizerFast</a> (MPT model)</li> <li><strong>mra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizerFast">RobertaTokenizerFast</a> (MRA model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Tokenizer">MT5Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5TokenizerFast">MT5TokenizerFast</a> (MT5 model)</li> <li><strong>musicgen</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Tokenizer">T5Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5TokenizerFast">T5TokenizerFast</a> (MusicGen model)</li> <li><strong>musicgen_melody</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Tokenizer">T5Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5TokenizerFast">T5TokenizerFast</a> (MusicGen Melody model)</li> <li><strong>mvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpTokenizer">MvpTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpTokenizerFast">MvpTokenizerFast</a> (MVP model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (Nezha model)</li> <li><strong>nllb</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nllb#transformers.NllbTokenizer">NllbTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/nllb#transformers.NllbTokenizerFast">NllbTokenizerFast</a> (NLLB model)</li> <li><strong>nllb-moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nllb#transformers.NllbTokenizer">NllbTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/nllb#transformers.NllbTokenizerFast">NllbTokenizerFast</a> (NLLB-MOE model)</li> <li><strong>nystromformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertTokenizer">AlbertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertTokenizerFast">AlbertTokenizerFast</a> (Nyströmformer model)</li> <li><strong>olmo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXTokenizerFast">GPTNeoXTokenizerFast</a> (OLMo model)</li> <li><strong>oneformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (OneFormer model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTTokenizer">OpenAIGPTTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTTokenizerFast">OpenAIGPTTokenizerFast</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (OPT model)</li> <li><strong>owlv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (OWLv2 model)</li> <li><strong>owlvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (OWL-ViT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (PaliGemma model)</li> <li><strong>pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusTokenizer">PegasusTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusTokenizerFast">PegasusTokenizerFast</a> (Pegasus model)</li> <li><strong>pegasus_x</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusTokenizer">PegasusTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusTokenizerFast">PegasusTokenizerFast</a> (PEGASUS-X model)</li> <li><strong>perceiver</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverTokenizer">PerceiverTokenizer</a> (Perceiver model)</li> <li><strong>persimmon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Persimmon model)</li> <li><strong>phi</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenTokenizer">CodeGenTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenTokenizerFast">CodeGenTokenizerFast</a> (Phi model)</li> <li><strong>phi3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (Phi3 model)</li> <li><strong>phobert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phobert#transformers.PhobertTokenizer">PhobertTokenizer</a> (PhoBERT model)</li> <li><strong>pix2struct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Tokenizer">T5Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5TokenizerFast">T5TokenizerFast</a> (Pix2Struct model)</li> <li><strong>plbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartTokenizer">PLBartTokenizer</a> (PLBart model)</li> <li><strong>prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetTokenizer">ProphetNetTokenizer</a> (ProphetNet model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (QDQBert model)</li> <li><strong>qwen2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2Tokenizer">Qwen2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2TokenizerFast">Qwen2TokenizerFast</a> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2Tokenizer">Qwen2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2TokenizerFast">Qwen2TokenizerFast</a> (Qwen2MoE model)</li> <li><strong>rag</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rag#transformers.RagTokenizer">RagTokenizer</a> (RAG model)</li> <li><strong>realm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/realm#transformers.RealmTokenizer">RealmTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/realm#transformers.RealmTokenizerFast">RealmTokenizerFast</a> (REALM model)</li> <li><strong>recurrent_gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaTokenizer">GemmaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaTokenizerFast">GemmaTokenizerFast</a> (RecurrentGemma model)</li> <li><strong>reformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerTokenizer">ReformerTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerTokenizerFast">ReformerTokenizerFast</a> (Reformer model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertTokenizer">RemBertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertTokenizerFast">RemBertTokenizerFast</a> (RemBERT model)</li> <li><strong>retribert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/retribert#transformers.RetriBertTokenizer">RetriBertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/retribert#transformers.RetriBertTokenizerFast">RetriBertTokenizerFast</a> (RetriBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizerFast">RobertaTokenizerFast</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizer">RobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaTokenizerFast">RobertaTokenizerFast</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertTokenizer">RoCBertTokenizer</a> (RoCBert model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerTokenizer">RoFormerTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerTokenizerFast">RoFormerTokenizerFast</a> (RoFormer model)</li> <li><strong>rwkv</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXTokenizerFast">GPTNeoXTokenizerFast</a> (RWKV model)</li> <li><strong>seamless_m4t</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TTokenizer">SeamlessM4TTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TTokenizerFast">SeamlessM4TTokenizerFast</a> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TTokenizer">SeamlessM4TTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TTokenizerFast">SeamlessM4TTokenizerFast</a> (SeamlessM4Tv2 model)</li> <li><strong>siglip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipTokenizer">SiglipTokenizer</a> (SigLIP model)</li> <li><strong>speech_to_text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextTokenizer">Speech2TextTokenizer</a> (Speech2Text model)</li> <li><strong>speech_to_text_2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text_2#transformers.Speech2Text2Tokenizer">Speech2Text2Tokenizer</a> (Speech2Text2 model)</li> <li><strong>speecht5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5Tokenizer">SpeechT5Tokenizer</a> (SpeechT5 model)</li> <li><strong>splinter</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterTokenizer">SplinterTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterTokenizerFast">SplinterTokenizerFast</a> (Splinter model)</li> <li><strong>squeezebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertTokenizer">SqueezeBertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertTokenizerFast">SqueezeBertTokenizerFast</a> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXTokenizerFast">GPTNeoXTokenizerFast</a> (StableLm model)</li> <li><strong>starcoder2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Tokenizer">GPT2Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2TokenizerFast">GPT2TokenizerFast</a> (Starcoder2 model)</li> <li><strong>switch_transformers</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Tokenizer">T5Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5TokenizerFast">T5TokenizerFast</a> (SwitchTransformers model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Tokenizer">T5Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5TokenizerFast">T5TokenizerFast</a> (T5 model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasTokenizer">TapasTokenizer</a> (TAPAS model)</li> <li><strong>tapex</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapex#transformers.TapexTokenizer">TapexTokenizer</a> (TAPEX model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLTokenizer">TransfoXLTokenizer</a> (Transformer-XL model)</li> <li><strong>tvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (TVP model)</li> <li><strong>udop</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/udop#transformers.UdopTokenizer">UdopTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/udop#transformers.UdopTokenizerFast">UdopTokenizerFast</a> (UDOP model)</li> <li><strong>umt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Tokenizer">T5Tokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5TokenizerFast">T5TokenizerFast</a> (UMT5 model)</li> <li><strong>video_llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (VideoLlava model)</li> <li><strong>vilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (ViLT model)</li> <li><strong>vipllava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizer">LlamaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaTokenizerFast">LlamaTokenizerFast</a> (VipLlava model)</li> <li><strong>visual_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizer">BertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertTokenizerFast">BertTokenizerFast</a> (VisualBERT model)</li> <li><strong>vits</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vits#transformers.VitsTokenizer">VitsTokenizer</a> (VITS model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer">Wav2Vec2CTCTokenizer</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer">Wav2Vec2CTCTokenizer</a> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2CTCTokenizer">Wav2Vec2CTCTokenizer</a> (Wav2Vec2-Conformer model)</li> <li><strong>wav2vec2_phoneme</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2_phoneme#transformers.Wav2Vec2PhonemeCTCTokenizer">Wav2Vec2PhonemeCTCTokenizer</a> (Wav2Vec2Phoneme model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperTokenizer">WhisperTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperTokenizerFast">WhisperTokenizerFast</a> (Whisper model)</li> <li><strong>xclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizer">CLIPTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPTokenizerFast">CLIPTokenizerFast</a> (X-CLIP model)</li> <li><strong>xglm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMTokenizer">XGLMTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMTokenizerFast">XGLMTokenizerFast</a> (XGLM model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMTokenizer">XLMTokenizer</a> (XLM model)</li> <li><strong>xlm-prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetTokenizer">XLMProphetNetTokenizer</a> (XLM-ProphetNet model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizer">XLMRobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizerFast">XLMRobertaTokenizerFast</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizer">XLMRobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizerFast">XLMRobertaTokenizerFast</a> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetTokenizer">XLNetTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetTokenizerFast">XLNetTokenizerFast</a> (XLNet model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizer">XLMRobertaTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaTokenizerFast">XLMRobertaTokenizerFast</a> (X-MOD model)</li> <li><strong>yoso</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertTokenizer">AlbertTokenizer</a> or <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertTokenizerFast">AlbertTokenizerFast</a> (YOSO model)</li>',s3,xs,i3,Ls,Wd,l3,$h,fW="Register a new tokenizer in this mapping.",qy,Jd,Xy,fe,Vd,d3,Bh,gW=`This is a generic feature extractor class that will be instantiated as one of the feature extractor classes of the
library when created with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoFeatureExtractor.from_pretrained">AutoFeatureExtractor.from_pretrained()</a> class method.`,m3,jh,pW="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",c3,j,Ed,f3,Ah,hW="Instantiate one of the feature extractor classes of the library from a pretrained model vocabulary.",g3,Zh,uW=`The feature extractor class to instantiate is selected based on the <code>model_type</code> property of the config object
(either passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s
missing, by falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,p3,Rh,_W='<li><strong>audio-spectrogram-transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/audio-spectrogram-transformer#transformers.ASTFeatureExtractor">ASTFeatureExtractor</a> (Audio Spectrogram Transformer model)</li> <li><strong>beit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitFeatureExtractor">BeitFeatureExtractor</a> (BEiT model)</li> <li><strong>chinese_clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPFeatureExtractor">ChineseCLIPFeatureExtractor</a> (Chinese-CLIP model)</li> <li><strong>clap</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clap#transformers.ClapFeatureExtractor">ClapFeatureExtractor</a> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPFeatureExtractor">CLIPFeatureExtractor</a> (CLIP model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (CLIPSeg model)</li> <li><strong>clvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clvp#transformers.ClvpFeatureExtractor">ClvpFeatureExtractor</a> (CLVP model)</li> <li><strong>conditional_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/conditional_detr#transformers.ConditionalDetrFeatureExtractor">ConditionalDetrFeatureExtractor</a> (Conditional DETR model)</li> <li><strong>convnext</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextFeatureExtractor">ConvNextFeatureExtractor</a> (ConvNeXT model)</li> <li><strong>cvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextFeatureExtractor">ConvNextFeatureExtractor</a> (CvT model)</li> <li><strong>data2vec-audio</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (Data2VecAudio model)</li> <li><strong>data2vec-vision</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitFeatureExtractor">BeitFeatureExtractor</a> (Data2VecVision model)</li> <li><strong>deformable_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deformable_detr#transformers.DeformableDetrFeatureExtractor">DeformableDetrFeatureExtractor</a> (Deformable DETR model)</li> <li><strong>deit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTFeatureExtractor">DeiTFeatureExtractor</a> (DeiT model)</li> <li><strong>detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrFeatureExtractor">DetrFeatureExtractor</a> (DETR model)</li> <li><strong>dinat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (DiNAT model)</li> <li><strong>donut-swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/donut#transformers.DonutFeatureExtractor">DonutFeatureExtractor</a> (DonutSwin model)</li> <li><strong>dpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTFeatureExtractor">DPTFeatureExtractor</a> (DPT model)</li> <li><strong>encodec</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/encodec#transformers.EncodecFeatureExtractor">EncodecFeatureExtractor</a> (EnCodec model)</li> <li><strong>flava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaFeatureExtractor">FlavaFeatureExtractor</a> (FLAVA model)</li> <li><strong>glpn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/glpn#transformers.GLPNFeatureExtractor">GLPNFeatureExtractor</a> (GLPN model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPFeatureExtractor">CLIPFeatureExtractor</a> (GroupViT model)</li> <li><strong>hubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (Hubert model)</li> <li><strong>imagegpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/imagegpt#transformers.ImageGPTFeatureExtractor">ImageGPTFeatureExtractor</a> (ImageGPT model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2FeatureExtractor">LayoutLMv2FeatureExtractor</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3FeatureExtractor">LayoutLMv3FeatureExtractor</a> (LayoutLMv3 model)</li> <li><strong>levit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitFeatureExtractor">LevitFeatureExtractor</a> (LeViT model)</li> <li><strong>maskformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerFeatureExtractor">MaskFormerFeatureExtractor</a> (MaskFormer model)</li> <li><strong>mctct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mctct#transformers.MCTCTFeatureExtractor">MCTCTFeatureExtractor</a> (M-CTC-T model)</li> <li><strong>mobilenet_v1</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v1#transformers.MobileNetV1FeatureExtractor">MobileNetV1FeatureExtractor</a> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2FeatureExtractor">MobileNetV2FeatureExtractor</a> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTFeatureExtractor">MobileViTFeatureExtractor</a> (MobileViT model)</li> <li><strong>nat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (NAT model)</li> <li><strong>owlvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTFeatureExtractor">OwlViTFeatureExtractor</a> (OWL-ViT model)</li> <li><strong>perceiver</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor">PerceiverFeatureExtractor</a> (Perceiver model)</li> <li><strong>poolformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/poolformer#transformers.PoolFormerFeatureExtractor">PoolFormerFeatureExtractor</a> (PoolFormer model)</li> <li><strong>pop2piano</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pop2piano#transformers.Pop2PianoFeatureExtractor">Pop2PianoFeatureExtractor</a> (Pop2Piano model)</li> <li><strong>regnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextFeatureExtractor">ConvNextFeatureExtractor</a> (RegNet model)</li> <li><strong>resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextFeatureExtractor">ConvNextFeatureExtractor</a> (ResNet model)</li> <li><strong>seamless_m4t</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TFeatureExtractor">SeamlessM4TFeatureExtractor</a> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TFeatureExtractor">SeamlessM4TFeatureExtractor</a> (SeamlessM4Tv2 model)</li> <li><strong>segformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerFeatureExtractor">SegformerFeatureExtractor</a> (SegFormer model)</li> <li><strong>sew</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (SEW model)</li> <li><strong>sew-d</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (SEW-D model)</li> <li><strong>speech_to_text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextFeatureExtractor">Speech2TextFeatureExtractor</a> (Speech2Text model)</li> <li><strong>speecht5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5FeatureExtractor">SpeechT5FeatureExtractor</a> (SpeechT5 model)</li> <li><strong>swiftformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (Swin Transformer model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (Swin Transformer V2 model)</li> <li><strong>table-transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrFeatureExtractor">DetrFeatureExtractor</a> (Table Transformer model)</li> <li><strong>timesformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor">VideoMAEFeatureExtractor</a> (TimeSformer model)</li> <li><strong>tvlt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltFeatureExtractor">TvltFeatureExtractor</a> (TVLT model)</li> <li><strong>unispeech</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (UniSpeechSat model)</li> <li><strong>univnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/univnet#transformers.UnivNetFeatureExtractor">UnivNetFeatureExtractor</a> (UnivNet model)</li> <li><strong>van</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextFeatureExtractor">ConvNextFeatureExtractor</a> (VAN model)</li> <li><strong>videomae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEFeatureExtractor">VideoMAEFeatureExtractor</a> (VideoMAE model)</li> <li><strong>vilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltFeatureExtractor">ViltFeatureExtractor</a> (ViLT model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (ViT model)</li> <li><strong>vit_mae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (ViTMAE model)</li> <li><strong>vit_msn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTFeatureExtractor">ViTFeatureExtractor</a> (ViTMSN model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2FeatureExtractor">Wav2Vec2FeatureExtractor</a> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperFeatureExtractor">WhisperFeatureExtractor</a> (Whisper model)</li> <li><strong>xclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPFeatureExtractor">CLIPFeatureExtractor</a> (X-CLIP model)</li> <li><strong>yolos</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yolos#transformers.YolosFeatureExtractor">YolosFeatureExtractor</a> (YOLOS model)</li>',h3,ks,u3,$s,_3,Bs,Id,b3,Gh,bW="Register a new feature extractor for this class.",Dy,Ud,Qy,ge,Nd,M3,Ph,MW=`This is a generic image processor class that will be instantiated as one of the image processor classes of the
library when created with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoImageProcessor.from_pretrained">AutoImageProcessor.from_pretrained()</a> class method.`,T3,Sh,TW="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",F3,A,qd,C3,Wh,FW="Instantiate one of the image processor classes of the library from a pretrained model vocabulary.",y3,Jh,CW=`The image processor class to instantiate is selected based on the <code>model_type</code> property of the config object
(either passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s
missing, by falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,v3,Vh,yW='<li><strong>align</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientnet#transformers.EfficientNetImageProcessor">EfficientNetImageProcessor</a> (ALIGN model)</li> <li><strong>beit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitImageProcessor">BeitImageProcessor</a> (BEiT model)</li> <li><strong>bit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitImageProcessor">BitImageProcessor</a> (BiT model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a> (BLIP-2 model)</li> <li><strong>bridgetower</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bridgetower#transformers.BridgeTowerImageProcessor">BridgeTowerImageProcessor</a> (BridgeTower model)</li> <li><strong>chinese_clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPImageProcessor">ChineseCLIPImageProcessor</a> (Chinese-CLIP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (CLIP model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (CLIPSeg model)</li> <li><strong>conditional_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/conditional_detr#transformers.ConditionalDetrImageProcessor">ConditionalDetrImageProcessor</a> (Conditional DETR model)</li> <li><strong>convnext</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a> (ConvNeXTV2 model)</li> <li><strong>cvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a> (CvT model)</li> <li><strong>data2vec-vision</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitImageProcessor">BeitImageProcessor</a> (Data2VecVision model)</li> <li><strong>deformable_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deformable_detr#transformers.DeformableDetrImageProcessor">DeformableDetrImageProcessor</a> (Deformable DETR model)</li> <li><strong>deit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTImageProcessor">DeiTImageProcessor</a> (DeiT model)</li> <li><strong>depth_anything</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTImageProcessor">DPTImageProcessor</a> (Depth Anything model)</li> <li><strong>deta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deta#transformers.DetaImageProcessor">DetaImageProcessor</a> (DETA model)</li> <li><strong>detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrImageProcessor">DetrImageProcessor</a> (DETR model)</li> <li><strong>dinat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (DiNAT model)</li> <li><strong>dinov2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitImageProcessor">BitImageProcessor</a> (DINOv2 model)</li> <li><strong>donut-swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/donut#transformers.DonutImageProcessor">DonutImageProcessor</a> (DonutSwin model)</li> <li><strong>dpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTImageProcessor">DPTImageProcessor</a> (DPT model)</li> <li><strong>efficientformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerImageProcessor">EfficientFormerImageProcessor</a> (EfficientFormer model)</li> <li><strong>efficientnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientnet#transformers.EfficientNetImageProcessor">EfficientNetImageProcessor</a> (EfficientNet model)</li> <li><strong>flava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaImageProcessor">FlavaImageProcessor</a> (FLAVA model)</li> <li><strong>focalnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitImageProcessor">BitImageProcessor</a> (FocalNet model)</li> <li><strong>fuyu</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fuyu#transformers.FuyuImageProcessor">FuyuImageProcessor</a> (Fuyu model)</li> <li><strong>git</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (GIT model)</li> <li><strong>glpn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/glpn#transformers.GLPNImageProcessor">GLPNImageProcessor</a> (GLPN model)</li> <li><strong>grounding-dino</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/grounding-dino#transformers.GroundingDinoImageProcessor">GroundingDinoImageProcessor</a> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (GroupViT model)</li> <li><strong>idefics</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsImageProcessor">IdeficsImageProcessor</a> (IDEFICS model)</li> <li><strong>idefics2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2ImageProcessor">Idefics2ImageProcessor</a> (Idefics2 model)</li> <li><strong>imagegpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/imagegpt#transformers.ImageGPTImageProcessor">ImageGPTImageProcessor</a> (ImageGPT model)</li> <li><strong>instructblip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipImageProcessor">BlipImageProcessor</a> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/instructblipvideo#transformers.InstructBlipVideoImageProcessor">InstructBlipVideoImageProcessor</a> (InstructBlipVideo model)</li> <li><strong>kosmos-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (KOSMOS-2 model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2ImageProcessor">LayoutLMv2ImageProcessor</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ImageProcessor">LayoutLMv3ImageProcessor</a> (LayoutLMv3 model)</li> <li><strong>levit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitImageProcessor">LevitImageProcessor</a> (LeViT model)</li> <li><strong>llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (LLaVa model)</li> <li><strong>llava_next</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava_next#transformers.LlavaNextImageProcessor">LlavaNextImageProcessor</a> (LLaVA-NeXT model)</li> <li><strong>mask2former</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mask2former#transformers.Mask2FormerImageProcessor">Mask2FormerImageProcessor</a> (Mask2Former model)</li> <li><strong>maskformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerImageProcessor">MaskFormerImageProcessor</a> (MaskFormer model)</li> <li><strong>mgp-str</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (MGP-STR model)</li> <li><strong>mobilenet_v1</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v1#transformers.MobileNetV1ImageProcessor">MobileNetV1ImageProcessor</a> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2ImageProcessor">MobileNetV2ImageProcessor</a> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTImageProcessor">MobileViTImageProcessor</a> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTImageProcessor">MobileViTImageProcessor</a> (MobileViTV2 model)</li> <li><strong>nat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (NAT model)</li> <li><strong>nougat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nougat#transformers.NougatImageProcessor">NougatImageProcessor</a> (Nougat model)</li> <li><strong>oneformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/oneformer#transformers.OneFormerImageProcessor">OneFormerImageProcessor</a> (OneFormer model)</li> <li><strong>owlv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlv2#transformers.Owlv2ImageProcessor">Owlv2ImageProcessor</a> (OWLv2 model)</li> <li><strong>owlvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTImageProcessor">OwlViTImageProcessor</a> (OWL-ViT model)</li> <li><strong>perceiver</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverImageProcessor">PerceiverImageProcessor</a> (Perceiver model)</li> <li><strong>pix2struct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pix2struct#transformers.Pix2StructImageProcessor">Pix2StructImageProcessor</a> (Pix2Struct model)</li> <li><strong>poolformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/poolformer#transformers.PoolFormerImageProcessor">PoolFormerImageProcessor</a> (PoolFormer model)</li> <li><strong>pvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pvt#transformers.PvtImageProcessor">PvtImageProcessor</a> (PVT model)</li> <li><strong>pvt_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pvt#transformers.PvtImageProcessor">PvtImageProcessor</a> (PVTv2 model)</li> <li><strong>regnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a> (RegNet model)</li> <li><strong>resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a> (ResNet model)</li> <li><strong>rt_detr</strong> — <code>R</code> or <code>T</code> (RT-DETR model)</li> <li><strong>sam</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sam#transformers.SamImageProcessor">SamImageProcessor</a> (SAM model)</li> <li><strong>segformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerImageProcessor">SegformerImageProcessor</a> (SegFormer model)</li> <li><strong>seggpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seggpt#transformers.SegGptImageProcessor">SegGptImageProcessor</a> (SegGPT model)</li> <li><strong>siglip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipImageProcessor">SiglipImageProcessor</a> (SigLIP model)</li> <li><strong>swiftformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (Swin Transformer model)</li> <li><strong>swin2sr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin2sr#transformers.Swin2SRImageProcessor">Swin2SRImageProcessor</a> (Swin2SR model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (Swin Transformer V2 model)</li> <li><strong>table-transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrImageProcessor">DetrImageProcessor</a> (Table Transformer model)</li> <li><strong>timesformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEImageProcessor">VideoMAEImageProcessor</a> (TimeSformer model)</li> <li><strong>tvlt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltImageProcessor">TvltImageProcessor</a> (TVLT model)</li> <li><strong>tvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvp#transformers.TvpImageProcessor">TvpImageProcessor</a> (TVP model)</li> <li><strong>udop</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ImageProcessor">LayoutLMv3ImageProcessor</a> (UDOP model)</li> <li><strong>upernet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerImageProcessor">SegformerImageProcessor</a> (UPerNet model)</li> <li><strong>van</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextImageProcessor">ConvNextImageProcessor</a> (VAN model)</li> <li><strong>videomae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEImageProcessor">VideoMAEImageProcessor</a> (VideoMAE model)</li> <li><strong>vilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltImageProcessor">ViltImageProcessor</a> (ViLT model)</li> <li><strong>vipllava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (VipLlava model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (ViT model)</li> <li><strong>vit_hybrid</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_hybrid#transformers.ViTHybridImageProcessor">ViTHybridImageProcessor</a> (ViT Hybrid model)</li> <li><strong>vit_mae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (ViTMAE model)</li> <li><strong>vit_msn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessor">ViTImageProcessor</a> or <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTImageProcessorFast">ViTImageProcessorFast</a> (ViTMSN model)</li> <li><strong>vitmatte</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vitmatte#transformers.VitMatteImageProcessor">VitMatteImageProcessor</a> (ViTMatte model)</li> <li><strong>xclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPImageProcessor">CLIPImageProcessor</a> (X-CLIP model)</li> <li><strong>yolos</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yolos#transformers.YolosImageProcessor">YolosImageProcessor</a> (YOLOS model)</li>',w3,js,x3,As,L3,Zs,Xd,k3,Eh,vW="Register a new image processor for this class.",Hy,Dd,Yy,pe,Qd,$3,Ih,wW=`This is a generic processor class that will be instantiated as one of the processor classes of the library when
created with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoProcessor.from_pretrained">AutoProcessor.from_pretrained()</a> class method.`,B3,Uh,xW="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",j3,Z,Hd,A3,Nh,LW="Instantiate one of the processor classes of the library from a pretrained model vocabulary.",Z3,qh,kW=`The processor class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible):`,R3,Xh,$W='<li><strong>align</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/align#transformers.AlignProcessor">AlignProcessor</a> (ALIGN model)</li> <li><strong>altclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/altclip#transformers.AltCLIPProcessor">AltCLIPProcessor</a> (AltCLIP model)</li> <li><strong>bark</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bark#transformers.BarkProcessor">BarkProcessor</a> (Bark model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipProcessor">BlipProcessor</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2Processor">Blip2Processor</a> (BLIP-2 model)</li> <li><strong>bridgetower</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bridgetower#transformers.BridgeTowerProcessor">BridgeTowerProcessor</a> (BridgeTower model)</li> <li><strong>chinese_clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPProcessor">ChineseCLIPProcessor</a> (Chinese-CLIP model)</li> <li><strong>clap</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clap#transformers.ClapProcessor">ClapProcessor</a> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPProcessor">CLIPProcessor</a> (CLIP model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clipseg#transformers.CLIPSegProcessor">CLIPSegProcessor</a> (CLIPSeg model)</li> <li><strong>clvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clvp#transformers.ClvpProcessor">ClvpProcessor</a> (CLVP model)</li> <li><strong>flava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaProcessor">FlavaProcessor</a> (FLAVA model)</li> <li><strong>fuyu</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fuyu#transformers.FuyuProcessor">FuyuProcessor</a> (Fuyu model)</li> <li><strong>git</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitProcessor">GitProcessor</a> (GIT model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPProcessor">CLIPProcessor</a> (GroupViT model)</li> <li><strong>hubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> (Hubert model)</li> <li><strong>idefics</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsProcessor">IdeficsProcessor</a> (IDEFICS model)</li> <li><strong>idefics2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2Processor">Idefics2Processor</a> (Idefics2 model)</li> <li><strong>instructblip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/instructblip#transformers.InstructBlipProcessor">InstructBlipProcessor</a> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/instructblipvideo#transformers.InstructBlipVideoProcessor">InstructBlipVideoProcessor</a> (InstructBlipVideo model)</li> <li><strong>kosmos-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/kosmos-2#transformers.Kosmos2Processor">Kosmos2Processor</a> (KOSMOS-2 model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Processor">LayoutLMv2Processor</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Processor">LayoutLMv3Processor</a> (LayoutLMv3 model)</li> <li><strong>llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava#transformers.LlavaProcessor">LlavaProcessor</a> (LLaVa model)</li> <li><strong>llava_next</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava_next#transformers.LlavaNextProcessor">LlavaNextProcessor</a> (LLaVA-NeXT model)</li> <li><strong>markuplm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMProcessor">MarkupLMProcessor</a> (MarkupLM model)</li> <li><strong>mctct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mctct#transformers.MCTCTProcessor">MCTCTProcessor</a> (M-CTC-T model)</li> <li><strong>mgp-str</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mgp-str#transformers.MgpstrProcessor">MgpstrProcessor</a> (MGP-STR model)</li> <li><strong>oneformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/oneformer#transformers.OneFormerProcessor">OneFormerProcessor</a> (OneFormer model)</li> <li><strong>owlv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlv2#transformers.Owlv2Processor">Owlv2Processor</a> (OWLv2 model)</li> <li><strong>owlvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTProcessor">OwlViTProcessor</a> (OWL-ViT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/paligemma#transformers.PaliGemmaProcessor">PaliGemmaProcessor</a> (PaliGemma model)</li> <li><strong>pix2struct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pix2struct#transformers.Pix2StructProcessor">Pix2StructProcessor</a> (Pix2Struct model)</li> <li><strong>pop2piano</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pop2piano#transformers.Pop2PianoProcessor">Pop2PianoProcessor</a> (Pop2Piano model)</li> <li><strong>sam</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sam#transformers.SamProcessor">SamProcessor</a> (SAM model)</li> <li><strong>seamless_m4t</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TProcessor">SeamlessM4TProcessor</a> (SeamlessM4T model)</li> <li><strong>sew</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> (SEW model)</li> <li><strong>sew-d</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> (SEW-D model)</li> <li><strong>siglip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipProcessor">SiglipProcessor</a> (SigLIP model)</li> <li><strong>speech_to_text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextProcessor">Speech2TextProcessor</a> (Speech2Text model)</li> <li><strong>speech_to_text_2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text_2#transformers.Speech2Text2Processor">Speech2Text2Processor</a> (Speech2Text2 model)</li> <li><strong>speecht5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5Processor">SpeechT5Processor</a> (SpeechT5 model)</li> <li><strong>trocr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/trocr#transformers.TrOCRProcessor">TrOCRProcessor</a> (TrOCR model)</li> <li><strong>tvlt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltProcessor">TvltProcessor</a> (TVLT model)</li> <li><strong>tvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvp#transformers.TvpProcessor">TvpProcessor</a> (TVP model)</li> <li><strong>unispeech</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> (UniSpeechSat model)</li> <li><strong>video_llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/video_llava#transformers.VideoLlavaProcessor">VideoLlavaProcessor</a> (VideoLlava model)</li> <li><strong>vilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltProcessor">ViltProcessor</a> (ViLT model)</li> <li><strong>vipllava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava#transformers.LlavaProcessor">LlavaProcessor</a> (VipLlava model)</li> <li><strong>vision-text-dual-encoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderProcessor">VisionTextDualEncoderProcessor</a> (VisionTextDualEncoder model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Processor">Wav2Vec2Processor</a> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperProcessor">WhisperProcessor</a> (Whisper model)</li> <li><strong>xclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xclip#transformers.XCLIPProcessor">XCLIPProcessor</a> (X-CLIP model)</li>',G3,Rs,P3,Gs,S3,Ps,Yd,W3,Dh,BW="Register a new processor for this class.",zy,zd,Oy,Od,jW="The following auto classes are available for instantiating a base model class without a specific head.",Ky,Kd,ev,he,em,J3,Qh,AW=`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,V3,Hh,ZW="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",E3,Gt,om,I3,Yh,RW="Instantiates one of the base model classes of the library from a configuration.",U3,zh,GW=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,N3,Ss,q3,R,rm,X3,Oh,PW="Instantiate one of the base model classes of the library from a pretrained model.",D3,Kh,SW=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Q3,eu,WW='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li> <li><strong>align</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li> <li><strong>altclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li> <li><strong>audio-spectrogram-transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/audio-spectrogram-transformer#transformers.ASTModel">ASTModel</a> (Audio Spectrogram Transformer model)</li> <li><strong>autoformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/autoformer#transformers.AutoformerModel">AutoformerModel</a> (Autoformer model)</li> <li><strong>bark</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bark#transformers.BarkModel">BarkModel</a> (Bark model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li> <li><strong>beit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li> <li><strong>bert-generation</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li> <li><strong>bit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitModel">BitModel</a> (BiT model)</li> <li><strong>blenderbot</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li> <li><strong>bridgetower</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bridgetower#transformers.BridgeTowerModel">BridgeTowerModel</a> (BridgeTower model)</li> <li><strong>bros</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bros#transformers.BrosModel">BrosModel</a> (BROS model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li> <li><strong>canine</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li> <li><strong>chinese_clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li> <li><strong>chinese_clip_vision_model</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPVisionModel">ChineseCLIPVisionModel</a> (ChineseCLIPVisionModel model)</li> <li><strong>clap</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clap#transformers.ClapModel">ClapModel</a> (CLAP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li> <li><strong>clip_vision_model</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPVisionModel">CLIPVisionModel</a> (CLIPVisionModel model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li> <li><strong>clvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clvp#transformers.ClvpModelForConditionalGeneration">ClvpModelForConditionalGeneration</a> (CLVP model)</li> <li><strong>code_llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaModel">LlamaModel</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li> <li><strong>cohere</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cohere#transformers.CohereModel">CohereModel</a> (Cohere model)</li> <li><strong>conditional_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li> <li><strong>convnext</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.ConvNextV2Model">ConvNextV2Model</a> (ConvNeXTV2 model)</li> <li><strong>cpmant</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a> (CPM-Ant model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li> <li><strong>cvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li> <li><strong>data2vec-audio</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li> <li><strong>data2vec-vision</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li> <li><strong>dbrx</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dbrx#transformers.DbrxModel">DbrxModel</a> (DBRX model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li> <li><strong>decision_transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li> <li><strong>deformable_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li> <li><strong>deit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li> <li><strong>deta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deta#transformers.DetaModel">DetaModel</a> (DETA model)</li> <li><strong>detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li> <li><strong>dinat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dinat#transformers.DinatModel">DinatModel</a> (DiNAT model)</li> <li><strong>dinov2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dinov2#transformers.Dinov2Model">Dinov2Model</a> (DINOv2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li> <li><strong>donut-swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li> <li><strong>dpr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li> <li><strong>dpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li> <li><strong>efficientformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerModel">EfficientFormerModel</a> (EfficientFormer model)</li> <li><strong>efficientnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientnet#transformers.EfficientNetModel">EfficientNetModel</a> (EfficientNet model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li> <li><strong>encodec</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/encodec#transformers.EncodecModel">EncodecModel</a> (EnCodec model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li> <li><strong>ernie_m</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMModel">ErnieMModel</a> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li> <li><strong>falcon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconModel">FalconModel</a> (Falcon model)</li> <li><strong>fastspeech2_conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fastspeech2_conformer#transformers.FastSpeech2ConformerModel">FastSpeech2ConformerModel</a> (FastSpeech2Conformer model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li> <li><strong>flava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li> <li><strong>focalnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetModel">FocalNetModel</a> (FocalNet model)</li> <li><strong>fsmt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaModel">GemmaModel</a> (Gemma model)</li> <li><strong>git</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitModel">GitModel</a> (GIT model)</li> <li><strong>glpn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeModel">GPTBigCodeModel</a> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li> <li><strong>gpt_neox_japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li> <li><strong>gptsan-japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li> <li><strong>graphormer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li> <li><strong>grounding-dino</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/grounding-dino#transformers.GroundingDinoModel">GroundingDinoModel</a> (Grounding DINO model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li> <li><strong>hubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li> <li><strong>ibert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li> <li><strong>idefics</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsModel">IdeficsModel</a> (IDEFICS model)</li> <li><strong>idefics2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2Model">Idefics2Model</a> (Idefics2 model)</li> <li><strong>imagegpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li> <li><strong>informer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li> <li><strong>jamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaModel">JambaModel</a> (Jamba model)</li> <li><strong>jetmoe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeModel">JetMoeModel</a> (JetMoe model)</li> <li><strong>jukebox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jukebox#transformers.JukeboxModel">JukeboxModel</a> (Jukebox model)</li> <li><strong>kosmos-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/kosmos-2#transformers.Kosmos2Model">Kosmos2Model</a> (KOSMOS-2 model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li> <li><strong>led</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li> <li><strong>levit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li> <li><strong>lilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaModel">LlamaModel</a> (LLaMA model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li> <li><strong>longt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li> <li><strong>luke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li> <li><strong>lxmert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li> <li><strong>m2m_100</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li> <li><strong>mamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaModel">MambaModel</a> (Mamba model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li> <li><strong>markuplm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li> <li><strong>mask2former</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mask2former#transformers.Mask2FormerModel">Mask2FormerModel</a> (Mask2Former model)</li> <li><strong>maskformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerModel">MaskFormerModel</a> (MaskFormer model)</li> <li><strong>maskformer-swin</strong> — <code>MaskFormerSwinModel</code> (MaskFormerSwin model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li> <li><strong>mctct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaModel">MegaModel</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li> <li><strong>mgp-str</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mgp-str#transformers.MgpstrForSceneTextRecognition">MgpstrForSceneTextRecognition</a> (MGP-STR model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralModel">MistralModel</a> (Mistral model)</li> <li><strong>mixtral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralModel">MixtralModel</a> (Mixtral model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li> <li><strong>mobilenet_v1</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v1#transformers.MobileNetV1Model">MobileNetV1Model</a> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2Model">MobileNetV2Model</a> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2Model">MobileViTV2Model</a> (MobileViTV2 model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li> <li><strong>mpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptModel">MptModel</a> (MPT model)</li> <li><strong>mra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraModel">MraModel</a> (MRA model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li> <li><strong>musicgen</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/musicgen#transformers.MusicgenModel">MusicgenModel</a> (MusicGen model)</li> <li><strong>musicgen_melody</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/musicgen_melody#transformers.MusicgenMelodyModel">MusicgenMelodyModel</a> (MusicGen Melody model)</li> <li><strong>mvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li> <li><strong>nat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nat#transformers.NatModel">NatModel</a> (NAT model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li> <li><strong>nllb-moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeModel">NllbMoeModel</a> (NLLB-MOE model)</li> <li><strong>nystromformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nyströmformer model)</li> <li><strong>olmo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/olmo#transformers.OlmoModel">OlmoModel</a> (OLMo model)</li> <li><strong>oneformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/oneformer#transformers.OneFormerModel">OneFormerModel</a> (OneFormer model)</li> <li><strong>open-llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a> (OpenLlama model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li> <li><strong>owlv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlv2#transformers.Owlv2Model">Owlv2Model</a> (OWLv2 model)</li> <li><strong>owlvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li> <li><strong>patchtsmixer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/patchtsmixer#transformers.PatchTSMixerModel">PatchTSMixerModel</a> (PatchTSMixer model)</li> <li><strong>patchtst</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/patchtst#transformers.PatchTSTModel">PatchTSTModel</a> (PatchTST model)</li> <li><strong>pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li> <li><strong>pegasus_x</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li> <li><strong>perceiver</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li> <li><strong>persimmon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonModel">PersimmonModel</a> (Persimmon model)</li> <li><strong>phi</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiModel">PhiModel</a> (Phi model)</li> <li><strong>phi3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3Model">Phi3Model</a> (Phi3 model)</li> <li><strong>plbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li> <li><strong>poolformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li> <li><strong>prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li> <li><strong>pvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pvt#transformers.PvtModel">PvtModel</a> (PVT model)</li> <li><strong>pvt_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pvt_v2#transformers.PvtV2Model">PvtV2Model</a> (PVTv2 model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li> <li><strong>qwen2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2Model">Qwen2Model</a> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeModel">Qwen2MoeModel</a> (Qwen2MoE model)</li> <li><strong>recurrent_gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/recurrent_gemma#transformers.RecurrentGemmaModel">RecurrentGemmaModel</a> (RecurrentGemma model)</li> <li><strong>reformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li> <li><strong>regnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li> <li><strong>resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li> <li><strong>retribert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertModel">RoCBertModel</a> (RoCBert model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li> <li><strong>rt_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rt_detr#transformers.RTDetrModel">RTDetrModel</a> (RT-DETR model)</li> <li><strong>rwkv</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvModel">RwkvModel</a> (RWKV model)</li> <li><strong>sam</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sam#transformers.SamModel">SamModel</a> (SAM model)</li> <li><strong>seamless_m4t</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel">SeamlessM4TModel</a> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2Model">SeamlessM4Tv2Model</a> (SeamlessM4Tv2 model)</li> <li><strong>segformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li> <li><strong>seggpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seggpt#transformers.SegGptModel">SegGptModel</a> (SegGPT model)</li> <li><strong>sew</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li> <li><strong>sew-d</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li> <li><strong>siglip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipModel">SiglipModel</a> (SigLIP model)</li> <li><strong>siglip_vision_model</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipVisionModel">SiglipVisionModel</a> (SiglipVisionModel model)</li> <li><strong>speech_to_text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li> <li><strong>speecht5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5Model">SpeechT5Model</a> (SpeechT5 model)</li> <li><strong>splinter</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li> <li><strong>squeezebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmModel">StableLmModel</a> (StableLm model)</li> <li><strong>starcoder2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2Model">Starcoder2Model</a> (Starcoder2 model)</li> <li><strong>swiftformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.SwiftFormerModel">SwiftFormerModel</a> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li> <li><strong>swin2sr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li> <li><strong>switch_transformers</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersModel">SwitchTransformersModel</a> (SwitchTransformers model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li> <li><strong>table-transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li> <li><strong>time_series_transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li> <li><strong>timesformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> (TimeSformer model)</li> <li><strong>timm_backbone</strong> — <a href="/docs/transformers/pr_31161/en/main_classes/backbones#transformers.TimmBackbone">TimmBackbone</a> (TimmBackbone model)</li> <li><strong>trajectory_transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li> <li><strong>tvlt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltModel">TvltModel</a> (TVLT model)</li> <li><strong>tvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvp#transformers.TvpModel">TvpModel</a> (TVP model)</li> <li><strong>udop</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/udop#transformers.UdopModel">UdopModel</a> (UDOP model)</li> <li><strong>umt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5Model">UMT5Model</a> (UMT5 model)</li> <li><strong>unispeech</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li> <li><strong>univnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/univnet#transformers.UnivNetModel">UnivNetModel</a> (UnivNet model)</li> <li><strong>van</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li> <li><strong>videomae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li> <li><strong>vilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li> <li><strong>vision-text-dual-encoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li> <li><strong>visual_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li> <li><strong>vit_hybrid</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_hybrid#transformers.ViTHybridModel">ViTHybridModel</a> (ViT Hybrid model)</li> <li><strong>vit_mae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li> <li><strong>vit_msn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li> <li><strong>vitdet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vitdet#transformers.VitDetModel">VitDetModel</a> (VitDet model)</li> <li><strong>vits</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vits#transformers.VitsModel">VitsModel</a> (VITS model)</li> <li><strong>vivit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vivit#transformers.VivitModel">VivitModel</a> (ViViT model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertModel">Wav2Vec2BertModel</a> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li> <li><strong>xclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li> <li><strong>xglm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li> <li><strong>xlm-prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodModel">XmodModel</a> (X-MOD model)</li> <li><strong>yolos</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li> <li><strong>yoso</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>',H3,ou,JW=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,Y3,Ws,ov,tm,rv,ue,nm,z3,ru,VW=`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,O3,tu,EW="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",K3,Pt,am,ex,nu,IW="Instantiates one of the base model classes of the library from a configuration.",ox,au,UW=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,rx,Js,tx,Uo,sm,nx,su,NW="Instantiate one of the base model classes of the library from a pretrained model.",ax,iu,qW=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,sx,lu,XW='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li> <li><strong>blenderbot</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li> <li><strong>convnext</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.TFConvNextV2Model">TFConvNextV2Model</a> (ConvNeXTV2 model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li> <li><strong>cvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li> <li><strong>data2vec-vision</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li> <li><strong>deit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li> <li><strong>dpr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li> <li><strong>efficientformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.TFEfficientFormerModel">TFEfficientFormerModel</a> (EfficientFormer model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li> <li><strong>groupvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li> <li><strong>hubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li> <li><strong>idefics</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.TFIdeficsModel">TFIdeficsModel</a> (IDEFICS model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li> <li><strong>led</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li> <li><strong>lxmert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.TFMistralModel">TFMistralModel</a> (Mistral model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li> <li><strong>mobilevit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li> <li><strong>pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li> <li><strong>regnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li> <li><strong>resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li> <li><strong>sam</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sam#transformers.TFSamModel">TFSamModel</a> (SAM model)</li> <li><strong>segformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li> <li><strong>speech_to_text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li> <li><strong>swiftformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.TFSwiftFormerModel">TFSwiftFormerModel</a> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li> <li><strong>vision-text-dual-encoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.TFVisionTextDualEncoderModel">TFVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li> <li><strong>vit_mae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li> <li><strong>xglm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>',ix,Vs,tv,im,nv,_e,lm,lx,du,DW=`This is a generic model class that will be instantiated as one of the base model classes of the library when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,dx,mu,QW="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",mx,St,dm,cx,cu,HW="Instantiates one of the base model classes of the library from a configuration.",fx,fu,YW=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,gx,Es,px,No,mm,hx,gu,zW="Instantiate one of the base model classes of the library from a pretrained model.",ux,pu,OW=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,_x,hu,KW='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li> <li><strong>beit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li> <li><strong>blenderbot</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.FlaxBloomModel">FlaxBloomModel</a> (BLOOM model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.FlaxGemmaModel">FlaxGemmaModel</a> (Gemma model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_neo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.FlaxLlamaModel">FlaxLlamaModel</a> (LLaMA model)</li> <li><strong>longt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.FlaxMistralModel">FlaxMistralModel</a> (Mistral model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li> <li><strong>pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li> <li><strong>regnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.FlaxRegNetModel">FlaxRegNetModel</a> (RegNet model)</li> <li><strong>resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.FlaxResNetModel">FlaxResNetModel</a> (ResNet model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormModel">FlaxRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li> <li><strong>vision-text-dual-encoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li> <li><strong>xglm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>',bx,Is,av,cm,sv,fm,eJ="The following auto classes are available for instantiating a model with a pretraining head.",iv,gm,lv,be,pm,Mx,uu,oJ=`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Tx,_u,rJ="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Fx,Wt,hm,Cx,bu,tJ="Instantiates one of the model classes of the library (with a pretraining head) from a configuration.",yx,Mu,nJ=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,vx,Us,wx,G,um,xx,Tu,aJ="Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.",Lx,Fu,sJ=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,kx,Cu,iJ='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li> <li><strong>flava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li> <li><strong>fsmt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li> <li><strong>gptsan-japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li> <li><strong>ibert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li> <li><strong>idefics</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsForVisionText2Text">IdeficsForVisionText2Text</a> (IDEFICS model)</li> <li><strong>idefics2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2ForConditionalGeneration">Idefics2ForConditionalGeneration</a> (Idefics2 model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li> <li><strong>llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava#transformers.LlavaForConditionalGeneration">LlavaForConditionalGeneration</a> (LLaVa model)</li> <li><strong>llava_next</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava_next#transformers.LlavaNextForConditionalGeneration">LlavaNextForConditionalGeneration</a> (LLaVA-NeXT model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li> <li><strong>luke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li> <li><strong>lxmert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li> <li><strong>mamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaForCausalLM">MambaForCausalLM</a> (Mamba model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li> <li><strong>mpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li> <li><strong>mra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li> <li><strong>mvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li> <li><strong>nllb-moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration">PaliGemmaForConditionalGeneration</a> (PaliGemma model)</li> <li><strong>retribert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForPreTraining">RoCBertForPreTraining</a> (RoCBert model)</li> <li><strong>rwkv</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li> <li><strong>splinter</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li> <li><strong>squeezebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li> <li><strong>switch_transformers</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li> <li><strong>tvlt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltForPreTraining">TvltForPreTraining</a> (TVLT model)</li> <li><strong>unispeech</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li> <li><strong>video_llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/video_llava#transformers.VideoLlavaForConditionalGeneration">VideoLlavaForConditionalGeneration</a> (VideoLlava model)</li> <li><strong>videomae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li> <li><strong>vipllava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vipllava#transformers.VipLlavaForConditionalGeneration">VipLlavaForConditionalGeneration</a> (VipLlava model)</li> <li><strong>visual_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li> <li><strong>vit_mae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>',$x,yu,lJ=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,Bx,Ns,dv,_m,mv,Me,bm,jx,vu,dJ=`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Ax,wu,mJ="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Zx,Jt,Mm,Rx,xu,cJ="Instantiates one of the model classes of the library (with a pretraining head) from a configuration.",Gx,Lu,fJ=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Px,qs,Sx,qo,Tm,Wx,ku,gJ="Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.",Jx,$u,pJ=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Vx,Bu,hJ='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li> <li><strong>idefics</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.TFIdeficsForVisionText2Text">TFIdeficsForVisionText2Text</a> (IDEFICS model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li> <li><strong>lxmert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li> <li><strong>vit_mae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>',Ex,Xs,cv,Fm,fv,Te,Cm,Ix,ju,uJ=`This is a generic model class that will be instantiated as one of the model classes of the library (with a pretraining head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Ux,Au,_J="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Nx,Vt,ym,qx,Zu,bJ="Instantiates one of the model classes of the library (with a pretraining head) from a configuration.",Xx,Ru,MJ=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Dx,Ds,Qx,Xo,vm,Hx,Gu,TJ="Instantiate one of the model classes of the library (with a pretraining head) from a pretrained model.",Yx,Pu,FJ=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,zx,Su,CJ='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li> <li><strong>longt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>',Ox,Qs,gv,wm,pv,xm,yJ="The following auto classes are available for the following natural language processing tasks.",hv,Lm,uv,Fe,km,Kx,Wu,vJ=`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,e6,Ju,wJ="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",o6,Et,$m,r6,Vu,xJ="Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.",t6,Eu,LJ=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,n6,Hs,a6,P,Bm,s6,Iu,kJ="Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.",i6,Uu,$J=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,l6,Nu,BJ='<li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li> <li><strong>bert-generation</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li> <li><strong>blenderbot</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li> <li><strong>code_llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (CodeLlama model)</li> <li><strong>codegen</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li> <li><strong>cohere</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cohere#transformers.CohereForCausalLM">CohereForCausalLM</a> (Cohere model)</li> <li><strong>cpmant</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cpmant#transformers.CpmAntForCausalLM">CpmAntForCausalLM</a> (CPM-Ant model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li> <li><strong>dbrx</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dbrx#transformers.DbrxForCausalLM">DbrxForCausalLM</a> (DBRX model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForCausalLM">ErnieForCausalLM</a> (ERNIE model)</li> <li><strong>falcon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconForCausalLM">FalconForCausalLM</a> (Falcon model)</li> <li><strong>fuyu</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fuyu#transformers.FuyuForCausalLM">FuyuForCausalLM</a> (Fuyu model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaForCausalLM">GemmaForCausalLM</a> (Gemma model)</li> <li><strong>git</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li> <li><strong>gpt_neox_japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li> <li><strong>jamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaForCausalLM">JambaForCausalLM</a> (Jamba model)</li> <li><strong>jetmoe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeForCausalLM">JetMoeForCausalLM</a> (JetMoe model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li> <li><strong>mamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaForCausalLM">MambaForCausalLM</a> (Mamba model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForCausalLM">MegaForCausalLM</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralForCausalLM">MistralForCausalLM</a> (Mistral model)</li> <li><strong>mixtral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralForCausalLM">MixtralForCausalLM</a> (Mixtral model)</li> <li><strong>mpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li> <li><strong>musicgen</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/musicgen#transformers.MusicgenForCausalLM">MusicgenForCausalLM</a> (MusicGen model)</li> <li><strong>musicgen_melody</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/musicgen_melody#transformers.MusicgenMelodyForCausalLM">MusicgenMelodyForCausalLM</a> (MusicGen Melody model)</li> <li><strong>mvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li> <li><strong>olmo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/olmo#transformers.OlmoForCausalLM">OlmoForCausalLM</a> (OLMo model)</li> <li><strong>open-llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaForCausalLM">OpenLlamaForCausalLM</a> (OpenLlama model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li> <li><strong>pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li> <li><strong>persimmon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonForCausalLM">PersimmonForCausalLM</a> (Persimmon model)</li> <li><strong>phi</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiForCausalLM">PhiForCausalLM</a> (Phi model)</li> <li><strong>phi3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3ForCausalLM">Phi3ForCausalLM</a> (Phi3 model)</li> <li><strong>plbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li> <li><strong>prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li> <li><strong>qwen2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2ForCausalLM">Qwen2ForCausalLM</a> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeForCausalLM">Qwen2MoeForCausalLM</a> (Qwen2MoE model)</li> <li><strong>recurrent_gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/recurrent_gemma#transformers.RecurrentGemmaForCausalLM">RecurrentGemmaForCausalLM</a> (RecurrentGemma model)</li> <li><strong>reformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForCausalLM">RobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForCausalLM">RoCBertForCausalLM</a> (RoCBert model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li> <li><strong>rwkv</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li> <li><strong>speech_to_text_2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li> <li><strong>stablelm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmForCausalLM">StableLmForCausalLM</a> (StableLm model)</li> <li><strong>starcoder2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2ForCausalLM">Starcoder2ForCausalLM</a> (Starcoder2 model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li> <li><strong>trocr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperForCausalLM">WhisperForCausalLM</a> (Whisper model)</li> <li><strong>xglm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li> <li><strong>xlm-prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForCausalLM">XmodForCausalLM</a> (X-MOD model)</li>',d6,qu,jJ=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,m6,Ys,_v,jm,bv,Ce,Am,c6,Xu,AJ=`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,f6,Du,ZJ="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",g6,It,Zm,p6,Qu,RJ="Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.",h6,Hu,GJ=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,u6,zs,_6,Do,Rm,b6,Yu,PJ="Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.",M6,zu,SJ=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,T6,Ou,WJ='<li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.TFMistralForCausalLM">TFMistralForCausalLM</a> (Mistral model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForCausalLM">TFRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li> <li><strong>xglm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForCausalLM">TFXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>',F6,Os,Mv,Gm,Tv,ye,Pm,C6,Ku,JJ=`This is a generic model class that will be instantiated as one of the model classes of the library (with a causal language modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,y6,e_,VJ="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",v6,Ut,Sm,w6,o_,EJ="Instantiates one of the model classes of the library (with a causal language modeling head) from a configuration.",x6,r_,IJ=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,L6,Ks,k6,Qo,Wm,$6,t_,UJ="Instantiate one of the model classes of the library (with a causal language modeling head) from a pretrained model.",B6,n_,NJ=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,j6,a_,qJ='<li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.FlaxBloomForCausalLM">FlaxBloomForCausalLM</a> (BLOOM model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.FlaxGemmaForCausalLM">FlaxGemmaForCausalLM</a> (Gemma model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_neo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.FlaxLlamaForCausalLM">FlaxLlamaForCausalLM</a> (LLaMA model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.FlaxMistralForCausalLM">FlaxMistralForCausalLM</a> (Mistral model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM">FlaxRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>xglm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM">FlaxXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>',A6,ei,Fv,Jm,Cv,ve,Vm,Z6,s_,XJ=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,R6,i_,DJ="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",G6,Nt,Em,P6,l_,QJ="Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.",S6,d_,HJ=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,W6,oi,J6,S,Im,V6,m_,YJ="Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.",E6,c_,zJ=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,I6,f_,OJ='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li> <li><strong>ibert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li> <li><strong>luke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li> <li><strong>mra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li> <li><strong>mvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li> <li><strong>nystromformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nyströmformer model)</li> <li><strong>perceiver</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li> <li><strong>reformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForMaskedLM">RoCBertForMaskedLM</a> (RoCBert model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li> <li><strong>squeezebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li> <li><strong>wav2vec2</strong> — <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li> <li><strong>yoso</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>',U6,g_,KJ=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,N6,ri,yv,Um,vv,we,Nm,q6,p_,eV=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,X6,h_,oV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",D6,qt,qm,Q6,u_,rV="Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.",H6,__,tV=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Y6,ti,z6,Ho,Xm,O6,b_,nV="Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.",K6,M_,aV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,eL,T_,sV='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>',oL,ni,wv,Dm,xv,xe,Qm,rL,F_,iV=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked language modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,tL,C_,lV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",nL,Xt,Hm,aL,y_,dV="Instantiates one of the model classes of the library (with a masked language modeling head) from a configuration.",sL,v_,mV=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,iL,ai,lL,Yo,Ym,dL,w_,cV="Instantiate one of the model classes of the library (with a masked language modeling head) from a pretrained model.",mL,x_,fV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,cL,L_,gV='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>',fL,si,Lv,zm,kv,Om,Km,$v,ec,Bv,oc,rc,jv,tc,Av,Le,nc,gL,k_,pV=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,pL,$_,hV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",hL,Dt,ac,uL,B_,uV="Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.",_L,j_,_V=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,bL,ii,ML,W,sc,TL,A_,bV="Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.",FL,Z_,MV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,CL,R_,TV='<li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li> <li><strong>bigbird_pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li> <li><strong>blenderbot</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li> <li><strong>encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li> <li><strong>fsmt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li> <li><strong>gptsan-japanese</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li> <li><strong>led</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li> <li><strong>longt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li> <li><strong>m2m_100</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li> <li><strong>mvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li> <li><strong>nllb-moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li> <li><strong>pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li> <li><strong>pegasus_x</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li> <li><strong>plbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li> <li><strong>prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li> <li><strong>seamless_m4t</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TForTextToText">SeamlessM4TForTextToText</a> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2ForTextToText">SeamlessM4Tv2ForTextToText</a> (SeamlessM4Tv2 model)</li> <li><strong>switch_transformers</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li> <li><strong>umt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5ForConditionalGeneration">UMT5ForConditionalGeneration</a> (UMT5 model)</li> <li><strong>xlm-prophetnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>',yL,G_,FV=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,vL,li,Zv,ic,Rv,ke,lc,wL,P_,CV=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,xL,S_,yV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",LL,Qt,dc,kL,W_,vV="Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.",$L,J_,wV=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,BL,di,jL,zo,mc,AL,V_,xV="Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.",ZL,E_,LV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,RL,I_,kV='<li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li> <li><strong>blenderbot</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li> <li><strong>encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li> <li><strong>led</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li> <li><strong>pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>',GL,mi,Gv,cc,Pv,$e,fc,PL,U_,$V=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence language modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,SL,N_,BV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",WL,Ht,gc,JL,q_,jV="Instantiates one of the model classes of the library (with a sequence-to-sequence language modeling head) from a configuration.",VL,X_,AV=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,EL,ci,IL,Oo,pc,UL,D_,ZV="Instantiate one of the model classes of the library (with a sequence-to-sequence language modeling head) from a pretrained model.",NL,Q_,RV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,qL,H_,GV='<li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li> <li><strong>blenderbot</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li> <li><strong>blenderbot-small</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li> <li><strong>encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li> <li><strong>longt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li> <li><strong>marian</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li> <li><strong>pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>',XL,fi,Sv,hc,Wv,Be,uc,DL,Y_,PV=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,QL,z_,SV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",HL,Yt,_c,YL,O_,WV="Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.",zL,K_,JV=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,OL,gi,KL,J,bc,ek,e1,VV="Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.",ok,o1,EV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,rk,r1,IV='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptForSequenceClassification">BioGptForSequenceClassification</a> (BioGpt model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li> <li><strong>canine</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li> <li><strong>code_llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (CodeLlama model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li> <li><strong>ernie_m</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMForSequenceClassification">ErnieMForSequenceClassification</a> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li> <li><strong>falcon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconForSequenceClassification">FalconForSequenceClassification</a> (Falcon model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaForSequenceClassification">GemmaForSequenceClassification</a> (Gemma model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForSequenceClassification">GPTBigCodeForSequenceClassification</a> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXForSequenceClassification">GPTNeoXForSequenceClassification</a> (GPT NeoX model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li> <li><strong>ibert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li> <li><strong>jamba</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaForSequenceClassification">JambaForSequenceClassification</a> (Jamba model)</li> <li><strong>jetmoe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeForSequenceClassification">JetMoeForSequenceClassification</a> (JetMoe model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li> <li><strong>led</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li> <li><strong>lilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (LLaMA model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li> <li><strong>luke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li> <li><strong>markuplm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForSequenceClassification">MegaForSequenceClassification</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralForSequenceClassification">MistralForSequenceClassification</a> (Mistral model)</li> <li><strong>mixtral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralForSequenceClassification">MixtralForSequenceClassification</a> (Mixtral model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li> <li><strong>mpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForSequenceClassification">MptForSequenceClassification</a> (MPT model)</li> <li><strong>mra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForSequenceClassification">MraForSequenceClassification</a> (MRA model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5ForSequenceClassification">MT5ForSequenceClassification</a> (MT5 model)</li> <li><strong>mvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li> <li><strong>nystromformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nyströmformer model)</li> <li><strong>open-llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaForSequenceClassification">OpenLlamaForSequenceClassification</a> (OpenLlama model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li> <li><strong>perceiver</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li> <li><strong>persimmon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonForSequenceClassification">PersimmonForSequenceClassification</a> (Persimmon model)</li> <li><strong>phi</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiForSequenceClassification">PhiForSequenceClassification</a> (Phi model)</li> <li><strong>phi3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3ForSequenceClassification">Phi3ForSequenceClassification</a> (Phi3 model)</li> <li><strong>plbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li> <li><strong>qwen2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2ForSequenceClassification">Qwen2ForSequenceClassification</a> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeForSequenceClassification">Qwen2MoeForSequenceClassification</a> (Qwen2MoE model)</li> <li><strong>reformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForSequenceClassification">RobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForSequenceClassification">RoCBertForSequenceClassification</a> (RoCBert model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li> <li><strong>squeezebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmForSequenceClassification">StableLmForSequenceClassification</a> (StableLm model)</li> <li><strong>starcoder2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2ForSequenceClassification">Starcoder2ForSequenceClassification</a> (Starcoder2 model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForSequenceClassification">T5ForSequenceClassification</a> (T5 model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li> <li><strong>umt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5ForSequenceClassification">UMT5ForSequenceClassification</a> (UMT5 model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForSequenceClassification">XmodForSequenceClassification</a> (X-MOD model)</li> <li><strong>yoso</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>',tk,t1,UV=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,nk,pi,Jv,Mc,Vv,je,Tc,ak,n1,NV=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,sk,a1,qV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",ik,zt,Fc,lk,s1,XV="Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.",dk,i1,DV=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,mk,hi,ck,Ko,Cc,fk,l1,QV="Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.",gk,d1,HV=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,pk,m1,YV='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li> <li><strong>ctrl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.TFMistralForSequenceClassification">TFMistralForSequenceClassification</a> (Mistral model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li> <li><strong>openai-gpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForSequenceClassification">TFRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li> <li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li> <li><strong>transfo-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>',hk,ui,Ev,yc,Iv,Ae,vc,uk,c1,zV=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,_k,f1,OV="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",bk,Ot,wc,Mk,g1,KV="Instantiates one of the model classes of the library (with a sequence classification head) from a configuration.",Tk,p1,e0=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Fk,_i,Ck,er,xc,yk,h1,o0="Instantiate one of the model classes of the library (with a sequence classification head) from a pretrained model.",vk,u1,r0=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,wk,_1,t0='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForSequenceClassification">FlaxRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>',xk,bi,Uv,Lc,Nv,Ze,kc,Lk,b1,n0=`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,kk,M1,a0="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",$k,Kt,$c,Bk,T1,s0="Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.",jk,F1,i0=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Ak,Mi,Zk,V,Bc,Rk,C1,l0="Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.",Gk,y1,d0=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Pk,v1,m0='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li> <li><strong>canine</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li> <li><strong>ernie_m</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMForMultipleChoice">ErnieMForMultipleChoice</a> (ErnieM model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li> <li><strong>ibert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li> <li><strong>luke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForMultipleChoice">MegaForMultipleChoice</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li> <li><strong>mra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForMultipleChoice">MraForMultipleChoice</a> (MRA model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li> <li><strong>nystromformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nyströmformer model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMultipleChoice">RobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForMultipleChoice">RoCBertForMultipleChoice</a> (RoCBert model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li> <li><strong>squeezebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForMultipleChoice">XmodForMultipleChoice</a> (X-MOD model)</li> <li><strong>yoso</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>',Sk,w1,c0=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,Wk,Ti,qv,jc,Xv,Re,Ac,Jk,x1,f0=`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Vk,L1,g0="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Ek,en,Zc,Ik,k1,p0="Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.",Uk,$1,h0=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Nk,Fi,qk,or,Rc,Xk,B1,u0="Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.",Dk,j1,_0=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Qk,A1,b0='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMultipleChoice">TFDebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMultipleChoice">TFRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>',Hk,Ci,Dv,Gc,Qv,Ge,Pc,Yk,Z1,M0=`This is a generic model class that will be instantiated as one of the model classes of the library (with a multiple choice head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,zk,R1,T0="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Ok,on,Sc,Kk,G1,F0="Instantiates one of the model classes of the library (with a multiple choice head) from a configuration.",e$,P1,C0=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,o$,yi,r$,rr,Wc,t$,S1,y0="Instantiate one of the model classes of the library (with a multiple choice head) from a pretrained model.",n$,W1,v0=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,a$,J1,w0='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMultipleChoice">FlaxRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>',s$,vi,Hv,Jc,Yv,Pe,Vc,i$,V1,x0=`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,l$,E1,L0="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",d$,rn,Ec,m$,I1,k0="Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.",c$,U1,$0=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,f$,wi,g$,E,Ic,p$,N1,B0="Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.",h$,q1,j0=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,u$,X1,A0='<li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>',_$,D1,Z0=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,b$,xi,zv,Uc,Ov,Se,Nc,M$,Q1,R0=`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,T$,H1,G0="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",F$,tn,qc,C$,Y1,P0="Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.",y$,z1,S0=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,v$,Li,w$,tr,Xc,x$,O1,W0="Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.",L$,K1,J0=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,k$,eb,V0='<li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>',$$,ki,Kv,Dc,e2,We,Qc,B$,ob,E0=`This is a generic model class that will be instantiated as one of the model classes of the library (with a next sentence prediction head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,j$,rb,I0="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",A$,nn,Hc,Z$,tb,U0="Instantiates one of the model classes of the library (with a next sentence prediction head) from a configuration.",R$,nb,N0=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,G$,$i,P$,nr,Yc,S$,ab,q0="Instantiate one of the model classes of the library (with a next sentence prediction head) from a pretrained model.",W$,sb,X0=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,J$,ib,D0='<li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>',V$,Bi,o2,zc,r2,Je,Oc,E$,lb,Q0=`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,I$,db,H0="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",U$,an,Kc,N$,mb,Y0="Instantiates one of the model classes of the library (with a token classification head) from a configuration.",q$,cb,z0=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,X$,ji,D$,I,ef,Q$,fb,O0="Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.",H$,gb,K0=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Y$,pb,eE='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li> <li><strong>biogpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptForTokenClassification">BioGptForTokenClassification</a> (BioGpt model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li> <li><strong>bros</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bros#transformers.BrosForTokenClassification">BrosForTokenClassification</a> (BROS model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li> <li><strong>canine</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li> <li><strong>ernie_m</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMForTokenClassification">ErnieMForTokenClassification</a> (ErnieM model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li> <li><strong>falcon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconForTokenClassification">FalconForTokenClassification</a> (Falcon model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li> <li><strong>gemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaForTokenClassification">GemmaForTokenClassification</a> (Gemma model)</li> <li><strong>gpt-sw3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (GPT-Sw3 model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_bigcode</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForTokenClassification">GPTBigCodeForTokenClassification</a> (GPTBigCode model)</li> <li><strong>gpt_neo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoForTokenClassification">GPTNeoForTokenClassification</a> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXForTokenClassification">GPTNeoXForTokenClassification</a> (GPT NeoX model)</li> <li><strong>ibert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li> <li><strong>lilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForTokenClassification">LlamaForTokenClassification</a> (LLaMA model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li> <li><strong>luke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li> <li><strong>markuplm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForTokenClassification">MegaForTokenClassification</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li> <li><strong>mistral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralForTokenClassification">MistralForTokenClassification</a> (Mistral model)</li> <li><strong>mixtral</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralForTokenClassification">MixtralForTokenClassification</a> (Mixtral model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li> <li><strong>mpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForTokenClassification">MptForTokenClassification</a> (MPT model)</li> <li><strong>mra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForTokenClassification">MraForTokenClassification</a> (MRA model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5ForTokenClassification">MT5ForTokenClassification</a> (MT5 model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li> <li><strong>nystromformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nyströmformer model)</li> <li><strong>persimmon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonForTokenClassification">PersimmonForTokenClassification</a> (Persimmon model)</li> <li><strong>phi</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiForTokenClassification">PhiForTokenClassification</a> (Phi model)</li> <li><strong>phi3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3ForTokenClassification">Phi3ForTokenClassification</a> (Phi3 model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li> <li><strong>qwen2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2ForTokenClassification">Qwen2ForTokenClassification</a> (Qwen2 model)</li> <li><strong>qwen2_moe</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeForTokenClassification">Qwen2MoeForTokenClassification</a> (Qwen2MoE model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForTokenClassification">RobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForTokenClassification">RoCBertForTokenClassification</a> (RoCBert model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li> <li><strong>squeezebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li> <li><strong>stablelm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmForTokenClassification">StableLmForTokenClassification</a> (StableLm model)</li> <li><strong>starcoder2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2ForTokenClassification">Starcoder2ForTokenClassification</a> (Starcoder2 model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForTokenClassification">T5ForTokenClassification</a> (T5 model)</li> <li><strong>umt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5ForTokenClassification">UMT5ForTokenClassification</a> (UMT5 model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForTokenClassification">XmodForTokenClassification</a> (X-MOD model)</li> <li><strong>yoso</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>',z$,hb,oE=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,O$,Ai,t2,of,n2,Ve,rf,K$,ub,rE=`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,eB,_b,tE="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",oB,sn,tf,rB,bb,nE="Instantiates one of the model classes of the library (with a token classification head) from a configuration.",tB,Mb,aE=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,nB,Zi,aB,ar,nf,sB,Tb,sE="Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.",iB,Fb,iE=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,lB,Cb,lE='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li> <li><strong>esm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li> <li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForTokenClassification">TFRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>',dB,Ri,a2,af,s2,Ee,sf,mB,yb,dE=`This is a generic model class that will be instantiated as one of the model classes of the library (with a token classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,cB,vb,mE="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",fB,ln,lf,gB,wb,cE="Instantiates one of the model classes of the library (with a token classification head) from a configuration.",pB,xb,fE=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,hB,Gi,uB,sr,df,_B,Lb,gE="Instantiate one of the model classes of the library (with a token classification head) from a pretrained model.",bB,kb,pE=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,MB,$b,hE='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForTokenClassification">FlaxRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>',TB,Pi,i2,mf,l2,Ie,cf,FB,Bb,uE=`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,CB,jb,_E="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",yB,dn,ff,vB,Ab,bE="Instantiates one of the model classes of the library (with a question answering head) from a configuration.",wB,Zb,ME=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,xB,Si,LB,U,gf,kB,Rb,TE="Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.",$B,Gb,FE=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,BB,Pb,CE='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li> <li><strong>bigbird_pegasus</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li> <li><strong>bloom</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li> <li><strong>canine</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li> <li><strong>data2vec-text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li> <li><strong>ernie</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li> <li><strong>ernie_m</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMForQuestionAnswering">ErnieMForQuestionAnswering</a> (ErnieM model)</li> <li><strong>falcon</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconForQuestionAnswering">FalconForQuestionAnswering</a> (Falcon model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li> <li><strong>fnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li> <li><strong>gpt2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2ForQuestionAnswering">GPT2ForQuestionAnswering</a> (OpenAI GPT-2 model)</li> <li><strong>gpt_neo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoForQuestionAnswering">GPTNeoForQuestionAnswering</a> (GPT Neo model)</li> <li><strong>gpt_neox</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXForQuestionAnswering">GPTNeoXForQuestionAnswering</a> (GPT NeoX model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li> <li><strong>ibert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li> <li><strong>led</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li> <li><strong>lilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li> <li><strong>llama</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForQuestionAnswering">LlamaForQuestionAnswering</a> (LLaMA model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li> <li><strong>luke</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li> <li><strong>lxmert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li> <li><strong>markuplm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li> <li><strong>mega</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForQuestionAnswering">MegaForQuestionAnswering</a> (MEGA model)</li> <li><strong>megatron-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li> <li><strong>mpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForQuestionAnswering">MptForQuestionAnswering</a> (MPT model)</li> <li><strong>mra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForQuestionAnswering">MraForQuestionAnswering</a> (MRA model)</li> <li><strong>mt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5ForQuestionAnswering">MT5ForQuestionAnswering</a> (MT5 model)</li> <li><strong>mvp</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li> <li><strong>nezha</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li> <li><strong>nystromformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nyströmformer model)</li> <li><strong>opt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li> <li><strong>qdqbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li> <li><strong>reformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForQuestionAnswering">RobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roc_bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForQuestionAnswering">RoCBertForQuestionAnswering</a> (RoCBert model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li> <li><strong>splinter</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li> <li><strong>squeezebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li> <li><strong>t5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForQuestionAnswering">T5ForQuestionAnswering</a> (T5 model)</li> <li><strong>umt5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5ForQuestionAnswering">UMT5ForQuestionAnswering</a> (UMT5 model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li> <li><strong>xlm-roberta-xl</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li> <li><strong>xmod</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForQuestionAnswering">XmodForQuestionAnswering</a> (X-MOD model)</li> <li><strong>yoso</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>',jB,Sb,yE=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,AB,Wi,d2,pf,m2,Ue,hf,ZB,Wb,vE=`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,RB,Jb,wE="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",GB,mn,uf,PB,Vb,xE="Instantiates one of the model classes of the library (with a question answering head) from a configuration.",SB,Eb,LE=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,WB,Ji,JB,ir,_f,VB,Ib,kE="Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.",EB,Ub,$E=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,IB,Nb,BE='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li> <li><strong>camembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li> <li><strong>convbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li> <li><strong>deberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li> <li><strong>deberta-v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li> <li><strong>flaubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li> <li><strong>funnel</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li> <li><strong>gptj</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li> <li><strong>longformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li> <li><strong>mobilebert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li> <li><strong>mpnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li> <li><strong>rembert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForQuestionAnswering">TFRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li> <li><strong>xlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li> <li><strong>xlnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>',UB,Vi,c2,bf,f2,Ne,Mf,NB,qb,jE=`This is a generic model class that will be instantiated as one of the model classes of the library (with a question answering head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,qB,Xb,AE="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",XB,cn,Tf,DB,Db,ZE="Instantiates one of the model classes of the library (with a question answering head) from a configuration.",QB,Qb,RE=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,HB,Ei,YB,lr,Ff,zB,Hb,GE="Instantiate one of the model classes of the library (with a question answering head) from a pretrained model.",OB,Yb,PE=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,KB,zb,SE='<li><strong>albert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li> <li><strong>bart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li> <li><strong>bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li> <li><strong>big_bird</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li> <li><strong>distilbert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li> <li><strong>electra</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li> <li><strong>mbart</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li> <li><strong>roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li> <li><strong>roberta-prelayernorm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForQuestionAnswering">FlaxRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li> <li><strong>roformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li> <li><strong>xlm-roberta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>',ej,Ii,g2,Cf,p2,yf,vf,h2,wf,u2,xf,Lf,_2,kf,b2,$f,WE="The following auto classes are available for the following computer vision tasks.",M2,Bf,T2,qe,jf,oj,Ob,JE=`This is a generic model class that will be instantiated as one of the model classes of the library (with a depth estimation head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,rj,Kb,VE="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",tj,fn,Af,nj,eM,EE="Instantiates one of the model classes of the library (with a depth estimation head) from a configuration.",aj,oM,IE=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,sj,Ui,ij,N,Zf,lj,rM,UE="Instantiate one of the model classes of the library (with a depth estimation head) from a pretrained model.",dj,tM,NE=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,mj,nM,qE='<li><strong>depth_anything</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/depth_anything#transformers.DepthAnythingForDepthEstimation">DepthAnythingForDepthEstimation</a> (Depth Anything model)</li> <li><strong>dpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li> <li><strong>glpn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>',cj,aM,XE=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,fj,Ni,F2,Rf,C2,Xe,Gf,gj,sM,DE=`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,pj,iM,QE="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",hj,gn,Pf,uj,lM,HE="Instantiates one of the model classes of the library (with a image classification head) from a configuration.",_j,dM,YE=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,bj,qi,Mj,q,Sf,Tj,mM,zE="Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.",Fj,cM,OE=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Cj,fM,KE='<li><strong>beit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li> <li><strong>bit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> (BiT model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPForImageClassification">CLIPForImageClassification</a> (CLIP model)</li> <li><strong>convnext</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification">ConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li> <li><strong>cvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li> <li><strong>data2vec-vision</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li> <li><strong>deit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li> <li><strong>dinat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dinat#transformers.DinatForImageClassification">DinatForImageClassification</a> (DiNAT model)</li> <li><strong>dinov2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dinov2#transformers.Dinov2ForImageClassification">Dinov2ForImageClassification</a> (DINOv2 model)</li> <li><strong>efficientformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification">EfficientFormerForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher">EfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li> <li><strong>efficientnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification">EfficientNetForImageClassification</a> (EfficientNet model)</li> <li><strong>focalnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetForImageClassification">FocalNetForImageClassification</a> (FocalNet model)</li> <li><strong>imagegpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li> <li><strong>levit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li> <li><strong>mobilenet_v1</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification">MobileNetV1ForImageClassification</a> (MobileNetV1 model)</li> <li><strong>mobilenet_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification">MobileNetV2ForImageClassification</a> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2ForImageClassification">MobileViTV2ForImageClassification</a> (MobileViTV2 model)</li> <li><strong>nat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/nat#transformers.NatForImageClassification">NatForImageClassification</a> (NAT model)</li> <li><strong>perceiver</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li> <li><strong>poolformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li> <li><strong>pvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pvt#transformers.PvtForImageClassification">PvtForImageClassification</a> (PVT model)</li> <li><strong>pvt_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pvt_v2#transformers.PvtV2ForImageClassification">PvtV2ForImageClassification</a> (PVTv2 model)</li> <li><strong>regnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li> <li><strong>resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li> <li><strong>segformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li> <li><strong>siglip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipForImageClassification">SiglipForImageClassification</a> (SigLIP model)</li> <li><strong>swiftformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.SwiftFormerForImageClassification">SwiftFormerForImageClassification</a> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li> <li><strong>van</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li> <li><strong>vit_hybrid</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification">ViTHybridForImageClassification</a> (ViT Hybrid model)</li> <li><strong>vit_msn</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>',yj,gM,eI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,vj,Xi,y2,Wf,v2,De,Jf,wj,pM,oI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,xj,hM,rI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Lj,pn,Vf,kj,uM,tI="Instantiates one of the model classes of the library (with a image classification head) from a configuration.",$j,_M,nI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Bj,Di,jj,dr,Ef,Aj,bM,aI="Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.",Zj,MM,sI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,Rj,TM,iI='<li><strong>convnext</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li> <li><strong>convnextv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.TFConvNextV2ForImageClassification">TFConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li> <li><strong>cvt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li> <li><strong>data2vec-vision</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li> <li><strong>deit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li> <li><strong>efficientformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassification">TFEfficientFormerForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassificationWithTeacher">TFEfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li> <li><strong>mobilevit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li> <li><strong>regnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li> <li><strong>resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li> <li><strong>segformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li> <li><strong>swiftformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.TFSwiftFormerForImageClassification">TFSwiftFormerForImageClassification</a> (SwiftFormer model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>',Gj,Qi,w2,If,x2,Qe,Uf,Pj,FM,lI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a image classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Sj,CM,dI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Wj,hn,Nf,Jj,yM,mI="Instantiates one of the model classes of the library (with a image classification head) from a configuration.",Vj,vM,cI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Ej,Hi,Ij,mr,qf,Uj,wM,fI="Instantiate one of the model classes of the library (with a image classification head) from a pretrained model.",Nj,xM,gI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,qj,LM,pI='<li><strong>beit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li> <li><strong>regnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.FlaxRegNetForImageClassification">FlaxRegNetForImageClassification</a> (RegNet model)</li> <li><strong>resnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.FlaxResNetForImageClassification">FlaxResNetForImageClassification</a> (ResNet model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>',Xj,Yi,L2,Xf,k2,He,Df,Dj,kM,hI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a video classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,Qj,$M,uI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",Hj,un,Qf,Yj,BM,_I="Instantiates one of the model classes of the library (with a video classification head) from a configuration.",zj,jM,bI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,Oj,zi,Kj,X,Hf,eA,AM,MI="Instantiate one of the model classes of the library (with a video classification head) from a pretrained model.",oA,ZM,TI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,rA,RM,FI='<li><strong>timesformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li> <li><strong>videomae</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li> <li><strong>vivit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vivit#transformers.VivitForVideoClassification">VivitForVideoClassification</a> (ViViT model)</li>',tA,GM,CI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,nA,Oi,$2,Yf,B2,zf,Of,j2,Kf,A2,Ye,eg,aA,PM,yI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,sA,SM,vI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",iA,_n,og,lA,WM,wI="Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.",dA,JM,xI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,mA,Ki,cA,D,rg,fA,VM,LI="Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.",gA,EM,kI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,pA,IM,$I='<li><strong>deit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li> <li><strong>focalnet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetForMaskedImageModeling">FocalNetForMaskedImageModeling</a> (FocalNet model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li> <li><strong>swinv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li> <li><strong>vit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>',hA,UM,BI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,uA,el,Z2,tg,R2,ze,ng,_A,NM,jI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a masked image modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,bA,qM,AI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",MA,bn,ag,TA,XM,ZI="Instantiates one of the model classes of the library (with a masked image modeling head) from a configuration.",FA,DM,RI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,CA,ol,yA,cr,sg,vA,QM,GI="Instantiate one of the model classes of the library (with a masked image modeling head) from a pretrained model.",wA,HM,PI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,xA,YM,SI='<li><strong>deit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.TFDeiTForMaskedImageModeling">TFDeiTForMaskedImageModeling</a> (DeiT model)</li> <li><strong>swin</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.TFSwinForMaskedImageModeling">TFSwinForMaskedImageModeling</a> (Swin Transformer model)</li>',LA,rl,G2,ig,P2,Oe,lg,kA,zM,WI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a object detection head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,$A,OM,JI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",BA,Mn,dg,jA,KM,VI="Instantiates one of the model classes of the library (with a object detection head) from a configuration.",AA,eT,EI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,ZA,tl,RA,Q,mg,GA,oT,II="Instantiate one of the model classes of the library (with a object detection head) from a pretrained model.",PA,rT,UI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,SA,tT,NI='<li><strong>conditional_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR model)</li> <li><strong>deformable_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li> <li><strong>deta</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/deta#transformers.DetaForObjectDetection">DetaForObjectDetection</a> (DETA model)</li> <li><strong>detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li> <li><strong>rt_detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/rt_detr#transformers.RTDetrForObjectDetection">RTDetrForObjectDetection</a> (RT-DETR model)</li> <li><strong>table-transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li> <li><strong>yolos</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>',WA,nT,qI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,JA,nl,S2,cg,W2,Ke,fg,VA,aT,XI=`This is a generic model class that will be instantiated as one of the model classes of the library (with a image segmentation head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,EA,sT,DI="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",IA,Tn,gg,UA,iT,QI="Instantiates one of the model classes of the library (with a image segmentation head) from a configuration.",NA,lT,HI=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,qA,al,XA,H,pg,DA,dT,YI="Instantiate one of the model classes of the library (with a image segmentation head) from a pretrained model.",QA,mT,zI=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,HA,cT,OI='<li><strong>detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>',YA,fT,KI=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,zA,sl,J2,hg,V2,ug,_g,E2,bg,I2,eo,Mg,OA,gT,eU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,KA,pT,oU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",eZ,Fn,Tg,oZ,hT,rU="Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.",rZ,uT,tU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,tZ,il,nZ,Y,Fg,aZ,_T,nU="Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.",sZ,bT,aU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,iZ,MT,sU='<li><strong>beit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li> <li><strong>data2vec-vision</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li> <li><strong>dpt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li> <li><strong>mobilenet_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForSemanticSegmentation">MobileNetV2ForSemanticSegmentation</a> (MobileNetV2 model)</li> <li><strong>mobilevit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li> <li><strong>mobilevitv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2ForSemanticSegmentation">MobileViTV2ForSemanticSegmentation</a> (MobileViTV2 model)</li> <li><strong>segformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li> <li><strong>upernet</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation">UperNetForSemanticSegmentation</a> (UPerNet model)</li>',lZ,TT,iU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,dZ,ll,U2,Cg,N2,oo,yg,mZ,FT,lU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a semantic segmentation head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,cZ,CT,dU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",fZ,Cn,vg,gZ,yT,mU="Instantiates one of the model classes of the library (with a semantic segmentation head) from a configuration.",pZ,vT,cU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,hZ,dl,uZ,fr,wg,_Z,wT,fU="Instantiate one of the model classes of the library (with a semantic segmentation head) from a pretrained model.",bZ,xT,gU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,MZ,LT,pU='<li><strong>data2vec-vision</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li> <li><strong>mobilevit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li> <li><strong>segformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer model)</li>',TZ,ml,q2,xg,X2,ro,Lg,FZ,kT,hU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a instance segmentation head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,CZ,$T,uU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",yZ,yn,kg,vZ,BT,_U="Instantiates one of the model classes of the library (with a instance segmentation head) from a configuration.",wZ,jT,bU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,xZ,cl,LZ,z,$g,kZ,AT,MU="Instantiate one of the model classes of the library (with a instance segmentation head) from a pretrained model.",$Z,ZT,TU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,BZ,RT,FU='<li><strong>maskformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>',jZ,GT,CU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,AZ,fl,D2,Bg,Q2,to,jg,ZZ,PT,yU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a universal image segmentation head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,RZ,ST,vU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",GZ,vn,Ag,PZ,WT,wU="Instantiates one of the model classes of the library (with a universal image segmentation head) from a configuration.",SZ,JT,xU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,WZ,gl,JZ,O,Zg,VZ,VT,LU="Instantiate one of the model classes of the library (with a universal image segmentation head) from a pretrained model.",EZ,ET,kU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,IZ,IT,$U='<li><strong>detr</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li> <li><strong>mask2former</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mask2former#transformers.Mask2FormerForUniversalSegmentation">Mask2FormerForUniversalSegmentation</a> (Mask2Former model)</li> <li><strong>maskformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li> <li><strong>oneformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/oneformer#transformers.OneFormerForUniversalSegmentation">OneFormerForUniversalSegmentation</a> (OneFormer model)</li>',UZ,UT,BU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,NZ,pl,H2,Rg,Y2,no,Gg,qZ,NT,jU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,XZ,qT,AU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",DZ,wn,Pg,QZ,XT,ZU="Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.",HZ,DT,RU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,YZ,hl,zZ,K,Sg,OZ,QT,GU="Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.",KZ,HT,PU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,eR,YT,SU='<li><strong>align</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li> <li><strong>altclip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li> <li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li> <li><strong>chinese_clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li> <li><strong>clipseg</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li> <li><strong>siglip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipModel">SiglipModel</a> (SigLIP model)</li>',oR,zT,WU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,rR,ul,z2,Wg,O2,ao,Jg,tR,OT,JU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot image classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,nR,KT,VU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",aR,xn,Vg,sR,eF,EU="Instantiates one of the model classes of the library (with a zero-shot image classification head) from a configuration.",iR,oF,IU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,lR,_l,dR,gr,Eg,mR,rF,UU="Instantiate one of the model classes of the library (with a zero-shot image classification head) from a pretrained model.",cR,tF,NU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,fR,nF,qU='<li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li> <li><strong>clip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>',gR,bl,K2,Ig,ew,so,Ug,pR,aF,XU=`This is a generic model class that will be instantiated as one of the model classes of the library (with a zero-shot object detection head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,hR,sF,DU="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",uR,Ln,Ng,_R,iF,QU="Instantiates one of the model classes of the library (with a zero-shot object detection head) from a configuration.",bR,lF,HU=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,MR,Ml,TR,ee,qg,FR,dF,YU="Instantiate one of the model classes of the library (with a zero-shot object detection head) from a pretrained model.",CR,mF,zU=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,yR,cF,OU='<li><strong>grounding-dino</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/grounding-dino#transformers.GroundingDinoForObjectDetection">GroundingDinoForObjectDetection</a> (Grounding DINO model)</li> <li><strong>owlv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlv2#transformers.Owlv2ForObjectDetection">Owlv2ForObjectDetection</a> (OWLv2 model)</li> <li><strong>owlvit</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>',vR,fF,KU=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,wR,Tl,ow,Xg,rw,Dg,e9="The following auto classes are available for the following audio tasks.",tw,Qg,nw,io,Hg,xR,gF,o9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,LR,pF,r9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",kR,kn,Yg,$R,hF,t9="Instantiates one of the model classes of the library (with a audio classification head) from a configuration.",BR,uF,n9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,jR,Fl,AR,oe,zg,ZR,_F,a9="Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.",RR,bF,s9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,GR,MF,i9='<li><strong>audio-spectrogram-transformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/audio-spectrogram-transformer#transformers.ASTForAudioClassification">ASTForAudioClassification</a> (Audio Spectrogram Transformer model)</li> <li><strong>data2vec-audio</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li> <li><strong>hubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li> <li><strong>sew</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li> <li><strong>sew-d</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li> <li><strong>unispeech</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForSequenceClassification">Wav2Vec2BertForSequenceClassification</a> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>',PR,TF,l9=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,SR,Cl,aw,Og,sw,lo,Kg,WR,FF,d9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,JR,CF,m9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",VR,$n,ep,ER,yF,c9="Instantiates one of the model classes of the library (with a audio classification head) from a configuration.",IR,vF,f9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,UR,yl,NR,pr,op,qR,wF,g9="Instantiate one of the model classes of the library (with a audio classification head) from a pretrained model.",XR,xF,p9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,DR,LF,h9='<li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.TFWav2Vec2ForSequenceClassification">TFWav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>',QR,vl,iw,rp,lw,mo,tp,HR,kF,u9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio frame (token) classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,YR,$F,_9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",zR,Bn,np,OR,BF,b9="Instantiates one of the model classes of the library (with a audio frame (token) classification head) from a configuration.",KR,jF,M9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,eG,wl,oG,re,ap,rG,AF,T9="Instantiate one of the model classes of the library (with a audio frame (token) classification head) from a pretrained model.",tG,ZF,F9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,nG,RF,C9='<li><strong>data2vec-audio</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li> <li><strong>unispeech-sat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForAudioFrameClassification">Wav2Vec2BertForAudioFrameClassification</a> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>',aG,GF,y9=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,sG,xl,dw,sp,mw,co,ip,iG,PF,v9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a connectionist temporal classification head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,lG,SF,w9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",dG,jn,lp,mG,WF,x9="Instantiates one of the model classes of the library (with a connectionist temporal classification head) from a configuration.",cG,JF,L9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,fG,Ll,gG,te,dp,pG,VF,k9="Instantiate one of the model classes of the library (with a connectionist temporal classification head) from a pretrained model.",hG,EF,$9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,uG,IF,B9='<li><strong>data2vec-audio</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li> <li><strong>hubert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li> <li><strong>mctct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li> <li><strong>sew</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li> <li><strong>sew-d</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li> <li><strong>unispeech</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li> <li><strong>unispeech-sat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForCTC">Wav2Vec2BertForCTC</a> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>',_G,UF,j9=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,bG,kl,cw,mp,fw,fo,cp,MG,NF,A9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,TG,qF,Z9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",FG,An,fp,CG,XF,R9="Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.",yG,DF,G9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,vG,$l,wG,ne,gp,xG,QF,P9="Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.",LG,HF,S9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,kG,YF,W9='<li><strong>pop2piano</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pop2piano#transformers.Pop2PianoForConditionalGeneration">Pop2PianoForConditionalGeneration</a> (Pop2Piano model)</li> <li><strong>seamless_m4t</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TForSpeechToText">SeamlessM4TForSpeechToText</a> (SeamlessM4T model)</li> <li><strong>seamless_m4t_v2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2ForSpeechToText">SeamlessM4Tv2ForSpeechToText</a> (SeamlessM4Tv2 model)</li> <li><strong>speech-encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li> <li><strong>speech_to_text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li> <li><strong>speecht5</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5ForSpeechToText">SpeechT5ForSpeechToText</a> (SpeechT5 model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>',$G,zF,J9=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,BG,Bl,gw,pp,pw,go,hp,jG,OF,V9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,AG,KF,E9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",ZG,Zn,up,RG,eC,I9="Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.",GG,oC,U9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,PG,jl,SG,hr,_p,WG,rC,N9="Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.",JG,tC,q9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,VG,nC,X9='<li><strong>speech_to_text</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>',EG,Al,hw,bp,uw,po,Mp,IG,aC,D9=`This is a generic model class that will be instantiated as one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,UG,sC,Q9="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",NG,Rn,Tp,qG,iC,H9="Instantiates one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a configuration.",XG,lC,Y9=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,DG,Zl,QG,ur,Fp,HG,dC,z9="Instantiate one of the model classes of the library (with a sequence-to-sequence speech-to-text modeling head) from a pretrained model.",YG,mC,O9=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,zG,cC,K9='<li><strong>speech-encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/speech-encoder-decoder#transformers.FlaxSpeechEncoderDecoderModel">FlaxSpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li> <li><strong>whisper</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>',OG,Rl,_w,Cp,bw,ho,yp,KG,fC,eN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a audio retrieval via x-vector head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,eP,gC,oN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",oP,Gn,vp,rP,pC,rN="Instantiates one of the model classes of the library (with a audio retrieval via x-vector head) from a configuration.",tP,hC,tN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,nP,Gl,aP,ae,wp,sP,uC,nN="Instantiate one of the model classes of the library (with a audio retrieval via x-vector head) from a pretrained model.",iP,_C,aN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,lP,bC,sN='<li><strong>data2vec-audio</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li> <li><strong>unispeech-sat</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li> <li><strong>wav2vec2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li> <li><strong>wav2vec2-bert</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForXVector">Wav2Vec2BertForXVector</a> (Wav2Vec2-BERT model)</li> <li><strong>wav2vec2-conformer</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li> <li><strong>wavlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>',dP,MC,iN=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,mP,Pl,Mw,xp,Tw,Lp,kp,Fw,$p,Cw,Bp,jp,yw,Ap,vw,Zp,lN="The following auto classes are available for the following multimodal tasks.",ww,Rp,xw,uo,Gp,cP,TC,dN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,fP,FC,mN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",gP,Pn,Pp,pP,CC,cN="Instantiates one of the model classes of the library (with a table question answering head) from a configuration.",hP,yC,fN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,uP,Sl,_P,se,Sp,bP,vC,gN="Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.",MP,wC,pN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,TP,xC,hN='<li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>',FP,LC,uN=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,CP,Wl,Lw,Wp,kw,_o,Jp,yP,kC,_N=`This is a generic model class that will be instantiated as one of the model classes of the library (with a table question answering head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,vP,$C,bN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",wP,Sn,Vp,xP,BC,MN="Instantiates one of the model classes of the library (with a table question answering head) from a configuration.",LP,jC,TN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,kP,Jl,$P,_r,Ep,BP,AC,FN="Instantiate one of the model classes of the library (with a table question answering head) from a pretrained model.",jP,ZC,CN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,AP,RC,yN='<li><strong>tapas</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>',ZP,Vl,$w,Ip,Bw,bo,Up,RP,GC,vN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,GP,PC,wN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",PP,Wn,Np,SP,SC,xN="Instantiates one of the model classes of the library (with a document question answering head) from a configuration.",WP,WC,LN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,JP,El,VP,ie,qp,EP,JC,kN="Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.",IP,VC,$N=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,UP,EC,BN='<li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li> <li><strong>layoutlmv2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>',NP,IC,jN=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,qP,Il,jw,Xp,Aw,Mo,Dp,XP,UC,AN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a document question answering head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,DP,NC,ZN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",QP,Jn,Qp,HP,qC,RN="Instantiates one of the model classes of the library (with a document question answering head) from a configuration.",YP,XC,GN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,zP,Ul,OP,br,Hp,KP,DC,PN="Instantiate one of the model classes of the library (with a document question answering head) from a pretrained model.",eS,QC,SN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,oS,HC,WN='<li><strong>layoutlm</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li> <li><strong>layoutlmv3</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>',rS,Nl,Zw,Yp,Rw,To,zp,tS,YC,JN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a visual question answering head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,nS,zC,VN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",aS,Vn,Op,sS,OC,EN="Instantiates one of the model classes of the library (with a visual question answering head) from a configuration.",iS,KC,IN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,lS,ql,dS,le,Kp,mS,ey,UN="Instantiate one of the model classes of the library (with a visual question answering head) from a pretrained model.",cS,oy,NN=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,fS,ry,qN='<li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipForQuestionAnswering">BlipForQuestionAnswering</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li> <li><strong>vilt</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>',gS,ty,XN=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,pS,Xl,Gw,eh,Pw,Fo,oh,hS,ny,DN=`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,uS,ay,QN="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",_S,En,rh,bS,sy,HN="Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.",MS,iy,YN=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,TS,Dl,FS,de,th,CS,ly,zN="Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.",yS,dy,ON=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,vS,my,KN='<li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li> <li><strong>blip-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li> <li><strong>git</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li> <li><strong>idefics2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2ForConditionalGeneration">Idefics2ForConditionalGeneration</a> (Idefics2 model)</li> <li><strong>instructblip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/instructblip#transformers.InstructBlipForConditionalGeneration">InstructBlipForConditionalGeneration</a> (InstructBLIP model)</li> <li><strong>instructblipvideo</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/instructblipvideo#transformers.InstructBlipVideoForConditionalGeneration">InstructBlipVideoForConditionalGeneration</a> (InstructBlipVideo model)</li> <li><strong>kosmos-2</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/kosmos-2#transformers.Kosmos2ForConditionalGeneration">Kosmos2ForConditionalGeneration</a> (KOSMOS-2 model)</li> <li><strong>llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava#transformers.LlavaForConditionalGeneration">LlavaForConditionalGeneration</a> (LLaVa model)</li> <li><strong>llava_next</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/llava_next#transformers.LlavaNextForConditionalGeneration">LlavaNextForConditionalGeneration</a> (LLaVA-NeXT model)</li> <li><strong>paligemma</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration">PaliGemmaForConditionalGeneration</a> (PaliGemma model)</li> <li><strong>pix2struct</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/pix2struct#transformers.Pix2StructForConditionalGeneration">Pix2StructForConditionalGeneration</a> (Pix2Struct model)</li> <li><strong>video_llava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/video_llava#transformers.VideoLlavaForConditionalGeneration">VideoLlavaForConditionalGeneration</a> (VideoLlava model)</li> <li><strong>vipllava</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vipllava#transformers.VipLlavaForConditionalGeneration">VipLlavaForConditionalGeneration</a> (VipLlava model)</li> <li><strong>vision-encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel">VisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>',wS,cy,e5=`The model is set in evaluation mode by default using <code>model.eval()</code> (so for instance, dropout modules are
deactivated). To train the model, you should first set it back in training mode with <code>model.train()</code>`,xS,Ql,Sw,nh,Ww,Co,ah,LS,fy,o5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,kS,gy,r5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",$S,In,sh,BS,py,t5="Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.",jS,hy,n5=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,AS,Hl,ZS,Mr,ih,RS,uy,a5="Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.",GS,_y,s5=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,PS,by,i5='<li><strong>blip</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.TFBlipForConditionalGeneration">TFBlipForConditionalGeneration</a> (BLIP model)</li> <li><strong>vision-encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>',SS,Yl,Jw,lh,Vw,yo,dh,WS,My,l5=`This is a generic model class that will be instantiated as one of the model classes of the library (with a vision-to-text modeling head) when created
with the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> class method or the <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_config">from_config()</a> class
method.`,JS,Ty,d5="This class cannot be instantiated directly using <code>__init__()</code> (throws an error).",VS,Un,mh,ES,Fy,m5="Instantiates one of the model classes of the library (with a vision-to-text modeling head) from a configuration.",IS,Cy,c5=`Note:
Loading a model from its configuration file does <strong>not</strong> load the model weights. It only affects the
model’s configuration. Use <a href="/docs/transformers/pr_31161/en/model_doc/auto#transformers.AutoModel.from_pretrained">from_pretrained()</a> to load the model weights.`,US,zl,NS,Tr,ch,qS,yy,f5="Instantiate one of the model classes of the library (with a vision-to-text modeling head) from a pretrained model.",XS,vy,g5=`The model class to instantiate is selected based on the <code>model_type</code> property of the config object (either
passed as an argument or loaded from <code>pretrained_model_name_or_path</code> if possible), or when it’s missing, by
falling back to using pattern matching on <code>pretrained_model_name_or_path</code>:`,DS,wy,p5='<li><strong>vision-encoder-decoder</strong> — <a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.FlaxVisionEncoderDecoderModel">FlaxVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>',QS,Ol,Ew,fh,Iw,xy,Uw;return c=new B({props:{title:"Auto Classes",local:"auto-classes",headingTag:"h1"}}),vd=new k({props:{code:"bW9kZWwlMjAlM0QlMjBBdXRvTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1iYXNlLWNhc2VkJTIyKQ==",highlighted:'model = AutoModel.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-base-cased&quot;</span>)',wrap:!1}}),Ld=new B({props:{title:"Extending the Auto Classes",local:"extending-the-auto-classes",headingTag:"h2"}}),$d=new k({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMkMlMjBBdXRvTW9kZWwlMEElMEFBdXRvQ29uZmlnLnJlZ2lzdGVyKCUyMm5ldy1tb2RlbCUyMiUyQyUyME5ld01vZGVsQ29uZmlnKSUwQUF1dG9Nb2RlbC5yZWdpc3RlcihOZXdNb2RlbENvbmZpZyUyQyUyME5ld01vZGVsKQ==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig, AutoModel

AutoConfig.register(<span class="hljs-string">&quot;new-model&quot;</span>, NewModelConfig)
AutoModel.register(NewModelConfig, NewModel)`,wrap:!1}}),ys=new Nw({props:{warning:!0,$$slots:{default:[v5]},$$scope:{ctx:y}}}),jd=new B({props:{title:"AutoConfig",local:"transformers.AutoConfig",headingTag:"h2"}}),Ad=new x({props:{name:"class transformers.AutoConfig",anchor:"transformers.AutoConfig",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/configuration_auto.py#L837"}}),Zd=new x({props:{name:"from_pretrained",anchor:"transformers.AutoConfig.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoConfig.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model configuration hosted inside a model repo on
huggingface.co.</li>
<li>A path to a <em>directory</em> containing a configuration file saved using the
<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.save_pretrained">save_pretrained()</a> method, or the <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> method,
e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a saved configuration JSON <em>file</em>, e.g.,
<code>./my_model_directory/configuration.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoConfig.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoConfig.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoConfig.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoConfig.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoConfig.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final configuration object.</p>
<p>If <code>True</code>, then this functions returns a <code>Tuple(config, unused_kwargs)</code> where <em>unused_kwargs</em> is a
dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e., the
part of <code>kwargs</code> which has not been used to update <code>config</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoConfig.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoConfig.from_pretrained.kwargs(additional",description:`<strong>kwargs(additional</strong> keyword arguments, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are configuration attributes will be used to override the loaded
values. Behavior concerning key/value pairs whose keys are <em>not</em> configuration attributes is controlled
by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs(additional"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/configuration_auto.py#L860"}}),vs=new $({props:{anchor:"transformers.AutoConfig.from_pretrained.example",$$slots:{default:[w5]},$$scope:{ctx:y}}}),Rd=new x({props:{name:"register",anchor:"transformers.AutoConfig.register",parameters:[{name:"model_type",val:""},{name:"config",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoConfig.register.model_type",description:"<strong>model_type</strong> (<code>str</code>) &#x2014; The model type like &#x201C;bert&#x201D; or &#x201C;gpt&#x201D;.",name:"model_type"},{anchor:"transformers.AutoConfig.register.config",description:'<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014; The config to register.',name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/configuration_auto.py#L999"}}),Gd=new B({props:{title:"AutoTokenizer",local:"transformers.AutoTokenizer",headingTag:"h2"}}),Pd=new x({props:{name:"class transformers.AutoTokenizer",anchor:"transformers.AutoTokenizer",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/tokenization_auto.py#L677"}}),Sd=new x({props:{name:"from_pretrained",anchor:"transformers.AutoTokenizer.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoTokenizer.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a predefined tokenizer hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing vocabulary files required by the tokenizer, for instance saved
using the <a href="/docs/transformers/pr_31161/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.save_pretrained">save_pretrained()</a> method, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a single saved vocabulary file if and only if the tokenizer only requires a
single vocabulary file (like Bert or XLNet), e.g.: <code>./my_model_directory/vocab.txt</code>. (Not
applicable to all derived classes)</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoTokenizer.from_pretrained.inputs",description:`<strong>inputs</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the Tokenizer <code>__init__()</code> method.`,name:"inputs"},{anchor:"transformers.AutoTokenizer.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
The configuration object used to determine the tokenizer class to instantiate.`,name:"config"},{anchor:"transformers.AutoTokenizer.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoTokenizer.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download the model weights and configuration files and override the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoTokenizer.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoTokenizer.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoTokenizer.from_pretrained.subfolder",description:`<strong>subfolder</strong> (<code>str</code>, <em>optional</em>) &#x2014;
In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
facebook/rag-token-base), specify it here.`,name:"subfolder"},{anchor:"transformers.AutoTokenizer.from_pretrained.use_fast",description:`<strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Use a <a href="https://huggingface.co/docs/tokenizers/index" rel="nofollow">fast Rust-based tokenizer</a> if it is supported for
a given model. If a fast tokenizer is not available for a given model, a normal Python-based tokenizer
is returned instead.`,name:"use_fast"},{anchor:"transformers.AutoTokenizer.from_pretrained.tokenizer_type",description:`<strong>tokenizer_type</strong> (<code>str</code>, <em>optional</em>) &#x2014;
Tokenizer type to be loaded.`,name:"tokenizer_type"},{anchor:"transformers.AutoTokenizer.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoTokenizer.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Will be passed to the Tokenizer <code>__init__()</code> method. Can be used to set special tokens like
<code>bos_token</code>, <code>eos_token</code>, <code>unk_token</code>, <code>sep_token</code>, <code>pad_token</code>, <code>cls_token</code>, <code>mask_token</code>,
<code>additional_special_tokens</code>. See parameters in the <code>__init__()</code> for more details.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/tokenization_auto.py#L691"}}),xs=new $({props:{anchor:"transformers.AutoTokenizer.from_pretrained.example",$$slots:{default:[x5]},$$scope:{ctx:y}}}),Wd=new x({props:{name:"register",anchor:"transformers.AutoTokenizer.register",parameters:[{name:"config_class",val:""},{name:"slow_tokenizer_class",val:" = None"},{name:"fast_tokenizer_class",val:" = None"},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoTokenizer.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoTokenizer.register.slow_tokenizer_class",description:`<strong>slow_tokenizer_class</strong> (<code>PretrainedTokenizer</code>, <em>optional</em>) &#x2014;
The slow tokenizer to register.`,name:"slow_tokenizer_class"},{anchor:"transformers.AutoTokenizer.register.fast_tokenizer_class",description:`<strong>fast_tokenizer_class</strong> (<code>PretrainedTokenizerFast</code>, <em>optional</em>) &#x2014;
The fast tokenizer to register.`,name:"fast_tokenizer_class"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/tokenization_auto.py#L915"}}),Jd=new B({props:{title:"AutoFeatureExtractor",local:"transformers.AutoFeatureExtractor",headingTag:"h2"}}),Vd=new x({props:{name:"class transformers.AutoFeatureExtractor",anchor:"transformers.AutoFeatureExtractor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/feature_extraction_auto.py#L240"}}),Ed=new x({props:{name:"from_pretrained",anchor:"transformers.AutoFeatureExtractor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co.</li>
<li>a path to a <em>directory</em> containing a feature extractor file saved using the
<a href="/docs/transformers/pr_31161/en/main_classes/feature_extractor#transformers.FeatureExtractionMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved feature extractor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoFeatureExtractor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/feature_extraction_auto.py#L254"}}),ks=new Nw({props:{$$slots:{default:[L5]},$$scope:{ctx:y}}}),$s=new $({props:{anchor:"transformers.AutoFeatureExtractor.from_pretrained.example",$$slots:{default:[k5]},$$scope:{ctx:y}}}),Id=new x({props:{name:"register",anchor:"transformers.AutoFeatureExtractor.register",parameters:[{name:"config_class",val:""},{name:"feature_extractor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoFeatureExtractor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoFeatureExtractor.register.feature_extractor_class",description:"<strong>feature_extractor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The feature extractor to register.",name:"feature_extractor_class"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/feature_extraction_auto.py#L388"}}),Ud=new B({props:{title:"AutoImageProcessor",local:"transformers.AutoImageProcessor",headingTag:"h2"}}),Nd=new x({props:{name:"class transformers.AutoImageProcessor",anchor:"transformers.AutoImageProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/image_processing_auto.py#L298"}}),qd=new x({props:{name:"from_pretrained",anchor:"transformers.AutoImageProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained image_processor hosted inside a model repo on
huggingface.co.</li>
<li>a path to a <em>directory</em> containing a image processor file saved using the
<a href="/docs/transformers/pr_31161/en/main_classes/image_processor#transformers.ImageProcessingMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved image processor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoImageProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoImageProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the image processor files and override the cached versions if
they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoImageProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoImageProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoImageProcessor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoImageProcessor.from_pretrained.use_fast",description:`<strong>use_fast</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Use a fast torchvision-base image processor if it is supported for a given model.
If a fast tokenizer is not available for a given model, a normal numpy-based image processor
is returned instead.`,name:"use_fast"},{anchor:"transformers.AutoImageProcessor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final image processor object. If <code>True</code>, then this
functions returns a <code>Tuple(image_processor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not image processor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>image_processor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoImageProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoImageProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are image processor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> image processor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/image_processing_auto.py#L312"}}),js=new Nw({props:{$$slots:{default:[$5]},$$scope:{ctx:y}}}),As=new $({props:{anchor:"transformers.AutoImageProcessor.from_pretrained.example",$$slots:{default:[B5]},$$scope:{ctx:y}}}),Xd=new x({props:{name:"register",anchor:"transformers.AutoImageProcessor.register",parameters:[{name:"config_class",val:""},{name:"image_processor_class",val:" = None"},{name:"slow_image_processor_class",val:" = None"},{name:"fast_image_processor_class",val:" = None"},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoImageProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoImageProcessor.register.image_processor_class",description:'<strong>image_processor_class</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/image_processor#transformers.ImageProcessingMixin">ImageProcessingMixin</a>) &#x2014; The image processor to register.',name:"image_processor_class"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/image_processing_auto.py#L490"}}),Dd=new B({props:{title:"AutoProcessor",local:"transformers.AutoProcessor",headingTag:"h2"}}),Qd=new x({props:{name:"class transformers.AutoProcessor",anchor:"transformers.AutoProcessor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/processing_auto.py#L135"}}),Hd=new x({props:{name:"from_pretrained",anchor:"transformers.AutoProcessor.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoProcessor.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained feature_extractor hosted inside a model repo on
huggingface.co.</li>
<li>a path to a <em>directory</em> containing a processor files saved using the <code>save_pretrained()</code> method,
e.g., <code>./my_model_directory/</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoProcessor.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model feature extractor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoProcessor.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the feature extractor files and override the cached versions
if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoProcessor.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoProcessor.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <em>bool</em>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, will use the token generated
when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.AutoProcessor.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoProcessor.from_pretrained.return_unused_kwargs",description:`<strong>return_unused_kwargs</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
If <code>False</code>, then this function returns just the final feature extractor object. If <code>True</code>, then this
functions returns a <code>Tuple(feature_extractor, unused_kwargs)</code> where <em>unused_kwargs</em> is a dictionary
consisting of the key/value pairs whose keys are not feature extractor attributes: i.e., the part of
<code>kwargs</code> which has not been used to update <code>feature_extractor</code> and is otherwise ignored.`,name:"return_unused_kwargs"},{anchor:"transformers.AutoProcessor.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoProcessor.from_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
The values in kwargs of any keys which are feature extractor attributes will be used to override the
loaded values. Behavior concerning key/value pairs whose keys are <em>not</em> feature extractor attributes is
controlled by the <code>return_unused_kwargs</code> keyword parameter.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/processing_auto.py#L149"}}),Rs=new Nw({props:{$$slots:{default:[j5]},$$scope:{ctx:y}}}),Gs=new $({props:{anchor:"transformers.AutoProcessor.from_pretrained.example",$$slots:{default:[A5]},$$scope:{ctx:y}}}),Yd=new x({props:{name:"register",anchor:"transformers.AutoProcessor.register",parameters:[{name:"config_class",val:""},{name:"processor_class",val:""},{name:"exist_ok",val:" = False"}],parametersDescription:[{anchor:"transformers.AutoProcessor.register.config_class",description:`<strong>config_class</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The configuration corresponding to the model to register.`,name:"config_class"},{anchor:"transformers.AutoProcessor.register.processor_class",description:"<strong>processor_class</strong> (<code>FeatureExtractorMixin</code>) &#x2014; The processor to register.",name:"processor_class"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/processing_auto.py#L352"}}),zd=new B({props:{title:"Generic model classes",local:"generic-model-classes",headingTag:"h2"}}),Kd=new B({props:{title:"AutoModel",local:"transformers.AutoModel",headingTag:"h3"}}),em=new x({props:{name:"class transformers.AutoModel",anchor:"transformers.AutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1448"}}),om=new x({props:{name:"from_config",anchor:"transformers.AutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/audio-spectrogram-transformer#transformers.ASTModel">ASTModel</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertModel">AlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/autoformer#transformers.AutoformerConfig">AutoformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/autoformer#transformers.AutoformerModel">AutoformerModel</a> (Autoformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bark#transformers.BarkConfig">BarkConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bark#transformers.BarkModel">BarkModel</a> (Bark model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartModel">BartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitModel">BeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertModel">BertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusModel">BigBirdPegasusModel</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptModel">BioGptModel</a> (BioGpt model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitModel">BitModel</a> (BiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotModel">BlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallModel">BlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2Model">Blip2Model</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomModel">BloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bridgetower#transformers.BridgeTowerConfig">BridgeTowerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bridgetower#transformers.BridgeTowerModel">BridgeTowerModel</a> (BridgeTower model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bros#transformers.BrosConfig">BrosConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bros#transformers.BrosModel">BrosModel</a> (BROS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPVisionConfig">CLIPVisionConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPVisionModel">CLIPVisionModel</a> (CLIPVisionModel model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertModel">CamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineModel">CanineModel</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPVisionConfig">ChineseCLIPVisionConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPVisionModel">ChineseCLIPVisionModel</a> (ChineseCLIPVisionModel model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clap#transformers.ClapConfig">ClapConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clap#transformers.ClapModel">ClapModel</a> (CLAP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clvp#transformers.ClvpConfig">ClvpConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clvp#transformers.ClvpModelForConditionalGeneration">ClvpModelForConditionalGeneration</a> (CLVP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenModel">CodeGenModel</a> (CodeGen model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/cohere#transformers.CohereConfig">CohereConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/cohere#transformers.CohereModel">CohereModel</a> (Cohere model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/conditional_detr#transformers.ConditionalDetrModel">ConditionalDetrModel</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertModel">ConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextModel">ConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.ConvNextV2Model">ConvNextV2Model</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.CvtModel">CvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dpr#transformers.DPRQuestionEncoder">DPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTModel">DPTModel</a> (DPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioModel">Data2VecAudioModel</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextModel">Data2VecTextModel</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionModel">Data2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dbrx#transformers.DbrxConfig">DbrxConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dbrx#transformers.DbrxModel">DbrxModel</a> (DBRX model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaModel">DebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Model">DebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/decision_transformer#transformers.DecisionTransformerConfig">DecisionTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/decision_transformer#transformers.DecisionTransformerModel">DecisionTransformerModel</a> (Decision Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTModel">DeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deta#transformers.DetaModel">DetaModel</a> (DETA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrModel">DetrModel</a> (DETR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dinat#transformers.DinatModel">DinatModel</a> (DiNAT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dinov2#transformers.Dinov2Model">Dinov2Model</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/donut#transformers.DonutSwinConfig">DonutSwinConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/donut#transformers.DonutSwinModel">DonutSwinModel</a> (DonutSwin model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerModel">EfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/efficientnet#transformers.EfficientNetModel">EfficientNetModel</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraModel">ElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/encodec#transformers.EncodecConfig">EncodecConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/encodec#transformers.EncodecModel">EncodecModel</a> (EnCodec model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieModel">ErnieModel</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMModel">ErnieMModel</a> (ErnieM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmModel">EsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetModel">FNetModel</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTModel">FSMTModel</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconModel">FalconModel</a> (Falcon model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fastspeech2_conformer#transformers.FastSpeech2ConformerConfig">FastSpeech2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fastspeech2_conformer#transformers.FastSpeech2ConformerModel">FastSpeech2ConformerModel</a> (FastSpeech2Conformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertModel">FlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaModel">FlavaModel</a> (FLAVA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetModel">FocalNetModel</a> (FocalNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelModel">FunnelModel</a> or <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelBaseModel">FunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/glpn#transformers.GLPNModel">GLPNModel</a> (GLPN model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Model">GPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeModel">GPTBigCodeModel</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJModel">GPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoModel">GPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXModel">GPTNeoXModel</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseModel">GPTNeoXJapaneseModel</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaModel">GemmaModel</a> (Gemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitModel">GitModel</a> (GIT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/graphormer#transformers.GraphormerConfig">GraphormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/graphormer#transformers.GraphormerModel">GraphormerModel</a> (Graphormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/grounding-dino#transformers.GroundingDinoConfig">GroundingDinoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/grounding-dino#transformers.GroundingDinoModel">GroundingDinoModel</a> (Grounding DINO model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/groupvit#transformers.GroupViTModel">GroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertModel">HubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertModel">IBertModel</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2Config">Idefics2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2Model">Idefics2Model</a> (Idefics2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsModel">IdeficsModel</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/imagegpt#transformers.ImageGPTModel">ImageGPTModel</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/informer#transformers.InformerConfig">InformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/informer#transformers.InformerModel">InformerModel</a> (Informer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaConfig">JambaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaModel">JambaModel</a> (Jamba model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeConfig">JetMoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeModel">JetMoeModel</a> (JetMoe model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/jukebox#transformers.JukeboxConfig">JukeboxConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/jukebox#transformers.JukeboxModel">JukeboxModel</a> (Jukebox model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/kosmos-2#transformers.Kosmos2Config">Kosmos2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/kosmos-2#transformers.Kosmos2Model">Kosmos2Model</a> (KOSMOS-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDModel">LEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMModel">LayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Model">LayoutLMv2Model</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Model">LayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitModel">LevitModel</a> (LeViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltModel">LiltModel</a> (LiLT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaModel">LlamaModel</a> (LLaMA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5Model">LongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerModel">LongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeModel">LukeModel</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertModel">LxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/m2m_100#transformers.M2M100Model">M2M100Model</a> (M2M100 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartModel">MBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mctct#transformers.MCTCTModel">MCTCTModel</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetModel">MPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Model">MT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaConfig">MambaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaModel">MambaModel</a> (Mamba model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianModel">MarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMModel">MarkupLMModel</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mask2former#transformers.Mask2FormerModel">Mask2FormerModel</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerModel">MaskFormerModel</a> (MaskFormer model)</li>
<li><code>MaskFormerSwinConfig</code> configuration class: <code>MaskFormerSwinModel</code> (MaskFormerSwin model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaModel">MegaModel</a> (MEGA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertModel">MegatronBertModel</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mgp-str#transformers.MgpstrConfig">MgpstrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mgp-str#transformers.MgpstrForSceneTextRecognition">MgpstrForSceneTextRecognition</a> (MGP-STR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralModel">MistralModel</a> (Mistral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralConfig">MixtralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralModel">MixtralModel</a> (Mixtral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertModel">MobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v1#transformers.MobileNetV1Model">MobileNetV1Model</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2Model">MobileNetV2Model</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTModel">MobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2Model">MobileViTV2Model</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptModel">MptModel</a> (MPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraModel">MraModel</a> (MRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/musicgen#transformers.MusicgenConfig">MusicgenConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/musicgen#transformers.MusicgenModel">MusicgenModel</a> (MusicGen model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/musicgen_melody#transformers.MusicgenMelodyConfig">MusicgenMelodyConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/musicgen_melody#transformers.MusicgenMelodyModel">MusicgenMelodyModel</a> (MusicGen Melody model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpModel">MvpModel</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nat#transformers.NatModel">NatModel</a> (NAT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaModel">NezhaModel</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeModel">NllbMoeModel</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerModel">NystromformerModel</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTModel">OPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/olmo#transformers.OlmoConfig">OlmoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/olmo#transformers.OlmoModel">OlmoModel</a> (OLMo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/oneformer#transformers.OneFormerModel">OneFormerModel</a> (OneFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTModel">OpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTModel">OwlViTModel</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/owlv2#transformers.Owlv2Config">Owlv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/owlv2#transformers.Owlv2Model">Owlv2Model</a> (OWLv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartModel">PLBartModel</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/patchtsmixer#transformers.PatchTSMixerConfig">PatchTSMixerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/patchtsmixer#transformers.PatchTSMixerModel">PatchTSMixerModel</a> (PatchTSMixer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/patchtst#transformers.PatchTSTConfig">PatchTSTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/patchtst#transformers.PatchTSTModel">PatchTSTModel</a> (PatchTST model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusModel">PegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pegasus_x#transformers.PegasusXModel">PegasusXModel</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverModel">PerceiverModel</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonConfig">PersimmonConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonModel">PersimmonModel</a> (Persimmon model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3Config">Phi3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3Model">Phi3Model</a> (Phi3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiConfig">PhiConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiModel">PhiModel</a> (Phi model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/poolformer#transformers.PoolFormerModel">PoolFormerModel</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetModel">ProphetNetModel</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pvt#transformers.PvtModel">PvtModel</a> (PVT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pvt_v2#transformers.PvtV2Config">PvtV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pvt_v2#transformers.PvtV2Model">PvtV2Model</a> (PVTv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertModel">QDQBertModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2Config">Qwen2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2Model">Qwen2Model</a> (Qwen2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeConfig">Qwen2MoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeModel">Qwen2MoeModel</a> (Qwen2MoE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rt_detr#transformers.RTDetrConfig">RTDetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rt_detr#transformers.RTDetrModel">RTDetrModel</a> (RT-DETR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/recurrent_gemma#transformers.RecurrentGemmaConfig">RecurrentGemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/recurrent_gemma#transformers.RecurrentGemmaModel">RecurrentGemmaModel</a> (RecurrentGemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerModel">ReformerModel</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetModel">RegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetModel">ResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertModel">RoCBertModel</a> (RoCBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerModel">RoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaModel">RobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormModel">RobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvModel">RwkvModel</a> (RWKV model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWModel">SEWModel</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDModel">SEWDModel</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/sam#transformers.SamModel">SamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TConfig">SeamlessM4TConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TModel">SeamlessM4TModel</a> (SeamlessM4T model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2Config">SeamlessM4Tv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2Model">SeamlessM4Tv2Model</a> (SeamlessM4Tv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/seggpt#transformers.SegGptConfig">SegGptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/seggpt#transformers.SegGptModel">SegGptModel</a> (SegGPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerModel">SegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipConfig">SiglipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipModel">SiglipModel</a> (SigLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipVisionConfig">SiglipVisionConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipVisionModel">SiglipVisionModel</a> (SiglipVisionModel model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextModel">Speech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5Model">SpeechT5Model</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterModel">SplinterModel</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertModel">SqueezeBertModel</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmConfig">StableLmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmModel">StableLmModel</a> (StableLm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2Config">Starcoder2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2Model">Starcoder2Model</a> (Starcoder2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.SwiftFormerModel">SwiftFormerModel</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swin2sr#transformers.Swin2SRConfig">Swin2SRConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swin2sr#transformers.Swin2SRModel">Swin2SRModel</a> (Swin2SR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinModel">SwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersModel">SwitchTransformersModel</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Model">T5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/table-transformer#transformers.TableTransformerModel">TableTransformerModel</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasModel">TapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerConfig">TimeSeriesTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/time_series_transformer#transformers.TimeSeriesTransformerModel">TimeSeriesTransformerModel</a> (Time Series Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/timesformer#transformers.TimesformerModel">TimesformerModel</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/main_classes/backbones#transformers.TimmBackboneConfig">TimmBackboneConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/main_classes/backbones#transformers.TimmBackbone">TimmBackbone</a> (TimmBackbone model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerConfig">TrajectoryTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/trajectory_transformer#transformers.TrajectoryTransformerModel">TrajectoryTransformerModel</a> (Trajectory Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLModel">TransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltModel">TvltModel</a> (TVLT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tvp#transformers.TvpConfig">TvpConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tvp#transformers.TvpModel">TvpModel</a> (TVP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5Model">UMT5Model</a> (UMT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/udop#transformers.UdopConfig">UdopConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/udop#transformers.UdopModel">UdopModel</a> (UDOP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechModel">UniSpeechModel</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatModel">UniSpeechSatModel</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/univnet#transformers.UnivNetConfig">UnivNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/univnet#transformers.UnivNetModel">UnivNetModel</a> (UnivNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/van#transformers.VanModel">VanModel</a> (VAN model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTModel">ViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit_hybrid#transformers.ViTHybridModel">ViTHybridModel</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.ViTMAEModel">ViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit_msn#transformers.ViTMSNModel">ViTMSNModel</a> (ViTMSN model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEModel">VideoMAEModel</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltModel">ViltModel</a> (ViLT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderModel">VisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/visual_bert#transformers.VisualBertModel">VisualBertModel</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vitdet#transformers.VitDetConfig">VitDetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vitdet#transformers.VitDetModel">VitDetModel</a> (VitDet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vits#transformers.VitsConfig">VitsConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vits#transformers.VitsModel">VitsModel</a> (VITS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vivit#transformers.VivitModel">VivitModel</a> (ViViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertConfig">Wav2Vec2BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertModel">Wav2Vec2BertModel</a> (Wav2Vec2-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Model">Wav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerModel">Wav2Vec2ConformerModel</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMModel">WavLMModel</a> (WavLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperModel">WhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xclip#transformers.XCLIPConfig">XCLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xclip#transformers.XCLIPModel">XCLIPModel</a> (X-CLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMModel">XGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMModel">XLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetModel">XLMProphetNetModel</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaModel">XLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLModel">XLMRobertaXLModel</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetModel">XLNetModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodModel">XmodModel</a> (X-MOD model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/yolos#transformers.YolosModel">YolosModel</a> (YOLOS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoModel">YosoModel</a> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModel.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ss=new $({props:{anchor:"transformers.AutoModel.from_config.example",$$slots:{default:[Z5]},$$scope:{ctx:y}}}),rm=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModel.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModel.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ws=new $({props:{anchor:"transformers.AutoModel.from_pretrained.example",$$slots:{default:[R5]},$$scope:{ctx:y}}}),tm=new B({props:{title:"TFAutoModel",local:"transformers.TFAutoModel",headingTag:"h3"}}),nm=new x({props:{name:"class transformers.TFAutoModel",anchor:"transformers.TFAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L537"}}),am=new x({props:{name:"from_config",anchor:"transformers.TFAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertModel">TFAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.TFBartModel">TFBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertModel">TFBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.TFBlenderbotModel">TFBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallModel">TFBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.TFCTRLModel">TFCTRLModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertModel">TFCamembertModel</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertModel">TFConvBertModel</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.TFConvNextModel">TFConvNextModel</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.TFConvNextV2Model">TFConvNextV2Model</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.TFCvtModel">TFCvtModel</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dpr#transformers.DPRConfig">DPRConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dpr#transformers.TFDPRQuestionEncoder">TFDPRQuestionEncoder</a> (DPR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.TFData2VecVisionModel">TFData2VecVisionModel</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaModel">TFDebertaModel</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2Model">TFDebertaV2Model</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.TFDeiTModel">TFDeiTModel</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.TFEfficientFormerModel">TFEfficientFormerModel</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraModel">TFElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.TFEsmModel">TFEsmModel</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertModel">TFFlaubertModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelModel">TFFunnelModel</a> or <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelBaseModel">TFFunnelBaseModel</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2Model">TFGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.TFGPTJModel">TFGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/groupvit#transformers.GroupViTConfig">GroupViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/groupvit#transformers.TFGroupViTModel">TFGroupViTModel</a> (GroupViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.TFHubertModel">TFHubertModel</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.TFIdeficsModel">TFIdeficsModel</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.TFLEDModel">TFLEDModel</a> (LED model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMModel">TFLayoutLMModel</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3Model">TFLayoutLMv3Model</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerModel">TFLongformerModel</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.TFLxmertModel">TFLxmertModel</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.TFMBartModel">TFMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetModel">TFMPNetModel</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.TFMT5Model">TFMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.TFMarianModel">TFMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.TFMistralModel">TFMistralModel</a> (Mistral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertModel">TFMobileBertModel</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.TFMobileViTModel">TFMobileViTModel</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.TFOPTModel">TFOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.TFOpenAIGPTModel">TFOpenAIGPTModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.TFPegasusModel">TFPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.TFRegNetModel">TFRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.TFResNetModel">TFResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerModel">TFRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaModel">TFRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormModel">TFRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/sam#transformers.SamConfig">SamConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/sam#transformers.TFSamModel">TFSamModel</a> (SAM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.TFSegformerModel">TFSegformerModel</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.TFSpeech2TextModel">TFSpeech2TextModel</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.TFSwiftFormerModel">TFSwiftFormerModel</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.TFSwinModel">TFSwinModel</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.TFT5Model">TFT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasModel">TFTapasModel</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TFTransfoXLModel">TFTransfoXLModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.TFViTModel">TFViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.TFViTMAEModel">TFViTMAEModel</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.TFVisionTextDualEncoderModel">TFVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.TFWav2Vec2Model">TFWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.TFWhisperModel">TFWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.TFXGLMModel">TFXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMModel">TFXLMModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaModel">TFXLMRobertaModel</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetModel">TFXLNetModel</a> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModel.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Js=new $({props:{anchor:"transformers.TFAutoModel.from_config.example",$$slots:{default:[G5]},$$scope:{ctx:y}}}),sm=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Vs=new $({props:{anchor:"transformers.TFAutoModel.from_pretrained.example",$$slots:{default:[P5]},$$scope:{ctx:y}}}),im=new B({props:{title:"FlaxAutoModel",local:"transformers.FlaxAutoModel",headingTag:"h3"}}),lm=new x({props:{name:"class transformers.FlaxAutoModel",anchor:"transformers.FlaxAutoModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L279"}}),dm=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModel.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertModel">FlaxAlbertModel</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartModel">FlaxBartModel</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.FlaxBeitModel">FlaxBeitModel</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertModel">FlaxBertModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdModel">FlaxBigBirdModel</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.FlaxBlenderbotModel">FlaxBlenderbotModel</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallModel">FlaxBlenderbotSmallModel</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.FlaxBloomModel">FlaxBloomModel</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.FlaxCLIPModel">FlaxCLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertModel">FlaxDistilBertModel</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraModel">FlaxElectraModel</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.FlaxGPT2Model">FlaxGPT2Model</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.FlaxGPTJModel">FlaxGPTJModel</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.FlaxGPTNeoModel">FlaxGPTNeoModel</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.FlaxGemmaModel">FlaxGemmaModel</a> (Gemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.FlaxLlamaModel">FlaxLlamaModel</a> (LLaMA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.FlaxLongT5Model">FlaxLongT5Model</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartModel">FlaxMBartModel</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.FlaxMT5Model">FlaxMT5Model</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.FlaxMarianModel">FlaxMarianModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.FlaxMistralModel">FlaxMistralModel</a> (Mistral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.FlaxOPTModel">FlaxOPTModel</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.FlaxPegasusModel">FlaxPegasusModel</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.FlaxRegNetModel">FlaxRegNetModel</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.FlaxResNetModel">FlaxResNetModel</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerModel">FlaxRoFormerModel</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaModel">FlaxRobertaModel</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormModel">FlaxRobertaPreLayerNormModel</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.FlaxT5Model">FlaxT5Model</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.FlaxViTModel">FlaxViTModel</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.VisionTextDualEncoderConfig">VisionTextDualEncoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vision-text-dual-encoder#transformers.FlaxVisionTextDualEncoderModel">FlaxVisionTextDualEncoderModel</a> (VisionTextDualEncoder model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2Model">FlaxWav2Vec2Model</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.FlaxWhisperModel">FlaxWhisperModel</a> (Whisper model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.FlaxXGLMModel">FlaxXGLMModel</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaModel">FlaxXLMRobertaModel</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModel.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Es=new $({props:{anchor:"transformers.FlaxAutoModel.from_config.example",$$slots:{default:[S5]},$$scope:{ctx:y}}}),mm=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModel.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModel.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModel.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModel.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModel.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModel.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModel.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModel.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModel.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModel.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModel.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModel.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Is=new $({props:{anchor:"transformers.FlaxAutoModel.from_pretrained.example",$$slots:{default:[W5]},$$scope:{ctx:y}}}),cm=new B({props:{title:"Generic pretraining classes",local:"generic-pretraining-classes",headingTag:"h2"}}),gm=new B({props:{title:"AutoModelForPreTraining",local:"transformers.AutoModelForPreTraining",headingTag:"h3"}}),pm=new x({props:{name:"class transformers.AutoModelForPreTraining",anchor:"transformers.AutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1455"}}),hm=new x({props:{name:"from_config",anchor:"transformers.AutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForPreTraining">AlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForPreTraining">BertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForPreTraining">ElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForPreTraining">ErnieForPreTraining</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForPreTraining">FNetForPreTraining</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaConfig">FlavaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flava#transformers.FlavaForPreTraining">FlavaForPreTraining</a> (FLAVA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForPreTraining">FunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2Config">Idefics2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2ForConditionalGeneration">Idefics2ForConditionalGeneration</a> (Idefics2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsForVisionText2Text">IdeficsForVisionText2Text</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llava#transformers.LlavaConfig">LlavaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llava#transformers.LlavaForConditionalGeneration">LlavaForConditionalGeneration</a> (LLaVa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llava_next#transformers.LlavaNextConfig">LlavaNextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llava_next#transformers.LlavaNextForConditionalGeneration">LlavaNextForConditionalGeneration</a> (LLaVA-NeXT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertForPreTraining">LxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaConfig">MambaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaForCausalLM">MambaForCausalLM</a> (Mamba model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForPreTraining">MegatronBertForPreTraining</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForPreTraining">MobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForPreTraining">NezhaForPreTraining</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/paligemma#transformers.PaliGemmaConfig">PaliGemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration">PaliGemmaForConditionalGeneration</a> (PaliGemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/retribert#transformers.RetriBertConfig">RetriBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/retribert#transformers.RetriBertModel">RetriBertModel</a> (RetriBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForPreTraining">RoCBertForPreTraining</a> (RoCBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterForPreTraining">SplinterForPreTraining</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltConfig">TvltConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tvlt#transformers.TvltForPreTraining">TvltForPreTraining</a> (TVLT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechForPreTraining">UniSpeechForPreTraining</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForPreTraining">UniSpeechSatForPreTraining</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.ViTMAEForPreTraining">ViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/video_llava#transformers.VideoLlavaConfig">VideoLlavaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/video_llava#transformers.VideoLlavaForConditionalGeneration">VideoLlavaForConditionalGeneration</a> (VideoLlava model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEForPreTraining">VideoMAEForPreTraining</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vipllava#transformers.VipLlavaConfig">VipLlavaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vipllava#transformers.VipLlavaForConditionalGeneration">VipLlavaForConditionalGeneration</a> (VipLlava model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/visual_bert#transformers.VisualBertConfig">VisualBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/visual_bert#transformers.VisualBertForPreTraining">VisualBertForPreTraining</a> (VisualBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForPreTraining">Wav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForPreTraining">Wav2Vec2ConformerForPreTraining</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForPreTraining.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Us=new $({props:{anchor:"transformers.AutoModelForPreTraining.from_config.example",$$slots:{default:[J5]},$$scope:{ctx:y}}}),um=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ns=new $({props:{anchor:"transformers.AutoModelForPreTraining.from_pretrained.example",$$slots:{default:[V5]},$$scope:{ctx:y}}}),_m=new B({props:{title:"TFAutoModelForPreTraining",local:"transformers.TFAutoModelForPreTraining",headingTag:"h3"}}),bm=new x({props:{name:"class transformers.TFAutoModelForPreTraining",anchor:"transformers.TFAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L553"}}),Mm=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForPreTraining">TFAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForPreTraining">TFBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForPreTraining">TFElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForPreTraining">TFFunnelForPreTraining</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.IdeficsConfig">IdeficsConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/idefics#transformers.TFIdeficsForVisionText2Text">TFIdeficsForVisionText2Text</a> (IDEFICS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.TFLxmertForPreTraining">TFLxmertForPreTraining</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForPreTraining">TFMobileBertForPreTraining</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.ViTMAEConfig">ViTMAEConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit_mae#transformers.TFViTMAEForPreTraining">TFViTMAEForPreTraining</a> (ViTMAE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForPreTraining.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),qs=new $({props:{anchor:"transformers.TFAutoModelForPreTraining.from_config.example",$$slots:{default:[E5]},$$scope:{ctx:y}}}),Tm=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Xs=new $({props:{anchor:"transformers.TFAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[I5]},$$scope:{ctx:y}}}),Fm=new B({props:{title:"FlaxAutoModelForPreTraining",local:"transformers.FlaxAutoModelForPreTraining",headingTag:"h3"}}),Cm=new x({props:{name:"class transformers.FlaxAutoModelForPreTraining",anchor:"transformers.FlaxAutoModelForPreTraining",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L286"}}),ym=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForPreTraining.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForPreTraining">FlaxAlbertForPreTraining</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForPreTraining">FlaxBertForPreTraining</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForPreTraining">FlaxBigBirdForPreTraining</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForPreTraining">FlaxElectraForPreTraining</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.FlaxWav2Vec2ForPreTraining">FlaxWav2Vec2ForPreTraining</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ds=new $({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_config.example",$$slots:{default:[U5]},$$scope:{ctx:y}}}),vm=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Qs=new $({props:{anchor:"transformers.FlaxAutoModelForPreTraining.from_pretrained.example",$$slots:{default:[N5]},$$scope:{ctx:y}}}),wm=new B({props:{title:"Natural Language Processing",local:"natural-language-processing",headingTag:"h2"}}),Lm=new B({props:{title:"AutoModelForCausalLM",local:"transformers.AutoModelForCausalLM",headingTag:"h3"}}),km=new x({props:{name:"class transformers.AutoModelForCausalLM",anchor:"transformers.AutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1470"}}),$m=new x({props:{name:"from_config",anchor:"transformers.AutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForCausalLM">BartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertLMHeadModel">BertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> (Bert Generation model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForCausalLM">BigBirdPegasusForCausalLM</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptForCausalLM">BioGptForCausalLM</a> (BioGpt model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotForCausalLM">BlenderbotForCausalLM</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForCausalLM">BlenderbotSmallForCausalLM</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForCausalLM">BloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForCausalLM">CamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenConfig">CodeGenConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/codegen#transformers.CodeGenForCausalLM">CodeGenForCausalLM</a> (CodeGen model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/cohere#transformers.CohereConfig">CohereConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/cohere#transformers.CohereForCausalLM">CohereForCausalLM</a> (Cohere model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/cpmant#transformers.CpmAntConfig">CpmAntConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/cpmant#transformers.CpmAntForCausalLM">CpmAntForCausalLM</a> (CPM-Ant model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForCausalLM">Data2VecTextForCausalLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dbrx#transformers.DbrxConfig">DbrxConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dbrx#transformers.DbrxForCausalLM">DbrxForCausalLM</a> (DBRX model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForCausalLM">ElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForCausalLM">ErnieForCausalLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconForCausalLM">FalconForCausalLM</a> (Falcon model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fuyu#transformers.FuyuConfig">FuyuConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fuyu#transformers.FuyuForCausalLM">FuyuForCausalLM</a> (Fuyu model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2LMHeadModel">GPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForCausalLM">GPTBigCodeForCausalLM</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJForCausalLM">GPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoForCausalLM">GPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXForCausalLM">GPTNeoXForCausalLM</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseConfig">GPTNeoXJapaneseConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox_japanese#transformers.GPTNeoXJapaneseForCausalLM">GPTNeoXJapaneseForCausalLM</a> (GPT NeoX Japanese model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaForCausalLM">GemmaForCausalLM</a> (Gemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaConfig">JambaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaForCausalLM">JambaForCausalLM</a> (Jamba model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeConfig">JetMoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeForCausalLM">JetMoeForCausalLM</a> (JetMoe model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForCausalLM">LlamaForCausalLM</a> (LLaMA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForCausalLM">MBartForCausalLM</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaConfig">MambaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mamba#transformers.MambaForCausalLM">MambaForCausalLM</a> (Mamba model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianForCausalLM">MarianForCausalLM</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForCausalLM">MegaForCausalLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForCausalLM">MegatronBertForCausalLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralForCausalLM">MistralForCausalLM</a> (Mistral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralConfig">MixtralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralForCausalLM">MixtralForCausalLM</a> (Mixtral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForCausalLM">MptForCausalLM</a> (MPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/musicgen#transformers.MusicgenConfig">MusicgenConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/musicgen#transformers.MusicgenForCausalLM">MusicgenForCausalLM</a> (MusicGen model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/musicgen_melody#transformers.MusicgenMelodyConfig">MusicgenMelodyConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/musicgen_melody#transformers.MusicgenMelodyForCausalLM">MusicgenMelodyForCausalLM</a> (MusicGen Melody model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForCausalLM">MvpForCausalLM</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTForCausalLM">OPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/olmo#transformers.OlmoConfig">OlmoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/olmo#transformers.OlmoForCausalLM">OlmoForCausalLM</a> (OLMo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTLMHeadModel">OpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaForCausalLM">OpenLlamaForCausalLM</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartForCausalLM">PLBartForCausalLM</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusForCausalLM">PegasusForCausalLM</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonConfig">PersimmonConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonForCausalLM">PersimmonForCausalLM</a> (Persimmon model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3Config">Phi3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3ForCausalLM">Phi3ForCausalLM</a> (Phi3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiConfig">PhiConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiForCausalLM">PhiForCausalLM</a> (Phi model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetForCausalLM">ProphetNetForCausalLM</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertLMHeadModel">QDQBertLMHeadModel</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2Config">Qwen2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2ForCausalLM">Qwen2ForCausalLM</a> (Qwen2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeConfig">Qwen2MoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeForCausalLM">Qwen2MoeForCausalLM</a> (Qwen2MoE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/recurrent_gemma#transformers.RecurrentGemmaConfig">RecurrentGemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/recurrent_gemma#transformers.RecurrentGemmaForCausalLM">RecurrentGemmaForCausalLM</a> (RecurrentGemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerModelWithLMHead">ReformerModelWithLMHead</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForCausalLM">RemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForCausalLM">RoCBertForCausalLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForCausalLM">RoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForCausalLM">RobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForCausalLM">RobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvConfig">RwkvConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rwkv#transformers.RwkvForCausalLM">RwkvForCausalLM</a> (RWKV model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text_2#transformers.Speech2Text2Config">Speech2Text2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text_2#transformers.Speech2Text2ForCausalLM">Speech2Text2ForCausalLM</a> (Speech2Text2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmConfig">StableLmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmForCausalLM">StableLmForCausalLM</a> (StableLm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2Config">Starcoder2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2ForCausalLM">Starcoder2ForCausalLM</a> (Starcoder2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/trocr#transformers.TrOCRConfig">TrOCRConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/trocr#transformers.TrOCRForCausalLM">TrOCRForCausalLM</a> (TrOCR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLLMHeadModel">TransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperForCausalLM">WhisperForCausalLM</a> (Whisper model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMForCausalLM">XGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForCausalLM">XLMProphetNetForCausalLM</a> (XLM-ProphetNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForCausalLM">XLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForCausalLM">XLMRobertaXLForCausalLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetLMHeadModel">XLNetLMHeadModel</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForCausalLM">XmodForCausalLM</a> (X-MOD model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCausalLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Hs=new $({props:{anchor:"transformers.AutoModelForCausalLM.from_config.example",$$slots:{default:[q5]},$$scope:{ctx:y}}}),Bm=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ys=new $({props:{anchor:"transformers.AutoModelForCausalLM.from_pretrained.example",$$slots:{default:[X5]},$$scope:{ctx:y}}}),jm=new B({props:{title:"TFAutoModelForCausalLM",local:"transformers.TFAutoModelForCausalLM",headingTag:"h3"}}),Am=new x({props:{name:"class transformers.TFAutoModelForCausalLM",anchor:"transformers.TFAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L568"}}),Zm=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertLMHeadModel">TFBertLMHeadModel</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.TFCTRLLMHeadModel">TFCTRLLMHeadModel</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForCausalLM">TFCamembertForCausalLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2LMHeadModel">TFGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.TFGPTJForCausalLM">TFGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.TFMistralForCausalLM">TFMistralForCausalLM</a> (Mistral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.TFOPTForCausalLM">TFOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.TFOpenAIGPTLMHeadModel">TFOpenAIGPTLMHeadModel</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForCausalLM">TFRemBertForCausalLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForCausalLM">TFRoFormerForCausalLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForCausalLM">TFRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForCausalLM">TFRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TFTransfoXLLMHeadModel">TFTransfoXLLMHeadModel</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.TFXGLMForCausalLM">TFXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForCausalLM">TFXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetLMHeadModel">TFXLNetLMHeadModel</a> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForCausalLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),zs=new $({props:{anchor:"transformers.TFAutoModelForCausalLM.from_config.example",$$slots:{default:[D5]},$$scope:{ctx:y}}}),Rm=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Os=new $({props:{anchor:"transformers.TFAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Q5]},$$scope:{ctx:y}}}),Gm=new B({props:{title:"FlaxAutoModelForCausalLM",local:"transformers.FlaxAutoModelForCausalLM",headingTag:"h3"}}),Pm=new x({props:{name:"class transformers.FlaxAutoModelForCausalLM",anchor:"transformers.FlaxAutoModelForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L293"}}),Sm=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForCausalLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForCausalLM">FlaxBartForCausalLM</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForCausalLM">FlaxBertForCausalLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForCausalLM">FlaxBigBirdForCausalLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.FlaxBloomForCausalLM">FlaxBloomForCausalLM</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForCausalLM">FlaxElectraForCausalLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.FlaxGPT2LMHeadModel">FlaxGPT2LMHeadModel</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.FlaxGPTJForCausalLM">FlaxGPTJForCausalLM</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.FlaxGPTNeoForCausalLM">FlaxGPTNeoForCausalLM</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.FlaxGemmaForCausalLM">FlaxGemmaForCausalLM</a> (Gemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.FlaxLlamaForCausalLM">FlaxLlamaForCausalLM</a> (LLaMA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.FlaxMistralForCausalLM">FlaxMistralForCausalLM</a> (Mistral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.FlaxOPTForCausalLM">FlaxOPTForCausalLM</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForCausalLM">FlaxRobertaForCausalLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForCausalLM">FlaxRobertaPreLayerNormForCausalLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.XGLMConfig">XGLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xglm#transformers.FlaxXGLMForCausalLM">FlaxXGLMForCausalLM</a> (XGLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForCausalLM">FlaxXLMRobertaForCausalLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ks=new $({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_config.example",$$slots:{default:[H5]},$$scope:{ctx:y}}}),Wm=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),ei=new $({props:{anchor:"transformers.FlaxAutoModelForCausalLM.from_pretrained.example",$$slots:{default:[Y5]},$$scope:{ctx:y}}}),Jm=new B({props:{title:"AutoModelForMaskedLM",local:"transformers.AutoModelForMaskedLM",headingTag:"h3"}}),Vm=new x({props:{name:"class transformers.AutoModelForMaskedLM",anchor:"transformers.AutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1477"}}),Em=new x({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForMaskedLM">AlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForMaskedLM">BertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForMaskedLM">CamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForMaskedLM">ConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForMaskedLM">Data2VecTextForMaskedLM</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForMaskedLM">DebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForMaskedLM">DebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForMaskedLM">ElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForMaskedLM">ErnieForMaskedLM</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmForMaskedLM">EsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForMaskedLM">FNetForMaskedLM</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertWithLMHeadModel">FlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForMaskedLM">FunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForMaskedLM">IBertForMaskedLM</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForMaskedLM">LayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForMaskedLM">LongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForMaskedLM">LukeForMaskedLM</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForMaskedLM">MPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForMaskedLM">MegaForMaskedLM</a> (MEGA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForMaskedLM">MegatronBertForMaskedLM</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForMaskedLM">MobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForMaskedLM">MraForMaskedLM</a> (MRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForMaskedLM">NezhaForMaskedLM</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForMaskedLM">NystromformerForMaskedLM</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForMaskedLM">PerceiverForMaskedLM</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForMaskedLM">QDQBertForMaskedLM</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerForMaskedLM">ReformerForMaskedLM</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForMaskedLM">RemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForMaskedLM">RoCBertForMaskedLM</a> (RoCBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForMaskedLM">RoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForMaskedLM">RobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMaskedLM">RobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForMaskedLM">SqueezeBertForMaskedLM</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasForMaskedLM">TapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <code>Wav2Vec2ForMaskedLM</code> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMWithLMHeadModel">XLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForMaskedLM">XLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMaskedLM">XLMRobertaXLForMaskedLM</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForMaskedLM">XmodForMaskedLM</a> (X-MOD model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForMaskedLM">YosoForMaskedLM</a> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),oi=new $({props:{anchor:"transformers.AutoModelForMaskedLM.from_config.example",$$slots:{default:[z5]},$$scope:{ctx:y}}}),Im=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),ri=new $({props:{anchor:"transformers.AutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[O5]},$$scope:{ctx:y}}}),Um=new B({props:{title:"TFAutoModelForMaskedLM",local:"transformers.TFAutoModelForMaskedLM",headingTag:"h3"}}),Nm=new x({props:{name:"class transformers.TFAutoModelForMaskedLM",anchor:"transformers.TFAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L618"}}),qm=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForMaskedLM">TFAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForMaskedLM">TFBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForMaskedLM">TFCamembertForMaskedLM</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForMaskedLM">TFConvBertForMaskedLM</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaForMaskedLM">TFDebertaForMaskedLM</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMaskedLM">TFDebertaV2ForMaskedLM</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForMaskedLM">TFElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.TFEsmForMaskedLM">TFEsmForMaskedLM</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertWithLMHeadModel">TFFlaubertWithLMHeadModel</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForMaskedLM">TFFunnelForMaskedLM</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForMaskedLM">TFLayoutLMForMaskedLM</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForMaskedLM">TFLongformerForMaskedLM</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForMaskedLM">TFMPNetForMaskedLM</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForMaskedLM">TFMobileBertForMaskedLM</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForMaskedLM">TFRemBertForMaskedLM</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForMaskedLM">TFRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForMaskedLM">TFRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMaskedLM">TFRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasForMaskedLM">TFTapasForMaskedLM</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMWithLMHeadModel">TFXLMWithLMHeadModel</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMaskedLM">TFXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),ti=new $({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_config.example",$$slots:{default:[K5]},$$scope:{ctx:y}}}),Xm=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),ni=new $({props:{anchor:"transformers.TFAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[eq]},$$scope:{ctx:y}}}),Dm=new B({props:{title:"FlaxAutoModelForMaskedLM",local:"transformers.FlaxAutoModelForMaskedLM",headingTag:"h3"}}),Qm=new x({props:{name:"class transformers.FlaxAutoModelForMaskedLM",anchor:"transformers.FlaxAutoModelForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L300"}}),Hm=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMaskedLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForMaskedLM">FlaxAlbertForMaskedLM</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForMaskedLM">FlaxBertForMaskedLM</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForMaskedLM">FlaxBigBirdForMaskedLM</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForMaskedLM">FlaxElectraForMaskedLM</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForMaskedLM">FlaxRoFormerForMaskedLM</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForMaskedLM">FlaxRobertaForMaskedLM</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMaskedLM">FlaxRobertaPreLayerNormForMaskedLM</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMaskedLM">FlaxXLMRobertaForMaskedLM</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),ai=new $({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_config.example",$$slots:{default:[oq]},$$scope:{ctx:y}}}),Ym=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),si=new $({props:{anchor:"transformers.FlaxAutoModelForMaskedLM.from_pretrained.example",$$slots:{default:[rq]},$$scope:{ctx:y}}}),zm=new B({props:{title:"AutoModelForMaskGeneration",local:"transformers.AutoModelForMaskGeneration",headingTag:"h3"}}),Km=new x({props:{name:"class transformers.AutoModelForMaskGeneration",anchor:"transformers.AutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1432"}}),ec=new B({props:{title:"TFAutoModelForMaskGeneration",local:"transformers.TFAutoModelForMaskGeneration",headingTag:"h3"}}),rc=new x({props:{name:"class transformers.TFAutoModelForMaskGeneration",anchor:"transformers.TFAutoModelForMaskGeneration",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L529"}}),tc=new B({props:{title:"AutoModelForSeq2SeqLM",local:"transformers.AutoModelForSeq2SeqLM",headingTag:"h3"}}),nc=new x({props:{name:"class transformers.AutoModelForSeq2SeqLM",anchor:"transformers.AutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1484"}}),ac=new x({props:{name:"from_config",anchor:"transformers.AutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForConditionalGeneration">BartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForConditionalGeneration">BigBirdPegasusForConditionalGeneration</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotForConditionalGeneration">BlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallForConditionalGeneration">BlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.EncoderDecoderModel">EncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTConfig">FSMTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fsmt#transformers.FSMTForConditionalGeneration">FSMTForConditionalGeneration</a> (FairSeq Machine-Translation model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseConfig">GPTSanJapaneseConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptsan-japanese#transformers.GPTSanJapaneseForConditionalGeneration">GPTSanJapaneseForConditionalGeneration</a> (GPTSAN-japanese model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDForConditionalGeneration">LEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5ForConditionalGeneration">LongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/m2m_100#transformers.M2M100Config">M2M100Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/m2m_100#transformers.M2M100ForConditionalGeneration">M2M100ForConditionalGeneration</a> (M2M100 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForConditionalGeneration">MBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5ForConditionalGeneration">MT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianMTModel">MarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForConditionalGeneration">MvpForConditionalGeneration</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeConfig">NllbMoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nllb-moe#transformers.NllbMoeForConditionalGeneration">NllbMoeForConditionalGeneration</a> (NLLB-MOE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartForConditionalGeneration">PLBartForConditionalGeneration</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusForConditionalGeneration">PegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pegasus_x#transformers.PegasusXConfig">PegasusXConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pegasus_x#transformers.PegasusXForConditionalGeneration">PegasusXForConditionalGeneration</a> (PEGASUS-X model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetConfig">ProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/prophetnet#transformers.ProphetNetForConditionalGeneration">ProphetNetForConditionalGeneration</a> (ProphetNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TConfig">SeamlessM4TConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TForTextToText">SeamlessM4TForTextToText</a> (SeamlessM4T model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2Config">SeamlessM4Tv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2ForTextToText">SeamlessM4Tv2ForTextToText</a> (SeamlessM4Tv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersConfig">SwitchTransformersConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/switch_transformers#transformers.SwitchTransformersForConditionalGeneration">SwitchTransformersForConditionalGeneration</a> (SwitchTransformers model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForConditionalGeneration">T5ForConditionalGeneration</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5ForConditionalGeneration">UMT5ForConditionalGeneration</a> (UMT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetConfig">XLMProphetNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-prophetnet#transformers.XLMProphetNetForConditionalGeneration">XLMProphetNetForConditionalGeneration</a> (XLM-ProphetNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),ii=new $({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[tq]},$$scope:{ctx:y}}}),sc=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),li=new $({props:{anchor:"transformers.AutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[nq]},$$scope:{ctx:y}}}),ic=new B({props:{title:"TFAutoModelForSeq2SeqLM",local:"transformers.TFAutoModelForSeq2SeqLM",headingTag:"h3"}}),lc=new x({props:{name:"class transformers.TFAutoModelForSeq2SeqLM",anchor:"transformers.TFAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L625"}}),dc=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.TFBartForConditionalGeneration">TFBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.TFBlenderbotForConditionalGeneration">TFBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.TFBlenderbotSmallForConditionalGeneration">TFBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.TFEncoderDecoderModel">TFEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.TFLEDForConditionalGeneration">TFLEDForConditionalGeneration</a> (LED model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.TFMBartForConditionalGeneration">TFMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.TFMT5ForConditionalGeneration">TFMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.TFMarianMTModel">TFMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.TFPegasusForConditionalGeneration">TFPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.TFT5ForConditionalGeneration">TFT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),di=new $({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[aq]},$$scope:{ctx:y}}}),mc=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),mi=new $({props:{anchor:"transformers.TFAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[sq]},$$scope:{ctx:y}}}),cc=new B({props:{title:"FlaxAutoModelForSeq2SeqLM",local:"transformers.FlaxAutoModelForSeq2SeqLM",headingTag:"h3"}}),fc=new x({props:{name:"class transformers.FlaxAutoModelForSeq2SeqLM",anchor:"transformers.FlaxAutoModelForSeq2SeqLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L307"}}),gc=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForConditionalGeneration">FlaxBartForConditionalGeneration</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.BlenderbotConfig">BlenderbotConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot#transformers.FlaxBlenderbotForConditionalGeneration">FlaxBlenderbotForConditionalGeneration</a> (Blenderbot model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.BlenderbotSmallConfig">BlenderbotSmallConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blenderbot-small#transformers.FlaxBlenderbotSmallForConditionalGeneration">FlaxBlenderbotSmallForConditionalGeneration</a> (BlenderbotSmall model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.EncoderDecoderConfig">EncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/encoder-decoder#transformers.FlaxEncoderDecoderModel">FlaxEncoderDecoderModel</a> (Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.LongT5Config">LongT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longt5#transformers.FlaxLongT5ForConditionalGeneration">FlaxLongT5ForConditionalGeneration</a> (LongT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForConditionalGeneration">FlaxMBartForConditionalGeneration</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.FlaxMT5ForConditionalGeneration">FlaxMT5ForConditionalGeneration</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.MarianConfig">MarianConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/marian#transformers.FlaxMarianMTModel">FlaxMarianMTModel</a> (Marian model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.PegasusConfig">PegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pegasus#transformers.FlaxPegasusForConditionalGeneration">FlaxPegasusForConditionalGeneration</a> (Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.FlaxT5ForConditionalGeneration">FlaxT5ForConditionalGeneration</a> (T5 model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),ci=new $({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_config.example",$$slots:{default:[iq]},$$scope:{ctx:y}}}),pc=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),fi=new $({props:{anchor:"transformers.FlaxAutoModelForSeq2SeqLM.from_pretrained.example",$$slots:{default:[lq]},$$scope:{ctx:y}}}),hc=new B({props:{title:"AutoModelForSequenceClassification",local:"transformers.AutoModelForSequenceClassification",headingTag:"h3"}}),uc=new x({props:{name:"class transformers.AutoModelForSequenceClassification",anchor:"transformers.AutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1495"}}),_c=new x({props:{name:"from_config",anchor:"transformers.AutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForSequenceClassification">AlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForSequenceClassification">BartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForSequenceClassification">BertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForSequenceClassification">BigBirdPegasusForSequenceClassification</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptForSequenceClassification">BioGptForSequenceClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForSequenceClassification">BloomForSequenceClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForSequenceClassification">CamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineForSequenceClassification">CanineForSequenceClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForSequenceClassification">ConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForSequenceClassification">Data2VecTextForSequenceClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForSequenceClassification">DebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForSequenceClassification">DebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForSequenceClassification">ElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForSequenceClassification">ErnieForSequenceClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMForSequenceClassification">ErnieMForSequenceClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmForSequenceClassification">EsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForSequenceClassification">FNetForSequenceClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconForSequenceClassification">FalconForSequenceClassification</a> (Falcon model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertForSequenceClassification">FlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForSequenceClassification">FunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2ForSequenceClassification">GPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForSequenceClassification">GPTBigCodeForSequenceClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJForSequenceClassification">GPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoForSequenceClassification">GPTNeoForSequenceClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXForSequenceClassification">GPTNeoXForSequenceClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaForSequenceClassification">GemmaForSequenceClassification</a> (Gemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForSequenceClassification">IBertForSequenceClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaConfig">JambaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/jamba#transformers.JambaForSequenceClassification">JambaForSequenceClassification</a> (Jamba model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeConfig">JetMoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/jetmoe#transformers.JetMoeForSequenceClassification">JetMoeForSequenceClassification</a> (JetMoe model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDForSequenceClassification">LEDForSequenceClassification</a> (LED model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForSequenceClassification">LayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForSequenceClassification">LayoutLMv2ForSequenceClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForSequenceClassification">LayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltForSequenceClassification">LiltForSequenceClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> (LLaMA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForSequenceClassification">LongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForSequenceClassification">LukeForSequenceClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForSequenceClassification">MBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForSequenceClassification">MPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5ForSequenceClassification">MT5ForSequenceClassification</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMForSequenceClassification">MarkupLMForSequenceClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForSequenceClassification">MegaForSequenceClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForSequenceClassification">MegatronBertForSequenceClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralForSequenceClassification">MistralForSequenceClassification</a> (Mistral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralConfig">MixtralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralForSequenceClassification">MixtralForSequenceClassification</a> (Mixtral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForSequenceClassification">MobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForSequenceClassification">MptForSequenceClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForSequenceClassification">MraForSequenceClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForSequenceClassification">MvpForSequenceClassification</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForSequenceClassification">NezhaForSequenceClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForSequenceClassification">NystromformerForSequenceClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTForSequenceClassification">OPTForSequenceClassification</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTForSequenceClassification">OpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/open-llama#transformers.OpenLlamaForSequenceClassification">OpenLlamaForSequenceClassification</a> (OpenLlama model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartConfig">PLBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/plbart#transformers.PLBartForSequenceClassification">PLBartForSequenceClassification</a> (PLBart model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForSequenceClassification">PerceiverForSequenceClassification</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonConfig">PersimmonConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonForSequenceClassification">PersimmonForSequenceClassification</a> (Persimmon model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3Config">Phi3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3ForSequenceClassification">Phi3ForSequenceClassification</a> (Phi3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiConfig">PhiConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiForSequenceClassification">PhiForSequenceClassification</a> (Phi model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForSequenceClassification">QDQBertForSequenceClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2Config">Qwen2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2ForSequenceClassification">Qwen2ForSequenceClassification</a> (Qwen2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeConfig">Qwen2MoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeForSequenceClassification">Qwen2MoeForSequenceClassification</a> (Qwen2MoE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerForSequenceClassification">ReformerForSequenceClassification</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForSequenceClassification">RemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForSequenceClassification">RoCBertForSequenceClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForSequenceClassification">RoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForSequenceClassification">RobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForSequenceClassification">RobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForSequenceClassification">SqueezeBertForSequenceClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmConfig">StableLmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmForSequenceClassification">StableLmForSequenceClassification</a> (StableLm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2Config">Starcoder2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2ForSequenceClassification">Starcoder2ForSequenceClassification</a> (Starcoder2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForSequenceClassification">T5ForSequenceClassification</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasForSequenceClassification">TapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLForSequenceClassification">TransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5ForSequenceClassification">UMT5ForSequenceClassification</a> (UMT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMForSequenceClassification">XLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForSequenceClassification">XLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForSequenceClassification">XLMRobertaXLForSequenceClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetForSequenceClassification">XLNetForSequenceClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForSequenceClassification">XmodForSequenceClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForSequenceClassification">YosoForSequenceClassification</a> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSequenceClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),gi=new $({props:{anchor:"transformers.AutoModelForSequenceClassification.from_config.example",$$slots:{default:[dq]},$$scope:{ctx:y}}}),bc=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),pi=new $({props:{anchor:"transformers.AutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[mq]},$$scope:{ctx:y}}}),Mc=new B({props:{title:"TFAutoModelForSequenceClassification",local:"transformers.TFAutoModelForSequenceClassification",headingTag:"h3"}}),Tc=new x({props:{name:"class transformers.TFAutoModelForSequenceClassification",anchor:"transformers.TFAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L636"}}),Fc=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForSequenceClassification">TFAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.TFBartForSequenceClassification">TFBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForSequenceClassification">TFBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.CTRLConfig">CTRLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ctrl#transformers.TFCTRLForSequenceClassification">TFCTRLForSequenceClassification</a> (CTRL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForSequenceClassification">TFCamembertForSequenceClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForSequenceClassification">TFConvBertForSequenceClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaForSequenceClassification">TFDebertaForSequenceClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForSequenceClassification">TFDebertaV2ForSequenceClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForSequenceClassification">TFElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.TFEsmForSequenceClassification">TFEsmForSequenceClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertForSequenceClassification">TFFlaubertForSequenceClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForSequenceClassification">TFFunnelForSequenceClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.TFGPT2ForSequenceClassification">TFGPT2ForSequenceClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.TFGPTJForSequenceClassification">TFGPTJForSequenceClassification</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForSequenceClassification">TFLayoutLMForSequenceClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForSequenceClassification">TFLayoutLMv3ForSequenceClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForSequenceClassification">TFLongformerForSequenceClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForSequenceClassification">TFMPNetForSequenceClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.TFMistralForSequenceClassification">TFMistralForSequenceClassification</a> (Mistral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForSequenceClassification">TFMobileBertForSequenceClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.OpenAIGPTConfig">OpenAIGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/openai-gpt#transformers.TFOpenAIGPTForSequenceClassification">TFOpenAIGPTForSequenceClassification</a> (OpenAI GPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForSequenceClassification">TFRemBertForSequenceClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForSequenceClassification">TFRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForSequenceClassification">TFRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForSequenceClassification">TFRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasForSequenceClassification">TFTapasForSequenceClassification</a> (TAPAS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TransfoXLConfig">TransfoXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/transfo-xl#transformers.TFTransfoXLForSequenceClassification">TFTransfoXLForSequenceClassification</a> (Transformer-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMForSequenceClassification">TFXLMForSequenceClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForSequenceClassification">TFXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetForSequenceClassification">TFXLNetForSequenceClassification</a> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),hi=new $({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_config.example",$$slots:{default:[cq]},$$scope:{ctx:y}}}),Cc=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),ui=new $({props:{anchor:"transformers.TFAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[fq]},$$scope:{ctx:y}}}),yc=new B({props:{title:"FlaxAutoModelForSequenceClassification",local:"transformers.FlaxAutoModelForSequenceClassification",headingTag:"h3"}}),vc=new x({props:{name:"class transformers.FlaxAutoModelForSequenceClassification",anchor:"transformers.FlaxAutoModelForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L318"}}),wc=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForSequenceClassification">FlaxAlbertForSequenceClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForSequenceClassification">FlaxBartForSequenceClassification</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForSequenceClassification">FlaxBertForSequenceClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForSequenceClassification">FlaxBigBirdForSequenceClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForSequenceClassification">FlaxElectraForSequenceClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForSequenceClassification">FlaxMBartForSequenceClassification</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForSequenceClassification">FlaxRoFormerForSequenceClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForSequenceClassification">FlaxRobertaForSequenceClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForSequenceClassification">FlaxRobertaPreLayerNormForSequenceClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForSequenceClassification">FlaxXLMRobertaForSequenceClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),_i=new $({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_config.example",$$slots:{default:[gq]},$$scope:{ctx:y}}}),xc=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),bi=new $({props:{anchor:"transformers.FlaxAutoModelForSequenceClassification.from_pretrained.example",$$slots:{default:[pq]},$$scope:{ctx:y}}}),Lc=new B({props:{title:"AutoModelForMultipleChoice",local:"transformers.AutoModelForMultipleChoice",headingTag:"h3"}}),kc=new x({props:{name:"class transformers.AutoModelForMultipleChoice",anchor:"transformers.AutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1551"}}),$c=new x({props:{name:"from_config",anchor:"transformers.AutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForMultipleChoice">AlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForMultipleChoice">BertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForMultipleChoice">CamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineForMultipleChoice">CanineForMultipleChoice</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForMultipleChoice">ConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForMultipleChoice">Data2VecTextForMultipleChoice</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForMultipleChoice">DebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForMultipleChoice">ElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForMultipleChoice">ErnieForMultipleChoice</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMForMultipleChoice">ErnieMForMultipleChoice</a> (ErnieM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForMultipleChoice">FNetForMultipleChoice</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertForMultipleChoice">FlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForMultipleChoice">FunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForMultipleChoice">IBertForMultipleChoice</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForMultipleChoice">LongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForMultipleChoice">LukeForMultipleChoice</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForMultipleChoice">MPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForMultipleChoice">MegaForMultipleChoice</a> (MEGA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForMultipleChoice">MegatronBertForMultipleChoice</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForMultipleChoice">MobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForMultipleChoice">MraForMultipleChoice</a> (MRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForMultipleChoice">NezhaForMultipleChoice</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForMultipleChoice">NystromformerForMultipleChoice</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForMultipleChoice">QDQBertForMultipleChoice</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForMultipleChoice">RemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForMultipleChoice">RoCBertForMultipleChoice</a> (RoCBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForMultipleChoice">RoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForMultipleChoice">RobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForMultipleChoice">RobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForMultipleChoice">SqueezeBertForMultipleChoice</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMForMultipleChoice">XLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForMultipleChoice">XLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForMultipleChoice">XLMRobertaXLForMultipleChoice</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetForMultipleChoice">XLNetForMultipleChoice</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForMultipleChoice">XmodForMultipleChoice</a> (X-MOD model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForMultipleChoice">YosoForMultipleChoice</a> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMultipleChoice.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Mi=new $({props:{anchor:"transformers.AutoModelForMultipleChoice.from_config.example",$$slots:{default:[hq]},$$scope:{ctx:y}}}),Bc=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ti=new $({props:{anchor:"transformers.AutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[uq]},$$scope:{ctx:y}}}),jc=new B({props:{title:"TFAutoModelForMultipleChoice",local:"transformers.TFAutoModelForMultipleChoice",headingTag:"h3"}}),Ac=new x({props:{name:"class transformers.TFAutoModelForMultipleChoice",anchor:"transformers.TFAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L683"}}),Zc=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForMultipleChoice">TFAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForMultipleChoice">TFBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForMultipleChoice">TFCamembertForMultipleChoice</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForMultipleChoice">TFConvBertForMultipleChoice</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForMultipleChoice">TFDebertaV2ForMultipleChoice</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForMultipleChoice">TFElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertForMultipleChoice">TFFlaubertForMultipleChoice</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForMultipleChoice">TFFunnelForMultipleChoice</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForMultipleChoice">TFLongformerForMultipleChoice</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForMultipleChoice">TFMPNetForMultipleChoice</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForMultipleChoice">TFMobileBertForMultipleChoice</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForMultipleChoice">TFRemBertForMultipleChoice</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForMultipleChoice">TFRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForMultipleChoice">TFRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForMultipleChoice">TFRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMForMultipleChoice">TFXLMForMultipleChoice</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForMultipleChoice">TFXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetForMultipleChoice">TFXLNetForMultipleChoice</a> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Fi=new $({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_config.example",$$slots:{default:[_q]},$$scope:{ctx:y}}}),Rc=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ci=new $({props:{anchor:"transformers.TFAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[bq]},$$scope:{ctx:y}}}),Gc=new B({props:{title:"FlaxAutoModelForMultipleChoice",local:"transformers.FlaxAutoModelForMultipleChoice",headingTag:"h3"}}),Pc=new x({props:{name:"class transformers.FlaxAutoModelForMultipleChoice",anchor:"transformers.FlaxAutoModelForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L343"}}),Sc=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForMultipleChoice">FlaxAlbertForMultipleChoice</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForMultipleChoice">FlaxBertForMultipleChoice</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForMultipleChoice">FlaxBigBirdForMultipleChoice</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForMultipleChoice">FlaxDistilBertForMultipleChoice</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForMultipleChoice">FlaxElectraForMultipleChoice</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForMultipleChoice">FlaxRoFormerForMultipleChoice</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForMultipleChoice">FlaxRobertaForMultipleChoice</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForMultipleChoice">FlaxRobertaPreLayerNormForMultipleChoice</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForMultipleChoice">FlaxXLMRobertaForMultipleChoice</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),yi=new $({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_config.example",$$slots:{default:[Mq]},$$scope:{ctx:y}}}),Wc=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),vi=new $({props:{anchor:"transformers.FlaxAutoModelForMultipleChoice.from_pretrained.example",$$slots:{default:[Tq]},$$scope:{ctx:y}}}),Jc=new B({props:{title:"AutoModelForNextSentencePrediction",local:"transformers.AutoModelForNextSentencePrediction",headingTag:"h3"}}),Vc=new x({props:{name:"class transformers.AutoModelForNextSentencePrediction",anchor:"transformers.AutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1558"}}),Ec=new x({props:{name:"from_config",anchor:"transformers.AutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForNextSentencePrediction">BertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForNextSentencePrediction">ErnieForNextSentencePrediction</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForNextSentencePrediction">FNetForNextSentencePrediction</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForNextSentencePrediction">MegatronBertForNextSentencePrediction</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForNextSentencePrediction">MobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForNextSentencePrediction">NezhaForNextSentencePrediction</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForNextSentencePrediction">QDQBertForNextSentencePrediction</a> (QDQBert model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),wi=new $({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[Fq]},$$scope:{ctx:y}}}),Ic=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),xi=new $({props:{anchor:"transformers.AutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[Cq]},$$scope:{ctx:y}}}),Uc=new B({props:{title:"TFAutoModelForNextSentencePrediction",local:"transformers.TFAutoModelForNextSentencePrediction",headingTag:"h3"}}),Nc=new x({props:{name:"class transformers.TFAutoModelForNextSentencePrediction",anchor:"transformers.TFAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L690"}}),qc=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForNextSentencePrediction">TFBertForNextSentencePrediction</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForNextSentencePrediction">TFMobileBertForNextSentencePrediction</a> (MobileBERT model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Li=new $({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[yq]},$$scope:{ctx:y}}}),Xc=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),ki=new $({props:{anchor:"transformers.TFAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[vq]},$$scope:{ctx:y}}}),Dc=new B({props:{title:"FlaxAutoModelForNextSentencePrediction",local:"transformers.FlaxAutoModelForNextSentencePrediction",headingTag:"h3"}}),Qc=new x({props:{name:"class transformers.FlaxAutoModelForNextSentencePrediction",anchor:"transformers.FlaxAutoModelForNextSentencePrediction",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L350"}}),Hc=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForNextSentencePrediction">FlaxBertForNextSentencePrediction</a> (BERT model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),$i=new $({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_config.example",$$slots:{default:[wq]},$$scope:{ctx:y}}}),Yc=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Bi=new $({props:{anchor:"transformers.FlaxAutoModelForNextSentencePrediction.from_pretrained.example",$$slots:{default:[xq]},$$scope:{ctx:y}}}),zc=new B({props:{title:"AutoModelForTokenClassification",local:"transformers.AutoModelForTokenClassification",headingTag:"h3"}}),Oc=new x({props:{name:"class transformers.AutoModelForTokenClassification",anchor:"transformers.AutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1544"}}),Kc=new x({props:{name:"from_config",anchor:"transformers.AutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForTokenClassification">AlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForTokenClassification">BertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptConfig">BioGptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/biogpt#transformers.BioGptForTokenClassification">BioGptForTokenClassification</a> (BioGpt model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForTokenClassification">BloomForTokenClassification</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bros#transformers.BrosConfig">BrosConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bros#transformers.BrosForTokenClassification">BrosForTokenClassification</a> (BROS model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForTokenClassification">CamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineForTokenClassification">CanineForTokenClassification</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForTokenClassification">ConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForTokenClassification">Data2VecTextForTokenClassification</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForTokenClassification">DebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForTokenClassification">DebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForTokenClassification">ElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForTokenClassification">ErnieForTokenClassification</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMForTokenClassification">ErnieMForTokenClassification</a> (ErnieM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmForTokenClassification">EsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForTokenClassification">FNetForTokenClassification</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconForTokenClassification">FalconForTokenClassification</a> (Falcon model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertForTokenClassification">FlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForTokenClassification">FunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2ForTokenClassification">GPT2ForTokenClassification</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeConfig">GPTBigCodeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_bigcode#transformers.GPTBigCodeForTokenClassification">GPTBigCodeForTokenClassification</a> (GPTBigCode model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoForTokenClassification">GPTNeoForTokenClassification</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXForTokenClassification">GPTNeoXForTokenClassification</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaConfig">GemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gemma#transformers.GemmaForTokenClassification">GemmaForTokenClassification</a> (Gemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForTokenClassification">IBertForTokenClassification</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForTokenClassification">LayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForTokenClassification">LayoutLMv2ForTokenClassification</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForTokenClassification">LayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltForTokenClassification">LiltForTokenClassification</a> (LiLT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForTokenClassification">LlamaForTokenClassification</a> (LLaMA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForTokenClassification">LongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForTokenClassification">LukeForTokenClassification</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForTokenClassification">MPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5ForTokenClassification">MT5ForTokenClassification</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMForTokenClassification">MarkupLMForTokenClassification</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForTokenClassification">MegaForTokenClassification</a> (MEGA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForTokenClassification">MegatronBertForTokenClassification</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralConfig">MistralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mistral#transformers.MistralForTokenClassification">MistralForTokenClassification</a> (Mistral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralConfig">MixtralConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mixtral#transformers.MixtralForTokenClassification">MixtralForTokenClassification</a> (Mixtral model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForTokenClassification">MobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForTokenClassification">MptForTokenClassification</a> (MPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForTokenClassification">MraForTokenClassification</a> (MRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForTokenClassification">NezhaForTokenClassification</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForTokenClassification">NystromformerForTokenClassification</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonConfig">PersimmonConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/persimmon#transformers.PersimmonForTokenClassification">PersimmonForTokenClassification</a> (Persimmon model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3Config">Phi3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/phi3#transformers.Phi3ForTokenClassification">Phi3ForTokenClassification</a> (Phi3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiConfig">PhiConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/phi#transformers.PhiForTokenClassification">PhiForTokenClassification</a> (Phi model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForTokenClassification">QDQBertForTokenClassification</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2Config">Qwen2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qwen2#transformers.Qwen2ForTokenClassification">Qwen2ForTokenClassification</a> (Qwen2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeConfig">Qwen2MoeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qwen2_moe#transformers.Qwen2MoeForTokenClassification">Qwen2MoeForTokenClassification</a> (Qwen2MoE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForTokenClassification">RemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForTokenClassification">RoCBertForTokenClassification</a> (RoCBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForTokenClassification">RoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForTokenClassification">RobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForTokenClassification">RobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForTokenClassification">SqueezeBertForTokenClassification</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmConfig">StableLmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/stablelm#transformers.StableLmForTokenClassification">StableLmForTokenClassification</a> (StableLm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2Config">Starcoder2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/starcoder2#transformers.Starcoder2ForTokenClassification">Starcoder2ForTokenClassification</a> (Starcoder2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForTokenClassification">T5ForTokenClassification</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5ForTokenClassification">UMT5ForTokenClassification</a> (UMT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMForTokenClassification">XLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForTokenClassification">XLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForTokenClassification">XLMRobertaXLForTokenClassification</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetForTokenClassification">XLNetForTokenClassification</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForTokenClassification">XmodForTokenClassification</a> (X-MOD model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForTokenClassification">YosoForTokenClassification</a> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTokenClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),ji=new $({props:{anchor:"transformers.AutoModelForTokenClassification.from_config.example",$$slots:{default:[Lq]},$$scope:{ctx:y}}}),ef=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ai=new $({props:{anchor:"transformers.AutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[kq]},$$scope:{ctx:y}}}),of=new B({props:{title:"TFAutoModelForTokenClassification",local:"transformers.TFAutoModelForTokenClassification",headingTag:"h3"}}),rf=new x({props:{name:"class transformers.TFAutoModelForTokenClassification",anchor:"transformers.TFAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L674"}}),tf=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForTokenClassification">TFAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForTokenClassification">TFBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForTokenClassification">TFCamembertForTokenClassification</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForTokenClassification">TFConvBertForTokenClassification</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaForTokenClassification">TFDebertaForTokenClassification</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForTokenClassification">TFDebertaV2ForTokenClassification</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForTokenClassification">TFElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.EsmConfig">EsmConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/esm#transformers.TFEsmForTokenClassification">TFEsmForTokenClassification</a> (ESM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertForTokenClassification">TFFlaubertForTokenClassification</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForTokenClassification">TFFunnelForTokenClassification</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForTokenClassification">TFLayoutLMForTokenClassification</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForTokenClassification">TFLayoutLMv3ForTokenClassification</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForTokenClassification">TFLongformerForTokenClassification</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForTokenClassification">TFMPNetForTokenClassification</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForTokenClassification">TFMobileBertForTokenClassification</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForTokenClassification">TFRemBertForTokenClassification</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForTokenClassification">TFRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForTokenClassification">TFRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForTokenClassification">TFRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMForTokenClassification">TFXLMForTokenClassification</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForTokenClassification">TFXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetForTokenClassification">TFXLNetForTokenClassification</a> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTokenClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Zi=new $({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_config.example",$$slots:{default:[$q]},$$scope:{ctx:y}}}),nf=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ri=new $({props:{anchor:"transformers.TFAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Bq]},$$scope:{ctx:y}}}),af=new B({props:{title:"FlaxAutoModelForTokenClassification",local:"transformers.FlaxAutoModelForTokenClassification",headingTag:"h3"}}),sf=new x({props:{name:"class transformers.FlaxAutoModelForTokenClassification",anchor:"transformers.FlaxAutoModelForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L334"}}),lf=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForTokenClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForTokenClassification">FlaxAlbertForTokenClassification</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForTokenClassification">FlaxBertForTokenClassification</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForTokenClassification">FlaxBigBirdForTokenClassification</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForTokenClassification">FlaxElectraForTokenClassification</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForTokenClassification">FlaxRoFormerForTokenClassification</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForTokenClassification">FlaxRobertaForTokenClassification</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForTokenClassification">FlaxRobertaPreLayerNormForTokenClassification</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForTokenClassification">FlaxXLMRobertaForTokenClassification</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Gi=new $({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_config.example",$$slots:{default:[jq]},$$scope:{ctx:y}}}),df=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Pi=new $({props:{anchor:"transformers.FlaxAutoModelForTokenClassification.from_pretrained.example",$$slots:{default:[Aq]},$$scope:{ctx:y}}}),mf=new B({props:{title:"AutoModelForQuestionAnswering",local:"transformers.AutoModelForQuestionAnswering",headingTag:"h3"}}),cf=new x({props:{name:"class transformers.AutoModelForQuestionAnswering",anchor:"transformers.AutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1504"}}),ff=new x({props:{name:"from_config",anchor:"transformers.AutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertForQuestionAnswering">AlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartForQuestionAnswering">BartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertForQuestionAnswering">BertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusConfig">BigBirdPegasusConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bigbird_pegasus#transformers.BigBirdPegasusForQuestionAnswering">BigBirdPegasusForQuestionAnswering</a> (BigBird-Pegasus model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomConfig">BloomConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bloom#transformers.BloomForQuestionAnswering">BloomForQuestionAnswering</a> (BLOOM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertForQuestionAnswering">CamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineConfig">CanineConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/canine#transformers.CanineForQuestionAnswering">CanineForQuestionAnswering</a> (CANINE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertForQuestionAnswering">ConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextConfig">Data2VecTextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecTextForQuestionAnswering">Data2VecTextForQuestionAnswering</a> (Data2VecText model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaForQuestionAnswering">DebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2ForQuestionAnswering">DebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraForQuestionAnswering">ElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieConfig">ErnieConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie#transformers.ErnieForQuestionAnswering">ErnieForQuestionAnswering</a> (ERNIE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMConfig">ErnieMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ernie_m#transformers.ErnieMForQuestionAnswering">ErnieMForQuestionAnswering</a> (ErnieM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetConfig">FNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/fnet#transformers.FNetForQuestionAnswering">FNetForQuestionAnswering</a> (FNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconConfig">FalconConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/falcon#transformers.FalconForQuestionAnswering">FalconForQuestionAnswering</a> (Falcon model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertForQuestionAnsweringSimple">FlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelForQuestionAnswering">FunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2Config">GPT2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt2#transformers.GPT2ForQuestionAnswering">GPT2ForQuestionAnswering</a> (OpenAI GPT-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJForQuestionAnswering">GPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoConfig">GPTNeoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neo#transformers.GPTNeoForQuestionAnswering">GPTNeoForQuestionAnswering</a> (GPT Neo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXConfig">GPTNeoXConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gpt_neox#transformers.GPTNeoXForQuestionAnswering">GPTNeoXForQuestionAnswering</a> (GPT NeoX model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertConfig">IBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/ibert#transformers.IBertForQuestionAnswering">IBertForQuestionAnswering</a> (I-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDConfig">LEDConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/led#transformers.LEDForQuestionAnswering">LEDForQuestionAnswering</a> (LED model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltConfig">LiltConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/lilt#transformers.LiltForQuestionAnswering">LiltForQuestionAnswering</a> (LiLT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llama#transformers.LlamaForQuestionAnswering">LlamaForQuestionAnswering</a> (LLaMA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerForQuestionAnswering">LongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeConfig">LukeConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/luke#transformers.LukeForQuestionAnswering">LukeForQuestionAnswering</a> (LUKE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertConfig">LxmertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/lxmert#transformers.LxmertForQuestionAnswering">LxmertForQuestionAnswering</a> (LXMERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartForQuestionAnswering">MBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetForQuestionAnswering">MPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5Config">MT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mt5#transformers.MT5ForQuestionAnswering">MT5ForQuestionAnswering</a> (MT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMConfig">MarkupLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/markuplm#transformers.MarkupLMForQuestionAnswering">MarkupLMForQuestionAnswering</a> (MarkupLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaConfig">MegaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mega#transformers.MegaForQuestionAnswering">MegaForQuestionAnswering</a> (MEGA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertConfig">MegatronBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/megatron-bert#transformers.MegatronBertForQuestionAnswering">MegatronBertForQuestionAnswering</a> (Megatron-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertForQuestionAnswering">MobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptConfig">MptConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpt#transformers.MptForQuestionAnswering">MptForQuestionAnswering</a> (MPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraConfig">MraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mra#transformers.MraForQuestionAnswering">MraForQuestionAnswering</a> (MRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpConfig">MvpConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mvp#transformers.MvpForQuestionAnswering">MvpForQuestionAnswering</a> (MVP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaConfig">NezhaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nezha#transformers.NezhaForQuestionAnswering">NezhaForQuestionAnswering</a> (Nezha model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerConfig">NystromformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nystromformer#transformers.NystromformerForQuestionAnswering">NystromformerForQuestionAnswering</a> (Nystr&#xF6;mformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTConfig">OPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/opt#transformers.OPTForQuestionAnswering">OPTForQuestionAnswering</a> (OPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertConfig">QDQBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/qdqbert#transformers.QDQBertForQuestionAnswering">QDQBertForQuestionAnswering</a> (QDQBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerConfig">ReformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/reformer#transformers.ReformerForQuestionAnswering">ReformerForQuestionAnswering</a> (Reformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertForQuestionAnswering">RemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertConfig">RoCBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roc_bert#transformers.RoCBertForQuestionAnswering">RoCBertForQuestionAnswering</a> (RoCBert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerForQuestionAnswering">RoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaForQuestionAnswering">RobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormForQuestionAnswering">RobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterConfig">SplinterConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/splinter#transformers.SplinterForQuestionAnswering">SplinterForQuestionAnswering</a> (Splinter model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertConfig">SqueezeBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/squeezebert#transformers.SqueezeBertForQuestionAnswering">SqueezeBertForQuestionAnswering</a> (SqueezeBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5Config">T5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/t5#transformers.T5ForQuestionAnswering">T5ForQuestionAnswering</a> (T5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5Config">UMT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/umt5#transformers.UMT5ForQuestionAnswering">UMT5ForQuestionAnswering</a> (UMT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMForQuestionAnsweringSimple">XLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaForQuestionAnswering">XLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLConfig">XLMRobertaXLConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta-xl#transformers.XLMRobertaXLForQuestionAnswering">XLMRobertaXLForQuestionAnswering</a> (XLM-RoBERTa-XL model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetForQuestionAnsweringSimple">XLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodConfig">XmodConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xmod#transformers.XmodForQuestionAnswering">XmodForQuestionAnswering</a> (X-MOD model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoConfig">YosoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/yoso#transformers.YosoForQuestionAnswering">YosoForQuestionAnswering</a> (YOSO model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Si=new $({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Zq]},$$scope:{ctx:y}}}),gf=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Wi=new $({props:{anchor:"transformers.AutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Rq]},$$scope:{ctx:y}}}),pf=new B({props:{title:"TFAutoModelForQuestionAnswering",local:"transformers.TFAutoModelForQuestionAnswering",headingTag:"h3"}}),hf=new x({props:{name:"class transformers.TFAutoModelForQuestionAnswering",anchor:"transformers.TFAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L645"}}),uf=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.TFAlbertForQuestionAnswering">TFAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.TFBertForQuestionAnswering">TFBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.CamembertConfig">CamembertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/camembert#transformers.TFCamembertForQuestionAnswering">TFCamembertForQuestionAnswering</a> (CamemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.ConvBertConfig">ConvBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convbert#transformers.TFConvBertForQuestionAnswering">TFConvBertForQuestionAnswering</a> (ConvBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.DebertaConfig">DebertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta#transformers.TFDebertaForQuestionAnswering">TFDebertaForQuestionAnswering</a> (DeBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.DebertaV2Config">DebertaV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deberta-v2#transformers.TFDebertaV2ForQuestionAnswering">TFDebertaV2ForQuestionAnswering</a> (DeBERTa-v2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.TFElectraForQuestionAnswering">TFElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.FlaubertConfig">FlaubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/flaubert#transformers.TFFlaubertForQuestionAnsweringSimple">TFFlaubertForQuestionAnsweringSimple</a> (FlauBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.FunnelConfig">FunnelConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/funnel#transformers.TFFunnelForQuestionAnswering">TFFunnelForQuestionAnswering</a> (Funnel Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.GPTJConfig">GPTJConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/gptj#transformers.TFGPTJForQuestionAnswering">TFGPTJForQuestionAnswering</a> (GPT-J model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.LongformerConfig">LongformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/longformer#transformers.TFLongformerForQuestionAnswering">TFLongformerForQuestionAnswering</a> (Longformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.MPNetConfig">MPNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mpnet#transformers.TFMPNetForQuestionAnswering">TFMPNetForQuestionAnswering</a> (MPNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.MobileBertConfig">MobileBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilebert#transformers.TFMobileBertForQuestionAnswering">TFMobileBertForQuestionAnswering</a> (MobileBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rembert#transformers.TFRemBertForQuestionAnswering">TFRemBertForQuestionAnswering</a> (RemBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.TFRoFormerForQuestionAnswering">TFRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.TFRobertaForQuestionAnswering">TFRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.TFRobertaPreLayerNormForQuestionAnswering">TFRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.XLMConfig">XLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm#transformers.TFXLMForQuestionAnsweringSimple">TFXLMForQuestionAnsweringSimple</a> (XLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.TFXLMRobertaForQuestionAnswering">TFXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.XLNetConfig">XLNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlnet#transformers.TFXLNetForQuestionAnsweringSimple">TFXLNetForQuestionAnsweringSimple</a> (XLNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ji=new $({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Gq]},$$scope:{ctx:y}}}),_f=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Vi=new $({props:{anchor:"transformers.TFAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Pq]},$$scope:{ctx:y}}}),bf=new B({props:{title:"FlaxAutoModelForQuestionAnswering",local:"transformers.FlaxAutoModelForQuestionAnswering",headingTag:"h3"}}),Mf=new x({props:{name:"class transformers.FlaxAutoModelForQuestionAnswering",anchor:"transformers.FlaxAutoModelForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L327"}}),Tf=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.AlbertConfig">AlbertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/albert#transformers.FlaxAlbertForQuestionAnswering">FlaxAlbertForQuestionAnswering</a> (ALBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.BartConfig">BartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bart#transformers.FlaxBartForQuestionAnswering">FlaxBartForQuestionAnswering</a> (BART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.BertConfig">BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bert#transformers.FlaxBertForQuestionAnswering">FlaxBertForQuestionAnswering</a> (BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> (BigBird model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> (DistilBERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.ElectraConfig">ElectraConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/electra#transformers.FlaxElectraForQuestionAnswering">FlaxElectraForQuestionAnswering</a> (ELECTRA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.MBartConfig">MBartConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mbart#transformers.FlaxMBartForQuestionAnswering">FlaxMBartForQuestionAnswering</a> (mBART model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.RoFormerConfig">RoFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roformer#transformers.FlaxRoFormerForQuestionAnswering">FlaxRoFormerForQuestionAnswering</a> (RoFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.RobertaConfig">RobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta#transformers.FlaxRobertaForQuestionAnswering">FlaxRobertaForQuestionAnswering</a> (RoBERTa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.RobertaPreLayerNormConfig">RobertaPreLayerNormConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/roberta-prelayernorm#transformers.FlaxRobertaPreLayerNormForQuestionAnswering">FlaxRobertaPreLayerNormForQuestionAnswering</a> (RoBERTa-PreLayerNorm model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.XLMRobertaConfig">XLMRobertaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/xlm-roberta#transformers.FlaxXLMRobertaForQuestionAnswering">FlaxXLMRobertaForQuestionAnswering</a> (XLM-RoBERTa model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ei=new $({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_config.example",$$slots:{default:[Sq]},$$scope:{ctx:y}}}),Ff=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ii=new $({props:{anchor:"transformers.FlaxAutoModelForQuestionAnswering.from_pretrained.example",$$slots:{default:[Wq]},$$scope:{ctx:y}}}),Cf=new B({props:{title:"AutoModelForTextEncoding",local:"transformers.AutoModelForTextEncoding",headingTag:"h3"}}),vf=new x({props:{name:"class transformers.AutoModelForTextEncoding",anchor:"transformers.AutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1440"}}),wf=new B({props:{title:"TFAutoModelForTextEncoding",local:"transformers.TFAutoModelForTextEncoding",headingTag:"h3"}}),Lf=new x({props:{name:"class transformers.TFAutoModelForTextEncoding",anchor:"transformers.TFAutoModelForTextEncoding",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L533"}}),kf=new B({props:{title:"Computer vision",local:"computer-vision",headingTag:"h2"}}),Bf=new B({props:{title:"AutoModelForDepthEstimation",local:"transformers.AutoModelForDepthEstimation",headingTag:"h3"}}),jf=new x({props:{name:"class transformers.AutoModelForDepthEstimation",anchor:"transformers.AutoModelForDepthEstimation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1633"}}),Af=new x({props:{name:"from_config",anchor:"transformers.AutoModelForDepthEstimation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTForDepthEstimation">DPTForDepthEstimation</a> (DPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/depth_anything#transformers.DepthAnythingConfig">DepthAnythingConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/depth_anything#transformers.DepthAnythingForDepthEstimation">DepthAnythingForDepthEstimation</a> (Depth Anything model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/glpn#transformers.GLPNConfig">GLPNConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/glpn#transformers.GLPNForDepthEstimation">GLPNForDepthEstimation</a> (GLPN model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDepthEstimation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ui=new $({props:{anchor:"transformers.AutoModelForDepthEstimation.from_config.example",$$slots:{default:[Jq]},$$scope:{ctx:y}}}),Zf=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForDepthEstimation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ni=new $({props:{anchor:"transformers.AutoModelForDepthEstimation.from_pretrained.example",$$slots:{default:[Vq]},$$scope:{ctx:y}}}),Rf=new B({props:{title:"AutoModelForImageClassification",local:"transformers.AutoModelForImageClassification",headingTag:"h3"}}),Gf=new x({props:{name:"class transformers.AutoModelForImageClassification",anchor:"transformers.AutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1567"}}),Pf=new x({props:{name:"from_config",anchor:"transformers.AutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitForImageClassification">BeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitConfig">BitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/bit#transformers.BitForImageClassification">BitForImageClassification</a> (BiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPForImageClassification">CLIPForImageClassification</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextForImageClassification">ConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.ConvNextV2ForImageClassification">ConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.CvtForImageClassification">CvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionForImageClassification">Data2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTForImageClassification">DeiTForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTForImageClassificationWithTeacher">DeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dinat#transformers.DinatConfig">DinatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dinat#transformers.DinatForImageClassification">DinatForImageClassification</a> (DiNAT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dinov2#transformers.Dinov2Config">Dinov2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dinov2#transformers.Dinov2ForImageClassification">Dinov2ForImageClassification</a> (DINOv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassification">EfficientFormerForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerForImageClassificationWithTeacher">EfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/efficientnet#transformers.EfficientNetConfig">EfficientNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/efficientnet#transformers.EfficientNetForImageClassification">EfficientNetForImageClassification</a> (EfficientNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetForImageClassification">FocalNetForImageClassification</a> (FocalNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/imagegpt#transformers.ImageGPTConfig">ImageGPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/imagegpt#transformers.ImageGPTForImageClassification">ImageGPTForImageClassification</a> (ImageGPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitConfig">LevitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitForImageClassification">LevitForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/levit#transformers.LevitForImageClassificationWithTeacher">LevitForImageClassificationWithTeacher</a> (LeViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v1#transformers.MobileNetV1Config">MobileNetV1Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v1#transformers.MobileNetV1ForImageClassification">MobileNetV1ForImageClassification</a> (MobileNetV1 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForImageClassification">MobileNetV2ForImageClassification</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTForImageClassification">MobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2ForImageClassification">MobileViTV2ForImageClassification</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/nat#transformers.NatConfig">NatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/nat#transformers.NatForImageClassification">NatForImageClassification</a> (NAT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverConfig">PerceiverConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForImageClassificationLearned">PerceiverForImageClassificationLearned</a> or <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForImageClassificationFourier">PerceiverForImageClassificationFourier</a> or <a href="/docs/transformers/pr_31161/en/model_doc/perceiver#transformers.PerceiverForImageClassificationConvProcessing">PerceiverForImageClassificationConvProcessing</a> (Perceiver model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/poolformer#transformers.PoolFormerConfig">PoolFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/poolformer#transformers.PoolFormerForImageClassification">PoolFormerForImageClassification</a> (PoolFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pvt#transformers.PvtConfig">PvtConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pvt#transformers.PvtForImageClassification">PvtForImageClassification</a> (PVT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pvt_v2#transformers.PvtV2Config">PvtV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pvt_v2#transformers.PvtV2ForImageClassification">PvtV2ForImageClassification</a> (PVTv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetForImageClassification">RegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetForImageClassification">ResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerForImageClassification">SegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipConfig">SiglipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipForImageClassification">SiglipForImageClassification</a> (SigLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.SwiftFormerForImageClassification">SwiftFormerForImageClassification</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinForImageClassification">SwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/van#transformers.VanConfig">VanConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/van#transformers.VanForImageClassification">VanForImageClassification</a> (VAN model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTForImageClassification">ViTForImageClassification</a> (ViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit_hybrid#transformers.ViTHybridConfig">ViTHybridConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit_hybrid#transformers.ViTHybridForImageClassification">ViTHybridForImageClassification</a> (ViT Hybrid model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit_msn#transformers.ViTMSNConfig">ViTMSNConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit_msn#transformers.ViTMSNForImageClassification">ViTMSNForImageClassification</a> (ViTMSN model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),qi=new $({props:{anchor:"transformers.AutoModelForImageClassification.from_config.example",$$slots:{default:[Eq]},$$scope:{ctx:y}}}),Sf=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Xi=new $({props:{anchor:"transformers.AutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Iq]},$$scope:{ctx:y}}}),Wf=new B({props:{title:"TFAutoModelForImageClassification",local:"transformers.TFAutoModelForImageClassification",headingTag:"h3"}}),Jf=new x({props:{name:"class transformers.TFAutoModelForImageClassification",anchor:"transformers.TFAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L584"}}),Vf=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.ConvNextConfig">ConvNextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convnext#transformers.TFConvNextForImageClassification">TFConvNextForImageClassification</a> (ConvNeXT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.ConvNextV2Config">ConvNextV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/convnextv2#transformers.TFConvNextV2ForImageClassification">TFConvNextV2ForImageClassification</a> (ConvNeXTV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.CvtConfig">CvtConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/cvt#transformers.TFCvtForImageClassification">TFCvtForImageClassification</a> (CvT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.TFData2VecVisionForImageClassification">TFData2VecVisionForImageClassification</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.TFDeiTForImageClassification">TFDeiTForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.TFDeiTForImageClassificationWithTeacher">TFDeiTForImageClassificationWithTeacher</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.EfficientFormerConfig">EfficientFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassification">TFEfficientFormerForImageClassification</a> or <a href="/docs/transformers/pr_31161/en/model_doc/efficientformer#transformers.TFEfficientFormerForImageClassificationWithTeacher">TFEfficientFormerForImageClassificationWithTeacher</a> (EfficientFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.TFMobileViTForImageClassification">TFMobileViTForImageClassification</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.TFRegNetForImageClassification">TFRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.TFResNetForImageClassification">TFResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.TFSegformerForImageClassification">TFSegformerForImageClassification</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.SwiftFormerConfig">SwiftFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swiftformer#transformers.TFSwiftFormerForImageClassification">TFSwiftFormerForImageClassification</a> (SwiftFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.TFSwinForImageClassification">TFSwinForImageClassification</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.TFViTForImageClassification">TFViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForImageClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Di=new $({props:{anchor:"transformers.TFAutoModelForImageClassification.from_config.example",$$slots:{default:[Uq]},$$scope:{ctx:y}}}),Ef=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Qi=new $({props:{anchor:"transformers.TFAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Nq]},$$scope:{ctx:y}}}),If=new B({props:{title:"FlaxAutoModelForImageClassification",local:"transformers.FlaxAutoModelForImageClassification",headingTag:"h3"}}),Uf=new x({props:{name:"class transformers.FlaxAutoModelForImageClassification",anchor:"transformers.FlaxAutoModelForImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L359"}}),Nf=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.FlaxBeitForImageClassification">FlaxBeitForImageClassification</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.RegNetConfig">RegNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/regnet#transformers.FlaxRegNetForImageClassification">FlaxRegNetForImageClassification</a> (RegNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.ResNetConfig">ResNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/resnet#transformers.FlaxResNetForImageClassification">FlaxResNetForImageClassification</a> (ResNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.FlaxViTForImageClassification">FlaxViTForImageClassification</a> (ViT model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Hi=new $({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_config.example",$$slots:{default:[qq]},$$scope:{ctx:y}}}),qf=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Yi=new $({props:{anchor:"transformers.FlaxAutoModelForImageClassification.from_pretrained.example",$$slots:{default:[Xq]},$$scope:{ctx:y}}}),Xf=new B({props:{title:"AutoModelForVideoClassification",local:"transformers.AutoModelForVideoClassification",headingTag:"h3"}}),Df=new x({props:{name:"class transformers.AutoModelForVideoClassification",anchor:"transformers.AutoModelForVideoClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1640"}}),Qf=new x({props:{name:"from_config",anchor:"transformers.AutoModelForVideoClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/timesformer#transformers.TimesformerConfig">TimesformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/timesformer#transformers.TimesformerForVideoClassification">TimesformerForVideoClassification</a> (TimeSformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEConfig">VideoMAEConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/videomae#transformers.VideoMAEForVideoClassification">VideoMAEForVideoClassification</a> (VideoMAE model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vivit#transformers.VivitConfig">VivitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vivit#transformers.VivitForVideoClassification">VivitForVideoClassification</a> (ViViT model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVideoClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),zi=new $({props:{anchor:"transformers.AutoModelForVideoClassification.from_config.example",$$slots:{default:[Dq]},$$scope:{ctx:y}}}),Hf=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVideoClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Oi=new $({props:{anchor:"transformers.AutoModelForVideoClassification.from_pretrained.example",$$slots:{default:[Qq]},$$scope:{ctx:y}}}),Yf=new B({props:{title:"AutoModelForKeypointDetection",local:"transformers.AutoModelForKeypointDetection",headingTag:"h3"}}),Of=new x({props:{name:"class transformers.AutoModelForKeypointDetection",anchor:"transformers.AutoModelForKeypointDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1436"}}),Kf=new B({props:{title:"AutoModelForMaskedImageModeling",local:"transformers.AutoModelForMaskedImageModeling",headingTag:"h3"}}),eg=new x({props:{name:"class transformers.AutoModelForMaskedImageModeling",anchor:"transformers.AutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1705"}}),og=new x({props:{name:"from_config",anchor:"transformers.AutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTForMaskedImageModeling">DeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetConfig">FocalNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/focalnet#transformers.FocalNetForMaskedImageModeling">FocalNetForMaskedImageModeling</a> (FocalNet model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinForMaskedImageModeling">SwinForMaskedImageModeling</a> (Swin Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> (Swin Transformer V2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTConfig">ViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vit#transformers.ViTForMaskedImageModeling">ViTForMaskedImageModeling</a> (ViT model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ki=new $({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[Hq]},$$scope:{ctx:y}}}),rg=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),el=new $({props:{anchor:"transformers.AutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Yq]},$$scope:{ctx:y}}}),tg=new B({props:{title:"TFAutoModelForMaskedImageModeling",local:"transformers.TFAutoModelForMaskedImageModeling",headingTag:"h3"}}),ng=new x({props:{name:"class transformers.TFAutoModelForMaskedImageModeling",anchor:"transformers.TFAutoModelForMaskedImageModeling",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L575"}}),ag=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.DeiTConfig">DeiTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deit#transformers.TFDeiTForMaskedImageModeling">TFDeiTForMaskedImageModeling</a> (DeiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.SwinConfig">SwinConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/swin#transformers.TFSwinForMaskedImageModeling">TFSwinForMaskedImageModeling</a> (Swin Transformer model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),ol=new $({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_config.example",$$slots:{default:[zq]},$$scope:{ctx:y}}}),sg=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),rl=new $({props:{anchor:"transformers.TFAutoModelForMaskedImageModeling.from_pretrained.example",$$slots:{default:[Oq]},$$scope:{ctx:y}}}),ig=new B({props:{title:"AutoModelForObjectDetection",local:"transformers.AutoModelForObjectDetection",headingTag:"h3"}}),lg=new x({props:{name:"class transformers.AutoModelForObjectDetection",anchor:"transformers.AutoModelForObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1617"}}),dg=new x({props:{name:"from_config",anchor:"transformers.AutoModelForObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/conditional_detr#transformers.ConditionalDetrConfig">ConditionalDetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/conditional_detr#transformers.ConditionalDetrForObjectDetection">ConditionalDetrForObjectDetection</a> (Conditional DETR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deformable_detr#transformers.DeformableDetrConfig">DeformableDetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deformable_detr#transformers.DeformableDetrForObjectDetection">DeformableDetrForObjectDetection</a> (Deformable DETR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/deta#transformers.DetaConfig">DetaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/deta#transformers.DetaForObjectDetection">DetaForObjectDetection</a> (DETA model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrForObjectDetection">DetrForObjectDetection</a> (DETR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/rt_detr#transformers.RTDetrConfig">RTDetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/rt_detr#transformers.RTDetrForObjectDetection">RTDetrForObjectDetection</a> (RT-DETR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/table-transformer#transformers.TableTransformerConfig">TableTransformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/table-transformer#transformers.TableTransformerForObjectDetection">TableTransformerForObjectDetection</a> (Table Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/yolos#transformers.YolosConfig">YolosConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/yolos#transformers.YolosForObjectDetection">YolosForObjectDetection</a> (YOLOS model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForObjectDetection.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),tl=new $({props:{anchor:"transformers.AutoModelForObjectDetection.from_config.example",$$slots:{default:[Kq]},$$scope:{ctx:y}}}),mg=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),nl=new $({props:{anchor:"transformers.AutoModelForObjectDetection.from_pretrained.example",$$slots:{default:[e4]},$$scope:{ctx:y}}}),cg=new B({props:{title:"AutoModelForImageSegmentation",local:"transformers.AutoModelForImageSegmentation",headingTag:"h3"}}),fg=new x({props:{name:"class transformers.AutoModelForImageSegmentation",anchor:"transformers.AutoModelForImageSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1583"}}),gg=new x({props:{name:"from_config",anchor:"transformers.AutoModelForImageSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),al=new $({props:{anchor:"transformers.AutoModelForImageSegmentation.from_config.example",$$slots:{default:[o4]},$$scope:{ctx:y}}}),pg=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForImageSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),sl=new $({props:{anchor:"transformers.AutoModelForImageSegmentation.from_pretrained.example",$$slots:{default:[r4]},$$scope:{ctx:y}}}),hg=new B({props:{title:"AutoModelForImageToImage",local:"transformers.AutoModelForImageToImage",headingTag:"h3"}}),_g=new x({props:{name:"class transformers.AutoModelForImageToImage",anchor:"transformers.AutoModelForImageToImage",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1444"}}),bg=new B({props:{title:"AutoModelForSemanticSegmentation",local:"transformers.AutoModelForSemanticSegmentation",headingTag:"h3"}}),Mg=new x({props:{name:"class transformers.AutoModelForSemanticSegmentation",anchor:"transformers.AutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1590"}}),Tg=new x({props:{name:"from_config",anchor:"transformers.AutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitConfig">BeitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/beit#transformers.BeitForSemanticSegmentation">BeitForSemanticSegmentation</a> (BEiT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTConfig">DPTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/dpt#transformers.DPTForSemanticSegmentation">DPTForSemanticSegmentation</a> (DPT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionForSemanticSegmentation">Data2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2Config">MobileNetV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilenet_v2#transformers.MobileNetV2ForSemanticSegmentation">MobileNetV2ForSemanticSegmentation</a> (MobileNetV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTForSemanticSegmentation">MobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilevitv2#transformers.MobileViTV2ForSemanticSegmentation">MobileViTV2ForSemanticSegmentation</a> (MobileViTV2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerForSemanticSegmentation">SegformerForSemanticSegmentation</a> (SegFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/upernet#transformers.UperNetConfig">UperNetConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/upernet#transformers.UperNetForSemanticSegmentation">UperNetForSemanticSegmentation</a> (UPerNet model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),il=new $({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[t4]},$$scope:{ctx:y}}}),Fg=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),ll=new $({props:{anchor:"transformers.AutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[n4]},$$scope:{ctx:y}}}),Cg=new B({props:{title:"TFAutoModelForSemanticSegmentation",local:"transformers.TFAutoModelForSemanticSegmentation",headingTag:"h3"}}),yg=new x({props:{name:"class transformers.TFAutoModelForSemanticSegmentation",anchor:"transformers.TFAutoModelForSemanticSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L602"}}),vg=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecVisionConfig">Data2VecVisionConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.TFData2VecVisionForSemanticSegmentation">TFData2VecVisionForSemanticSegmentation</a> (Data2VecVision model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.MobileViTConfig">MobileViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mobilevit#transformers.TFMobileViTForSemanticSegmentation">TFMobileViTForSemanticSegmentation</a> (MobileViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.SegformerConfig">SegformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/segformer#transformers.TFSegformerForSemanticSegmentation">TFSegformerForSemanticSegmentation</a> (SegFormer model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),dl=new $({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_config.example",$$slots:{default:[a4]},$$scope:{ctx:y}}}),wg=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),ml=new $({props:{anchor:"transformers.TFAutoModelForSemanticSegmentation.from_pretrained.example",$$slots:{default:[s4]},$$scope:{ctx:y}}}),xg=new B({props:{title:"AutoModelForInstanceSegmentation",local:"transformers.AutoModelForInstanceSegmentation",headingTag:"h3"}}),Lg=new x({props:{name:"class transformers.AutoModelForInstanceSegmentation",anchor:"transformers.AutoModelForInstanceSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1608"}}),kg=new x({props:{name:"from_config",anchor:"transformers.AutoModelForInstanceSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),cl=new $({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_config.example",$$slots:{default:[i4]},$$scope:{ctx:y}}}),$g=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),fl=new $({props:{anchor:"transformers.AutoModelForInstanceSegmentation.from_pretrained.example",$$slots:{default:[l4]},$$scope:{ctx:y}}}),Bg=new B({props:{title:"AutoModelForUniversalSegmentation",local:"transformers.AutoModelForUniversalSegmentation",headingTag:"h3"}}),jg=new x({props:{name:"class transformers.AutoModelForUniversalSegmentation",anchor:"transformers.AutoModelForUniversalSegmentation",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1599"}}),Ag=new x({props:{name:"from_config",anchor:"transformers.AutoModelForUniversalSegmentation.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrConfig">DetrConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/detr#transformers.DetrForSegmentation">DetrForSegmentation</a> (DETR model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mask2former#transformers.Mask2FormerConfig">Mask2FormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mask2former#transformers.Mask2FormerForUniversalSegmentation">Mask2FormerForUniversalSegmentation</a> (Mask2Former model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerConfig">MaskFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/maskformer#transformers.MaskFormerForInstanceSegmentation">MaskFormerForInstanceSegmentation</a> (MaskFormer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/oneformer#transformers.OneFormerConfig">OneFormerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/oneformer#transformers.OneFormerForUniversalSegmentation">OneFormerForUniversalSegmentation</a> (OneFormer model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),gl=new $({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_config.example",$$slots:{default:[d4]},$$scope:{ctx:y}}}),Zg=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),pl=new $({props:{anchor:"transformers.AutoModelForUniversalSegmentation.from_pretrained.example",$$slots:{default:[m4]},$$scope:{ctx:y}}}),Rg=new B({props:{title:"AutoModelForZeroShotImageClassification",local:"transformers.AutoModelForZeroShotImageClassification",headingTag:"h3"}}),Gg=new x({props:{name:"class transformers.AutoModelForZeroShotImageClassification",anchor:"transformers.AutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1574"}}),Pg=new x({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/align#transformers.AlignConfig">AlignConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/align#transformers.AlignModel">AlignModel</a> (ALIGN model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/altclip#transformers.AltCLIPConfig">AltCLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/altclip#transformers.AltCLIPModel">AltCLIPModel</a> (AltCLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipModel">BlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPModel">CLIPModel</a> (CLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clipseg#transformers.CLIPSegConfig">CLIPSegConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clipseg#transformers.CLIPSegModel">CLIPSegModel</a> (CLIPSeg model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPConfig">ChineseCLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/chinese_clip#transformers.ChineseCLIPModel">ChineseCLIPModel</a> (Chinese-CLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipConfig">SiglipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/siglip#transformers.SiglipModel">SiglipModel</a> (SigLIP model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),hl=new $({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[c4]},$$scope:{ctx:y}}}),Sg=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),ul=new $({props:{anchor:"transformers.AutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[f4]},$$scope:{ctx:y}}}),Wg=new B({props:{title:"TFAutoModelForZeroShotImageClassification",local:"transformers.TFAutoModelForZeroShotImageClassification",headingTag:"h3"}}),Jg=new x({props:{name:"class transformers.TFAutoModelForZeroShotImageClassification",anchor:"transformers.TFAutoModelForZeroShotImageClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L593"}}),Vg=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.TFBlipModel">TFBlipModel</a> (BLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.CLIPConfig">CLIPConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/clip#transformers.TFCLIPModel">TFCLIPModel</a> (CLIP model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),_l=new $({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_config.example",$$slots:{default:[g4]},$$scope:{ctx:y}}}),Eg=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),bl=new $({props:{anchor:"transformers.TFAutoModelForZeroShotImageClassification.from_pretrained.example",$$slots:{default:[p4]},$$scope:{ctx:y}}}),Ig=new B({props:{title:"AutoModelForZeroShotObjectDetection",local:"transformers.AutoModelForZeroShotObjectDetection",headingTag:"h3"}}),Ug=new x({props:{name:"class transformers.AutoModelForZeroShotObjectDetection",anchor:"transformers.AutoModelForZeroShotObjectDetection",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1624"}}),Ng=new x({props:{name:"from_config",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/grounding-dino#transformers.GroundingDinoConfig">GroundingDinoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/grounding-dino#transformers.GroundingDinoForObjectDetection">GroundingDinoForObjectDetection</a> (Grounding DINO model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTConfig">OwlViTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/owlvit#transformers.OwlViTForObjectDetection">OwlViTForObjectDetection</a> (OWL-ViT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/owlv2#transformers.Owlv2Config">Owlv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/owlv2#transformers.Owlv2ForObjectDetection">Owlv2ForObjectDetection</a> (OWLv2 model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ml=new $({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_config.example",$$slots:{default:[h4]},$$scope:{ctx:y}}}),qg=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Tl=new $({props:{anchor:"transformers.AutoModelForZeroShotObjectDetection.from_pretrained.example",$$slots:{default:[u4]},$$scope:{ctx:y}}}),Xg=new B({props:{title:"Audio",local:"audio",headingTag:"h2"}}),Qg=new B({props:{title:"AutoModelForAudioClassification",local:"transformers.AutoModelForAudioClassification",headingTag:"h3"}}),Hg=new x({props:{name:"class transformers.AutoModelForAudioClassification",anchor:"transformers.AutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1654"}}),Yg=new x({props:{name:"from_config",anchor:"transformers.AutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/audio-spectrogram-transformer#transformers.ASTConfig">ASTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/audio-spectrogram-transformer#transformers.ASTForAudioClassification">ASTForAudioClassification</a> (Audio Spectrogram Transformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioForSequenceClassification">Data2VecAudioForSequenceClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertForSequenceClassification">HubertForSequenceClassification</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWForSequenceClassification">SEWForSequenceClassification</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDForSequenceClassification">SEWDForSequenceClassification</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechForSequenceClassification">UniSpeechForSequenceClassification</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForSequenceClassification">UniSpeechSatForSequenceClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertConfig">Wav2Vec2BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForSequenceClassification">Wav2Vec2BertForSequenceClassification</a> (Wav2Vec2-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForSequenceClassification">Wav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForSequenceClassification">Wav2Vec2ConformerForSequenceClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMForSequenceClassification">WavLMForSequenceClassification</a> (WavLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperForAudioClassification">WhisperForAudioClassification</a> (Whisper model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Fl=new $({props:{anchor:"transformers.AutoModelForAudioClassification.from_config.example",$$slots:{default:[_4]},$$scope:{ctx:y}}}),zg=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Cl=new $({props:{anchor:"transformers.AutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[b4]},$$scope:{ctx:y}}}),Og=new B({props:{title:"AutoModelForAudioFrameClassification",local:"transformers.TFAutoModelForAudioClassification",headingTag:"h3"}}),Kg=new x({props:{name:"class transformers.TFAutoModelForAudioClassification",anchor:"transformers.TFAutoModelForAudioClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L544"}}),ep=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForAudioClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.TFWav2Vec2ForSequenceClassification">TFWav2Vec2ForSequenceClassification</a> (Wav2Vec2 model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForAudioClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),yl=new $({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_config.example",$$slots:{default:[M4]},$$scope:{ctx:y}}}),op=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),vl=new $({props:{anchor:"transformers.TFAutoModelForAudioClassification.from_pretrained.example",$$slots:{default:[T4]},$$scope:{ctx:y}}}),rp=new B({props:{title:"TFAutoModelForAudioFrameClassification",local:"transformers.AutoModelForAudioFrameClassification",headingTag:"h3"}}),tp=new x({props:{name:"class transformers.AutoModelForAudioFrameClassification",anchor:"transformers.AutoModelForAudioFrameClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1677"}}),np=new x({props:{name:"from_config",anchor:"transformers.AutoModelForAudioFrameClassification.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioForAudioFrameClassification">Data2VecAudioForAudioFrameClassification</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForAudioFrameClassification">UniSpeechSatForAudioFrameClassification</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertConfig">Wav2Vec2BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForAudioFrameClassification">Wav2Vec2BertForAudioFrameClassification</a> (Wav2Vec2-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForAudioFrameClassification">Wav2Vec2ForAudioFrameClassification</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForAudioFrameClassification">Wav2Vec2ConformerForAudioFrameClassification</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMForAudioFrameClassification">WavLMForAudioFrameClassification</a> (WavLM model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),wl=new $({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_config.example",$$slots:{default:[F4]},$$scope:{ctx:y}}}),ap=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),xl=new $({props:{anchor:"transformers.AutoModelForAudioFrameClassification.from_pretrained.example",$$slots:{default:[C4]},$$scope:{ctx:y}}}),sp=new B({props:{title:"AutoModelForCTC",local:"transformers.AutoModelForCTC",headingTag:"h3"}}),ip=new x({props:{name:"class transformers.AutoModelForCTC",anchor:"transformers.AutoModelForCTC",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1661"}}),lp=new x({props:{name:"from_config",anchor:"transformers.AutoModelForCTC.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioForCTC">Data2VecAudioForCTC</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertConfig">HubertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/hubert#transformers.HubertForCTC">HubertForCTC</a> (Hubert model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/mctct#transformers.MCTCTConfig">MCTCTConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/mctct#transformers.MCTCTForCTC">MCTCTForCTC</a> (M-CTC-T model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWConfig">SEWConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/sew#transformers.SEWForCTC">SEWForCTC</a> (SEW model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDConfig">SEWDConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/sew-d#transformers.SEWDForCTC">SEWDForCTC</a> (SEW-D model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechConfig">UniSpeechConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech#transformers.UniSpeechForCTC">UniSpeechForCTC</a> (UniSpeech model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForCTC">UniSpeechSatForCTC</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertConfig">Wav2Vec2BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForCTC">Wav2Vec2BertForCTC</a> (Wav2Vec2-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC">Wav2Vec2ForCTC</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForCTC">Wav2Vec2ConformerForCTC</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMForCTC">WavLMForCTC</a> (WavLM model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCTC.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ll=new $({props:{anchor:"transformers.AutoModelForCTC.from_config.example",$$slots:{default:[y4]},$$scope:{ctx:y}}}),dp=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForCTC.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForCTC.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForCTC.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForCTC.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForCTC.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForCTC.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForCTC.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForCTC.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForCTC.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForCTC.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForCTC.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForCTC.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForCTC.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),kl=new $({props:{anchor:"transformers.AutoModelForCTC.from_pretrained.example",$$slots:{default:[v4]},$$scope:{ctx:y}}}),mp=new B({props:{title:"AutoModelForSpeechSeq2Seq",local:"transformers.AutoModelForSpeechSeq2Seq",headingTag:"h3"}}),cp=new x({props:{name:"class transformers.AutoModelForSpeechSeq2Seq",anchor:"transformers.AutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1668"}}),fp=new x({props:{name:"from_config",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pop2piano#transformers.Pop2PianoConfig">Pop2PianoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pop2piano#transformers.Pop2PianoForConditionalGeneration">Pop2PianoForConditionalGeneration</a> (Pop2Piano model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TConfig">SeamlessM4TConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t#transformers.SeamlessM4TForSpeechToText">SeamlessM4TForSpeechToText</a> (SeamlessM4T model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2Config">SeamlessM4Tv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/seamless_m4t_v2#transformers.SeamlessM4Tv2ForSpeechToText">SeamlessM4Tv2ForSpeechToText</a> (SeamlessM4Tv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextForConditionalGeneration">Speech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderModel">SpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5Config">SpeechT5Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/speecht5#transformers.SpeechT5ForSpeechToText">SpeechT5ForSpeechToText</a> (SpeechT5 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperForConditionalGeneration">WhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),$l=new $({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[w4]},$$scope:{ctx:y}}}),gp=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Bl=new $({props:{anchor:"transformers.AutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[x4]},$$scope:{ctx:y}}}),pp=new B({props:{title:"TFAutoModelForSpeechSeq2Seq",local:"transformers.TFAutoModelForSpeechSeq2Seq",headingTag:"h3"}}),hp=new x({props:{name:"class transformers.TFAutoModelForSpeechSeq2Seq",anchor:"transformers.TFAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L699"}}),up=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.Speech2TextConfig">Speech2TextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/speech_to_text#transformers.TFSpeech2TextForConditionalGeneration">TFSpeech2TextForConditionalGeneration</a> (Speech2Text model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.TFWhisperForConditionalGeneration">TFWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),jl=new $({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[L4]},$$scope:{ctx:y}}}),_p=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Al=new $({props:{anchor:"transformers.TFAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[k4]},$$scope:{ctx:y}}}),bp=new B({props:{title:"FlaxAutoModelForSpeechSeq2Seq",local:"transformers.FlaxAutoModelForSpeechSeq2Seq",headingTag:"h3"}}),Mp=new x({props:{name:"class transformers.FlaxAutoModelForSpeechSeq2Seq",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L375"}}),Tp=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/speech-encoder-decoder#transformers.SpeechEncoderDecoderConfig">SpeechEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/speech-encoder-decoder#transformers.FlaxSpeechEncoderDecoderModel">FlaxSpeechEncoderDecoderModel</a> (Speech Encoder decoder model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.WhisperConfig">WhisperConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/whisper#transformers.FlaxWhisperForConditionalGeneration">FlaxWhisperForConditionalGeneration</a> (Whisper model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Zl=new $({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_config.example",$$slots:{default:[$4]},$$scope:{ctx:y}}}),Fp=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Rl=new $({props:{anchor:"transformers.FlaxAutoModelForSpeechSeq2Seq.from_pretrained.example",$$slots:{default:[B4]},$$scope:{ctx:y}}}),Cp=new B({props:{title:"AutoModelForAudioXVector",local:"transformers.AutoModelForAudioXVector",headingTag:"h3"}}),yp=new x({props:{name:"class transformers.AutoModelForAudioXVector",anchor:"transformers.AutoModelForAudioXVector",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1686"}}),vp=new x({props:{name:"from_config",anchor:"transformers.AutoModelForAudioXVector.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioConfig">Data2VecAudioConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/data2vec#transformers.Data2VecAudioForXVector">Data2VecAudioForXVector</a> (Data2VecAudio model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatConfig">UniSpeechSatConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/unispeech-sat#transformers.UniSpeechSatForXVector">UniSpeechSatForXVector</a> (UniSpeechSat model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertConfig">Wav2Vec2BertConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-bert#transformers.Wav2Vec2BertForXVector">Wav2Vec2BertForXVector</a> (Wav2Vec2-BERT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2Config">Wav2Vec2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2#transformers.Wav2Vec2ForXVector">Wav2Vec2ForXVector</a> (Wav2Vec2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerConfig">Wav2Vec2ConformerConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wav2vec2-conformer#transformers.Wav2Vec2ConformerForXVector">Wav2Vec2ConformerForXVector</a> (Wav2Vec2-Conformer model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMConfig">WavLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/wavlm#transformers.WavLMForXVector">WavLMForXVector</a> (WavLM model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioXVector.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Gl=new $({props:{anchor:"transformers.AutoModelForAudioXVector.from_config.example",$$slots:{default:[j4]},$$scope:{ctx:y}}}),wp=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForAudioXVector.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Pl=new $({props:{anchor:"transformers.AutoModelForAudioXVector.from_pretrained.example",$$slots:{default:[A4]},$$scope:{ctx:y}}}),xp=new B({props:{title:"AutoModelForTextToSpectrogram",local:"transformers.AutoModelForTextToSpectrogram",headingTag:"h3"}}),kp=new x({props:{name:"class transformers.AutoModelForTextToSpectrogram",anchor:"transformers.AutoModelForTextToSpectrogram",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1690"}}),$p=new B({props:{title:"AutoModelForTextToWaveform",local:"transformers.AutoModelForTextToWaveform",headingTag:"h3"}}),jp=new x({props:{name:"class transformers.AutoModelForTextToWaveform",anchor:"transformers.AutoModelForTextToWaveform",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1694"}}),Ap=new B({props:{title:"Multimodal",local:"multimodal",headingTag:"h2"}}),Rp=new B({props:{title:"AutoModelForTableQuestionAnswering",local:"transformers.AutoModelForTableQuestionAnswering",headingTag:"h3"}}),Gp=new x({props:{name:"class transformers.AutoModelForTableQuestionAnswering",anchor:"transformers.AutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1511"}}),Pp=new x({props:{name:"from_config",anchor:"transformers.AutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasForQuestionAnswering">TapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Sl=new $({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[Z4]},$$scope:{ctx:y}}}),Sp=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Wl=new $({props:{anchor:"transformers.AutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[R4]},$$scope:{ctx:y}}}),Wp=new B({props:{title:"TFAutoModelForTableQuestionAnswering",local:"transformers.TFAutoModelForTableQuestionAnswering",headingTag:"h3"}}),Jp=new x({props:{name:"class transformers.TFAutoModelForTableQuestionAnswering",anchor:"transformers.TFAutoModelForTableQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L663"}}),Vp=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TapasConfig">TapasConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/tapas#transformers.TFTapasForQuestionAnswering">TFTapasForQuestionAnswering</a> (TAPAS model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Jl=new $({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_config.example",$$slots:{default:[G4]},$$scope:{ctx:y}}}),Ep=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Vl=new $({props:{anchor:"transformers.TFAutoModelForTableQuestionAnswering.from_pretrained.example",$$slots:{default:[P4]},$$scope:{ctx:y}}}),Ip=new B({props:{title:"AutoModelForDocumentQuestionAnswering",local:"transformers.AutoModelForDocumentQuestionAnswering",headingTag:"h3"}}),Up=new x({props:{name:"class transformers.AutoModelForDocumentQuestionAnswering",anchor:"transformers.AutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1533"}}),Np=new x({props:{name:"from_config",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMForQuestionAnswering">LayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2Config">LayoutLMv2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv2#transformers.LayoutLMv2ForQuestionAnswering">LayoutLMv2ForQuestionAnswering</a> (LayoutLMv2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3ForQuestionAnswering">LayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),El=new $({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[S4]},$$scope:{ctx:y}}}),qp=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Il=new $({props:{anchor:"transformers.AutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[W4]},$$scope:{ctx:y}}}),Xp=new B({props:{title:"TFAutoModelForDocumentQuestionAnswering",local:"transformers.TFAutoModelForDocumentQuestionAnswering",headingTag:"h3"}}),Dp=new x({props:{name:"class transformers.TFAutoModelForDocumentQuestionAnswering",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L652"}}),Qp=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.LayoutLMConfig">LayoutLMConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlm#transformers.TFLayoutLMForQuestionAnswering">TFLayoutLMForQuestionAnswering</a> (LayoutLM model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.LayoutLMv3Config">LayoutLMv3Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/layoutlmv3#transformers.TFLayoutLMv3ForQuestionAnswering">TFLayoutLMv3ForQuestionAnswering</a> (LayoutLMv3 model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Ul=new $({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_config.example",$$slots:{default:[J4]},$$scope:{ctx:y}}}),Hp=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Nl=new $({props:{anchor:"transformers.TFAutoModelForDocumentQuestionAnswering.from_pretrained.example",$$slots:{default:[V4]},$$scope:{ctx:y}}}),Yp=new B({props:{title:"AutoModelForVisualQuestionAnswering",local:"transformers.AutoModelForVisualQuestionAnswering",headingTag:"h3"}}),zp=new x({props:{name:"class transformers.AutoModelForVisualQuestionAnswering",anchor:"transformers.AutoModelForVisualQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1522"}}),Op=new x({props:{name:"from_config",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipForQuestionAnswering">BlipForQuestionAnswering</a> (BLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltConfig">ViltConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vilt#transformers.ViltForQuestionAnswering">ViltForQuestionAnswering</a> (ViLT model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),ql=new $({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_config.example",$$slots:{default:[E4]},$$scope:{ctx:y}}}),Kp=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Xl=new $({props:{anchor:"transformers.AutoModelForVisualQuestionAnswering.from_pretrained.example",$$slots:{default:[I4]},$$scope:{ctx:y}}}),eh=new B({props:{title:"AutoModelForVision2Seq",local:"transformers.AutoModelForVision2Seq",headingTag:"h3"}}),oh=new x({props:{name:"class transformers.AutoModelForVision2Seq",anchor:"transformers.AutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_auto.py#L1647"}}),rh=new x({props:{name:"from_config",anchor:"transformers.AutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2Config">Blip2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration">Blip2ForConditionalGeneration</a> (BLIP-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipForConditionalGeneration">BlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitConfig">GitConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/git#transformers.GitForCausalLM">GitForCausalLM</a> (GIT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2Config">Idefics2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/idefics2#transformers.Idefics2ForConditionalGeneration">Idefics2ForConditionalGeneration</a> (Idefics2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/instructblip#transformers.InstructBlipConfig">InstructBlipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/instructblip#transformers.InstructBlipForConditionalGeneration">InstructBlipForConditionalGeneration</a> (InstructBLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/instructblipvideo#transformers.InstructBlipVideoConfig">InstructBlipVideoConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/instructblipvideo#transformers.InstructBlipVideoForConditionalGeneration">InstructBlipVideoForConditionalGeneration</a> (InstructBlipVideo model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/kosmos-2#transformers.Kosmos2Config">Kosmos2Config</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/kosmos-2#transformers.Kosmos2ForConditionalGeneration">Kosmos2ForConditionalGeneration</a> (KOSMOS-2 model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llava#transformers.LlavaConfig">LlavaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llava#transformers.LlavaForConditionalGeneration">LlavaForConditionalGeneration</a> (LLaVa model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/llava_next#transformers.LlavaNextConfig">LlavaNextConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/llava_next#transformers.LlavaNextForConditionalGeneration">LlavaNextForConditionalGeneration</a> (LLaVA-NeXT model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/paligemma#transformers.PaliGemmaConfig">PaliGemmaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/paligemma#transformers.PaliGemmaForConditionalGeneration">PaliGemmaForConditionalGeneration</a> (PaliGemma model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/pix2struct#transformers.Pix2StructConfig">Pix2StructConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/pix2struct#transformers.Pix2StructForConditionalGeneration">Pix2StructForConditionalGeneration</a> (Pix2Struct model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/video_llava#transformers.VideoLlavaConfig">VideoLlavaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/video_llava#transformers.VideoLlavaForConditionalGeneration">VideoLlavaForConditionalGeneration</a> (VideoLlava model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vipllava#transformers.VipLlavaConfig">VipLlavaConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vipllava#transformers.VipLlavaForConditionalGeneration">VipLlavaForConditionalGeneration</a> (VipLlava model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderModel">VisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVision2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Dl=new $({props:{anchor:"transformers.AutoModelForVision2Seq.from_config.example",$$slots:{default:[U4]},$$scope:{ctx:y}}}),th=new x({props:{name:"from_pretrained",anchor:"transformers.AutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>tensorflow index checkpoint file</em> (e.g, <code>./tf_model/model.ckpt.index</code>). In
this case, <code>from_tf</code> should be set to <code>True</code> and a configuration object should be provided as
<code>config</code> argument. This loading path is slower than converting the TensorFlow checkpoint in a
PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.state_dict",description:`<strong>state_dict</strong> (<em>Dict[str, torch.Tensor]</em>, <em>optional</em>) &#x2014;
A state dictionary to use instead of a state dictionary loaded from saved weights file.</p>
<p>This option can be used if you want to create a model from a pretrained configuration but load your own
weights. In this case though, you should check if using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> is not a simpler option.`,name:"state_dict"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.from_tf",description:`<strong>from_tf</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a TensorFlow checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_tf"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ql=new $({props:{anchor:"transformers.AutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[N4]},$$scope:{ctx:y}}}),nh=new B({props:{title:"TFAutoModelForVision2Seq",local:"transformers.TFAutoModelForVision2Seq",headingTag:"h3"}}),ah=new x({props:{name:"class transformers.TFAutoModelForVision2Seq",anchor:"transformers.TFAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_tf_auto.py#L611"}}),sh=new x({props:{name:"from_config",anchor:"transformers.TFAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.BlipConfig">BlipConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/blip#transformers.TFBlipForConditionalGeneration">TFBlipForConditionalGeneration</a> (BLIP model)</li>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.TFVisionEncoderDecoderModel">TFVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForVision2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),Hl=new $({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_config.example",$$slots:{default:[q4]},$$scope:{ctx:y}}}),ih=new x({props:{name:"from_pretrained",anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Yl=new $({props:{anchor:"transformers.TFAutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[X4]},$$scope:{ctx:y}}}),lh=new B({props:{title:"FlaxAutoModelForVision2Seq",local:"transformers.FlaxAutoModelForVision2Seq",headingTag:"h3"}}),dh=new x({props:{name:"class transformers.FlaxAutoModelForVision2Seq",anchor:"transformers.FlaxAutoModelForVision2Seq",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/modeling_flax_auto.py#L368"}}),mh=new x({props:{name:"from_config",anchor:"transformers.FlaxAutoModelForVision2Seq.from_config",parameters:[{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForVision2Seq.from_config.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>) &#x2014;
The model class to instantiate is selected based on the configuration class:</p>
<ul>
<li><a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.VisionEncoderDecoderConfig">VisionEncoderDecoderConfig</a> configuration class: <a href="/docs/transformers/pr_31161/en/model_doc/vision-encoder-decoder#transformers.FlaxVisionEncoderDecoderModel">FlaxVisionEncoderDecoderModel</a> (Vision Encoder decoder model)</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_config.attn_implementation",description:`<strong>attn_implementation</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The attention implementation to use in the model (if relevant). Can be any of <code>&quot;eager&quot;</code> (manual implementation of the attention), <code>&quot;sdpa&quot;</code> (using <a href="https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html" rel="nofollow"><code>F.scaled_dot_product_attention</code></a>), or <code>&quot;flash_attention_2&quot;</code> (using <a href="https://github.com/Dao-AILab/flash-attention" rel="nofollow">Dao-AILab/flash-attention</a>). By default, if available, SDPA will be used for torch&gt;=2.1.1. The default is otherwise the manual <code>&quot;eager&quot;</code> implementation.`,name:"attn_implementation"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L414"}}),zl=new $({props:{anchor:"transformers.FlaxAutoModelForVision2Seq.from_config.example",$$slots:{default:[D4]},$$scope:{ctx:y}}}),ch=new x({props:{name:"from_pretrained",anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained",parameters:[{name:"*model_args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Can be either:</p>
<ul>
<li>A string, the <em>model id</em> of a pretrained model hosted inside a model repo on huggingface.co.</li>
<li>A path to a <em>directory</em> containing model weights saved using
<a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a>, e.g., <code>./my_model_directory/</code>.</li>
<li>A path or url to a <em>PyTorch state_dict save file</em> (e.g, <code>./pt_model/pytorch_model.bin</code>). In this
case, <code>from_pt</code> should be set to <code>True</code> and a configuration object should be provided as <code>config</code>
argument. This loading path is slower than converting the PyTorch model in a TensorFlow model
using the provided conversion scripts and loading the TensorFlow model afterwards.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.model_args",description:`<strong>model_args</strong> (additional positional arguments, <em>optional</em>) &#x2014;
Will be passed along to the underlying model <code>__init__()</code> method.`,name:"model_args"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a>, <em>optional</em>) &#x2014;
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when:</p>
<ul>
<li>The model is a model provided by the library (loaded with the <em>model id</em> string of a pretrained
model).</li>
<li>The model was saved using <a href="/docs/transformers/pr_31161/en/main_classes/model#transformers.PreTrainedModel.save_pretrained">save_pretrained()</a> and is reloaded by supplying the
save directory.</li>
<li>The model is loaded by supplying a local directory as <code>pretrained_model_name_or_path</code> and a
configuration JSON file named <em>config.json</em> is found in the directory.</li>
</ul>`,name:"config"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.from_pt",description:`<strong>from_pt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Load the model weights from a PyTorch checkpoint save file (see docstring of
<code>pretrained_model_name_or_path</code> argument).`,name:"from_pt"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"force_download"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}</code>. The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.output_loading_info(bool,",description:`<strong>output_loading_info(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.`,name:"output_loading_info(bool,"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.local_files_only(bool,",description:`<strong>local_files_only(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to only look at local files (e.g., not try downloading the model).`,name:"local_files_only(bool,"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.trust_remote_code",description:`<strong>trust_remote_code</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to allow for custom models defined on the Hub in their own modeling files. This option
should only be set to <code>True</code> for repositories you trust and in which you have read the code, as it will
execute code present on the Hub on your local machine.`,name:"trust_remote_code"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.code_revision",description:`<strong>code_revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific revision to use for the code on the Hub, if the code leaves in a different repository than
the rest of the model. It can be a branch name, a tag name, or a commit id, since we use a git-based
system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any identifier
allowed by git.`,name:"code_revision"},{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.kwargs",description:`<strong>kwargs</strong> (additional keyword arguments, <em>optional</em>) &#x2014;
Can be used to update the configuration object (after it being loaded) and initiate the model (e.g.,
<code>output_attentions=True</code>). Behaves differently depending on whether a <code>config</code> is provided or
automatically loaded:</p>
<ul>
<li>If a configuration is provided with <code>config</code>, <code>**kwargs</code> will be directly passed to the
underlying model&#x2019;s <code>__init__</code> method (we assume all relevant updates to the configuration have
already been done)</li>
<li>If a configuration is not provided, <code>kwargs</code> will be first passed to the configuration class
initialization function (<a href="/docs/transformers/pr_31161/en/main_classes/configuration#transformers.PretrainedConfig.from_pretrained">from_pretrained()</a>). Each key of <code>kwargs</code> that
corresponds to a configuration attribute will be used to override said attribute with the
supplied <code>kwargs</code> value. Remaining keys that do not correspond to any configuration attribute
will be passed to the underlying model&#x2019;s <code>__init__</code> function.</li>
</ul>`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/auto/auto_factory.py#L445"}}),Ol=new $({props:{anchor:"transformers.FlaxAutoModelForVision2Seq.from_pretrained.example",$$slots:{default:[Q4]},$$scope:{ctx:y}}}),fh=new y5({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/auto.md"}}),{c(){t=i("meta"),C=n(),m=i("p"),s=n(),p(c.$$.fragment),e=n(),F=i("p"),F.innerHTML=HS,Ay=n(),yd=i("p"),yd.innerHTML=YS,Zy=n(),p(vd.$$.fragment),Ry=n(),wd=i("p"),wd.innerHTML=zS,Gy=n(),xd=i("p"),xd.innerHTML=OS,Py=n(),p(Ld.$$.fragment),Sy=n(),kd=i("p"),kd.innerHTML=KS,Wy=n(),p($d.$$.fragment),Jy=n(),Bd=i("p"),Bd.textContent=eW,Vy=n(),p(ys.$$.fragment),Ey=n(),p(jd.$$.fragment),Iy=n(),me=i("div"),p(Ad.$$.fragment),qw=n(),bh=i("p"),bh.innerHTML=oW,Xw=n(),Mh=i("p"),Mh.innerHTML=rW,Dw=n(),Eo=i("div"),p(Zd.$$.fragment),Qw=n(),Th=i("p"),Th.textContent=tW,Hw=n(),Fh=i("p"),Fh.innerHTML=nW,Yw=n(),Ch=i("ul"),Ch.innerHTML=aW,zw=n(),p(vs.$$.fragment),Ow=n(),ws=i("div"),p(Rd.$$.fragment),Kw=n(),yh=i("p"),yh.textContent=sW,Uy=n(),p(Gd.$$.fragment),Ny=n(),ce=i("div"),p(Pd.$$.fragment),e3=n(),vh=i("p"),vh.innerHTML=iW,o3=n(),wh=i("p"),wh.innerHTML=lW,r3=n(),Io=i("div"),p(Sd.$$.fragment),t3=n(),xh=i("p"),xh.textContent=dW,n3=n(),Lh=i("p"),Lh.innerHTML=mW,a3=n(),kh=i("ul"),kh.innerHTML=cW,s3=n(),p(xs.$$.fragment),i3=n(),Ls=i("div"),p(Wd.$$.fragment),l3=n(),$h=i("p"),$h.textContent=fW,qy=n(),p(Jd.$$.fragment),Xy=n(),fe=i("div"),p(Vd.$$.fragment),d3=n(),Bh=i("p"),Bh.innerHTML=gW,m3=n(),jh=i("p"),jh.innerHTML=pW,c3=n(),j=i("div"),p(Ed.$$.fragment),f3=n(),Ah=i("p"),Ah.textContent=hW,g3=n(),Zh=i("p"),Zh.innerHTML=uW,p3=n(),Rh=i("ul"),Rh.innerHTML=_W,h3=n(),p(ks.$$.fragment),u3=n(),p($s.$$.fragment),_3=n(),Bs=i("div"),p(Id.$$.fragment),b3=n(),Gh=i("p"),Gh.textContent=bW,Dy=n(),p(Ud.$$.fragment),Qy=n(),ge=i("div"),p(Nd.$$.fragment),M3=n(),Ph=i("p"),Ph.innerHTML=MW,T3=n(),Sh=i("p"),Sh.innerHTML=TW,F3=n(),A=i("div"),p(qd.$$.fragment),C3=n(),Wh=i("p"),Wh.textContent=FW,y3=n(),Jh=i("p"),Jh.innerHTML=CW,v3=n(),Vh=i("ul"),Vh.innerHTML=yW,w3=n(),p(js.$$.fragment),x3=n(),p(As.$$.fragment),L3=n(),Zs=i("div"),p(Xd.$$.fragment),k3=n(),Eh=i("p"),Eh.textContent=vW,Hy=n(),p(Dd.$$.fragment),Yy=n(),pe=i("div"),p(Qd.$$.fragment),$3=n(),Ih=i("p"),Ih.innerHTML=wW,B3=n(),Uh=i("p"),Uh.innerHTML=xW,j3=n(),Z=i("div"),p(Hd.$$.fragment),A3=n(),Nh=i("p"),Nh.textContent=LW,Z3=n(),qh=i("p"),qh.innerHTML=kW,R3=n(),Xh=i("ul"),Xh.innerHTML=$W,G3=n(),p(Rs.$$.fragment),P3=n(),p(Gs.$$.fragment),S3=n(),Ps=i("div"),p(Yd.$$.fragment),W3=n(),Dh=i("p"),Dh.textContent=BW,zy=n(),p(zd.$$.fragment),Oy=n(),Od=i("p"),Od.textContent=jW,Ky=n(),p(Kd.$$.fragment),ev=n(),he=i("div"),p(em.$$.fragment),J3=n(),Qh=i("p"),Qh.innerHTML=AW,V3=n(),Hh=i("p"),Hh.innerHTML=ZW,E3=n(),Gt=i("div"),p(om.$$.fragment),I3=n(),Yh=i("p"),Yh.textContent=RW,U3=n(),zh=i("p"),zh.innerHTML=GW,N3=n(),p(Ss.$$.fragment),q3=n(),R=i("div"),p(rm.$$.fragment),X3=n(),Oh=i("p"),Oh.textContent=PW,D3=n(),Kh=i("p"),Kh.innerHTML=SW,Q3=n(),eu=i("ul"),eu.innerHTML=WW,H3=n(),ou=i("p"),ou.innerHTML=JW,Y3=n(),p(Ws.$$.fragment),ov=n(),p(tm.$$.fragment),rv=n(),ue=i("div"),p(nm.$$.fragment),z3=n(),ru=i("p"),ru.innerHTML=VW,O3=n(),tu=i("p"),tu.innerHTML=EW,K3=n(),Pt=i("div"),p(am.$$.fragment),ex=n(),nu=i("p"),nu.textContent=IW,ox=n(),au=i("p"),au.innerHTML=UW,rx=n(),p(Js.$$.fragment),tx=n(),Uo=i("div"),p(sm.$$.fragment),nx=n(),su=i("p"),su.textContent=NW,ax=n(),iu=i("p"),iu.innerHTML=qW,sx=n(),lu=i("ul"),lu.innerHTML=XW,ix=n(),p(Vs.$$.fragment),tv=n(),p(im.$$.fragment),nv=n(),_e=i("div"),p(lm.$$.fragment),lx=n(),du=i("p"),du.innerHTML=DW,dx=n(),mu=i("p"),mu.innerHTML=QW,mx=n(),St=i("div"),p(dm.$$.fragment),cx=n(),cu=i("p"),cu.textContent=HW,fx=n(),fu=i("p"),fu.innerHTML=YW,gx=n(),p(Es.$$.fragment),px=n(),No=i("div"),p(mm.$$.fragment),hx=n(),gu=i("p"),gu.textContent=zW,ux=n(),pu=i("p"),pu.innerHTML=OW,_x=n(),hu=i("ul"),hu.innerHTML=KW,bx=n(),p(Is.$$.fragment),av=n(),p(cm.$$.fragment),sv=n(),fm=i("p"),fm.textContent=eJ,iv=n(),p(gm.$$.fragment),lv=n(),be=i("div"),p(pm.$$.fragment),Mx=n(),uu=i("p"),uu.innerHTML=oJ,Tx=n(),_u=i("p"),_u.innerHTML=rJ,Fx=n(),Wt=i("div"),p(hm.$$.fragment),Cx=n(),bu=i("p"),bu.textContent=tJ,yx=n(),Mu=i("p"),Mu.innerHTML=nJ,vx=n(),p(Us.$$.fragment),wx=n(),G=i("div"),p(um.$$.fragment),xx=n(),Tu=i("p"),Tu.textContent=aJ,Lx=n(),Fu=i("p"),Fu.innerHTML=sJ,kx=n(),Cu=i("ul"),Cu.innerHTML=iJ,$x=n(),yu=i("p"),yu.innerHTML=lJ,Bx=n(),p(Ns.$$.fragment),dv=n(),p(_m.$$.fragment),mv=n(),Me=i("div"),p(bm.$$.fragment),jx=n(),vu=i("p"),vu.innerHTML=dJ,Ax=n(),wu=i("p"),wu.innerHTML=mJ,Zx=n(),Jt=i("div"),p(Mm.$$.fragment),Rx=n(),xu=i("p"),xu.textContent=cJ,Gx=n(),Lu=i("p"),Lu.innerHTML=fJ,Px=n(),p(qs.$$.fragment),Sx=n(),qo=i("div"),p(Tm.$$.fragment),Wx=n(),ku=i("p"),ku.textContent=gJ,Jx=n(),$u=i("p"),$u.innerHTML=pJ,Vx=n(),Bu=i("ul"),Bu.innerHTML=hJ,Ex=n(),p(Xs.$$.fragment),cv=n(),p(Fm.$$.fragment),fv=n(),Te=i("div"),p(Cm.$$.fragment),Ix=n(),ju=i("p"),ju.innerHTML=uJ,Ux=n(),Au=i("p"),Au.innerHTML=_J,Nx=n(),Vt=i("div"),p(ym.$$.fragment),qx=n(),Zu=i("p"),Zu.textContent=bJ,Xx=n(),Ru=i("p"),Ru.innerHTML=MJ,Dx=n(),p(Ds.$$.fragment),Qx=n(),Xo=i("div"),p(vm.$$.fragment),Hx=n(),Gu=i("p"),Gu.textContent=TJ,Yx=n(),Pu=i("p"),Pu.innerHTML=FJ,zx=n(),Su=i("ul"),Su.innerHTML=CJ,Ox=n(),p(Qs.$$.fragment),gv=n(),p(wm.$$.fragment),pv=n(),xm=i("p"),xm.textContent=yJ,hv=n(),p(Lm.$$.fragment),uv=n(),Fe=i("div"),p(km.$$.fragment),Kx=n(),Wu=i("p"),Wu.innerHTML=vJ,e6=n(),Ju=i("p"),Ju.innerHTML=wJ,o6=n(),Et=i("div"),p($m.$$.fragment),r6=n(),Vu=i("p"),Vu.textContent=xJ,t6=n(),Eu=i("p"),Eu.innerHTML=LJ,n6=n(),p(Hs.$$.fragment),a6=n(),P=i("div"),p(Bm.$$.fragment),s6=n(),Iu=i("p"),Iu.textContent=kJ,i6=n(),Uu=i("p"),Uu.innerHTML=$J,l6=n(),Nu=i("ul"),Nu.innerHTML=BJ,d6=n(),qu=i("p"),qu.innerHTML=jJ,m6=n(),p(Ys.$$.fragment),_v=n(),p(jm.$$.fragment),bv=n(),Ce=i("div"),p(Am.$$.fragment),c6=n(),Xu=i("p"),Xu.innerHTML=AJ,f6=n(),Du=i("p"),Du.innerHTML=ZJ,g6=n(),It=i("div"),p(Zm.$$.fragment),p6=n(),Qu=i("p"),Qu.textContent=RJ,h6=n(),Hu=i("p"),Hu.innerHTML=GJ,u6=n(),p(zs.$$.fragment),_6=n(),Do=i("div"),p(Rm.$$.fragment),b6=n(),Yu=i("p"),Yu.textContent=PJ,M6=n(),zu=i("p"),zu.innerHTML=SJ,T6=n(),Ou=i("ul"),Ou.innerHTML=WJ,F6=n(),p(Os.$$.fragment),Mv=n(),p(Gm.$$.fragment),Tv=n(),ye=i("div"),p(Pm.$$.fragment),C6=n(),Ku=i("p"),Ku.innerHTML=JJ,y6=n(),e_=i("p"),e_.innerHTML=VJ,v6=n(),Ut=i("div"),p(Sm.$$.fragment),w6=n(),o_=i("p"),o_.textContent=EJ,x6=n(),r_=i("p"),r_.innerHTML=IJ,L6=n(),p(Ks.$$.fragment),k6=n(),Qo=i("div"),p(Wm.$$.fragment),$6=n(),t_=i("p"),t_.textContent=UJ,B6=n(),n_=i("p"),n_.innerHTML=NJ,j6=n(),a_=i("ul"),a_.innerHTML=qJ,A6=n(),p(ei.$$.fragment),Fv=n(),p(Jm.$$.fragment),Cv=n(),ve=i("div"),p(Vm.$$.fragment),Z6=n(),s_=i("p"),s_.innerHTML=XJ,R6=n(),i_=i("p"),i_.innerHTML=DJ,G6=n(),Nt=i("div"),p(Em.$$.fragment),P6=n(),l_=i("p"),l_.textContent=QJ,S6=n(),d_=i("p"),d_.innerHTML=HJ,W6=n(),p(oi.$$.fragment),J6=n(),S=i("div"),p(Im.$$.fragment),V6=n(),m_=i("p"),m_.textContent=YJ,E6=n(),c_=i("p"),c_.innerHTML=zJ,I6=n(),f_=i("ul"),f_.innerHTML=OJ,U6=n(),g_=i("p"),g_.innerHTML=KJ,N6=n(),p(ri.$$.fragment),yv=n(),p(Um.$$.fragment),vv=n(),we=i("div"),p(Nm.$$.fragment),q6=n(),p_=i("p"),p_.innerHTML=eV,X6=n(),h_=i("p"),h_.innerHTML=oV,D6=n(),qt=i("div"),p(qm.$$.fragment),Q6=n(),u_=i("p"),u_.textContent=rV,H6=n(),__=i("p"),__.innerHTML=tV,Y6=n(),p(ti.$$.fragment),z6=n(),Ho=i("div"),p(Xm.$$.fragment),O6=n(),b_=i("p"),b_.textContent=nV,K6=n(),M_=i("p"),M_.innerHTML=aV,eL=n(),T_=i("ul"),T_.innerHTML=sV,oL=n(),p(ni.$$.fragment),wv=n(),p(Dm.$$.fragment),xv=n(),xe=i("div"),p(Qm.$$.fragment),rL=n(),F_=i("p"),F_.innerHTML=iV,tL=n(),C_=i("p"),C_.innerHTML=lV,nL=n(),Xt=i("div"),p(Hm.$$.fragment),aL=n(),y_=i("p"),y_.textContent=dV,sL=n(),v_=i("p"),v_.innerHTML=mV,iL=n(),p(ai.$$.fragment),lL=n(),Yo=i("div"),p(Ym.$$.fragment),dL=n(),w_=i("p"),w_.textContent=cV,mL=n(),x_=i("p"),x_.innerHTML=fV,cL=n(),L_=i("ul"),L_.innerHTML=gV,fL=n(),p(si.$$.fragment),Lv=n(),p(zm.$$.fragment),kv=n(),Om=i("div"),p(Km.$$.fragment),$v=n(),p(ec.$$.fragment),Bv=n(),oc=i("div"),p(rc.$$.fragment),jv=n(),p(tc.$$.fragment),Av=n(),Le=i("div"),p(nc.$$.fragment),gL=n(),k_=i("p"),k_.innerHTML=pV,pL=n(),$_=i("p"),$_.innerHTML=hV,hL=n(),Dt=i("div"),p(ac.$$.fragment),uL=n(),B_=i("p"),B_.textContent=uV,_L=n(),j_=i("p"),j_.innerHTML=_V,bL=n(),p(ii.$$.fragment),ML=n(),W=i("div"),p(sc.$$.fragment),TL=n(),A_=i("p"),A_.textContent=bV,FL=n(),Z_=i("p"),Z_.innerHTML=MV,CL=n(),R_=i("ul"),R_.innerHTML=TV,yL=n(),G_=i("p"),G_.innerHTML=FV,vL=n(),p(li.$$.fragment),Zv=n(),p(ic.$$.fragment),Rv=n(),ke=i("div"),p(lc.$$.fragment),wL=n(),P_=i("p"),P_.innerHTML=CV,xL=n(),S_=i("p"),S_.innerHTML=yV,LL=n(),Qt=i("div"),p(dc.$$.fragment),kL=n(),W_=i("p"),W_.textContent=vV,$L=n(),J_=i("p"),J_.innerHTML=wV,BL=n(),p(di.$$.fragment),jL=n(),zo=i("div"),p(mc.$$.fragment),AL=n(),V_=i("p"),V_.textContent=xV,ZL=n(),E_=i("p"),E_.innerHTML=LV,RL=n(),I_=i("ul"),I_.innerHTML=kV,GL=n(),p(mi.$$.fragment),Gv=n(),p(cc.$$.fragment),Pv=n(),$e=i("div"),p(fc.$$.fragment),PL=n(),U_=i("p"),U_.innerHTML=$V,SL=n(),N_=i("p"),N_.innerHTML=BV,WL=n(),Ht=i("div"),p(gc.$$.fragment),JL=n(),q_=i("p"),q_.textContent=jV,VL=n(),X_=i("p"),X_.innerHTML=AV,EL=n(),p(ci.$$.fragment),IL=n(),Oo=i("div"),p(pc.$$.fragment),UL=n(),D_=i("p"),D_.textContent=ZV,NL=n(),Q_=i("p"),Q_.innerHTML=RV,qL=n(),H_=i("ul"),H_.innerHTML=GV,XL=n(),p(fi.$$.fragment),Sv=n(),p(hc.$$.fragment),Wv=n(),Be=i("div"),p(uc.$$.fragment),DL=n(),Y_=i("p"),Y_.innerHTML=PV,QL=n(),z_=i("p"),z_.innerHTML=SV,HL=n(),Yt=i("div"),p(_c.$$.fragment),YL=n(),O_=i("p"),O_.textContent=WV,zL=n(),K_=i("p"),K_.innerHTML=JV,OL=n(),p(gi.$$.fragment),KL=n(),J=i("div"),p(bc.$$.fragment),ek=n(),e1=i("p"),e1.textContent=VV,ok=n(),o1=i("p"),o1.innerHTML=EV,rk=n(),r1=i("ul"),r1.innerHTML=IV,tk=n(),t1=i("p"),t1.innerHTML=UV,nk=n(),p(pi.$$.fragment),Jv=n(),p(Mc.$$.fragment),Vv=n(),je=i("div"),p(Tc.$$.fragment),ak=n(),n1=i("p"),n1.innerHTML=NV,sk=n(),a1=i("p"),a1.innerHTML=qV,ik=n(),zt=i("div"),p(Fc.$$.fragment),lk=n(),s1=i("p"),s1.textContent=XV,dk=n(),i1=i("p"),i1.innerHTML=DV,mk=n(),p(hi.$$.fragment),ck=n(),Ko=i("div"),p(Cc.$$.fragment),fk=n(),l1=i("p"),l1.textContent=QV,gk=n(),d1=i("p"),d1.innerHTML=HV,pk=n(),m1=i("ul"),m1.innerHTML=YV,hk=n(),p(ui.$$.fragment),Ev=n(),p(yc.$$.fragment),Iv=n(),Ae=i("div"),p(vc.$$.fragment),uk=n(),c1=i("p"),c1.innerHTML=zV,_k=n(),f1=i("p"),f1.innerHTML=OV,bk=n(),Ot=i("div"),p(wc.$$.fragment),Mk=n(),g1=i("p"),g1.textContent=KV,Tk=n(),p1=i("p"),p1.innerHTML=e0,Fk=n(),p(_i.$$.fragment),Ck=n(),er=i("div"),p(xc.$$.fragment),yk=n(),h1=i("p"),h1.textContent=o0,vk=n(),u1=i("p"),u1.innerHTML=r0,wk=n(),_1=i("ul"),_1.innerHTML=t0,xk=n(),p(bi.$$.fragment),Uv=n(),p(Lc.$$.fragment),Nv=n(),Ze=i("div"),p(kc.$$.fragment),Lk=n(),b1=i("p"),b1.innerHTML=n0,kk=n(),M1=i("p"),M1.innerHTML=a0,$k=n(),Kt=i("div"),p($c.$$.fragment),Bk=n(),T1=i("p"),T1.textContent=s0,jk=n(),F1=i("p"),F1.innerHTML=i0,Ak=n(),p(Mi.$$.fragment),Zk=n(),V=i("div"),p(Bc.$$.fragment),Rk=n(),C1=i("p"),C1.textContent=l0,Gk=n(),y1=i("p"),y1.innerHTML=d0,Pk=n(),v1=i("ul"),v1.innerHTML=m0,Sk=n(),w1=i("p"),w1.innerHTML=c0,Wk=n(),p(Ti.$$.fragment),qv=n(),p(jc.$$.fragment),Xv=n(),Re=i("div"),p(Ac.$$.fragment),Jk=n(),x1=i("p"),x1.innerHTML=f0,Vk=n(),L1=i("p"),L1.innerHTML=g0,Ek=n(),en=i("div"),p(Zc.$$.fragment),Ik=n(),k1=i("p"),k1.textContent=p0,Uk=n(),$1=i("p"),$1.innerHTML=h0,Nk=n(),p(Fi.$$.fragment),qk=n(),or=i("div"),p(Rc.$$.fragment),Xk=n(),B1=i("p"),B1.textContent=u0,Dk=n(),j1=i("p"),j1.innerHTML=_0,Qk=n(),A1=i("ul"),A1.innerHTML=b0,Hk=n(),p(Ci.$$.fragment),Dv=n(),p(Gc.$$.fragment),Qv=n(),Ge=i("div"),p(Pc.$$.fragment),Yk=n(),Z1=i("p"),Z1.innerHTML=M0,zk=n(),R1=i("p"),R1.innerHTML=T0,Ok=n(),on=i("div"),p(Sc.$$.fragment),Kk=n(),G1=i("p"),G1.textContent=F0,e$=n(),P1=i("p"),P1.innerHTML=C0,o$=n(),p(yi.$$.fragment),r$=n(),rr=i("div"),p(Wc.$$.fragment),t$=n(),S1=i("p"),S1.textContent=y0,n$=n(),W1=i("p"),W1.innerHTML=v0,a$=n(),J1=i("ul"),J1.innerHTML=w0,s$=n(),p(vi.$$.fragment),Hv=n(),p(Jc.$$.fragment),Yv=n(),Pe=i("div"),p(Vc.$$.fragment),i$=n(),V1=i("p"),V1.innerHTML=x0,l$=n(),E1=i("p"),E1.innerHTML=L0,d$=n(),rn=i("div"),p(Ec.$$.fragment),m$=n(),I1=i("p"),I1.textContent=k0,c$=n(),U1=i("p"),U1.innerHTML=$0,f$=n(),p(wi.$$.fragment),g$=n(),E=i("div"),p(Ic.$$.fragment),p$=n(),N1=i("p"),N1.textContent=B0,h$=n(),q1=i("p"),q1.innerHTML=j0,u$=n(),X1=i("ul"),X1.innerHTML=A0,_$=n(),D1=i("p"),D1.innerHTML=Z0,b$=n(),p(xi.$$.fragment),zv=n(),p(Uc.$$.fragment),Ov=n(),Se=i("div"),p(Nc.$$.fragment),M$=n(),Q1=i("p"),Q1.innerHTML=R0,T$=n(),H1=i("p"),H1.innerHTML=G0,F$=n(),tn=i("div"),p(qc.$$.fragment),C$=n(),Y1=i("p"),Y1.textContent=P0,y$=n(),z1=i("p"),z1.innerHTML=S0,v$=n(),p(Li.$$.fragment),w$=n(),tr=i("div"),p(Xc.$$.fragment),x$=n(),O1=i("p"),O1.textContent=W0,L$=n(),K1=i("p"),K1.innerHTML=J0,k$=n(),eb=i("ul"),eb.innerHTML=V0,$$=n(),p(ki.$$.fragment),Kv=n(),p(Dc.$$.fragment),e2=n(),We=i("div"),p(Qc.$$.fragment),B$=n(),ob=i("p"),ob.innerHTML=E0,j$=n(),rb=i("p"),rb.innerHTML=I0,A$=n(),nn=i("div"),p(Hc.$$.fragment),Z$=n(),tb=i("p"),tb.textContent=U0,R$=n(),nb=i("p"),nb.innerHTML=N0,G$=n(),p($i.$$.fragment),P$=n(),nr=i("div"),p(Yc.$$.fragment),S$=n(),ab=i("p"),ab.textContent=q0,W$=n(),sb=i("p"),sb.innerHTML=X0,J$=n(),ib=i("ul"),ib.innerHTML=D0,V$=n(),p(Bi.$$.fragment),o2=n(),p(zc.$$.fragment),r2=n(),Je=i("div"),p(Oc.$$.fragment),E$=n(),lb=i("p"),lb.innerHTML=Q0,I$=n(),db=i("p"),db.innerHTML=H0,U$=n(),an=i("div"),p(Kc.$$.fragment),N$=n(),mb=i("p"),mb.textContent=Y0,q$=n(),cb=i("p"),cb.innerHTML=z0,X$=n(),p(ji.$$.fragment),D$=n(),I=i("div"),p(ef.$$.fragment),Q$=n(),fb=i("p"),fb.textContent=O0,H$=n(),gb=i("p"),gb.innerHTML=K0,Y$=n(),pb=i("ul"),pb.innerHTML=eE,z$=n(),hb=i("p"),hb.innerHTML=oE,O$=n(),p(Ai.$$.fragment),t2=n(),p(of.$$.fragment),n2=n(),Ve=i("div"),p(rf.$$.fragment),K$=n(),ub=i("p"),ub.innerHTML=rE,eB=n(),_b=i("p"),_b.innerHTML=tE,oB=n(),sn=i("div"),p(tf.$$.fragment),rB=n(),bb=i("p"),bb.textContent=nE,tB=n(),Mb=i("p"),Mb.innerHTML=aE,nB=n(),p(Zi.$$.fragment),aB=n(),ar=i("div"),p(nf.$$.fragment),sB=n(),Tb=i("p"),Tb.textContent=sE,iB=n(),Fb=i("p"),Fb.innerHTML=iE,lB=n(),Cb=i("ul"),Cb.innerHTML=lE,dB=n(),p(Ri.$$.fragment),a2=n(),p(af.$$.fragment),s2=n(),Ee=i("div"),p(sf.$$.fragment),mB=n(),yb=i("p"),yb.innerHTML=dE,cB=n(),vb=i("p"),vb.innerHTML=mE,fB=n(),ln=i("div"),p(lf.$$.fragment),gB=n(),wb=i("p"),wb.textContent=cE,pB=n(),xb=i("p"),xb.innerHTML=fE,hB=n(),p(Gi.$$.fragment),uB=n(),sr=i("div"),p(df.$$.fragment),_B=n(),Lb=i("p"),Lb.textContent=gE,bB=n(),kb=i("p"),kb.innerHTML=pE,MB=n(),$b=i("ul"),$b.innerHTML=hE,TB=n(),p(Pi.$$.fragment),i2=n(),p(mf.$$.fragment),l2=n(),Ie=i("div"),p(cf.$$.fragment),FB=n(),Bb=i("p"),Bb.innerHTML=uE,CB=n(),jb=i("p"),jb.innerHTML=_E,yB=n(),dn=i("div"),p(ff.$$.fragment),vB=n(),Ab=i("p"),Ab.textContent=bE,wB=n(),Zb=i("p"),Zb.innerHTML=ME,xB=n(),p(Si.$$.fragment),LB=n(),U=i("div"),p(gf.$$.fragment),kB=n(),Rb=i("p"),Rb.textContent=TE,$B=n(),Gb=i("p"),Gb.innerHTML=FE,BB=n(),Pb=i("ul"),Pb.innerHTML=CE,jB=n(),Sb=i("p"),Sb.innerHTML=yE,AB=n(),p(Wi.$$.fragment),d2=n(),p(pf.$$.fragment),m2=n(),Ue=i("div"),p(hf.$$.fragment),ZB=n(),Wb=i("p"),Wb.innerHTML=vE,RB=n(),Jb=i("p"),Jb.innerHTML=wE,GB=n(),mn=i("div"),p(uf.$$.fragment),PB=n(),Vb=i("p"),Vb.textContent=xE,SB=n(),Eb=i("p"),Eb.innerHTML=LE,WB=n(),p(Ji.$$.fragment),JB=n(),ir=i("div"),p(_f.$$.fragment),VB=n(),Ib=i("p"),Ib.textContent=kE,EB=n(),Ub=i("p"),Ub.innerHTML=$E,IB=n(),Nb=i("ul"),Nb.innerHTML=BE,UB=n(),p(Vi.$$.fragment),c2=n(),p(bf.$$.fragment),f2=n(),Ne=i("div"),p(Mf.$$.fragment),NB=n(),qb=i("p"),qb.innerHTML=jE,qB=n(),Xb=i("p"),Xb.innerHTML=AE,XB=n(),cn=i("div"),p(Tf.$$.fragment),DB=n(),Db=i("p"),Db.textContent=ZE,QB=n(),Qb=i("p"),Qb.innerHTML=RE,HB=n(),p(Ei.$$.fragment),YB=n(),lr=i("div"),p(Ff.$$.fragment),zB=n(),Hb=i("p"),Hb.textContent=GE,OB=n(),Yb=i("p"),Yb.innerHTML=PE,KB=n(),zb=i("ul"),zb.innerHTML=SE,ej=n(),p(Ii.$$.fragment),g2=n(),p(Cf.$$.fragment),p2=n(),yf=i("div"),p(vf.$$.fragment),h2=n(),p(wf.$$.fragment),u2=n(),xf=i("div"),p(Lf.$$.fragment),_2=n(),p(kf.$$.fragment),b2=n(),$f=i("p"),$f.textContent=WE,M2=n(),p(Bf.$$.fragment),T2=n(),qe=i("div"),p(jf.$$.fragment),oj=n(),Ob=i("p"),Ob.innerHTML=JE,rj=n(),Kb=i("p"),Kb.innerHTML=VE,tj=n(),fn=i("div"),p(Af.$$.fragment),nj=n(),eM=i("p"),eM.textContent=EE,aj=n(),oM=i("p"),oM.innerHTML=IE,sj=n(),p(Ui.$$.fragment),ij=n(),N=i("div"),p(Zf.$$.fragment),lj=n(),rM=i("p"),rM.textContent=UE,dj=n(),tM=i("p"),tM.innerHTML=NE,mj=n(),nM=i("ul"),nM.innerHTML=qE,cj=n(),aM=i("p"),aM.innerHTML=XE,fj=n(),p(Ni.$$.fragment),F2=n(),p(Rf.$$.fragment),C2=n(),Xe=i("div"),p(Gf.$$.fragment),gj=n(),sM=i("p"),sM.innerHTML=DE,pj=n(),iM=i("p"),iM.innerHTML=QE,hj=n(),gn=i("div"),p(Pf.$$.fragment),uj=n(),lM=i("p"),lM.textContent=HE,_j=n(),dM=i("p"),dM.innerHTML=YE,bj=n(),p(qi.$$.fragment),Mj=n(),q=i("div"),p(Sf.$$.fragment),Tj=n(),mM=i("p"),mM.textContent=zE,Fj=n(),cM=i("p"),cM.innerHTML=OE,Cj=n(),fM=i("ul"),fM.innerHTML=KE,yj=n(),gM=i("p"),gM.innerHTML=eI,vj=n(),p(Xi.$$.fragment),y2=n(),p(Wf.$$.fragment),v2=n(),De=i("div"),p(Jf.$$.fragment),wj=n(),pM=i("p"),pM.innerHTML=oI,xj=n(),hM=i("p"),hM.innerHTML=rI,Lj=n(),pn=i("div"),p(Vf.$$.fragment),kj=n(),uM=i("p"),uM.textContent=tI,$j=n(),_M=i("p"),_M.innerHTML=nI,Bj=n(),p(Di.$$.fragment),jj=n(),dr=i("div"),p(Ef.$$.fragment),Aj=n(),bM=i("p"),bM.textContent=aI,Zj=n(),MM=i("p"),MM.innerHTML=sI,Rj=n(),TM=i("ul"),TM.innerHTML=iI,Gj=n(),p(Qi.$$.fragment),w2=n(),p(If.$$.fragment),x2=n(),Qe=i("div"),p(Uf.$$.fragment),Pj=n(),FM=i("p"),FM.innerHTML=lI,Sj=n(),CM=i("p"),CM.innerHTML=dI,Wj=n(),hn=i("div"),p(Nf.$$.fragment),Jj=n(),yM=i("p"),yM.textContent=mI,Vj=n(),vM=i("p"),vM.innerHTML=cI,Ej=n(),p(Hi.$$.fragment),Ij=n(),mr=i("div"),p(qf.$$.fragment),Uj=n(),wM=i("p"),wM.textContent=fI,Nj=n(),xM=i("p"),xM.innerHTML=gI,qj=n(),LM=i("ul"),LM.innerHTML=pI,Xj=n(),p(Yi.$$.fragment),L2=n(),p(Xf.$$.fragment),k2=n(),He=i("div"),p(Df.$$.fragment),Dj=n(),kM=i("p"),kM.innerHTML=hI,Qj=n(),$M=i("p"),$M.innerHTML=uI,Hj=n(),un=i("div"),p(Qf.$$.fragment),Yj=n(),BM=i("p"),BM.textContent=_I,zj=n(),jM=i("p"),jM.innerHTML=bI,Oj=n(),p(zi.$$.fragment),Kj=n(),X=i("div"),p(Hf.$$.fragment),eA=n(),AM=i("p"),AM.textContent=MI,oA=n(),ZM=i("p"),ZM.innerHTML=TI,rA=n(),RM=i("ul"),RM.innerHTML=FI,tA=n(),GM=i("p"),GM.innerHTML=CI,nA=n(),p(Oi.$$.fragment),$2=n(),p(Yf.$$.fragment),B2=n(),zf=i("div"),p(Of.$$.fragment),j2=n(),p(Kf.$$.fragment),A2=n(),Ye=i("div"),p(eg.$$.fragment),aA=n(),PM=i("p"),PM.innerHTML=yI,sA=n(),SM=i("p"),SM.innerHTML=vI,iA=n(),_n=i("div"),p(og.$$.fragment),lA=n(),WM=i("p"),WM.textContent=wI,dA=n(),JM=i("p"),JM.innerHTML=xI,mA=n(),p(Ki.$$.fragment),cA=n(),D=i("div"),p(rg.$$.fragment),fA=n(),VM=i("p"),VM.textContent=LI,gA=n(),EM=i("p"),EM.innerHTML=kI,pA=n(),IM=i("ul"),IM.innerHTML=$I,hA=n(),UM=i("p"),UM.innerHTML=BI,uA=n(),p(el.$$.fragment),Z2=n(),p(tg.$$.fragment),R2=n(),ze=i("div"),p(ng.$$.fragment),_A=n(),NM=i("p"),NM.innerHTML=jI,bA=n(),qM=i("p"),qM.innerHTML=AI,MA=n(),bn=i("div"),p(ag.$$.fragment),TA=n(),XM=i("p"),XM.textContent=ZI,FA=n(),DM=i("p"),DM.innerHTML=RI,CA=n(),p(ol.$$.fragment),yA=n(),cr=i("div"),p(sg.$$.fragment),vA=n(),QM=i("p"),QM.textContent=GI,wA=n(),HM=i("p"),HM.innerHTML=PI,xA=n(),YM=i("ul"),YM.innerHTML=SI,LA=n(),p(rl.$$.fragment),G2=n(),p(ig.$$.fragment),P2=n(),Oe=i("div"),p(lg.$$.fragment),kA=n(),zM=i("p"),zM.innerHTML=WI,$A=n(),OM=i("p"),OM.innerHTML=JI,BA=n(),Mn=i("div"),p(dg.$$.fragment),jA=n(),KM=i("p"),KM.textContent=VI,AA=n(),eT=i("p"),eT.innerHTML=EI,ZA=n(),p(tl.$$.fragment),RA=n(),Q=i("div"),p(mg.$$.fragment),GA=n(),oT=i("p"),oT.textContent=II,PA=n(),rT=i("p"),rT.innerHTML=UI,SA=n(),tT=i("ul"),tT.innerHTML=NI,WA=n(),nT=i("p"),nT.innerHTML=qI,JA=n(),p(nl.$$.fragment),S2=n(),p(cg.$$.fragment),W2=n(),Ke=i("div"),p(fg.$$.fragment),VA=n(),aT=i("p"),aT.innerHTML=XI,EA=n(),sT=i("p"),sT.innerHTML=DI,IA=n(),Tn=i("div"),p(gg.$$.fragment),UA=n(),iT=i("p"),iT.textContent=QI,NA=n(),lT=i("p"),lT.innerHTML=HI,qA=n(),p(al.$$.fragment),XA=n(),H=i("div"),p(pg.$$.fragment),DA=n(),dT=i("p"),dT.textContent=YI,QA=n(),mT=i("p"),mT.innerHTML=zI,HA=n(),cT=i("ul"),cT.innerHTML=OI,YA=n(),fT=i("p"),fT.innerHTML=KI,zA=n(),p(sl.$$.fragment),J2=n(),p(hg.$$.fragment),V2=n(),ug=i("div"),p(_g.$$.fragment),E2=n(),p(bg.$$.fragment),I2=n(),eo=i("div"),p(Mg.$$.fragment),OA=n(),gT=i("p"),gT.innerHTML=eU,KA=n(),pT=i("p"),pT.innerHTML=oU,eZ=n(),Fn=i("div"),p(Tg.$$.fragment),oZ=n(),hT=i("p"),hT.textContent=rU,rZ=n(),uT=i("p"),uT.innerHTML=tU,tZ=n(),p(il.$$.fragment),nZ=n(),Y=i("div"),p(Fg.$$.fragment),aZ=n(),_T=i("p"),_T.textContent=nU,sZ=n(),bT=i("p"),bT.innerHTML=aU,iZ=n(),MT=i("ul"),MT.innerHTML=sU,lZ=n(),TT=i("p"),TT.innerHTML=iU,dZ=n(),p(ll.$$.fragment),U2=n(),p(Cg.$$.fragment),N2=n(),oo=i("div"),p(yg.$$.fragment),mZ=n(),FT=i("p"),FT.innerHTML=lU,cZ=n(),CT=i("p"),CT.innerHTML=dU,fZ=n(),Cn=i("div"),p(vg.$$.fragment),gZ=n(),yT=i("p"),yT.textContent=mU,pZ=n(),vT=i("p"),vT.innerHTML=cU,hZ=n(),p(dl.$$.fragment),uZ=n(),fr=i("div"),p(wg.$$.fragment),_Z=n(),wT=i("p"),wT.textContent=fU,bZ=n(),xT=i("p"),xT.innerHTML=gU,MZ=n(),LT=i("ul"),LT.innerHTML=pU,TZ=n(),p(ml.$$.fragment),q2=n(),p(xg.$$.fragment),X2=n(),ro=i("div"),p(Lg.$$.fragment),FZ=n(),kT=i("p"),kT.innerHTML=hU,CZ=n(),$T=i("p"),$T.innerHTML=uU,yZ=n(),yn=i("div"),p(kg.$$.fragment),vZ=n(),BT=i("p"),BT.textContent=_U,wZ=n(),jT=i("p"),jT.innerHTML=bU,xZ=n(),p(cl.$$.fragment),LZ=n(),z=i("div"),p($g.$$.fragment),kZ=n(),AT=i("p"),AT.textContent=MU,$Z=n(),ZT=i("p"),ZT.innerHTML=TU,BZ=n(),RT=i("ul"),RT.innerHTML=FU,jZ=n(),GT=i("p"),GT.innerHTML=CU,AZ=n(),p(fl.$$.fragment),D2=n(),p(Bg.$$.fragment),Q2=n(),to=i("div"),p(jg.$$.fragment),ZZ=n(),PT=i("p"),PT.innerHTML=yU,RZ=n(),ST=i("p"),ST.innerHTML=vU,GZ=n(),vn=i("div"),p(Ag.$$.fragment),PZ=n(),WT=i("p"),WT.textContent=wU,SZ=n(),JT=i("p"),JT.innerHTML=xU,WZ=n(),p(gl.$$.fragment),JZ=n(),O=i("div"),p(Zg.$$.fragment),VZ=n(),VT=i("p"),VT.textContent=LU,EZ=n(),ET=i("p"),ET.innerHTML=kU,IZ=n(),IT=i("ul"),IT.innerHTML=$U,UZ=n(),UT=i("p"),UT.innerHTML=BU,NZ=n(),p(pl.$$.fragment),H2=n(),p(Rg.$$.fragment),Y2=n(),no=i("div"),p(Gg.$$.fragment),qZ=n(),NT=i("p"),NT.innerHTML=jU,XZ=n(),qT=i("p"),qT.innerHTML=AU,DZ=n(),wn=i("div"),p(Pg.$$.fragment),QZ=n(),XT=i("p"),XT.textContent=ZU,HZ=n(),DT=i("p"),DT.innerHTML=RU,YZ=n(),p(hl.$$.fragment),zZ=n(),K=i("div"),p(Sg.$$.fragment),OZ=n(),QT=i("p"),QT.textContent=GU,KZ=n(),HT=i("p"),HT.innerHTML=PU,eR=n(),YT=i("ul"),YT.innerHTML=SU,oR=n(),zT=i("p"),zT.innerHTML=WU,rR=n(),p(ul.$$.fragment),z2=n(),p(Wg.$$.fragment),O2=n(),ao=i("div"),p(Jg.$$.fragment),tR=n(),OT=i("p"),OT.innerHTML=JU,nR=n(),KT=i("p"),KT.innerHTML=VU,aR=n(),xn=i("div"),p(Vg.$$.fragment),sR=n(),eF=i("p"),eF.textContent=EU,iR=n(),oF=i("p"),oF.innerHTML=IU,lR=n(),p(_l.$$.fragment),dR=n(),gr=i("div"),p(Eg.$$.fragment),mR=n(),rF=i("p"),rF.textContent=UU,cR=n(),tF=i("p"),tF.innerHTML=NU,fR=n(),nF=i("ul"),nF.innerHTML=qU,gR=n(),p(bl.$$.fragment),K2=n(),p(Ig.$$.fragment),ew=n(),so=i("div"),p(Ug.$$.fragment),pR=n(),aF=i("p"),aF.innerHTML=XU,hR=n(),sF=i("p"),sF.innerHTML=DU,uR=n(),Ln=i("div"),p(Ng.$$.fragment),_R=n(),iF=i("p"),iF.textContent=QU,bR=n(),lF=i("p"),lF.innerHTML=HU,MR=n(),p(Ml.$$.fragment),TR=n(),ee=i("div"),p(qg.$$.fragment),FR=n(),dF=i("p"),dF.textContent=YU,CR=n(),mF=i("p"),mF.innerHTML=zU,yR=n(),cF=i("ul"),cF.innerHTML=OU,vR=n(),fF=i("p"),fF.innerHTML=KU,wR=n(),p(Tl.$$.fragment),ow=n(),p(Xg.$$.fragment),rw=n(),Dg=i("p"),Dg.textContent=e9,tw=n(),p(Qg.$$.fragment),nw=n(),io=i("div"),p(Hg.$$.fragment),xR=n(),gF=i("p"),gF.innerHTML=o9,LR=n(),pF=i("p"),pF.innerHTML=r9,kR=n(),kn=i("div"),p(Yg.$$.fragment),$R=n(),hF=i("p"),hF.textContent=t9,BR=n(),uF=i("p"),uF.innerHTML=n9,jR=n(),p(Fl.$$.fragment),AR=n(),oe=i("div"),p(zg.$$.fragment),ZR=n(),_F=i("p"),_F.textContent=a9,RR=n(),bF=i("p"),bF.innerHTML=s9,GR=n(),MF=i("ul"),MF.innerHTML=i9,PR=n(),TF=i("p"),TF.innerHTML=l9,SR=n(),p(Cl.$$.fragment),aw=n(),p(Og.$$.fragment),sw=n(),lo=i("div"),p(Kg.$$.fragment),WR=n(),FF=i("p"),FF.innerHTML=d9,JR=n(),CF=i("p"),CF.innerHTML=m9,VR=n(),$n=i("div"),p(ep.$$.fragment),ER=n(),yF=i("p"),yF.textContent=c9,IR=n(),vF=i("p"),vF.innerHTML=f9,UR=n(),p(yl.$$.fragment),NR=n(),pr=i("div"),p(op.$$.fragment),qR=n(),wF=i("p"),wF.textContent=g9,XR=n(),xF=i("p"),xF.innerHTML=p9,DR=n(),LF=i("ul"),LF.innerHTML=h9,QR=n(),p(vl.$$.fragment),iw=n(),p(rp.$$.fragment),lw=n(),mo=i("div"),p(tp.$$.fragment),HR=n(),kF=i("p"),kF.innerHTML=u9,YR=n(),$F=i("p"),$F.innerHTML=_9,zR=n(),Bn=i("div"),p(np.$$.fragment),OR=n(),BF=i("p"),BF.textContent=b9,KR=n(),jF=i("p"),jF.innerHTML=M9,eG=n(),p(wl.$$.fragment),oG=n(),re=i("div"),p(ap.$$.fragment),rG=n(),AF=i("p"),AF.textContent=T9,tG=n(),ZF=i("p"),ZF.innerHTML=F9,nG=n(),RF=i("ul"),RF.innerHTML=C9,aG=n(),GF=i("p"),GF.innerHTML=y9,sG=n(),p(xl.$$.fragment),dw=n(),p(sp.$$.fragment),mw=n(),co=i("div"),p(ip.$$.fragment),iG=n(),PF=i("p"),PF.innerHTML=v9,lG=n(),SF=i("p"),SF.innerHTML=w9,dG=n(),jn=i("div"),p(lp.$$.fragment),mG=n(),WF=i("p"),WF.textContent=x9,cG=n(),JF=i("p"),JF.innerHTML=L9,fG=n(),p(Ll.$$.fragment),gG=n(),te=i("div"),p(dp.$$.fragment),pG=n(),VF=i("p"),VF.textContent=k9,hG=n(),EF=i("p"),EF.innerHTML=$9,uG=n(),IF=i("ul"),IF.innerHTML=B9,_G=n(),UF=i("p"),UF.innerHTML=j9,bG=n(),p(kl.$$.fragment),cw=n(),p(mp.$$.fragment),fw=n(),fo=i("div"),p(cp.$$.fragment),MG=n(),NF=i("p"),NF.innerHTML=A9,TG=n(),qF=i("p"),qF.innerHTML=Z9,FG=n(),An=i("div"),p(fp.$$.fragment),CG=n(),XF=i("p"),XF.textContent=R9,yG=n(),DF=i("p"),DF.innerHTML=G9,vG=n(),p($l.$$.fragment),wG=n(),ne=i("div"),p(gp.$$.fragment),xG=n(),QF=i("p"),QF.textContent=P9,LG=n(),HF=i("p"),HF.innerHTML=S9,kG=n(),YF=i("ul"),YF.innerHTML=W9,$G=n(),zF=i("p"),zF.innerHTML=J9,BG=n(),p(Bl.$$.fragment),gw=n(),p(pp.$$.fragment),pw=n(),go=i("div"),p(hp.$$.fragment),jG=n(),OF=i("p"),OF.innerHTML=V9,AG=n(),KF=i("p"),KF.innerHTML=E9,ZG=n(),Zn=i("div"),p(up.$$.fragment),RG=n(),eC=i("p"),eC.textContent=I9,GG=n(),oC=i("p"),oC.innerHTML=U9,PG=n(),p(jl.$$.fragment),SG=n(),hr=i("div"),p(_p.$$.fragment),WG=n(),rC=i("p"),rC.textContent=N9,JG=n(),tC=i("p"),tC.innerHTML=q9,VG=n(),nC=i("ul"),nC.innerHTML=X9,EG=n(),p(Al.$$.fragment),hw=n(),p(bp.$$.fragment),uw=n(),po=i("div"),p(Mp.$$.fragment),IG=n(),aC=i("p"),aC.innerHTML=D9,UG=n(),sC=i("p"),sC.innerHTML=Q9,NG=n(),Rn=i("div"),p(Tp.$$.fragment),qG=n(),iC=i("p"),iC.textContent=H9,XG=n(),lC=i("p"),lC.innerHTML=Y9,DG=n(),p(Zl.$$.fragment),QG=n(),ur=i("div"),p(Fp.$$.fragment),HG=n(),dC=i("p"),dC.textContent=z9,YG=n(),mC=i("p"),mC.innerHTML=O9,zG=n(),cC=i("ul"),cC.innerHTML=K9,OG=n(),p(Rl.$$.fragment),_w=n(),p(Cp.$$.fragment),bw=n(),ho=i("div"),p(yp.$$.fragment),KG=n(),fC=i("p"),fC.innerHTML=eN,eP=n(),gC=i("p"),gC.innerHTML=oN,oP=n(),Gn=i("div"),p(vp.$$.fragment),rP=n(),pC=i("p"),pC.textContent=rN,tP=n(),hC=i("p"),hC.innerHTML=tN,nP=n(),p(Gl.$$.fragment),aP=n(),ae=i("div"),p(wp.$$.fragment),sP=n(),uC=i("p"),uC.textContent=nN,iP=n(),_C=i("p"),_C.innerHTML=aN,lP=n(),bC=i("ul"),bC.innerHTML=sN,dP=n(),MC=i("p"),MC.innerHTML=iN,mP=n(),p(Pl.$$.fragment),Mw=n(),p(xp.$$.fragment),Tw=n(),Lp=i("div"),p(kp.$$.fragment),Fw=n(),p($p.$$.fragment),Cw=n(),Bp=i("div"),p(jp.$$.fragment),yw=n(),p(Ap.$$.fragment),vw=n(),Zp=i("p"),Zp.textContent=lN,ww=n(),p(Rp.$$.fragment),xw=n(),uo=i("div"),p(Gp.$$.fragment),cP=n(),TC=i("p"),TC.innerHTML=dN,fP=n(),FC=i("p"),FC.innerHTML=mN,gP=n(),Pn=i("div"),p(Pp.$$.fragment),pP=n(),CC=i("p"),CC.textContent=cN,hP=n(),yC=i("p"),yC.innerHTML=fN,uP=n(),p(Sl.$$.fragment),_P=n(),se=i("div"),p(Sp.$$.fragment),bP=n(),vC=i("p"),vC.textContent=gN,MP=n(),wC=i("p"),wC.innerHTML=pN,TP=n(),xC=i("ul"),xC.innerHTML=hN,FP=n(),LC=i("p"),LC.innerHTML=uN,CP=n(),p(Wl.$$.fragment),Lw=n(),p(Wp.$$.fragment),kw=n(),_o=i("div"),p(Jp.$$.fragment),yP=n(),kC=i("p"),kC.innerHTML=_N,vP=n(),$C=i("p"),$C.innerHTML=bN,wP=n(),Sn=i("div"),p(Vp.$$.fragment),xP=n(),BC=i("p"),BC.textContent=MN,LP=n(),jC=i("p"),jC.innerHTML=TN,kP=n(),p(Jl.$$.fragment),$P=n(),_r=i("div"),p(Ep.$$.fragment),BP=n(),AC=i("p"),AC.textContent=FN,jP=n(),ZC=i("p"),ZC.innerHTML=CN,AP=n(),RC=i("ul"),RC.innerHTML=yN,ZP=n(),p(Vl.$$.fragment),$w=n(),p(Ip.$$.fragment),Bw=n(),bo=i("div"),p(Up.$$.fragment),RP=n(),GC=i("p"),GC.innerHTML=vN,GP=n(),PC=i("p"),PC.innerHTML=wN,PP=n(),Wn=i("div"),p(Np.$$.fragment),SP=n(),SC=i("p"),SC.textContent=xN,WP=n(),WC=i("p"),WC.innerHTML=LN,JP=n(),p(El.$$.fragment),VP=n(),ie=i("div"),p(qp.$$.fragment),EP=n(),JC=i("p"),JC.textContent=kN,IP=n(),VC=i("p"),VC.innerHTML=$N,UP=n(),EC=i("ul"),EC.innerHTML=BN,NP=n(),IC=i("p"),IC.innerHTML=jN,qP=n(),p(Il.$$.fragment),jw=n(),p(Xp.$$.fragment),Aw=n(),Mo=i("div"),p(Dp.$$.fragment),XP=n(),UC=i("p"),UC.innerHTML=AN,DP=n(),NC=i("p"),NC.innerHTML=ZN,QP=n(),Jn=i("div"),p(Qp.$$.fragment),HP=n(),qC=i("p"),qC.textContent=RN,YP=n(),XC=i("p"),XC.innerHTML=GN,zP=n(),p(Ul.$$.fragment),OP=n(),br=i("div"),p(Hp.$$.fragment),KP=n(),DC=i("p"),DC.textContent=PN,eS=n(),QC=i("p"),QC.innerHTML=SN,oS=n(),HC=i("ul"),HC.innerHTML=WN,rS=n(),p(Nl.$$.fragment),Zw=n(),p(Yp.$$.fragment),Rw=n(),To=i("div"),p(zp.$$.fragment),tS=n(),YC=i("p"),YC.innerHTML=JN,nS=n(),zC=i("p"),zC.innerHTML=VN,aS=n(),Vn=i("div"),p(Op.$$.fragment),sS=n(),OC=i("p"),OC.textContent=EN,iS=n(),KC=i("p"),KC.innerHTML=IN,lS=n(),p(ql.$$.fragment),dS=n(),le=i("div"),p(Kp.$$.fragment),mS=n(),ey=i("p"),ey.textContent=UN,cS=n(),oy=i("p"),oy.innerHTML=NN,fS=n(),ry=i("ul"),ry.innerHTML=qN,gS=n(),ty=i("p"),ty.innerHTML=XN,pS=n(),p(Xl.$$.fragment),Gw=n(),p(eh.$$.fragment),Pw=n(),Fo=i("div"),p(oh.$$.fragment),hS=n(),ny=i("p"),ny.innerHTML=DN,uS=n(),ay=i("p"),ay.innerHTML=QN,_S=n(),En=i("div"),p(rh.$$.fragment),bS=n(),sy=i("p"),sy.textContent=HN,MS=n(),iy=i("p"),iy.innerHTML=YN,TS=n(),p(Dl.$$.fragment),FS=n(),de=i("div"),p(th.$$.fragment),CS=n(),ly=i("p"),ly.textContent=zN,yS=n(),dy=i("p"),dy.innerHTML=ON,vS=n(),my=i("ul"),my.innerHTML=KN,wS=n(),cy=i("p"),cy.innerHTML=e5,xS=n(),p(Ql.$$.fragment),Sw=n(),p(nh.$$.fragment),Ww=n(),Co=i("div"),p(ah.$$.fragment),LS=n(),fy=i("p"),fy.innerHTML=o5,kS=n(),gy=i("p"),gy.innerHTML=r5,$S=n(),In=i("div"),p(sh.$$.fragment),BS=n(),py=i("p"),py.textContent=t5,jS=n(),hy=i("p"),hy.innerHTML=n5,AS=n(),p(Hl.$$.fragment),ZS=n(),Mr=i("div"),p(ih.$$.fragment),RS=n(),uy=i("p"),uy.textContent=a5,GS=n(),_y=i("p"),_y.innerHTML=s5,PS=n(),by=i("ul"),by.innerHTML=i5,SS=n(),p(Yl.$$.fragment),Jw=n(),p(lh.$$.fragment),Vw=n(),yo=i("div"),p(dh.$$.fragment),WS=n(),My=i("p"),My.innerHTML=l5,JS=n(),Ty=i("p"),Ty.innerHTML=d5,VS=n(),Un=i("div"),p(mh.$$.fragment),ES=n(),Fy=i("p"),Fy.textContent=m5,IS=n(),Cy=i("p"),Cy.innerHTML=c5,US=n(),p(zl.$$.fragment),NS=n(),Tr=i("div"),p(ch.$$.fragment),qS=n(),yy=i("p"),yy.textContent=f5,XS=n(),vy=i("p"),vy.innerHTML=g5,DS=n(),wy=i("ul"),wy.innerHTML=p5,QS=n(),p(Ol.$$.fragment),Ew=n(),p(fh.$$.fragment),Iw=n(),xy=i("p"),this.h()},l(o){const g=C5("svelte-u9bgzb",document.head);t=l(g,"META",{name:!0,content:!0}),g.forEach(d),C=a(o),m=l(o,"P",{}),v(m).forEach(d),s=a(o),h(c.$$.fragment,o),e=a(o),F=l(o,"P",{"data-svelte-h":!0}),f(F)!=="svelte-1sl6sct"&&(F.innerHTML=HS),Ay=a(o),yd=l(o,"P",{"data-svelte-h":!0}),f(yd)!=="svelte-3mg9is"&&(yd.innerHTML=YS),Zy=a(o),h(vd.$$.fragment,o),Ry=a(o),wd=l(o,"P",{"data-svelte-h":!0}),f(wd)!=="svelte-exw8vq"&&(wd.innerHTML=zS),Gy=a(o),xd=l(o,"P",{"data-svelte-h":!0}),f(xd)!=="svelte-km6kzj"&&(xd.innerHTML=OS),Py=a(o),h(Ld.$$.fragment,o),Sy=a(o),kd=l(o,"P",{"data-svelte-h":!0}),f(kd)!=="svelte-11r6347"&&(kd.innerHTML=KS),Wy=a(o),h($d.$$.fragment,o),Jy=a(o),Bd=l(o,"P",{"data-svelte-h":!0}),f(Bd)!=="svelte-18w8uiz"&&(Bd.textContent=eW),Vy=a(o),h(ys.$$.fragment,o),Ey=a(o),h(jd.$$.fragment,o),Iy=a(o),me=l(o,"DIV",{class:!0});var Fr=v(me);h(Ad.$$.fragment,Fr),qw=a(Fr),bh=l(Fr,"P",{"data-svelte-h":!0}),f(bh)!=="svelte-qgr7n9"&&(bh.innerHTML=oW),Xw=a(Fr),Mh=l(Fr,"P",{"data-svelte-h":!0}),f(Mh)!=="svelte-1n2cphd"&&(Mh.innerHTML=rW),Dw=a(Fr),Eo=l(Fr,"DIV",{class:!0});var Cr=v(Eo);h(Zd.$$.fragment,Cr),Qw=a(Cr),Th=l(Cr,"P",{"data-svelte-h":!0}),f(Th)!=="svelte-102bmt5"&&(Th.textContent=tW),Hw=a(Cr),Fh=l(Cr,"P",{"data-svelte-h":!0}),f(Fh)!=="svelte-dfwk4w"&&(Fh.innerHTML=nW),Yw=a(Cr),Ch=l(Cr,"UL",{"data-svelte-h":!0}),f(Ch)!=="svelte-1p73bnn"&&(Ch.innerHTML=aW),zw=a(Cr),h(vs.$$.fragment,Cr),Cr.forEach(d),Ow=a(Fr),ws=l(Fr,"DIV",{class:!0});var gh=v(ws);h(Rd.$$.fragment,gh),Kw=a(gh),yh=l(gh,"P",{"data-svelte-h":!0}),f(yh)!=="svelte-3x5rn5"&&(yh.textContent=sW),gh.forEach(d),Fr.forEach(d),Uy=a(o),h(Gd.$$.fragment,o),Ny=a(o),ce=l(o,"DIV",{class:!0});var yr=v(ce);h(Pd.$$.fragment,yr),e3=a(yr),vh=l(yr,"P",{"data-svelte-h":!0}),f(vh)!=="svelte-1o4w43u"&&(vh.innerHTML=iW),o3=a(yr),wh=l(yr,"P",{"data-svelte-h":!0}),f(wh)!=="svelte-1n2cphd"&&(wh.innerHTML=lW),r3=a(yr),Io=l(yr,"DIV",{class:!0});var vr=v(Io);h(Sd.$$.fragment,vr),t3=a(vr),xh=l(vr,"P",{"data-svelte-h":!0}),f(xh)!=="svelte-1cgdf6a"&&(xh.textContent=dW),n3=a(vr),Lh=l(vr,"P",{"data-svelte-h":!0}),f(Lh)!=="svelte-yjv30u"&&(Lh.innerHTML=mW),a3=a(vr),kh=l(vr,"UL",{"data-svelte-h":!0}),f(kh)!=="svelte-hfaxuv"&&(kh.innerHTML=cW),s3=a(vr),h(xs.$$.fragment,vr),vr.forEach(d),i3=a(yr),Ls=l(yr,"DIV",{class:!0});var ph=v(Ls);h(Wd.$$.fragment,ph),l3=a(ph),$h=l(ph,"P",{"data-svelte-h":!0}),f($h)!=="svelte-1dj9ip8"&&($h.textContent=fW),ph.forEach(d),yr.forEach(d),qy=a(o),h(Jd.$$.fragment,o),Xy=a(o),fe=l(o,"DIV",{class:!0});var wr=v(fe);h(Vd.$$.fragment,wr),d3=a(wr),Bh=l(wr,"P",{"data-svelte-h":!0}),f(Bh)!=="svelte-x1yoqq"&&(Bh.innerHTML=gW),m3=a(wr),jh=l(wr,"P",{"data-svelte-h":!0}),f(jh)!=="svelte-1n2cphd"&&(jh.innerHTML=pW),c3=a(wr),j=l(wr,"DIV",{class:!0});var vo=v(j);h(Ed.$$.fragment,vo),f3=a(vo),Ah=l(vo,"P",{"data-svelte-h":!0}),f(Ah)!=="svelte-brndlr"&&(Ah.textContent=hW),g3=a(vo),Zh=l(vo,"P",{"data-svelte-h":!0}),f(Zh)!=="svelte-8hskw5"&&(Zh.innerHTML=uW),p3=a(vo),Rh=l(vo,"UL",{"data-svelte-h":!0}),f(Rh)!=="svelte-4bc7em"&&(Rh.innerHTML=_W),h3=a(vo),h(ks.$$.fragment,vo),u3=a(vo),h($s.$$.fragment,vo),vo.forEach(d),_3=a(wr),Bs=l(wr,"DIV",{class:!0});var hh=v(Bs);h(Id.$$.fragment,hh),b3=a(hh),Gh=l(hh,"P",{"data-svelte-h":!0}),f(Gh)!=="svelte-1a46i61"&&(Gh.textContent=bW),hh.forEach(d),wr.forEach(d),Dy=a(o),h(Ud.$$.fragment,o),Qy=a(o),ge=l(o,"DIV",{class:!0});var xr=v(ge);h(Nd.$$.fragment,xr),M3=a(xr),Ph=l(xr,"P",{"data-svelte-h":!0}),f(Ph)!=="svelte-1vz0t3g"&&(Ph.innerHTML=MW),T3=a(xr),Sh=l(xr,"P",{"data-svelte-h":!0}),f(Sh)!=="svelte-1n2cphd"&&(Sh.innerHTML=TW),F3=a(xr),A=l(xr,"DIV",{class:!0});var wo=v(A);h(qd.$$.fragment,wo),C3=a(wo),Wh=l(wo,"P",{"data-svelte-h":!0}),f(Wh)!=="svelte-cyiy9y"&&(Wh.textContent=FW),y3=a(wo),Jh=l(wo,"P",{"data-svelte-h":!0}),f(Jh)!=="svelte-1vjaq5m"&&(Jh.innerHTML=CW),v3=a(wo),Vh=l(wo,"UL",{"data-svelte-h":!0}),f(Vh)!=="svelte-1lnq4g8"&&(Vh.innerHTML=yW),w3=a(wo),h(js.$$.fragment,wo),x3=a(wo),h(As.$$.fragment,wo),wo.forEach(d),L3=a(xr),Zs=l(xr,"DIV",{class:!0});var uh=v(Zs);h(Xd.$$.fragment,uh),k3=a(uh),Eh=l(uh,"P",{"data-svelte-h":!0}),f(Eh)!=="svelte-8qw1k0"&&(Eh.textContent=vW),uh.forEach(d),xr.forEach(d),Hy=a(o),h(Dd.$$.fragment,o),Yy=a(o),pe=l(o,"DIV",{class:!0});var Lr=v(pe);h(Qd.$$.fragment,Lr),$3=a(Lr),Ih=l(Lr,"P",{"data-svelte-h":!0}),f(Ih)!=="svelte-eslkie"&&(Ih.innerHTML=wW),B3=a(Lr),Uh=l(Lr,"P",{"data-svelte-h":!0}),f(Uh)!=="svelte-1n2cphd"&&(Uh.innerHTML=xW),j3=a(Lr),Z=l(Lr,"DIV",{class:!0});var xo=v(Z);h(Hd.$$.fragment,xo),A3=a(xo),Nh=l(xo,"P",{"data-svelte-h":!0}),f(Nh)!=="svelte-ampft1"&&(Nh.textContent=LW),Z3=a(xo),qh=l(xo,"P",{"data-svelte-h":!0}),f(qh)!=="svelte-1lz4kgk"&&(qh.innerHTML=kW),R3=a(xo),Xh=l(xo,"UL",{"data-svelte-h":!0}),f(Xh)!=="svelte-1rjv72z"&&(Xh.innerHTML=$W),G3=a(xo),h(Rs.$$.fragment,xo),P3=a(xo),h(Gs.$$.fragment,xo),xo.forEach(d),S3=a(Lr),Ps=l(Lr,"DIV",{class:!0});var _h=v(Ps);h(Yd.$$.fragment,_h),W3=a(_h),Dh=l(_h,"P",{"data-svelte-h":!0}),f(Dh)!=="svelte-1rfmb83"&&(Dh.textContent=BW),_h.forEach(d),Lr.forEach(d),zy=a(o),h(zd.$$.fragment,o),Oy=a(o),Od=l(o,"P",{"data-svelte-h":!0}),f(Od)!=="svelte-waxqds"&&(Od.textContent=jW),Ky=a(o),h(Kd.$$.fragment,o),ev=a(o),he=l(o,"DIV",{class:!0});var kr=v(he);h(em.$$.fragment,kr),J3=a(kr),Qh=l(kr,"P",{"data-svelte-h":!0}),f(Qh)!=="svelte-1f7ifqz"&&(Qh.innerHTML=AW),V3=a(kr),Hh=l(kr,"P",{"data-svelte-h":!0}),f(Hh)!=="svelte-1n2cphd"&&(Hh.innerHTML=ZW),E3=a(kr),Gt=l(kr,"DIV",{class:!0});var sa=v(Gt);h(om.$$.fragment,sa),I3=a(sa),Yh=l(sa,"P",{"data-svelte-h":!0}),f(Yh)!=="svelte-ws3pq1"&&(Yh.textContent=RW),U3=a(sa),zh=l(sa,"P",{"data-svelte-h":!0}),f(zh)!=="svelte-1s2prmj"&&(zh.innerHTML=GW),N3=a(sa),h(Ss.$$.fragment,sa),sa.forEach(d),q3=a(kr),R=l(kr,"DIV",{class:!0});var Lo=v(R);h(rm.$$.fragment,Lo),X3=a(Lo),Oh=l(Lo,"P",{"data-svelte-h":!0}),f(Oh)!=="svelte-mt76tr"&&(Oh.textContent=PW),D3=a(Lo),Kh=l(Lo,"P",{"data-svelte-h":!0}),f(Kh)!=="svelte-1enn9p2"&&(Kh.innerHTML=SW),Q3=a(Lo),eu=l(Lo,"UL",{"data-svelte-h":!0}),f(eu)!=="svelte-gnrv6n"&&(eu.innerHTML=WW),H3=a(Lo),ou=l(Lo,"P",{"data-svelte-h":!0}),f(ou)!=="svelte-tebhz6"&&(ou.innerHTML=JW),Y3=a(Lo),h(Ws.$$.fragment,Lo),Lo.forEach(d),kr.forEach(d),ov=a(o),h(tm.$$.fragment,o),rv=a(o),ue=l(o,"DIV",{class:!0});var $r=v(ue);h(nm.$$.fragment,$r),z3=a($r),ru=l($r,"P",{"data-svelte-h":!0}),f(ru)!=="svelte-1f7ifqz"&&(ru.innerHTML=VW),O3=a($r),tu=l($r,"P",{"data-svelte-h":!0}),f(tu)!=="svelte-1n2cphd"&&(tu.innerHTML=EW),K3=a($r),Pt=l($r,"DIV",{class:!0});var ia=v(Pt);h(am.$$.fragment,ia),ex=a(ia),nu=l(ia,"P",{"data-svelte-h":!0}),f(nu)!=="svelte-ws3pq1"&&(nu.textContent=IW),ox=a(ia),au=l(ia,"P",{"data-svelte-h":!0}),f(au)!=="svelte-1s2prmj"&&(au.innerHTML=UW),rx=a(ia),h(Js.$$.fragment,ia),ia.forEach(d),tx=a($r),Uo=l($r,"DIV",{class:!0});var Br=v(Uo);h(sm.$$.fragment,Br),nx=a(Br),su=l(Br,"P",{"data-svelte-h":!0}),f(su)!=="svelte-mt76tr"&&(su.textContent=NW),ax=a(Br),iu=l(Br,"P",{"data-svelte-h":!0}),f(iu)!=="svelte-1enn9p2"&&(iu.innerHTML=qW),sx=a(Br),lu=l(Br,"UL",{"data-svelte-h":!0}),f(lu)!=="svelte-1jssekc"&&(lu.innerHTML=XW),ix=a(Br),h(Vs.$$.fragment,Br),Br.forEach(d),$r.forEach(d),tv=a(o),h(im.$$.fragment,o),nv=a(o),_e=l(o,"DIV",{class:!0});var jr=v(_e);h(lm.$$.fragment,jr),lx=a(jr),du=l(jr,"P",{"data-svelte-h":!0}),f(du)!=="svelte-1f7ifqz"&&(du.innerHTML=DW),dx=a(jr),mu=l(jr,"P",{"data-svelte-h":!0}),f(mu)!=="svelte-1n2cphd"&&(mu.innerHTML=QW),mx=a(jr),St=l(jr,"DIV",{class:!0});var la=v(St);h(dm.$$.fragment,la),cx=a(la),cu=l(la,"P",{"data-svelte-h":!0}),f(cu)!=="svelte-ws3pq1"&&(cu.textContent=HW),fx=a(la),fu=l(la,"P",{"data-svelte-h":!0}),f(fu)!=="svelte-1s2prmj"&&(fu.innerHTML=YW),gx=a(la),h(Es.$$.fragment,la),la.forEach(d),px=a(jr),No=l(jr,"DIV",{class:!0});var Ar=v(No);h(mm.$$.fragment,Ar),hx=a(Ar),gu=l(Ar,"P",{"data-svelte-h":!0}),f(gu)!=="svelte-mt76tr"&&(gu.textContent=zW),ux=a(Ar),pu=l(Ar,"P",{"data-svelte-h":!0}),f(pu)!=="svelte-1enn9p2"&&(pu.innerHTML=OW),_x=a(Ar),hu=l(Ar,"UL",{"data-svelte-h":!0}),f(hu)!=="svelte-1gm35ri"&&(hu.innerHTML=KW),bx=a(Ar),h(Is.$$.fragment,Ar),Ar.forEach(d),jr.forEach(d),av=a(o),h(cm.$$.fragment,o),sv=a(o),fm=l(o,"P",{"data-svelte-h":!0}),f(fm)!=="svelte-1lm34wk"&&(fm.textContent=eJ),iv=a(o),h(gm.$$.fragment,o),lv=a(o),be=l(o,"DIV",{class:!0});var Zr=v(be);h(pm.$$.fragment,Zr),Mx=a(Zr),uu=l(Zr,"P",{"data-svelte-h":!0}),f(uu)!=="svelte-1in6r81"&&(uu.innerHTML=oJ),Tx=a(Zr),_u=l(Zr,"P",{"data-svelte-h":!0}),f(_u)!=="svelte-1n2cphd"&&(_u.innerHTML=rJ),Fx=a(Zr),Wt=l(Zr,"DIV",{class:!0});var da=v(Wt);h(hm.$$.fragment,da),Cx=a(da),bu=l(da,"P",{"data-svelte-h":!0}),f(bu)!=="svelte-qeb48j"&&(bu.textContent=tJ),yx=a(da),Mu=l(da,"P",{"data-svelte-h":!0}),f(Mu)!=="svelte-1s2prmj"&&(Mu.innerHTML=nJ),vx=a(da),h(Us.$$.fragment,da),da.forEach(d),wx=a(Zr),G=l(Zr,"DIV",{class:!0});var ko=v(G);h(um.$$.fragment,ko),xx=a(ko),Tu=l(ko,"P",{"data-svelte-h":!0}),f(Tu)!=="svelte-141b7sb"&&(Tu.textContent=aJ),Lx=a(ko),Fu=l(ko,"P",{"data-svelte-h":!0}),f(Fu)!=="svelte-1enn9p2"&&(Fu.innerHTML=sJ),kx=a(ko),Cu=l(ko,"UL",{"data-svelte-h":!0}),f(Cu)!=="svelte-10sb0zs"&&(Cu.innerHTML=iJ),$x=a(ko),yu=l(ko,"P",{"data-svelte-h":!0}),f(yu)!=="svelte-tebhz6"&&(yu.innerHTML=lJ),Bx=a(ko),h(Ns.$$.fragment,ko),ko.forEach(d),Zr.forEach(d),dv=a(o),h(_m.$$.fragment,o),mv=a(o),Me=l(o,"DIV",{class:!0});var Rr=v(Me);h(bm.$$.fragment,Rr),jx=a(Rr),vu=l(Rr,"P",{"data-svelte-h":!0}),f(vu)!=="svelte-1in6r81"&&(vu.innerHTML=dJ),Ax=a(Rr),wu=l(Rr,"P",{"data-svelte-h":!0}),f(wu)!=="svelte-1n2cphd"&&(wu.innerHTML=mJ),Zx=a(Rr),Jt=l(Rr,"DIV",{class:!0});var ma=v(Jt);h(Mm.$$.fragment,ma),Rx=a(ma),xu=l(ma,"P",{"data-svelte-h":!0}),f(xu)!=="svelte-qeb48j"&&(xu.textContent=cJ),Gx=a(ma),Lu=l(ma,"P",{"data-svelte-h":!0}),f(Lu)!=="svelte-1s2prmj"&&(Lu.innerHTML=fJ),Px=a(ma),h(qs.$$.fragment,ma),ma.forEach(d),Sx=a(Rr),qo=l(Rr,"DIV",{class:!0});var Gr=v(qo);h(Tm.$$.fragment,Gr),Wx=a(Gr),ku=l(Gr,"P",{"data-svelte-h":!0}),f(ku)!=="svelte-141b7sb"&&(ku.textContent=gJ),Jx=a(Gr),$u=l(Gr,"P",{"data-svelte-h":!0}),f($u)!=="svelte-1enn9p2"&&($u.innerHTML=pJ),Vx=a(Gr),Bu=l(Gr,"UL",{"data-svelte-h":!0}),f(Bu)!=="svelte-yfev9s"&&(Bu.innerHTML=hJ),Ex=a(Gr),h(Xs.$$.fragment,Gr),Gr.forEach(d),Rr.forEach(d),cv=a(o),h(Fm.$$.fragment,o),fv=a(o),Te=l(o,"DIV",{class:!0});var Pr=v(Te);h(Cm.$$.fragment,Pr),Ix=a(Pr),ju=l(Pr,"P",{"data-svelte-h":!0}),f(ju)!=="svelte-1in6r81"&&(ju.innerHTML=uJ),Ux=a(Pr),Au=l(Pr,"P",{"data-svelte-h":!0}),f(Au)!=="svelte-1n2cphd"&&(Au.innerHTML=_J),Nx=a(Pr),Vt=l(Pr,"DIV",{class:!0});var ca=v(Vt);h(ym.$$.fragment,ca),qx=a(ca),Zu=l(ca,"P",{"data-svelte-h":!0}),f(Zu)!=="svelte-qeb48j"&&(Zu.textContent=bJ),Xx=a(ca),Ru=l(ca,"P",{"data-svelte-h":!0}),f(Ru)!=="svelte-1s2prmj"&&(Ru.innerHTML=MJ),Dx=a(ca),h(Ds.$$.fragment,ca),ca.forEach(d),Qx=a(Pr),Xo=l(Pr,"DIV",{class:!0});var Sr=v(Xo);h(vm.$$.fragment,Sr),Hx=a(Sr),Gu=l(Sr,"P",{"data-svelte-h":!0}),f(Gu)!=="svelte-141b7sb"&&(Gu.textContent=TJ),Yx=a(Sr),Pu=l(Sr,"P",{"data-svelte-h":!0}),f(Pu)!=="svelte-1enn9p2"&&(Pu.innerHTML=FJ),zx=a(Sr),Su=l(Sr,"UL",{"data-svelte-h":!0}),f(Su)!=="svelte-6co4aq"&&(Su.innerHTML=CJ),Ox=a(Sr),h(Qs.$$.fragment,Sr),Sr.forEach(d),Pr.forEach(d),gv=a(o),h(wm.$$.fragment,o),pv=a(o),xm=l(o,"P",{"data-svelte-h":!0}),f(xm)!=="svelte-pqqo9r"&&(xm.textContent=yJ),hv=a(o),h(Lm.$$.fragment,o),uv=a(o),Fe=l(o,"DIV",{class:!0});var Wr=v(Fe);h(km.$$.fragment,Wr),Kx=a(Wr),Wu=l(Wr,"P",{"data-svelte-h":!0}),f(Wu)!=="svelte-cs7x5g"&&(Wu.innerHTML=vJ),e6=a(Wr),Ju=l(Wr,"P",{"data-svelte-h":!0}),f(Ju)!=="svelte-1n2cphd"&&(Ju.innerHTML=wJ),o6=a(Wr),Et=l(Wr,"DIV",{class:!0});var fa=v(Et);h($m.$$.fragment,fa),r6=a(fa),Vu=l(fa,"P",{"data-svelte-h":!0}),f(Vu)!=="svelte-6spxaa"&&(Vu.textContent=xJ),t6=a(fa),Eu=l(fa,"P",{"data-svelte-h":!0}),f(Eu)!=="svelte-1s2prmj"&&(Eu.innerHTML=LJ),n6=a(fa),h(Hs.$$.fragment,fa),fa.forEach(d),a6=a(Wr),P=l(Wr,"DIV",{class:!0});var $o=v(P);h(Bm.$$.fragment,$o),s6=a($o),Iu=l($o,"P",{"data-svelte-h":!0}),f(Iu)!=="svelte-1b19um"&&(Iu.textContent=kJ),i6=a($o),Uu=l($o,"P",{"data-svelte-h":!0}),f(Uu)!=="svelte-1enn9p2"&&(Uu.innerHTML=$J),l6=a($o),Nu=l($o,"UL",{"data-svelte-h":!0}),f(Nu)!=="svelte-jxh3zx"&&(Nu.innerHTML=BJ),d6=a($o),qu=l($o,"P",{"data-svelte-h":!0}),f(qu)!=="svelte-tebhz6"&&(qu.innerHTML=jJ),m6=a($o),h(Ys.$$.fragment,$o),$o.forEach(d),Wr.forEach(d),_v=a(o),h(jm.$$.fragment,o),bv=a(o),Ce=l(o,"DIV",{class:!0});var Jr=v(Ce);h(Am.$$.fragment,Jr),c6=a(Jr),Xu=l(Jr,"P",{"data-svelte-h":!0}),f(Xu)!=="svelte-cs7x5g"&&(Xu.innerHTML=AJ),f6=a(Jr),Du=l(Jr,"P",{"data-svelte-h":!0}),f(Du)!=="svelte-1n2cphd"&&(Du.innerHTML=ZJ),g6=a(Jr),It=l(Jr,"DIV",{class:!0});var ga=v(It);h(Zm.$$.fragment,ga),p6=a(ga),Qu=l(ga,"P",{"data-svelte-h":!0}),f(Qu)!=="svelte-6spxaa"&&(Qu.textContent=RJ),h6=a(ga),Hu=l(ga,"P",{"data-svelte-h":!0}),f(Hu)!=="svelte-1s2prmj"&&(Hu.innerHTML=GJ),u6=a(ga),h(zs.$$.fragment,ga),ga.forEach(d),_6=a(Jr),Do=l(Jr,"DIV",{class:!0});var Vr=v(Do);h(Rm.$$.fragment,Vr),b6=a(Vr),Yu=l(Vr,"P",{"data-svelte-h":!0}),f(Yu)!=="svelte-1b19um"&&(Yu.textContent=PJ),M6=a(Vr),zu=l(Vr,"P",{"data-svelte-h":!0}),f(zu)!=="svelte-1enn9p2"&&(zu.innerHTML=SJ),T6=a(Vr),Ou=l(Vr,"UL",{"data-svelte-h":!0}),f(Ou)!=="svelte-d4l1r0"&&(Ou.innerHTML=WJ),F6=a(Vr),h(Os.$$.fragment,Vr),Vr.forEach(d),Jr.forEach(d),Mv=a(o),h(Gm.$$.fragment,o),Tv=a(o),ye=l(o,"DIV",{class:!0});var Er=v(ye);h(Pm.$$.fragment,Er),C6=a(Er),Ku=l(Er,"P",{"data-svelte-h":!0}),f(Ku)!=="svelte-cs7x5g"&&(Ku.innerHTML=JJ),y6=a(Er),e_=l(Er,"P",{"data-svelte-h":!0}),f(e_)!=="svelte-1n2cphd"&&(e_.innerHTML=VJ),v6=a(Er),Ut=l(Er,"DIV",{class:!0});var pa=v(Ut);h(Sm.$$.fragment,pa),w6=a(pa),o_=l(pa,"P",{"data-svelte-h":!0}),f(o_)!=="svelte-6spxaa"&&(o_.textContent=EJ),x6=a(pa),r_=l(pa,"P",{"data-svelte-h":!0}),f(r_)!=="svelte-1s2prmj"&&(r_.innerHTML=IJ),L6=a(pa),h(Ks.$$.fragment,pa),pa.forEach(d),k6=a(Er),Qo=l(Er,"DIV",{class:!0});var Ir=v(Qo);h(Wm.$$.fragment,Ir),$6=a(Ir),t_=l(Ir,"P",{"data-svelte-h":!0}),f(t_)!=="svelte-1b19um"&&(t_.textContent=UJ),B6=a(Ir),n_=l(Ir,"P",{"data-svelte-h":!0}),f(n_)!=="svelte-1enn9p2"&&(n_.innerHTML=NJ),j6=a(Ir),a_=l(Ir,"UL",{"data-svelte-h":!0}),f(a_)!=="svelte-1iddlra"&&(a_.innerHTML=qJ),A6=a(Ir),h(ei.$$.fragment,Ir),Ir.forEach(d),Er.forEach(d),Fv=a(o),h(Jm.$$.fragment,o),Cv=a(o),ve=l(o,"DIV",{class:!0});var Ur=v(ve);h(Vm.$$.fragment,Ur),Z6=a(Ur),s_=l(Ur,"P",{"data-svelte-h":!0}),f(s_)!=="svelte-153anbo"&&(s_.innerHTML=XJ),R6=a(Ur),i_=l(Ur,"P",{"data-svelte-h":!0}),f(i_)!=="svelte-1n2cphd"&&(i_.innerHTML=DJ),G6=a(Ur),Nt=l(Ur,"DIV",{class:!0});var ha=v(Nt);h(Em.$$.fragment,ha),P6=a(ha),l_=l(ha,"P",{"data-svelte-h":!0}),f(l_)!=="svelte-1l4n5m2"&&(l_.textContent=QJ),S6=a(ha),d_=l(ha,"P",{"data-svelte-h":!0}),f(d_)!=="svelte-1s2prmj"&&(d_.innerHTML=HJ),W6=a(ha),h(oi.$$.fragment,ha),ha.forEach(d),J6=a(Ur),S=l(Ur,"DIV",{class:!0});var Bo=v(S);h(Im.$$.fragment,Bo),V6=a(Bo),m_=l(Bo,"P",{"data-svelte-h":!0}),f(m_)!=="svelte-1hs4l6u"&&(m_.textContent=YJ),E6=a(Bo),c_=l(Bo,"P",{"data-svelte-h":!0}),f(c_)!=="svelte-1enn9p2"&&(c_.innerHTML=zJ),I6=a(Bo),f_=l(Bo,"UL",{"data-svelte-h":!0}),f(f_)!=="svelte-1yfnou6"&&(f_.innerHTML=OJ),U6=a(Bo),g_=l(Bo,"P",{"data-svelte-h":!0}),f(g_)!=="svelte-tebhz6"&&(g_.innerHTML=KJ),N6=a(Bo),h(ri.$$.fragment,Bo),Bo.forEach(d),Ur.forEach(d),yv=a(o),h(Um.$$.fragment,o),vv=a(o),we=l(o,"DIV",{class:!0});var Nr=v(we);h(Nm.$$.fragment,Nr),q6=a(Nr),p_=l(Nr,"P",{"data-svelte-h":!0}),f(p_)!=="svelte-153anbo"&&(p_.innerHTML=eV),X6=a(Nr),h_=l(Nr,"P",{"data-svelte-h":!0}),f(h_)!=="svelte-1n2cphd"&&(h_.innerHTML=oV),D6=a(Nr),qt=l(Nr,"DIV",{class:!0});var ua=v(qt);h(qm.$$.fragment,ua),Q6=a(ua),u_=l(ua,"P",{"data-svelte-h":!0}),f(u_)!=="svelte-1l4n5m2"&&(u_.textContent=rV),H6=a(ua),__=l(ua,"P",{"data-svelte-h":!0}),f(__)!=="svelte-1s2prmj"&&(__.innerHTML=tV),Y6=a(ua),h(ti.$$.fragment,ua),ua.forEach(d),z6=a(Nr),Ho=l(Nr,"DIV",{class:!0});var qr=v(Ho);h(Xm.$$.fragment,qr),O6=a(qr),b_=l(qr,"P",{"data-svelte-h":!0}),f(b_)!=="svelte-1hs4l6u"&&(b_.textContent=nV),K6=a(qr),M_=l(qr,"P",{"data-svelte-h":!0}),f(M_)!=="svelte-1enn9p2"&&(M_.innerHTML=aV),eL=a(qr),T_=l(qr,"UL",{"data-svelte-h":!0}),f(T_)!=="svelte-1srn27c"&&(T_.innerHTML=sV),oL=a(qr),h(ni.$$.fragment,qr),qr.forEach(d),Nr.forEach(d),wv=a(o),h(Dm.$$.fragment,o),xv=a(o),xe=l(o,"DIV",{class:!0});var Xr=v(xe);h(Qm.$$.fragment,Xr),rL=a(Xr),F_=l(Xr,"P",{"data-svelte-h":!0}),f(F_)!=="svelte-153anbo"&&(F_.innerHTML=iV),tL=a(Xr),C_=l(Xr,"P",{"data-svelte-h":!0}),f(C_)!=="svelte-1n2cphd"&&(C_.innerHTML=lV),nL=a(Xr),Xt=l(Xr,"DIV",{class:!0});var _a=v(Xt);h(Hm.$$.fragment,_a),aL=a(_a),y_=l(_a,"P",{"data-svelte-h":!0}),f(y_)!=="svelte-1l4n5m2"&&(y_.textContent=dV),sL=a(_a),v_=l(_a,"P",{"data-svelte-h":!0}),f(v_)!=="svelte-1s2prmj"&&(v_.innerHTML=mV),iL=a(_a),h(ai.$$.fragment,_a),_a.forEach(d),lL=a(Xr),Yo=l(Xr,"DIV",{class:!0});var Dr=v(Yo);h(Ym.$$.fragment,Dr),dL=a(Dr),w_=l(Dr,"P",{"data-svelte-h":!0}),f(w_)!=="svelte-1hs4l6u"&&(w_.textContent=cV),mL=a(Dr),x_=l(Dr,"P",{"data-svelte-h":!0}),f(x_)!=="svelte-1enn9p2"&&(x_.innerHTML=fV),cL=a(Dr),L_=l(Dr,"UL",{"data-svelte-h":!0}),f(L_)!=="svelte-1lduz2m"&&(L_.innerHTML=gV),fL=a(Dr),h(si.$$.fragment,Dr),Dr.forEach(d),Xr.forEach(d),Lv=a(o),h(zm.$$.fragment,o),kv=a(o),Om=l(o,"DIV",{class:!0});var Ly=v(Om);h(Km.$$.fragment,Ly),Ly.forEach(d),$v=a(o),h(ec.$$.fragment,o),Bv=a(o),oc=l(o,"DIV",{class:!0});var ky=v(oc);h(rc.$$.fragment,ky),ky.forEach(d),jv=a(o),h(tc.$$.fragment,o),Av=a(o),Le=l(o,"DIV",{class:!0});var Qr=v(Le);h(nc.$$.fragment,Qr),gL=a(Qr),k_=l(Qr,"P",{"data-svelte-h":!0}),f(k_)!=="svelte-1wqdaw8"&&(k_.innerHTML=pV),pL=a(Qr),$_=l(Qr,"P",{"data-svelte-h":!0}),f($_)!=="svelte-1n2cphd"&&($_.innerHTML=hV),hL=a(Qr),Dt=l(Qr,"DIV",{class:!0});var ba=v(Dt);h(ac.$$.fragment,ba),uL=a(ba),B_=l(ba,"P",{"data-svelte-h":!0}),f(B_)!=="svelte-1d0i3aa"&&(B_.textContent=uV),_L=a(ba),j_=l(ba,"P",{"data-svelte-h":!0}),f(j_)!=="svelte-1s2prmj"&&(j_.innerHTML=_V),bL=a(ba),h(ii.$$.fragment,ba),ba.forEach(d),ML=a(Qr),W=l(Qr,"DIV",{class:!0});var jo=v(W);h(sc.$$.fragment,jo),TL=a(jo),A_=l(jo,"P",{"data-svelte-h":!0}),f(A_)!=="svelte-196fe96"&&(A_.textContent=bV),FL=a(jo),Z_=l(jo,"P",{"data-svelte-h":!0}),f(Z_)!=="svelte-1enn9p2"&&(Z_.innerHTML=MV),CL=a(jo),R_=l(jo,"UL",{"data-svelte-h":!0}),f(R_)!=="svelte-1sbqszk"&&(R_.innerHTML=TV),yL=a(jo),G_=l(jo,"P",{"data-svelte-h":!0}),f(G_)!=="svelte-tebhz6"&&(G_.innerHTML=FV),vL=a(jo),h(li.$$.fragment,jo),jo.forEach(d),Qr.forEach(d),Zv=a(o),h(ic.$$.fragment,o),Rv=a(o),ke=l(o,"DIV",{class:!0});var Hr=v(ke);h(lc.$$.fragment,Hr),wL=a(Hr),P_=l(Hr,"P",{"data-svelte-h":!0}),f(P_)!=="svelte-1wqdaw8"&&(P_.innerHTML=CV),xL=a(Hr),S_=l(Hr,"P",{"data-svelte-h":!0}),f(S_)!=="svelte-1n2cphd"&&(S_.innerHTML=yV),LL=a(Hr),Qt=l(Hr,"DIV",{class:!0});var Ma=v(Qt);h(dc.$$.fragment,Ma),kL=a(Ma),W_=l(Ma,"P",{"data-svelte-h":!0}),f(W_)!=="svelte-1d0i3aa"&&(W_.textContent=vV),$L=a(Ma),J_=l(Ma,"P",{"data-svelte-h":!0}),f(J_)!=="svelte-1s2prmj"&&(J_.innerHTML=wV),BL=a(Ma),h(di.$$.fragment,Ma),Ma.forEach(d),jL=a(Hr),zo=l(Hr,"DIV",{class:!0});var Yr=v(zo);h(mc.$$.fragment,Yr),AL=a(Yr),V_=l(Yr,"P",{"data-svelte-h":!0}),f(V_)!=="svelte-196fe96"&&(V_.textContent=xV),ZL=a(Yr),E_=l(Yr,"P",{"data-svelte-h":!0}),f(E_)!=="svelte-1enn9p2"&&(E_.innerHTML=LV),RL=a(Yr),I_=l(Yr,"UL",{"data-svelte-h":!0}),f(I_)!=="svelte-1jqiof2"&&(I_.innerHTML=kV),GL=a(Yr),h(mi.$$.fragment,Yr),Yr.forEach(d),Hr.forEach(d),Gv=a(o),h(cc.$$.fragment,o),Pv=a(o),$e=l(o,"DIV",{class:!0});var zr=v($e);h(fc.$$.fragment,zr),PL=a(zr),U_=l(zr,"P",{"data-svelte-h":!0}),f(U_)!=="svelte-1wqdaw8"&&(U_.innerHTML=$V),SL=a(zr),N_=l(zr,"P",{"data-svelte-h":!0}),f(N_)!=="svelte-1n2cphd"&&(N_.innerHTML=BV),WL=a(zr),Ht=l(zr,"DIV",{class:!0});var Ta=v(Ht);h(gc.$$.fragment,Ta),JL=a(Ta),q_=l(Ta,"P",{"data-svelte-h":!0}),f(q_)!=="svelte-1d0i3aa"&&(q_.textContent=jV),VL=a(Ta),X_=l(Ta,"P",{"data-svelte-h":!0}),f(X_)!=="svelte-1s2prmj"&&(X_.innerHTML=AV),EL=a(Ta),h(ci.$$.fragment,Ta),Ta.forEach(d),IL=a(zr),Oo=l(zr,"DIV",{class:!0});var Or=v(Oo);h(pc.$$.fragment,Or),UL=a(Or),D_=l(Or,"P",{"data-svelte-h":!0}),f(D_)!=="svelte-196fe96"&&(D_.textContent=ZV),NL=a(Or),Q_=l(Or,"P",{"data-svelte-h":!0}),f(Q_)!=="svelte-1enn9p2"&&(Q_.innerHTML=RV),qL=a(Or),H_=l(Or,"UL",{"data-svelte-h":!0}),f(H_)!=="svelte-1cxph3k"&&(H_.innerHTML=GV),XL=a(Or),h(fi.$$.fragment,Or),Or.forEach(d),zr.forEach(d),Sv=a(o),h(hc.$$.fragment,o),Wv=a(o),Be=l(o,"DIV",{class:!0});var Kr=v(Be);h(uc.$$.fragment,Kr),DL=a(Kr),Y_=l(Kr,"P",{"data-svelte-h":!0}),f(Y_)!=="svelte-jiubdl"&&(Y_.innerHTML=PV),QL=a(Kr),z_=l(Kr,"P",{"data-svelte-h":!0}),f(z_)!=="svelte-1n2cphd"&&(z_.innerHTML=SV),HL=a(Kr),Yt=l(Kr,"DIV",{class:!0});var Fa=v(Yt);h(_c.$$.fragment,Fa),YL=a(Fa),O_=l(Fa,"P",{"data-svelte-h":!0}),f(O_)!=="svelte-6xp6fj"&&(O_.textContent=WV),zL=a(Fa),K_=l(Fa,"P",{"data-svelte-h":!0}),f(K_)!=="svelte-1s2prmj"&&(K_.innerHTML=JV),OL=a(Fa),h(gi.$$.fragment,Fa),Fa.forEach(d),KL=a(Kr),J=l(Kr,"DIV",{class:!0});var Ao=v(J);h(bc.$$.fragment,Ao),ek=a(Ao),e1=l(Ao,"P",{"data-svelte-h":!0}),f(e1)!=="svelte-10e8w47"&&(e1.textContent=VV),ok=a(Ao),o1=l(Ao,"P",{"data-svelte-h":!0}),f(o1)!=="svelte-1enn9p2"&&(o1.innerHTML=EV),rk=a(Ao),r1=l(Ao,"UL",{"data-svelte-h":!0}),f(r1)!=="svelte-akg4ju"&&(r1.innerHTML=IV),tk=a(Ao),t1=l(Ao,"P",{"data-svelte-h":!0}),f(t1)!=="svelte-tebhz6"&&(t1.innerHTML=UV),nk=a(Ao),h(pi.$$.fragment,Ao),Ao.forEach(d),Kr.forEach(d),Jv=a(o),h(Mc.$$.fragment,o),Vv=a(o),je=l(o,"DIV",{class:!0});var et=v(je);h(Tc.$$.fragment,et),ak=a(et),n1=l(et,"P",{"data-svelte-h":!0}),f(n1)!=="svelte-jiubdl"&&(n1.innerHTML=NV),sk=a(et),a1=l(et,"P",{"data-svelte-h":!0}),f(a1)!=="svelte-1n2cphd"&&(a1.innerHTML=qV),ik=a(et),zt=l(et,"DIV",{class:!0});var Ca=v(zt);h(Fc.$$.fragment,Ca),lk=a(Ca),s1=l(Ca,"P",{"data-svelte-h":!0}),f(s1)!=="svelte-6xp6fj"&&(s1.textContent=XV),dk=a(Ca),i1=l(Ca,"P",{"data-svelte-h":!0}),f(i1)!=="svelte-1s2prmj"&&(i1.innerHTML=DV),mk=a(Ca),h(hi.$$.fragment,Ca),Ca.forEach(d),ck=a(et),Ko=l(et,"DIV",{class:!0});var ot=v(Ko);h(Cc.$$.fragment,ot),fk=a(ot),l1=l(ot,"P",{"data-svelte-h":!0}),f(l1)!=="svelte-10e8w47"&&(l1.textContent=QV),gk=a(ot),d1=l(ot,"P",{"data-svelte-h":!0}),f(d1)!=="svelte-1enn9p2"&&(d1.innerHTML=HV),pk=a(ot),m1=l(ot,"UL",{"data-svelte-h":!0}),f(m1)!=="svelte-1wr9hlr"&&(m1.innerHTML=YV),hk=a(ot),h(ui.$$.fragment,ot),ot.forEach(d),et.forEach(d),Ev=a(o),h(yc.$$.fragment,o),Iv=a(o),Ae=l(o,"DIV",{class:!0});var rt=v(Ae);h(vc.$$.fragment,rt),uk=a(rt),c1=l(rt,"P",{"data-svelte-h":!0}),f(c1)!=="svelte-jiubdl"&&(c1.innerHTML=zV),_k=a(rt),f1=l(rt,"P",{"data-svelte-h":!0}),f(f1)!=="svelte-1n2cphd"&&(f1.innerHTML=OV),bk=a(rt),Ot=l(rt,"DIV",{class:!0});var ya=v(Ot);h(wc.$$.fragment,ya),Mk=a(ya),g1=l(ya,"P",{"data-svelte-h":!0}),f(g1)!=="svelte-6xp6fj"&&(g1.textContent=KV),Tk=a(ya),p1=l(ya,"P",{"data-svelte-h":!0}),f(p1)!=="svelte-1s2prmj"&&(p1.innerHTML=e0),Fk=a(ya),h(_i.$$.fragment,ya),ya.forEach(d),Ck=a(rt),er=l(rt,"DIV",{class:!0});var tt=v(er);h(xc.$$.fragment,tt),yk=a(tt),h1=l(tt,"P",{"data-svelte-h":!0}),f(h1)!=="svelte-10e8w47"&&(h1.textContent=o0),vk=a(tt),u1=l(tt,"P",{"data-svelte-h":!0}),f(u1)!=="svelte-1enn9p2"&&(u1.innerHTML=r0),wk=a(tt),_1=l(tt,"UL",{"data-svelte-h":!0}),f(_1)!=="svelte-16zih92"&&(_1.innerHTML=t0),xk=a(tt),h(bi.$$.fragment,tt),tt.forEach(d),rt.forEach(d),Uv=a(o),h(Lc.$$.fragment,o),Nv=a(o),Ze=l(o,"DIV",{class:!0});var nt=v(Ze);h(kc.$$.fragment,nt),Lk=a(nt),b1=l(nt,"P",{"data-svelte-h":!0}),f(b1)!=="svelte-3d7c3p"&&(b1.innerHTML=n0),kk=a(nt),M1=l(nt,"P",{"data-svelte-h":!0}),f(M1)!=="svelte-1n2cphd"&&(M1.innerHTML=a0),$k=a(nt),Kt=l(nt,"DIV",{class:!0});var va=v(Kt);h($c.$$.fragment,va),Bk=a(va),T1=l(va,"P",{"data-svelte-h":!0}),f(T1)!=="svelte-snlhsn"&&(T1.textContent=s0),jk=a(va),F1=l(va,"P",{"data-svelte-h":!0}),f(F1)!=="svelte-1s2prmj"&&(F1.innerHTML=i0),Ak=a(va),h(Mi.$$.fragment,va),va.forEach(d),Zk=a(nt),V=l(nt,"DIV",{class:!0});var Zo=v(V);h(Bc.$$.fragment,Zo),Rk=a(Zo),C1=l(Zo,"P",{"data-svelte-h":!0}),f(C1)!=="svelte-jtqyi7"&&(C1.textContent=l0),Gk=a(Zo),y1=l(Zo,"P",{"data-svelte-h":!0}),f(y1)!=="svelte-1enn9p2"&&(y1.innerHTML=d0),Pk=a(Zo),v1=l(Zo,"UL",{"data-svelte-h":!0}),f(v1)!=="svelte-hx4nss"&&(v1.innerHTML=m0),Sk=a(Zo),w1=l(Zo,"P",{"data-svelte-h":!0}),f(w1)!=="svelte-tebhz6"&&(w1.innerHTML=c0),Wk=a(Zo),h(Ti.$$.fragment,Zo),Zo.forEach(d),nt.forEach(d),qv=a(o),h(jc.$$.fragment,o),Xv=a(o),Re=l(o,"DIV",{class:!0});var at=v(Re);h(Ac.$$.fragment,at),Jk=a(at),x1=l(at,"P",{"data-svelte-h":!0}),f(x1)!=="svelte-3d7c3p"&&(x1.innerHTML=f0),Vk=a(at),L1=l(at,"P",{"data-svelte-h":!0}),f(L1)!=="svelte-1n2cphd"&&(L1.innerHTML=g0),Ek=a(at),en=l(at,"DIV",{class:!0});var wa=v(en);h(Zc.$$.fragment,wa),Ik=a(wa),k1=l(wa,"P",{"data-svelte-h":!0}),f(k1)!=="svelte-snlhsn"&&(k1.textContent=p0),Uk=a(wa),$1=l(wa,"P",{"data-svelte-h":!0}),f($1)!=="svelte-1s2prmj"&&($1.innerHTML=h0),Nk=a(wa),h(Fi.$$.fragment,wa),wa.forEach(d),qk=a(at),or=l(at,"DIV",{class:!0});var st=v(or);h(Rc.$$.fragment,st),Xk=a(st),B1=l(st,"P",{"data-svelte-h":!0}),f(B1)!=="svelte-jtqyi7"&&(B1.textContent=u0),Dk=a(st),j1=l(st,"P",{"data-svelte-h":!0}),f(j1)!=="svelte-1enn9p2"&&(j1.innerHTML=_0),Qk=a(st),A1=l(st,"UL",{"data-svelte-h":!0}),f(A1)!=="svelte-1duyzsy"&&(A1.innerHTML=b0),Hk=a(st),h(Ci.$$.fragment,st),st.forEach(d),at.forEach(d),Dv=a(o),h(Gc.$$.fragment,o),Qv=a(o),Ge=l(o,"DIV",{class:!0});var it=v(Ge);h(Pc.$$.fragment,it),Yk=a(it),Z1=l(it,"P",{"data-svelte-h":!0}),f(Z1)!=="svelte-3d7c3p"&&(Z1.innerHTML=M0),zk=a(it),R1=l(it,"P",{"data-svelte-h":!0}),f(R1)!=="svelte-1n2cphd"&&(R1.innerHTML=T0),Ok=a(it),on=l(it,"DIV",{class:!0});var xa=v(on);h(Sc.$$.fragment,xa),Kk=a(xa),G1=l(xa,"P",{"data-svelte-h":!0}),f(G1)!=="svelte-snlhsn"&&(G1.textContent=F0),e$=a(xa),P1=l(xa,"P",{"data-svelte-h":!0}),f(P1)!=="svelte-1s2prmj"&&(P1.innerHTML=C0),o$=a(xa),h(yi.$$.fragment,xa),xa.forEach(d),r$=a(it),rr=l(it,"DIV",{class:!0});var lt=v(rr);h(Wc.$$.fragment,lt),t$=a(lt),S1=l(lt,"P",{"data-svelte-h":!0}),f(S1)!=="svelte-jtqyi7"&&(S1.textContent=y0),n$=a(lt),W1=l(lt,"P",{"data-svelte-h":!0}),f(W1)!=="svelte-1enn9p2"&&(W1.innerHTML=v0),a$=a(lt),J1=l(lt,"UL",{"data-svelte-h":!0}),f(J1)!=="svelte-1nybht1"&&(J1.innerHTML=w0),s$=a(lt),h(vi.$$.fragment,lt),lt.forEach(d),it.forEach(d),Hv=a(o),h(Jc.$$.fragment,o),Yv=a(o),Pe=l(o,"DIV",{class:!0});var dt=v(Pe);h(Vc.$$.fragment,dt),i$=a(dt),V1=l(dt,"P",{"data-svelte-h":!0}),f(V1)!=="svelte-t89tvj"&&(V1.innerHTML=x0),l$=a(dt),E1=l(dt,"P",{"data-svelte-h":!0}),f(E1)!=="svelte-1n2cphd"&&(E1.innerHTML=L0),d$=a(dt),rn=l(dt,"DIV",{class:!0});var La=v(rn);h(Ec.$$.fragment,La),m$=a(La),I1=l(La,"P",{"data-svelte-h":!0}),f(I1)!=="svelte-1yu679h"&&(I1.textContent=k0),c$=a(La),U1=l(La,"P",{"data-svelte-h":!0}),f(U1)!=="svelte-1s2prmj"&&(U1.innerHTML=$0),f$=a(La),h(wi.$$.fragment,La),La.forEach(d),g$=a(dt),E=l(dt,"DIV",{class:!0});var Ro=v(E);h(Ic.$$.fragment,Ro),p$=a(Ro),N1=l(Ro,"P",{"data-svelte-h":!0}),f(N1)!=="svelte-hx8awn"&&(N1.textContent=B0),h$=a(Ro),q1=l(Ro,"P",{"data-svelte-h":!0}),f(q1)!=="svelte-1enn9p2"&&(q1.innerHTML=j0),u$=a(Ro),X1=l(Ro,"UL",{"data-svelte-h":!0}),f(X1)!=="svelte-dh7kgz"&&(X1.innerHTML=A0),_$=a(Ro),D1=l(Ro,"P",{"data-svelte-h":!0}),f(D1)!=="svelte-tebhz6"&&(D1.innerHTML=Z0),b$=a(Ro),h(xi.$$.fragment,Ro),Ro.forEach(d),dt.forEach(d),zv=a(o),h(Uc.$$.fragment,o),Ov=a(o),Se=l(o,"DIV",{class:!0});var mt=v(Se);h(Nc.$$.fragment,mt),M$=a(mt),Q1=l(mt,"P",{"data-svelte-h":!0}),f(Q1)!=="svelte-t89tvj"&&(Q1.innerHTML=R0),T$=a(mt),H1=l(mt,"P",{"data-svelte-h":!0}),f(H1)!=="svelte-1n2cphd"&&(H1.innerHTML=G0),F$=a(mt),tn=l(mt,"DIV",{class:!0});var ka=v(tn);h(qc.$$.fragment,ka),C$=a(ka),Y1=l(ka,"P",{"data-svelte-h":!0}),f(Y1)!=="svelte-1yu679h"&&(Y1.textContent=P0),y$=a(ka),z1=l(ka,"P",{"data-svelte-h":!0}),f(z1)!=="svelte-1s2prmj"&&(z1.innerHTML=S0),v$=a(ka),h(Li.$$.fragment,ka),ka.forEach(d),w$=a(mt),tr=l(mt,"DIV",{class:!0});var ct=v(tr);h(Xc.$$.fragment,ct),x$=a(ct),O1=l(ct,"P",{"data-svelte-h":!0}),f(O1)!=="svelte-hx8awn"&&(O1.textContent=W0),L$=a(ct),K1=l(ct,"P",{"data-svelte-h":!0}),f(K1)!=="svelte-1enn9p2"&&(K1.innerHTML=J0),k$=a(ct),eb=l(ct,"UL",{"data-svelte-h":!0}),f(eb)!=="svelte-1071l5q"&&(eb.innerHTML=V0),$$=a(ct),h(ki.$$.fragment,ct),ct.forEach(d),mt.forEach(d),Kv=a(o),h(Dc.$$.fragment,o),e2=a(o),We=l(o,"DIV",{class:!0});var ft=v(We);h(Qc.$$.fragment,ft),B$=a(ft),ob=l(ft,"P",{"data-svelte-h":!0}),f(ob)!=="svelte-t89tvj"&&(ob.innerHTML=E0),j$=a(ft),rb=l(ft,"P",{"data-svelte-h":!0}),f(rb)!=="svelte-1n2cphd"&&(rb.innerHTML=I0),A$=a(ft),nn=l(ft,"DIV",{class:!0});var $a=v(nn);h(Hc.$$.fragment,$a),Z$=a($a),tb=l($a,"P",{"data-svelte-h":!0}),f(tb)!=="svelte-1yu679h"&&(tb.textContent=U0),R$=a($a),nb=l($a,"P",{"data-svelte-h":!0}),f(nb)!=="svelte-1s2prmj"&&(nb.innerHTML=N0),G$=a($a),h($i.$$.fragment,$a),$a.forEach(d),P$=a(ft),nr=l(ft,"DIV",{class:!0});var gt=v(nr);h(Yc.$$.fragment,gt),S$=a(gt),ab=l(gt,"P",{"data-svelte-h":!0}),f(ab)!=="svelte-hx8awn"&&(ab.textContent=q0),W$=a(gt),sb=l(gt,"P",{"data-svelte-h":!0}),f(sb)!=="svelte-1enn9p2"&&(sb.innerHTML=X0),J$=a(gt),ib=l(gt,"UL",{"data-svelte-h":!0}),f(ib)!=="svelte-aaijfw"&&(ib.innerHTML=D0),V$=a(gt),h(Bi.$$.fragment,gt),gt.forEach(d),ft.forEach(d),o2=a(o),h(zc.$$.fragment,o),r2=a(o),Je=l(o,"DIV",{class:!0});var pt=v(Je);h(Oc.$$.fragment,pt),E$=a(pt),lb=l(pt,"P",{"data-svelte-h":!0}),f(lb)!=="svelte-1hq2dbf"&&(lb.innerHTML=Q0),I$=a(pt),db=l(pt,"P",{"data-svelte-h":!0}),f(db)!=="svelte-1n2cphd"&&(db.innerHTML=H0),U$=a(pt),an=l(pt,"DIV",{class:!0});var Ba=v(an);h(Kc.$$.fragment,Ba),N$=a(Ba),mb=l(Ba,"P",{"data-svelte-h":!0}),f(mb)!=="svelte-81lf85"&&(mb.textContent=Y0),q$=a(Ba),cb=l(Ba,"P",{"data-svelte-h":!0}),f(cb)!=="svelte-1s2prmj"&&(cb.innerHTML=z0),X$=a(Ba),h(ji.$$.fragment,Ba),Ba.forEach(d),D$=a(pt),I=l(pt,"DIV",{class:!0});var Go=v(I);h(ef.$$.fragment,Go),Q$=a(Go),fb=l(Go,"P",{"data-svelte-h":!0}),f(fb)!=="svelte-16vvmzn"&&(fb.textContent=O0),H$=a(Go),gb=l(Go,"P",{"data-svelte-h":!0}),f(gb)!=="svelte-1enn9p2"&&(gb.innerHTML=K0),Y$=a(Go),pb=l(Go,"UL",{"data-svelte-h":!0}),f(pb)!=="svelte-dww090"&&(pb.innerHTML=eE),z$=a(Go),hb=l(Go,"P",{"data-svelte-h":!0}),f(hb)!=="svelte-tebhz6"&&(hb.innerHTML=oE),O$=a(Go),h(Ai.$$.fragment,Go),Go.forEach(d),pt.forEach(d),t2=a(o),h(of.$$.fragment,o),n2=a(o),Ve=l(o,"DIV",{class:!0});var ht=v(Ve);h(rf.$$.fragment,ht),K$=a(ht),ub=l(ht,"P",{"data-svelte-h":!0}),f(ub)!=="svelte-1hq2dbf"&&(ub.innerHTML=rE),eB=a(ht),_b=l(ht,"P",{"data-svelte-h":!0}),f(_b)!=="svelte-1n2cphd"&&(_b.innerHTML=tE),oB=a(ht),sn=l(ht,"DIV",{class:!0});var ja=v(sn);h(tf.$$.fragment,ja),rB=a(ja),bb=l(ja,"P",{"data-svelte-h":!0}),f(bb)!=="svelte-81lf85"&&(bb.textContent=nE),tB=a(ja),Mb=l(ja,"P",{"data-svelte-h":!0}),f(Mb)!=="svelte-1s2prmj"&&(Mb.innerHTML=aE),nB=a(ja),h(Zi.$$.fragment,ja),ja.forEach(d),aB=a(ht),ar=l(ht,"DIV",{class:!0});var ut=v(ar);h(nf.$$.fragment,ut),sB=a(ut),Tb=l(ut,"P",{"data-svelte-h":!0}),f(Tb)!=="svelte-16vvmzn"&&(Tb.textContent=sE),iB=a(ut),Fb=l(ut,"P",{"data-svelte-h":!0}),f(Fb)!=="svelte-1enn9p2"&&(Fb.innerHTML=iE),lB=a(ut),Cb=l(ut,"UL",{"data-svelte-h":!0}),f(Cb)!=="svelte-mvoisv"&&(Cb.innerHTML=lE),dB=a(ut),h(Ri.$$.fragment,ut),ut.forEach(d),ht.forEach(d),a2=a(o),h(af.$$.fragment,o),s2=a(o),Ee=l(o,"DIV",{class:!0});var _t=v(Ee);h(sf.$$.fragment,_t),mB=a(_t),yb=l(_t,"P",{"data-svelte-h":!0}),f(yb)!=="svelte-1hq2dbf"&&(yb.innerHTML=dE),cB=a(_t),vb=l(_t,"P",{"data-svelte-h":!0}),f(vb)!=="svelte-1n2cphd"&&(vb.innerHTML=mE),fB=a(_t),ln=l(_t,"DIV",{class:!0});var Aa=v(ln);h(lf.$$.fragment,Aa),gB=a(Aa),wb=l(Aa,"P",{"data-svelte-h":!0}),f(wb)!=="svelte-81lf85"&&(wb.textContent=cE),pB=a(Aa),xb=l(Aa,"P",{"data-svelte-h":!0}),f(xb)!=="svelte-1s2prmj"&&(xb.innerHTML=fE),hB=a(Aa),h(Gi.$$.fragment,Aa),Aa.forEach(d),uB=a(_t),sr=l(_t,"DIV",{class:!0});var bt=v(sr);h(df.$$.fragment,bt),_B=a(bt),Lb=l(bt,"P",{"data-svelte-h":!0}),f(Lb)!=="svelte-16vvmzn"&&(Lb.textContent=gE),bB=a(bt),kb=l(bt,"P",{"data-svelte-h":!0}),f(kb)!=="svelte-1enn9p2"&&(kb.innerHTML=pE),MB=a(bt),$b=l(bt,"UL",{"data-svelte-h":!0}),f($b)!=="svelte-130p9tj"&&($b.innerHTML=hE),TB=a(bt),h(Pi.$$.fragment,bt),bt.forEach(d),_t.forEach(d),i2=a(o),h(mf.$$.fragment,o),l2=a(o),Ie=l(o,"DIV",{class:!0});var Mt=v(Ie);h(cf.$$.fragment,Mt),FB=a(Mt),Bb=l(Mt,"P",{"data-svelte-h":!0}),f(Bb)!=="svelte-1f9ymy2"&&(Bb.innerHTML=uE),CB=a(Mt),jb=l(Mt,"P",{"data-svelte-h":!0}),f(jb)!=="svelte-1n2cphd"&&(jb.innerHTML=_E),yB=a(Mt),dn=l(Mt,"DIV",{class:!0});var Za=v(dn);h(ff.$$.fragment,Za),vB=a(Za),Ab=l(Za,"P",{"data-svelte-h":!0}),f(Ab)!=="svelte-n52yuc"&&(Ab.textContent=bE),wB=a(Za),Zb=l(Za,"P",{"data-svelte-h":!0}),f(Zb)!=="svelte-1s2prmj"&&(Zb.innerHTML=ME),xB=a(Za),h(Si.$$.fragment,Za),Za.forEach(d),LB=a(Mt),U=l(Mt,"DIV",{class:!0});var Po=v(U);h(gf.$$.fragment,Po),kB=a(Po),Rb=l(Po,"P",{"data-svelte-h":!0}),f(Rb)!=="svelte-1h7oepk"&&(Rb.textContent=TE),$B=a(Po),Gb=l(Po,"P",{"data-svelte-h":!0}),f(Gb)!=="svelte-1enn9p2"&&(Gb.innerHTML=FE),BB=a(Po),Pb=l(Po,"UL",{"data-svelte-h":!0}),f(Pb)!=="svelte-snax4o"&&(Pb.innerHTML=CE),jB=a(Po),Sb=l(Po,"P",{"data-svelte-h":!0}),f(Sb)!=="svelte-tebhz6"&&(Sb.innerHTML=yE),AB=a(Po),h(Wi.$$.fragment,Po),Po.forEach(d),Mt.forEach(d),d2=a(o),h(pf.$$.fragment,o),m2=a(o),Ue=l(o,"DIV",{class:!0});var Tt=v(Ue);h(hf.$$.fragment,Tt),ZB=a(Tt),Wb=l(Tt,"P",{"data-svelte-h":!0}),f(Wb)!=="svelte-1f9ymy2"&&(Wb.innerHTML=vE),RB=a(Tt),Jb=l(Tt,"P",{"data-svelte-h":!0}),f(Jb)!=="svelte-1n2cphd"&&(Jb.innerHTML=wE),GB=a(Tt),mn=l(Tt,"DIV",{class:!0});var Ra=v(mn);h(uf.$$.fragment,Ra),PB=a(Ra),Vb=l(Ra,"P",{"data-svelte-h":!0}),f(Vb)!=="svelte-n52yuc"&&(Vb.textContent=xE),SB=a(Ra),Eb=l(Ra,"P",{"data-svelte-h":!0}),f(Eb)!=="svelte-1s2prmj"&&(Eb.innerHTML=LE),WB=a(Ra),h(Ji.$$.fragment,Ra),Ra.forEach(d),JB=a(Tt),ir=l(Tt,"DIV",{class:!0});var Ft=v(ir);h(_f.$$.fragment,Ft),VB=a(Ft),Ib=l(Ft,"P",{"data-svelte-h":!0}),f(Ib)!=="svelte-1h7oepk"&&(Ib.textContent=kE),EB=a(Ft),Ub=l(Ft,"P",{"data-svelte-h":!0}),f(Ub)!=="svelte-1enn9p2"&&(Ub.innerHTML=$E),IB=a(Ft),Nb=l(Ft,"UL",{"data-svelte-h":!0}),f(Nb)!=="svelte-xaw1sy"&&(Nb.innerHTML=BE),UB=a(Ft),h(Vi.$$.fragment,Ft),Ft.forEach(d),Tt.forEach(d),c2=a(o),h(bf.$$.fragment,o),f2=a(o),Ne=l(o,"DIV",{class:!0});var Ct=v(Ne);h(Mf.$$.fragment,Ct),NB=a(Ct),qb=l(Ct,"P",{"data-svelte-h":!0}),f(qb)!=="svelte-1f9ymy2"&&(qb.innerHTML=jE),qB=a(Ct),Xb=l(Ct,"P",{"data-svelte-h":!0}),f(Xb)!=="svelte-1n2cphd"&&(Xb.innerHTML=AE),XB=a(Ct),cn=l(Ct,"DIV",{class:!0});var Ga=v(cn);h(Tf.$$.fragment,Ga),DB=a(Ga),Db=l(Ga,"P",{"data-svelte-h":!0}),f(Db)!=="svelte-n52yuc"&&(Db.textContent=ZE),QB=a(Ga),Qb=l(Ga,"P",{"data-svelte-h":!0}),f(Qb)!=="svelte-1s2prmj"&&(Qb.innerHTML=RE),HB=a(Ga),h(Ei.$$.fragment,Ga),Ga.forEach(d),YB=a(Ct),lr=l(Ct,"DIV",{class:!0});var yt=v(lr);h(Ff.$$.fragment,yt),zB=a(yt),Hb=l(yt,"P",{"data-svelte-h":!0}),f(Hb)!=="svelte-1h7oepk"&&(Hb.textContent=GE),OB=a(yt),Yb=l(yt,"P",{"data-svelte-h":!0}),f(Yb)!=="svelte-1enn9p2"&&(Yb.innerHTML=PE),KB=a(yt),zb=l(yt,"UL",{"data-svelte-h":!0}),f(zb)!=="svelte-19uysi"&&(zb.innerHTML=SE),ej=a(yt),h(Ii.$$.fragment,yt),yt.forEach(d),Ct.forEach(d),g2=a(o),h(Cf.$$.fragment,o),p2=a(o),yf=l(o,"DIV",{class:!0});var $y=v(yf);h(vf.$$.fragment,$y),$y.forEach(d),h2=a(o),h(wf.$$.fragment,o),u2=a(o),xf=l(o,"DIV",{class:!0});var By=v(xf);h(Lf.$$.fragment,By),By.forEach(d),_2=a(o),h(kf.$$.fragment,o),b2=a(o),$f=l(o,"P",{"data-svelte-h":!0}),f($f)!=="svelte-1a2lih2"&&($f.textContent=WE),M2=a(o),h(Bf.$$.fragment,o),T2=a(o),qe=l(o,"DIV",{class:!0});var vt=v(qe);h(jf.$$.fragment,vt),oj=a(vt),Ob=l(vt,"P",{"data-svelte-h":!0}),f(Ob)!=="svelte-1t7y12w"&&(Ob.innerHTML=JE),rj=a(vt),Kb=l(vt,"P",{"data-svelte-h":!0}),f(Kb)!=="svelte-1n2cphd"&&(Kb.innerHTML=VE),tj=a(vt),fn=l(vt,"DIV",{class:!0});var Pa=v(fn);h(Af.$$.fragment,Pa),nj=a(Pa),eM=l(Pa,"P",{"data-svelte-h":!0}),f(eM)!=="svelte-1o4g80a"&&(eM.textContent=EE),aj=a(Pa),oM=l(Pa,"P",{"data-svelte-h":!0}),f(oM)!=="svelte-1s2prmj"&&(oM.innerHTML=IE),sj=a(Pa),h(Ui.$$.fragment,Pa),Pa.forEach(d),ij=a(vt),N=l(vt,"DIV",{class:!0});var So=v(N);h(Zf.$$.fragment,So),lj=a(So),rM=l(So,"P",{"data-svelte-h":!0}),f(rM)!=="svelte-100to3e"&&(rM.textContent=UE),dj=a(So),tM=l(So,"P",{"data-svelte-h":!0}),f(tM)!=="svelte-1enn9p2"&&(tM.innerHTML=NE),mj=a(So),nM=l(So,"UL",{"data-svelte-h":!0}),f(nM)!=="svelte-okdct"&&(nM.innerHTML=qE),cj=a(So),aM=l(So,"P",{"data-svelte-h":!0}),f(aM)!=="svelte-tebhz6"&&(aM.innerHTML=XE),fj=a(So),h(Ni.$$.fragment,So),So.forEach(d),vt.forEach(d),F2=a(o),h(Rf.$$.fragment,o),C2=a(o),Xe=l(o,"DIV",{class:!0});var wt=v(Xe);h(Gf.$$.fragment,wt),gj=a(wt),sM=l(wt,"P",{"data-svelte-h":!0}),f(sM)!=="svelte-k4ptab"&&(sM.innerHTML=DE),pj=a(wt),iM=l(wt,"P",{"data-svelte-h":!0}),f(iM)!=="svelte-1n2cphd"&&(iM.innerHTML=QE),hj=a(wt),gn=l(wt,"DIV",{class:!0});var Sa=v(gn);h(Pf.$$.fragment,Sa),uj=a(Sa),lM=l(Sa,"P",{"data-svelte-h":!0}),f(lM)!=="svelte-3mnohx"&&(lM.textContent=HE),_j=a(Sa),dM=l(Sa,"P",{"data-svelte-h":!0}),f(dM)!=="svelte-1s2prmj"&&(dM.innerHTML=YE),bj=a(Sa),h(qi.$$.fragment,Sa),Sa.forEach(d),Mj=a(wt),q=l(wt,"DIV",{class:!0});var Wo=v(q);h(Sf.$$.fragment,Wo),Tj=a(Wo),mM=l(Wo,"P",{"data-svelte-h":!0}),f(mM)!=="svelte-1ezjudv"&&(mM.textContent=zE),Fj=a(Wo),cM=l(Wo,"P",{"data-svelte-h":!0}),f(cM)!=="svelte-1enn9p2"&&(cM.innerHTML=OE),Cj=a(Wo),fM=l(Wo,"UL",{"data-svelte-h":!0}),f(fM)!=="svelte-cc6x9g"&&(fM.innerHTML=KE),yj=a(Wo),gM=l(Wo,"P",{"data-svelte-h":!0}),f(gM)!=="svelte-tebhz6"&&(gM.innerHTML=eI),vj=a(Wo),h(Xi.$$.fragment,Wo),Wo.forEach(d),wt.forEach(d),y2=a(o),h(Wf.$$.fragment,o),v2=a(o),De=l(o,"DIV",{class:!0});var xt=v(De);h(Jf.$$.fragment,xt),wj=a(xt),pM=l(xt,"P",{"data-svelte-h":!0}),f(pM)!=="svelte-k4ptab"&&(pM.innerHTML=oI),xj=a(xt),hM=l(xt,"P",{"data-svelte-h":!0}),f(hM)!=="svelte-1n2cphd"&&(hM.innerHTML=rI),Lj=a(xt),pn=l(xt,"DIV",{class:!0});var Wa=v(pn);h(Vf.$$.fragment,Wa),kj=a(Wa),uM=l(Wa,"P",{"data-svelte-h":!0}),f(uM)!=="svelte-3mnohx"&&(uM.textContent=tI),$j=a(Wa),_M=l(Wa,"P",{"data-svelte-h":!0}),f(_M)!=="svelte-1s2prmj"&&(_M.innerHTML=nI),Bj=a(Wa),h(Di.$$.fragment,Wa),Wa.forEach(d),jj=a(xt),dr=l(xt,"DIV",{class:!0});var Lt=v(dr);h(Ef.$$.fragment,Lt),Aj=a(Lt),bM=l(Lt,"P",{"data-svelte-h":!0}),f(bM)!=="svelte-1ezjudv"&&(bM.textContent=aI),Zj=a(Lt),MM=l(Lt,"P",{"data-svelte-h":!0}),f(MM)!=="svelte-1enn9p2"&&(MM.innerHTML=sI),Rj=a(Lt),TM=l(Lt,"UL",{"data-svelte-h":!0}),f(TM)!=="svelte-d57sft"&&(TM.innerHTML=iI),Gj=a(Lt),h(Qi.$$.fragment,Lt),Lt.forEach(d),xt.forEach(d),w2=a(o),h(If.$$.fragment,o),x2=a(o),Qe=l(o,"DIV",{class:!0});var kt=v(Qe);h(Uf.$$.fragment,kt),Pj=a(kt),FM=l(kt,"P",{"data-svelte-h":!0}),f(FM)!=="svelte-k4ptab"&&(FM.innerHTML=lI),Sj=a(kt),CM=l(kt,"P",{"data-svelte-h":!0}),f(CM)!=="svelte-1n2cphd"&&(CM.innerHTML=dI),Wj=a(kt),hn=l(kt,"DIV",{class:!0});var Ja=v(hn);h(Nf.$$.fragment,Ja),Jj=a(Ja),yM=l(Ja,"P",{"data-svelte-h":!0}),f(yM)!=="svelte-3mnohx"&&(yM.textContent=mI),Vj=a(Ja),vM=l(Ja,"P",{"data-svelte-h":!0}),f(vM)!=="svelte-1s2prmj"&&(vM.innerHTML=cI),Ej=a(Ja),h(Hi.$$.fragment,Ja),Ja.forEach(d),Ij=a(kt),mr=l(kt,"DIV",{class:!0});var $t=v(mr);h(qf.$$.fragment,$t),Uj=a($t),wM=l($t,"P",{"data-svelte-h":!0}),f(wM)!=="svelte-1ezjudv"&&(wM.textContent=fI),Nj=a($t),xM=l($t,"P",{"data-svelte-h":!0}),f(xM)!=="svelte-1enn9p2"&&(xM.innerHTML=gI),qj=a($t),LM=l($t,"UL",{"data-svelte-h":!0}),f(LM)!=="svelte-8903en"&&(LM.innerHTML=pI),Xj=a($t),h(Yi.$$.fragment,$t),$t.forEach(d),kt.forEach(d),L2=a(o),h(Xf.$$.fragment,o),k2=a(o),He=l(o,"DIV",{class:!0});var Bt=v(He);h(Df.$$.fragment,Bt),Dj=a(Bt),kM=l(Bt,"P",{"data-svelte-h":!0}),f(kM)!=="svelte-5wl3ux"&&(kM.innerHTML=hI),Qj=a(Bt),$M=l(Bt,"P",{"data-svelte-h":!0}),f($M)!=="svelte-1n2cphd"&&($M.innerHTML=uI),Hj=a(Bt),un=l(Bt,"DIV",{class:!0});var Va=v(un);h(Qf.$$.fragment,Va),Yj=a(Va),BM=l(Va,"P",{"data-svelte-h":!0}),f(BM)!=="svelte-1269scv"&&(BM.textContent=_I),zj=a(Va),jM=l(Va,"P",{"data-svelte-h":!0}),f(jM)!=="svelte-1s2prmj"&&(jM.innerHTML=bI),Oj=a(Va),h(zi.$$.fragment,Va),Va.forEach(d),Kj=a(Bt),X=l(Bt,"DIV",{class:!0});var Jo=v(X);h(Hf.$$.fragment,Jo),eA=a(Jo),AM=l(Jo,"P",{"data-svelte-h":!0}),f(AM)!=="svelte-xeutyd"&&(AM.textContent=MI),oA=a(Jo),ZM=l(Jo,"P",{"data-svelte-h":!0}),f(ZM)!=="svelte-1enn9p2"&&(ZM.innerHTML=TI),rA=a(Jo),RM=l(Jo,"UL",{"data-svelte-h":!0}),f(RM)!=="svelte-be8zec"&&(RM.innerHTML=FI),tA=a(Jo),GM=l(Jo,"P",{"data-svelte-h":!0}),f(GM)!=="svelte-tebhz6"&&(GM.innerHTML=CI),nA=a(Jo),h(Oi.$$.fragment,Jo),Jo.forEach(d),Bt.forEach(d),$2=a(o),h(Yf.$$.fragment,o),B2=a(o),zf=l(o,"DIV",{class:!0});var jy=v(zf);h(Of.$$.fragment,jy),jy.forEach(d),j2=a(o),h(Kf.$$.fragment,o),A2=a(o),Ye=l(o,"DIV",{class:!0});var jt=v(Ye);h(eg.$$.fragment,jt),aA=a(jt),PM=l(jt,"P",{"data-svelte-h":!0}),f(PM)!=="svelte-1826trx"&&(PM.innerHTML=yI),sA=a(jt),SM=l(jt,"P",{"data-svelte-h":!0}),f(SM)!=="svelte-1n2cphd"&&(SM.innerHTML=vI),iA=a(jt),_n=l(jt,"DIV",{class:!0});var Ea=v(_n);h(og.$$.fragment,Ea),lA=a(Ea),WM=l(Ea,"P",{"data-svelte-h":!0}),f(WM)!=="svelte-vmkzub"&&(WM.textContent=wI),dA=a(Ea),JM=l(Ea,"P",{"data-svelte-h":!0}),f(JM)!=="svelte-1s2prmj"&&(JM.innerHTML=xI),mA=a(Ea),h(Ki.$$.fragment,Ea),Ea.forEach(d),cA=a(jt),D=l(jt,"DIV",{class:!0});var Vo=v(D);h(rg.$$.fragment,Vo),fA=a(Vo),VM=l(Vo,"P",{"data-svelte-h":!0}),f(VM)!=="svelte-up2an7"&&(VM.textContent=LI),gA=a(Vo),EM=l(Vo,"P",{"data-svelte-h":!0}),f(EM)!=="svelte-1enn9p2"&&(EM.innerHTML=kI),pA=a(Vo),IM=l(Vo,"UL",{"data-svelte-h":!0}),f(IM)!=="svelte-1r69sxi"&&(IM.innerHTML=$I),hA=a(Vo),UM=l(Vo,"P",{"data-svelte-h":!0}),f(UM)!=="svelte-tebhz6"&&(UM.innerHTML=BI),uA=a(Vo),h(el.$$.fragment,Vo),Vo.forEach(d),jt.forEach(d),Z2=a(o),h(tg.$$.fragment,o),R2=a(o),ze=l(o,"DIV",{class:!0});var At=v(ze);h(ng.$$.fragment,At),_A=a(At),NM=l(At,"P",{"data-svelte-h":!0}),f(NM)!=="svelte-1826trx"&&(NM.innerHTML=jI),bA=a(At),qM=l(At,"P",{"data-svelte-h":!0}),f(qM)!=="svelte-1n2cphd"&&(qM.innerHTML=AI),MA=a(At),bn=l(At,"DIV",{class:!0});var Ia=v(bn);h(ag.$$.fragment,Ia),TA=a(Ia),XM=l(Ia,"P",{"data-svelte-h":!0}),f(XM)!=="svelte-vmkzub"&&(XM.textContent=ZI),FA=a(Ia),DM=l(Ia,"P",{"data-svelte-h":!0}),f(DM)!=="svelte-1s2prmj"&&(DM.innerHTML=RI),CA=a(Ia),h(ol.$$.fragment,Ia),Ia.forEach(d),yA=a(At),cr=l(At,"DIV",{class:!0});var Zt=v(cr);h(sg.$$.fragment,Zt),vA=a(Zt),QM=l(Zt,"P",{"data-svelte-h":!0}),f(QM)!=="svelte-up2an7"&&(QM.textContent=GI),wA=a(Zt),HM=l(Zt,"P",{"data-svelte-h":!0}),f(HM)!=="svelte-1enn9p2"&&(HM.innerHTML=PI),xA=a(Zt),YM=l(Zt,"UL",{"data-svelte-h":!0}),f(YM)!=="svelte-1wabiu"&&(YM.innerHTML=SI),LA=a(Zt),h(rl.$$.fragment,Zt),Zt.forEach(d),At.forEach(d),G2=a(o),h(ig.$$.fragment,o),P2=a(o),Oe=l(o,"DIV",{class:!0});var Rt=v(Oe);h(lg.$$.fragment,Rt),kA=a(Rt),zM=l(Rt,"P",{"data-svelte-h":!0}),f(zM)!=="svelte-tejn5k"&&(zM.innerHTML=WI),$A=a(Rt),OM=l(Rt,"P",{"data-svelte-h":!0}),f(OM)!=="svelte-1n2cphd"&&(OM.innerHTML=JI),BA=a(Rt),Mn=l(Rt,"DIV",{class:!0});var Ua=v(Mn);h(dg.$$.fragment,Ua),jA=a(Ua),KM=l(Ua,"P",{"data-svelte-h":!0}),f(KM)!=="svelte-1dstum2"&&(KM.textContent=VI),AA=a(Ua),eT=l(Ua,"P",{"data-svelte-h":!0}),f(eT)!=="svelte-1s2prmj"&&(eT.innerHTML=EI),ZA=a(Ua),h(tl.$$.fragment,Ua),Ua.forEach(d),RA=a(Rt),Q=l(Rt,"DIV",{class:!0});var Nn=v(Q);h(mg.$$.fragment,Nn),GA=a(Nn),oT=l(Nn,"P",{"data-svelte-h":!0}),f(oT)!=="svelte-7xylam"&&(oT.textContent=II),PA=a(Nn),rT=l(Nn,"P",{"data-svelte-h":!0}),f(rT)!=="svelte-1enn9p2"&&(rT.innerHTML=UI),SA=a(Nn),tT=l(Nn,"UL",{"data-svelte-h":!0}),f(tT)!=="svelte-112vyqm"&&(tT.innerHTML=NI),WA=a(Nn),nT=l(Nn,"P",{"data-svelte-h":!0}),f(nT)!=="svelte-tebhz6"&&(nT.innerHTML=qI),JA=a(Nn),h(nl.$$.fragment,Nn),Nn.forEach(d),Rt.forEach(d),S2=a(o),h(cg.$$.fragment,o),W2=a(o),Ke=l(o,"DIV",{class:!0});var Na=v(Ke);h(fg.$$.fragment,Na),VA=a(Na),aT=l(Na,"P",{"data-svelte-h":!0}),f(aT)!=="svelte-1v69xb9"&&(aT.innerHTML=XI),EA=a(Na),sT=l(Na,"P",{"data-svelte-h":!0}),f(sT)!=="svelte-1n2cphd"&&(sT.innerHTML=DI),IA=a(Na),Tn=l(Na,"DIV",{class:!0});var Kl=v(Tn);h(gg.$$.fragment,Kl),UA=a(Kl),iT=l(Kl,"P",{"data-svelte-h":!0}),f(iT)!=="svelte-a638l3"&&(iT.textContent=QI),NA=a(Kl),lT=l(Kl,"P",{"data-svelte-h":!0}),f(lT)!=="svelte-1s2prmj"&&(lT.innerHTML=HI),qA=a(Kl),h(al.$$.fragment,Kl),Kl.forEach(d),XA=a(Na),H=l(Na,"DIV",{class:!0});var qn=v(H);h(pg.$$.fragment,qn),DA=a(qn),dT=l(qn,"P",{"data-svelte-h":!0}),f(dT)!=="svelte-1rj5rvl"&&(dT.textContent=YI),QA=a(qn),mT=l(qn,"P",{"data-svelte-h":!0}),f(mT)!=="svelte-1enn9p2"&&(mT.innerHTML=zI),HA=a(qn),cT=l(qn,"UL",{"data-svelte-h":!0}),f(cT)!=="svelte-62axr6"&&(cT.innerHTML=OI),YA=a(qn),fT=l(qn,"P",{"data-svelte-h":!0}),f(fT)!=="svelte-tebhz6"&&(fT.innerHTML=KI),zA=a(qn),h(sl.$$.fragment,qn),qn.forEach(d),Na.forEach(d),J2=a(o),h(hg.$$.fragment,o),V2=a(o),ug=l(o,"DIV",{class:!0});var h5=v(ug);h(_g.$$.fragment,h5),h5.forEach(d),E2=a(o),h(bg.$$.fragment,o),I2=a(o),eo=l(o,"DIV",{class:!0});var qa=v(eo);h(Mg.$$.fragment,qa),OA=a(qa),gT=l(qa,"P",{"data-svelte-h":!0}),f(gT)!=="svelte-512nfe"&&(gT.innerHTML=eU),KA=a(qa),pT=l(qa,"P",{"data-svelte-h":!0}),f(pT)!=="svelte-1n2cphd"&&(pT.innerHTML=oU),eZ=a(qa),Fn=l(qa,"DIV",{class:!0});var ed=v(Fn);h(Tg.$$.fragment,ed),oZ=a(ed),hT=l(ed,"P",{"data-svelte-h":!0}),f(hT)!=="svelte-ne8wj0"&&(hT.textContent=rU),rZ=a(ed),uT=l(ed,"P",{"data-svelte-h":!0}),f(uT)!=="svelte-1s2prmj"&&(uT.innerHTML=tU),tZ=a(ed),h(il.$$.fragment,ed),ed.forEach(d),nZ=a(qa),Y=l(qa,"DIV",{class:!0});var Xn=v(Y);h(Fg.$$.fragment,Xn),aZ=a(Xn),_T=l(Xn,"P",{"data-svelte-h":!0}),f(_T)!=="svelte-xit1mq"&&(_T.textContent=nU),sZ=a(Xn),bT=l(Xn,"P",{"data-svelte-h":!0}),f(bT)!=="svelte-1enn9p2"&&(bT.innerHTML=aU),iZ=a(Xn),MT=l(Xn,"UL",{"data-svelte-h":!0}),f(MT)!=="svelte-mmy7v5"&&(MT.innerHTML=sU),lZ=a(Xn),TT=l(Xn,"P",{"data-svelte-h":!0}),f(TT)!=="svelte-tebhz6"&&(TT.innerHTML=iU),dZ=a(Xn),h(ll.$$.fragment,Xn),Xn.forEach(d),qa.forEach(d),U2=a(o),h(Cg.$$.fragment,o),N2=a(o),oo=l(o,"DIV",{class:!0});var Xa=v(oo);h(yg.$$.fragment,Xa),mZ=a(Xa),FT=l(Xa,"P",{"data-svelte-h":!0}),f(FT)!=="svelte-512nfe"&&(FT.innerHTML=lU),cZ=a(Xa),CT=l(Xa,"P",{"data-svelte-h":!0}),f(CT)!=="svelte-1n2cphd"&&(CT.innerHTML=dU),fZ=a(Xa),Cn=l(Xa,"DIV",{class:!0});var od=v(Cn);h(vg.$$.fragment,od),gZ=a(od),yT=l(od,"P",{"data-svelte-h":!0}),f(yT)!=="svelte-ne8wj0"&&(yT.textContent=mU),pZ=a(od),vT=l(od,"P",{"data-svelte-h":!0}),f(vT)!=="svelte-1s2prmj"&&(vT.innerHTML=cU),hZ=a(od),h(dl.$$.fragment,od),od.forEach(d),uZ=a(Xa),fr=l(Xa,"DIV",{class:!0});var Da=v(fr);h(wg.$$.fragment,Da),_Z=a(Da),wT=l(Da,"P",{"data-svelte-h":!0}),f(wT)!=="svelte-xit1mq"&&(wT.textContent=fU),bZ=a(Da),xT=l(Da,"P",{"data-svelte-h":!0}),f(xT)!=="svelte-1enn9p2"&&(xT.innerHTML=gU),MZ=a(Da),LT=l(Da,"UL",{"data-svelte-h":!0}),f(LT)!=="svelte-10ycj6j"&&(LT.innerHTML=pU),TZ=a(Da),h(ml.$$.fragment,Da),Da.forEach(d),Xa.forEach(d),q2=a(o),h(xg.$$.fragment,o),X2=a(o),ro=l(o,"DIV",{class:!0});var Qa=v(ro);h(Lg.$$.fragment,Qa),FZ=a(Qa),kT=l(Qa,"P",{"data-svelte-h":!0}),f(kT)!=="svelte-17hl2sx"&&(kT.innerHTML=hU),CZ=a(Qa),$T=l(Qa,"P",{"data-svelte-h":!0}),f($T)!=="svelte-1n2cphd"&&($T.innerHTML=uU),yZ=a(Qa),yn=l(Qa,"DIV",{class:!0});var rd=v(yn);h(kg.$$.fragment,rd),vZ=a(rd),BT=l(rd,"P",{"data-svelte-h":!0}),f(BT)!=="svelte-15xznv"&&(BT.textContent=_U),wZ=a(rd),jT=l(rd,"P",{"data-svelte-h":!0}),f(jT)!=="svelte-1s2prmj"&&(jT.innerHTML=bU),xZ=a(rd),h(cl.$$.fragment,rd),rd.forEach(d),LZ=a(Qa),z=l(Qa,"DIV",{class:!0});var Dn=v(z);h($g.$$.fragment,Dn),kZ=a(Dn),AT=l(Dn,"P",{"data-svelte-h":!0}),f(AT)!=="svelte-1r6a2oz"&&(AT.textContent=MU),$Z=a(Dn),ZT=l(Dn,"P",{"data-svelte-h":!0}),f(ZT)!=="svelte-1enn9p2"&&(ZT.innerHTML=TU),BZ=a(Dn),RT=l(Dn,"UL",{"data-svelte-h":!0}),f(RT)!=="svelte-b9g6xe"&&(RT.innerHTML=FU),jZ=a(Dn),GT=l(Dn,"P",{"data-svelte-h":!0}),f(GT)!=="svelte-tebhz6"&&(GT.innerHTML=CU),AZ=a(Dn),h(fl.$$.fragment,Dn),Dn.forEach(d),Qa.forEach(d),D2=a(o),h(Bg.$$.fragment,o),Q2=a(o),to=l(o,"DIV",{class:!0});var Ha=v(to);h(jg.$$.fragment,Ha),ZZ=a(Ha),PT=l(Ha,"P",{"data-svelte-h":!0}),f(PT)!=="svelte-7xo2vu"&&(PT.innerHTML=yU),RZ=a(Ha),ST=l(Ha,"P",{"data-svelte-h":!0}),f(ST)!=="svelte-1n2cphd"&&(ST.innerHTML=vU),GZ=a(Ha),vn=l(Ha,"DIV",{class:!0});var td=v(vn);h(Ag.$$.fragment,td),PZ=a(td),WT=l(td,"P",{"data-svelte-h":!0}),f(WT)!=="svelte-zddwso"&&(WT.textContent=wU),SZ=a(td),JT=l(td,"P",{"data-svelte-h":!0}),f(JT)!=="svelte-1s2prmj"&&(JT.innerHTML=xU),WZ=a(td),h(gl.$$.fragment,td),td.forEach(d),JZ=a(Ha),O=l(Ha,"DIV",{class:!0});var Qn=v(O);h(Zg.$$.fragment,Qn),VZ=a(Qn),VT=l(Qn,"P",{"data-svelte-h":!0}),f(VT)!=="svelte-1wyb2t8"&&(VT.textContent=LU),EZ=a(Qn),ET=l(Qn,"P",{"data-svelte-h":!0}),f(ET)!=="svelte-1enn9p2"&&(ET.innerHTML=kU),IZ=a(Qn),IT=l(Qn,"UL",{"data-svelte-h":!0}),f(IT)!=="svelte-9zbs3u"&&(IT.innerHTML=$U),UZ=a(Qn),UT=l(Qn,"P",{"data-svelte-h":!0}),f(UT)!=="svelte-tebhz6"&&(UT.innerHTML=BU),NZ=a(Qn),h(pl.$$.fragment,Qn),Qn.forEach(d),Ha.forEach(d),H2=a(o),h(Rg.$$.fragment,o),Y2=a(o),no=l(o,"DIV",{class:!0});var Ya=v(no);h(Gg.$$.fragment,Ya),qZ=a(Ya),NT=l(Ya,"P",{"data-svelte-h":!0}),f(NT)!=="svelte-4pu8eo"&&(NT.innerHTML=jU),XZ=a(Ya),qT=l(Ya,"P",{"data-svelte-h":!0}),f(qT)!=="svelte-1n2cphd"&&(qT.innerHTML=AU),DZ=a(Ya),wn=l(Ya,"DIV",{class:!0});var nd=v(wn);h(Pg.$$.fragment,nd),QZ=a(nd),XT=l(nd,"P",{"data-svelte-h":!0}),f(XT)!=="svelte-1xqzdzm"&&(XT.textContent=ZU),HZ=a(nd),DT=l(nd,"P",{"data-svelte-h":!0}),f(DT)!=="svelte-1s2prmj"&&(DT.innerHTML=RU),YZ=a(nd),h(hl.$$.fragment,nd),nd.forEach(d),zZ=a(Ya),K=l(Ya,"DIV",{class:!0});var Hn=v(K);h(Sg.$$.fragment,Hn),OZ=a(Hn),QT=l(Hn,"P",{"data-svelte-h":!0}),f(QT)!=="svelte-1kp6e9m"&&(QT.textContent=GU),KZ=a(Hn),HT=l(Hn,"P",{"data-svelte-h":!0}),f(HT)!=="svelte-1enn9p2"&&(HT.innerHTML=PU),eR=a(Hn),YT=l(Hn,"UL",{"data-svelte-h":!0}),f(YT)!=="svelte-1dmy2l7"&&(YT.innerHTML=SU),oR=a(Hn),zT=l(Hn,"P",{"data-svelte-h":!0}),f(zT)!=="svelte-tebhz6"&&(zT.innerHTML=WU),rR=a(Hn),h(ul.$$.fragment,Hn),Hn.forEach(d),Ya.forEach(d),z2=a(o),h(Wg.$$.fragment,o),O2=a(o),ao=l(o,"DIV",{class:!0});var za=v(ao);h(Jg.$$.fragment,za),tR=a(za),OT=l(za,"P",{"data-svelte-h":!0}),f(OT)!=="svelte-4pu8eo"&&(OT.innerHTML=JU),nR=a(za),KT=l(za,"P",{"data-svelte-h":!0}),f(KT)!=="svelte-1n2cphd"&&(KT.innerHTML=VU),aR=a(za),xn=l(za,"DIV",{class:!0});var ad=v(xn);h(Vg.$$.fragment,ad),sR=a(ad),eF=l(ad,"P",{"data-svelte-h":!0}),f(eF)!=="svelte-1xqzdzm"&&(eF.textContent=EU),iR=a(ad),oF=l(ad,"P",{"data-svelte-h":!0}),f(oF)!=="svelte-1s2prmj"&&(oF.innerHTML=IU),lR=a(ad),h(_l.$$.fragment,ad),ad.forEach(d),dR=a(za),gr=l(za,"DIV",{class:!0});var Oa=v(gr);h(Eg.$$.fragment,Oa),mR=a(Oa),rF=l(Oa,"P",{"data-svelte-h":!0}),f(rF)!=="svelte-1kp6e9m"&&(rF.textContent=UU),cR=a(Oa),tF=l(Oa,"P",{"data-svelte-h":!0}),f(tF)!=="svelte-1enn9p2"&&(tF.innerHTML=NU),fR=a(Oa),nF=l(Oa,"UL",{"data-svelte-h":!0}),f(nF)!=="svelte-ic96xh"&&(nF.innerHTML=qU),gR=a(Oa),h(bl.$$.fragment,Oa),Oa.forEach(d),za.forEach(d),K2=a(o),h(Ig.$$.fragment,o),ew=a(o),so=l(o,"DIV",{class:!0});var Ka=v(so);h(Ug.$$.fragment,Ka),pR=a(Ka),aF=l(Ka,"P",{"data-svelte-h":!0}),f(aF)!=="svelte-m6ffr"&&(aF.innerHTML=XU),hR=a(Ka),sF=l(Ka,"P",{"data-svelte-h":!0}),f(sF)!=="svelte-1n2cphd"&&(sF.innerHTML=DU),uR=a(Ka),Ln=l(Ka,"DIV",{class:!0});var sd=v(Ln);h(Ng.$$.fragment,sd),_R=a(sd),iF=l(sd,"P",{"data-svelte-h":!0}),f(iF)!=="svelte-1t88i0p"&&(iF.textContent=QU),bR=a(sd),lF=l(sd,"P",{"data-svelte-h":!0}),f(lF)!=="svelte-1s2prmj"&&(lF.innerHTML=HU),MR=a(sd),h(Ml.$$.fragment,sd),sd.forEach(d),TR=a(Ka),ee=l(Ka,"DIV",{class:!0});var Yn=v(ee);h(qg.$$.fragment,Yn),FR=a(Yn),dF=l(Yn,"P",{"data-svelte-h":!0}),f(dF)!=="svelte-xtgygr"&&(dF.textContent=YU),CR=a(Yn),mF=l(Yn,"P",{"data-svelte-h":!0}),f(mF)!=="svelte-1enn9p2"&&(mF.innerHTML=zU),yR=a(Yn),cF=l(Yn,"UL",{"data-svelte-h":!0}),f(cF)!=="svelte-1n0x18s"&&(cF.innerHTML=OU),vR=a(Yn),fF=l(Yn,"P",{"data-svelte-h":!0}),f(fF)!=="svelte-tebhz6"&&(fF.innerHTML=KU),wR=a(Yn),h(Tl.$$.fragment,Yn),Yn.forEach(d),Ka.forEach(d),ow=a(o),h(Xg.$$.fragment,o),rw=a(o),Dg=l(o,"P",{"data-svelte-h":!0}),f(Dg)!=="svelte-1htpfc5"&&(Dg.textContent=e9),tw=a(o),h(Qg.$$.fragment,o),nw=a(o),io=l(o,"DIV",{class:!0});var es=v(io);h(Hg.$$.fragment,es),xR=a(es),gF=l(es,"P",{"data-svelte-h":!0}),f(gF)!=="svelte-11rbyoy"&&(gF.innerHTML=o9),LR=a(es),pF=l(es,"P",{"data-svelte-h":!0}),f(pF)!=="svelte-1n2cphd"&&(pF.innerHTML=r9),kR=a(es),kn=l(es,"DIV",{class:!0});var id=v(kn);h(Yg.$$.fragment,id),$R=a(id),hF=l(id,"P",{"data-svelte-h":!0}),f(hF)!=="svelte-1jtlz68"&&(hF.textContent=t9),BR=a(id),uF=l(id,"P",{"data-svelte-h":!0}),f(uF)!=="svelte-1s2prmj"&&(uF.innerHTML=n9),jR=a(id),h(Fl.$$.fragment,id),id.forEach(d),AR=a(es),oe=l(es,"DIV",{class:!0});var zn=v(oe);h(zg.$$.fragment,zn),ZR=a(zn),_F=l(zn,"P",{"data-svelte-h":!0}),f(_F)!=="svelte-mj39cs"&&(_F.textContent=a9),RR=a(zn),bF=l(zn,"P",{"data-svelte-h":!0}),f(bF)!=="svelte-1enn9p2"&&(bF.innerHTML=s9),GR=a(zn),MF=l(zn,"UL",{"data-svelte-h":!0}),f(MF)!=="svelte-nko42j"&&(MF.innerHTML=i9),PR=a(zn),TF=l(zn,"P",{"data-svelte-h":!0}),f(TF)!=="svelte-tebhz6"&&(TF.innerHTML=l9),SR=a(zn),h(Cl.$$.fragment,zn),zn.forEach(d),es.forEach(d),aw=a(o),h(Og.$$.fragment,o),sw=a(o),lo=l(o,"DIV",{class:!0});var os=v(lo);h(Kg.$$.fragment,os),WR=a(os),FF=l(os,"P",{"data-svelte-h":!0}),f(FF)!=="svelte-11rbyoy"&&(FF.innerHTML=d9),JR=a(os),CF=l(os,"P",{"data-svelte-h":!0}),f(CF)!=="svelte-1n2cphd"&&(CF.innerHTML=m9),VR=a(os),$n=l(os,"DIV",{class:!0});var ld=v($n);h(ep.$$.fragment,ld),ER=a(ld),yF=l(ld,"P",{"data-svelte-h":!0}),f(yF)!=="svelte-1jtlz68"&&(yF.textContent=c9),IR=a(ld),vF=l(ld,"P",{"data-svelte-h":!0}),f(vF)!=="svelte-1s2prmj"&&(vF.innerHTML=f9),UR=a(ld),h(yl.$$.fragment,ld),ld.forEach(d),NR=a(os),pr=l(os,"DIV",{class:!0});var rs=v(pr);h(op.$$.fragment,rs),qR=a(rs),wF=l(rs,"P",{"data-svelte-h":!0}),f(wF)!=="svelte-mj39cs"&&(wF.textContent=g9),XR=a(rs),xF=l(rs,"P",{"data-svelte-h":!0}),f(xF)!=="svelte-1enn9p2"&&(xF.innerHTML=p9),DR=a(rs),LF=l(rs,"UL",{"data-svelte-h":!0}),f(LF)!=="svelte-1sh8je7"&&(LF.innerHTML=h9),QR=a(rs),h(vl.$$.fragment,rs),rs.forEach(d),os.forEach(d),iw=a(o),h(rp.$$.fragment,o),lw=a(o),mo=l(o,"DIV",{class:!0});var ts=v(mo);h(tp.$$.fragment,ts),HR=a(ts),kF=l(ts,"P",{"data-svelte-h":!0}),f(kF)!=="svelte-1q0vojd"&&(kF.innerHTML=u9),YR=a(ts),$F=l(ts,"P",{"data-svelte-h":!0}),f($F)!=="svelte-1n2cphd"&&($F.innerHTML=_9),zR=a(ts),Bn=l(ts,"DIV",{class:!0});var dd=v(Bn);h(np.$$.fragment,dd),OR=a(dd),BF=l(dd,"P",{"data-svelte-h":!0}),f(BF)!=="svelte-qoovm7"&&(BF.textContent=b9),KR=a(dd),jF=l(dd,"P",{"data-svelte-h":!0}),f(jF)!=="svelte-1s2prmj"&&(jF.innerHTML=M9),eG=a(dd),h(wl.$$.fragment,dd),dd.forEach(d),oG=a(ts),re=l(ts,"DIV",{class:!0});var On=v(re);h(ap.$$.fragment,On),rG=a(On),AF=l(On,"P",{"data-svelte-h":!0}),f(AF)!=="svelte-1ldaoop"&&(AF.textContent=T9),tG=a(On),ZF=l(On,"P",{"data-svelte-h":!0}),f(ZF)!=="svelte-1enn9p2"&&(ZF.innerHTML=F9),nG=a(On),RF=l(On,"UL",{"data-svelte-h":!0}),f(RF)!=="svelte-jagt8s"&&(RF.innerHTML=C9),aG=a(On),GF=l(On,"P",{"data-svelte-h":!0}),f(GF)!=="svelte-tebhz6"&&(GF.innerHTML=y9),sG=a(On),h(xl.$$.fragment,On),On.forEach(d),ts.forEach(d),dw=a(o),h(sp.$$.fragment,o),mw=a(o),co=l(o,"DIV",{class:!0});var ns=v(co);h(ip.$$.fragment,ns),iG=a(ns),PF=l(ns,"P",{"data-svelte-h":!0}),f(PF)!=="svelte-14dzufi"&&(PF.innerHTML=v9),lG=a(ns),SF=l(ns,"P",{"data-svelte-h":!0}),f(SF)!=="svelte-1n2cphd"&&(SF.innerHTML=w9),dG=a(ns),jn=l(ns,"DIV",{class:!0});var md=v(jn);h(lp.$$.fragment,md),mG=a(md),WF=l(md,"P",{"data-svelte-h":!0}),f(WF)!=="svelte-ovue0k"&&(WF.textContent=x9),cG=a(md),JF=l(md,"P",{"data-svelte-h":!0}),f(JF)!=="svelte-1s2prmj"&&(JF.innerHTML=L9),fG=a(md),h(Ll.$$.fragment,md),md.forEach(d),gG=a(ns),te=l(ns,"DIV",{class:!0});var Kn=v(te);h(dp.$$.fragment,Kn),pG=a(Kn),VF=l(Kn,"P",{"data-svelte-h":!0}),f(VF)!=="svelte-1jrxhfe"&&(VF.textContent=k9),hG=a(Kn),EF=l(Kn,"P",{"data-svelte-h":!0}),f(EF)!=="svelte-1enn9p2"&&(EF.innerHTML=$9),uG=a(Kn),IF=l(Kn,"UL",{"data-svelte-h":!0}),f(IF)!=="svelte-3jtkzz"&&(IF.innerHTML=B9),_G=a(Kn),UF=l(Kn,"P",{"data-svelte-h":!0}),f(UF)!=="svelte-tebhz6"&&(UF.innerHTML=j9),bG=a(Kn),h(kl.$$.fragment,Kn),Kn.forEach(d),ns.forEach(d),cw=a(o),h(mp.$$.fragment,o),fw=a(o),fo=l(o,"DIV",{class:!0});var as=v(fo);h(cp.$$.fragment,as),MG=a(as),NF=l(as,"P",{"data-svelte-h":!0}),f(NF)!=="svelte-5a1z7u"&&(NF.innerHTML=A9),TG=a(as),qF=l(as,"P",{"data-svelte-h":!0}),f(qF)!=="svelte-1n2cphd"&&(qF.innerHTML=Z9),FG=a(as),An=l(as,"DIV",{class:!0});var cd=v(An);h(fp.$$.fragment,cd),CG=a(cd),XF=l(cd,"P",{"data-svelte-h":!0}),f(XF)!=="svelte-1aufdt0"&&(XF.textContent=R9),yG=a(cd),DF=l(cd,"P",{"data-svelte-h":!0}),f(DF)!=="svelte-1s2prmj"&&(DF.innerHTML=G9),vG=a(cd),h($l.$$.fragment,cd),cd.forEach(d),wG=a(as),ne=l(as,"DIV",{class:!0});var ea=v(ne);h(gp.$$.fragment,ea),xG=a(ea),QF=l(ea,"P",{"data-svelte-h":!0}),f(QF)!=="svelte-73ck9g"&&(QF.textContent=P9),LG=a(ea),HF=l(ea,"P",{"data-svelte-h":!0}),f(HF)!=="svelte-1enn9p2"&&(HF.innerHTML=S9),kG=a(ea),YF=l(ea,"UL",{"data-svelte-h":!0}),f(YF)!=="svelte-1l5wuil"&&(YF.innerHTML=W9),$G=a(ea),zF=l(ea,"P",{"data-svelte-h":!0}),f(zF)!=="svelte-tebhz6"&&(zF.innerHTML=J9),BG=a(ea),h(Bl.$$.fragment,ea),ea.forEach(d),as.forEach(d),gw=a(o),h(pp.$$.fragment,o),pw=a(o),go=l(o,"DIV",{class:!0});var ss=v(go);h(hp.$$.fragment,ss),jG=a(ss),OF=l(ss,"P",{"data-svelte-h":!0}),f(OF)!=="svelte-5a1z7u"&&(OF.innerHTML=V9),AG=a(ss),KF=l(ss,"P",{"data-svelte-h":!0}),f(KF)!=="svelte-1n2cphd"&&(KF.innerHTML=E9),ZG=a(ss),Zn=l(ss,"DIV",{class:!0});var fd=v(Zn);h(up.$$.fragment,fd),RG=a(fd),eC=l(fd,"P",{"data-svelte-h":!0}),f(eC)!=="svelte-1aufdt0"&&(eC.textContent=I9),GG=a(fd),oC=l(fd,"P",{"data-svelte-h":!0}),f(oC)!=="svelte-1s2prmj"&&(oC.innerHTML=U9),PG=a(fd),h(jl.$$.fragment,fd),fd.forEach(d),SG=a(ss),hr=l(ss,"DIV",{class:!0});var is=v(hr);h(_p.$$.fragment,is),WG=a(is),rC=l(is,"P",{"data-svelte-h":!0}),f(rC)!=="svelte-73ck9g"&&(rC.textContent=N9),JG=a(is),tC=l(is,"P",{"data-svelte-h":!0}),f(tC)!=="svelte-1enn9p2"&&(tC.innerHTML=q9),VG=a(is),nC=l(is,"UL",{"data-svelte-h":!0}),f(nC)!=="svelte-1sm3uy9"&&(nC.innerHTML=X9),EG=a(is),h(Al.$$.fragment,is),is.forEach(d),ss.forEach(d),hw=a(o),h(bp.$$.fragment,o),uw=a(o),po=l(o,"DIV",{class:!0});var ls=v(po);h(Mp.$$.fragment,ls),IG=a(ls),aC=l(ls,"P",{"data-svelte-h":!0}),f(aC)!=="svelte-5a1z7u"&&(aC.innerHTML=D9),UG=a(ls),sC=l(ls,"P",{"data-svelte-h":!0}),f(sC)!=="svelte-1n2cphd"&&(sC.innerHTML=Q9),NG=a(ls),Rn=l(ls,"DIV",{class:!0});var gd=v(Rn);h(Tp.$$.fragment,gd),qG=a(gd),iC=l(gd,"P",{"data-svelte-h":!0}),f(iC)!=="svelte-1aufdt0"&&(iC.textContent=H9),XG=a(gd),lC=l(gd,"P",{"data-svelte-h":!0}),f(lC)!=="svelte-1s2prmj"&&(lC.innerHTML=Y9),DG=a(gd),h(Zl.$$.fragment,gd),gd.forEach(d),QG=a(ls),ur=l(ls,"DIV",{class:!0});var ds=v(ur);h(Fp.$$.fragment,ds),HG=a(ds),dC=l(ds,"P",{"data-svelte-h":!0}),f(dC)!=="svelte-73ck9g"&&(dC.textContent=z9),YG=a(ds),mC=l(ds,"P",{"data-svelte-h":!0}),f(mC)!=="svelte-1enn9p2"&&(mC.innerHTML=O9),zG=a(ds),cC=l(ds,"UL",{"data-svelte-h":!0}),f(cC)!=="svelte-6m2jte"&&(cC.innerHTML=K9),OG=a(ds),h(Rl.$$.fragment,ds),ds.forEach(d),ls.forEach(d),_w=a(o),h(Cp.$$.fragment,o),bw=a(o),ho=l(o,"DIV",{class:!0});var ms=v(ho);h(yp.$$.fragment,ms),KG=a(ms),fC=l(ms,"P",{"data-svelte-h":!0}),f(fC)!=="svelte-zvcvpi"&&(fC.innerHTML=eN),eP=a(ms),gC=l(ms,"P",{"data-svelte-h":!0}),f(gC)!=="svelte-1n2cphd"&&(gC.innerHTML=oN),oP=a(ms),Gn=l(ms,"DIV",{class:!0});var pd=v(Gn);h(vp.$$.fragment,pd),rP=a(pd),pC=l(pd,"P",{"data-svelte-h":!0}),f(pC)!=="svelte-1wxna3s"&&(pC.textContent=rN),tP=a(pd),hC=l(pd,"P",{"data-svelte-h":!0}),f(hC)!=="svelte-1s2prmj"&&(hC.innerHTML=tN),nP=a(pd),h(Gl.$$.fragment,pd),pd.forEach(d),aP=a(ms),ae=l(ms,"DIV",{class:!0});var oa=v(ae);h(wp.$$.fragment,oa),sP=a(oa),uC=l(oa,"P",{"data-svelte-h":!0}),f(uC)!=="svelte-1n6mjno"&&(uC.textContent=nN),iP=a(oa),_C=l(oa,"P",{"data-svelte-h":!0}),f(_C)!=="svelte-1enn9p2"&&(_C.innerHTML=aN),lP=a(oa),bC=l(oa,"UL",{"data-svelte-h":!0}),f(bC)!=="svelte-1l1t3ia"&&(bC.innerHTML=sN),dP=a(oa),MC=l(oa,"P",{"data-svelte-h":!0}),f(MC)!=="svelte-tebhz6"&&(MC.innerHTML=iN),mP=a(oa),h(Pl.$$.fragment,oa),oa.forEach(d),ms.forEach(d),Mw=a(o),h(xp.$$.fragment,o),Tw=a(o),Lp=l(o,"DIV",{class:!0});var u5=v(Lp);h(kp.$$.fragment,u5),u5.forEach(d),Fw=a(o),h($p.$$.fragment,o),Cw=a(o),Bp=l(o,"DIV",{class:!0});var _5=v(Bp);h(jp.$$.fragment,_5),_5.forEach(d),yw=a(o),h(Ap.$$.fragment,o),vw=a(o),Zp=l(o,"P",{"data-svelte-h":!0}),f(Zp)!=="svelte-1rj2vi9"&&(Zp.textContent=lN),ww=a(o),h(Rp.$$.fragment,o),xw=a(o),uo=l(o,"DIV",{class:!0});var cs=v(uo);h(Gp.$$.fragment,cs),cP=a(cs),TC=l(cs,"P",{"data-svelte-h":!0}),f(TC)!=="svelte-1eiabpy"&&(TC.innerHTML=dN),fP=a(cs),FC=l(cs,"P",{"data-svelte-h":!0}),f(FC)!=="svelte-1n2cphd"&&(FC.innerHTML=mN),gP=a(cs),Pn=l(cs,"DIV",{class:!0});var hd=v(Pn);h(Pp.$$.fragment,hd),pP=a(hd),CC=l(hd,"P",{"data-svelte-h":!0}),f(CC)!=="svelte-syrugc"&&(CC.textContent=cN),hP=a(hd),yC=l(hd,"P",{"data-svelte-h":!0}),f(yC)!=="svelte-1s2prmj"&&(yC.innerHTML=fN),uP=a(hd),h(Sl.$$.fragment,hd),hd.forEach(d),_P=a(cs),se=l(cs,"DIV",{class:!0});var ra=v(se);h(Sp.$$.fragment,ra),bP=a(ra),vC=l(ra,"P",{"data-svelte-h":!0}),f(vC)!=="svelte-1ontymo"&&(vC.textContent=gN),MP=a(ra),wC=l(ra,"P",{"data-svelte-h":!0}),f(wC)!=="svelte-1enn9p2"&&(wC.innerHTML=pN),TP=a(ra),xC=l(ra,"UL",{"data-svelte-h":!0}),f(xC)!=="svelte-bvkkm4"&&(xC.innerHTML=hN),FP=a(ra),LC=l(ra,"P",{"data-svelte-h":!0}),f(LC)!=="svelte-tebhz6"&&(LC.innerHTML=uN),CP=a(ra),h(Wl.$$.fragment,ra),ra.forEach(d),cs.forEach(d),Lw=a(o),h(Wp.$$.fragment,o),kw=a(o),_o=l(o,"DIV",{class:!0});var fs=v(_o);h(Jp.$$.fragment,fs),yP=a(fs),kC=l(fs,"P",{"data-svelte-h":!0}),f(kC)!=="svelte-1eiabpy"&&(kC.innerHTML=_N),vP=a(fs),$C=l(fs,"P",{"data-svelte-h":!0}),f($C)!=="svelte-1n2cphd"&&($C.innerHTML=bN),wP=a(fs),Sn=l(fs,"DIV",{class:!0});var ud=v(Sn);h(Vp.$$.fragment,ud),xP=a(ud),BC=l(ud,"P",{"data-svelte-h":!0}),f(BC)!=="svelte-syrugc"&&(BC.textContent=MN),LP=a(ud),jC=l(ud,"P",{"data-svelte-h":!0}),f(jC)!=="svelte-1s2prmj"&&(jC.innerHTML=TN),kP=a(ud),h(Jl.$$.fragment,ud),ud.forEach(d),$P=a(fs),_r=l(fs,"DIV",{class:!0});var gs=v(_r);h(Ep.$$.fragment,gs),BP=a(gs),AC=l(gs,"P",{"data-svelte-h":!0}),f(AC)!=="svelte-1ontymo"&&(AC.textContent=FN),jP=a(gs),ZC=l(gs,"P",{"data-svelte-h":!0}),f(ZC)!=="svelte-1enn9p2"&&(ZC.innerHTML=CN),AP=a(gs),RC=l(gs,"UL",{"data-svelte-h":!0}),f(RC)!=="svelte-o0a5pw"&&(RC.innerHTML=yN),ZP=a(gs),h(Vl.$$.fragment,gs),gs.forEach(d),fs.forEach(d),$w=a(o),h(Ip.$$.fragment,o),Bw=a(o),bo=l(o,"DIV",{class:!0});var ps=v(bo);h(Up.$$.fragment,ps),RP=a(ps),GC=l(ps,"P",{"data-svelte-h":!0}),f(GC)!=="svelte-1nhkrl7"&&(GC.innerHTML=vN),GP=a(ps),PC=l(ps,"P",{"data-svelte-h":!0}),f(PC)!=="svelte-1n2cphd"&&(PC.innerHTML=wN),PP=a(ps),Wn=l(ps,"DIV",{class:!0});var _d=v(Wn);h(Np.$$.fragment,_d),SP=a(_d),SC=l(_d,"P",{"data-svelte-h":!0}),f(SC)!=="svelte-axumax"&&(SC.textContent=xN),WP=a(_d),WC=l(_d,"P",{"data-svelte-h":!0}),f(WC)!=="svelte-1s2prmj"&&(WC.innerHTML=LN),JP=a(_d),h(El.$$.fragment,_d),_d.forEach(d),VP=a(ps),ie=l(ps,"DIV",{class:!0});var ta=v(ie);h(qp.$$.fragment,ta),EP=a(ta),JC=l(ta,"P",{"data-svelte-h":!0}),f(JC)!=="svelte-dbgbst"&&(JC.textContent=kN),IP=a(ta),VC=l(ta,"P",{"data-svelte-h":!0}),f(VC)!=="svelte-1enn9p2"&&(VC.innerHTML=$N),UP=a(ta),EC=l(ta,"UL",{"data-svelte-h":!0}),f(EC)!=="svelte-khdwtr"&&(EC.innerHTML=BN),NP=a(ta),IC=l(ta,"P",{"data-svelte-h":!0}),f(IC)!=="svelte-tebhz6"&&(IC.innerHTML=jN),qP=a(ta),h(Il.$$.fragment,ta),ta.forEach(d),ps.forEach(d),jw=a(o),h(Xp.$$.fragment,o),Aw=a(o),Mo=l(o,"DIV",{class:!0});var hs=v(Mo);h(Dp.$$.fragment,hs),XP=a(hs),UC=l(hs,"P",{"data-svelte-h":!0}),f(UC)!=="svelte-1nhkrl7"&&(UC.innerHTML=AN),DP=a(hs),NC=l(hs,"P",{"data-svelte-h":!0}),f(NC)!=="svelte-1n2cphd"&&(NC.innerHTML=ZN),QP=a(hs),Jn=l(hs,"DIV",{class:!0});var bd=v(Jn);h(Qp.$$.fragment,bd),HP=a(bd),qC=l(bd,"P",{"data-svelte-h":!0}),f(qC)!=="svelte-axumax"&&(qC.textContent=RN),YP=a(bd),XC=l(bd,"P",{"data-svelte-h":!0}),f(XC)!=="svelte-1s2prmj"&&(XC.innerHTML=GN),zP=a(bd),h(Ul.$$.fragment,bd),bd.forEach(d),OP=a(hs),br=l(hs,"DIV",{class:!0});var us=v(br);h(Hp.$$.fragment,us),KP=a(us),DC=l(us,"P",{"data-svelte-h":!0}),f(DC)!=="svelte-dbgbst"&&(DC.textContent=PN),eS=a(us),QC=l(us,"P",{"data-svelte-h":!0}),f(QC)!=="svelte-1enn9p2"&&(QC.innerHTML=SN),oS=a(us),HC=l(us,"UL",{"data-svelte-h":!0}),f(HC)!=="svelte-13lo641"&&(HC.innerHTML=WN),rS=a(us),h(Nl.$$.fragment,us),us.forEach(d),hs.forEach(d),Zw=a(o),h(Yp.$$.fragment,o),Rw=a(o),To=l(o,"DIV",{class:!0});var _s=v(To);h(zp.$$.fragment,_s),tS=a(_s),YC=l(_s,"P",{"data-svelte-h":!0}),f(YC)!=="svelte-16v192y"&&(YC.innerHTML=JN),nS=a(_s),zC=l(_s,"P",{"data-svelte-h":!0}),f(zC)!=="svelte-1n2cphd"&&(zC.innerHTML=VN),aS=a(_s),Vn=l(_s,"DIV",{class:!0});var Md=v(Vn);h(Op.$$.fragment,Md),sS=a(Md),OC=l(Md,"P",{"data-svelte-h":!0}),f(OC)!=="svelte-1nom7tk"&&(OC.textContent=EN),iS=a(Md),KC=l(Md,"P",{"data-svelte-h":!0}),f(KC)!=="svelte-1s2prmj"&&(KC.innerHTML=IN),lS=a(Md),h(ql.$$.fragment,Md),Md.forEach(d),dS=a(_s),le=l(_s,"DIV",{class:!0});var na=v(le);h(Kp.$$.fragment,na),mS=a(na),ey=l(na,"P",{"data-svelte-h":!0}),f(ey)!=="svelte-7pdp5q"&&(ey.textContent=UN),cS=a(na),oy=l(na,"P",{"data-svelte-h":!0}),f(oy)!=="svelte-1enn9p2"&&(oy.innerHTML=NN),fS=a(na),ry=l(na,"UL",{"data-svelte-h":!0}),f(ry)!=="svelte-nfr351"&&(ry.innerHTML=qN),gS=a(na),ty=l(na,"P",{"data-svelte-h":!0}),f(ty)!=="svelte-tebhz6"&&(ty.innerHTML=XN),pS=a(na),h(Xl.$$.fragment,na),na.forEach(d),_s.forEach(d),Gw=a(o),h(eh.$$.fragment,o),Pw=a(o),Fo=l(o,"DIV",{class:!0});var bs=v(Fo);h(oh.$$.fragment,bs),hS=a(bs),ny=l(bs,"P",{"data-svelte-h":!0}),f(ny)!=="svelte-10rtpgb"&&(ny.innerHTML=DN),uS=a(bs),ay=l(bs,"P",{"data-svelte-h":!0}),f(ay)!=="svelte-1n2cphd"&&(ay.innerHTML=QN),_S=a(bs),En=l(bs,"DIV",{class:!0});var Td=v(En);h(rh.$$.fragment,Td),bS=a(Td),sy=l(Td,"P",{"data-svelte-h":!0}),f(sy)!=="svelte-10jw89"&&(sy.textContent=HN),MS=a(Td),iy=l(Td,"P",{"data-svelte-h":!0}),f(iy)!=="svelte-1s2prmj"&&(iy.innerHTML=YN),TS=a(Td),h(Dl.$$.fragment,Td),Td.forEach(d),FS=a(bs),de=l(bs,"DIV",{class:!0});var aa=v(de);h(th.$$.fragment,aa),CS=a(aa),ly=l(aa,"P",{"data-svelte-h":!0}),f(ly)!=="svelte-1mjzf2l"&&(ly.textContent=zN),yS=a(aa),dy=l(aa,"P",{"data-svelte-h":!0}),f(dy)!=="svelte-1enn9p2"&&(dy.innerHTML=ON),vS=a(aa),my=l(aa,"UL",{"data-svelte-h":!0}),f(my)!=="svelte-vbkvmt"&&(my.innerHTML=KN),wS=a(aa),cy=l(aa,"P",{"data-svelte-h":!0}),f(cy)!=="svelte-tebhz6"&&(cy.innerHTML=e5),xS=a(aa),h(Ql.$$.fragment,aa),aa.forEach(d),bs.forEach(d),Sw=a(o),h(nh.$$.fragment,o),Ww=a(o),Co=l(o,"DIV",{class:!0});var Ms=v(Co);h(ah.$$.fragment,Ms),LS=a(Ms),fy=l(Ms,"P",{"data-svelte-h":!0}),f(fy)!=="svelte-10rtpgb"&&(fy.innerHTML=o5),kS=a(Ms),gy=l(Ms,"P",{"data-svelte-h":!0}),f(gy)!=="svelte-1n2cphd"&&(gy.innerHTML=r5),$S=a(Ms),In=l(Ms,"DIV",{class:!0});var Fd=v(In);h(sh.$$.fragment,Fd),BS=a(Fd),py=l(Fd,"P",{"data-svelte-h":!0}),f(py)!=="svelte-10jw89"&&(py.textContent=t5),jS=a(Fd),hy=l(Fd,"P",{"data-svelte-h":!0}),f(hy)!=="svelte-1s2prmj"&&(hy.innerHTML=n5),AS=a(Fd),h(Hl.$$.fragment,Fd),Fd.forEach(d),ZS=a(Ms),Mr=l(Ms,"DIV",{class:!0});var Ts=v(Mr);h(ih.$$.fragment,Ts),RS=a(Ts),uy=l(Ts,"P",{"data-svelte-h":!0}),f(uy)!=="svelte-1mjzf2l"&&(uy.textContent=a5),GS=a(Ts),_y=l(Ts,"P",{"data-svelte-h":!0}),f(_y)!=="svelte-1enn9p2"&&(_y.innerHTML=s5),PS=a(Ts),by=l(Ts,"UL",{"data-svelte-h":!0}),f(by)!=="svelte-164zl9b"&&(by.innerHTML=i5),SS=a(Ts),h(Yl.$$.fragment,Ts),Ts.forEach(d),Ms.forEach(d),Jw=a(o),h(lh.$$.fragment,o),Vw=a(o),yo=l(o,"DIV",{class:!0});var Fs=v(yo);h(dh.$$.fragment,Fs),WS=a(Fs),My=l(Fs,"P",{"data-svelte-h":!0}),f(My)!=="svelte-10rtpgb"&&(My.innerHTML=l5),JS=a(Fs),Ty=l(Fs,"P",{"data-svelte-h":!0}),f(Ty)!=="svelte-1n2cphd"&&(Ty.innerHTML=d5),VS=a(Fs),Un=l(Fs,"DIV",{class:!0});var Cd=v(Un);h(mh.$$.fragment,Cd),ES=a(Cd),Fy=l(Cd,"P",{"data-svelte-h":!0}),f(Fy)!=="svelte-10jw89"&&(Fy.textContent=m5),IS=a(Cd),Cy=l(Cd,"P",{"data-svelte-h":!0}),f(Cy)!=="svelte-1s2prmj"&&(Cy.innerHTML=c5),US=a(Cd),h(zl.$$.fragment,Cd),Cd.forEach(d),NS=a(Fs),Tr=l(Fs,"DIV",{class:!0});var Cs=v(Tr);h(ch.$$.fragment,Cs),qS=a(Cs),yy=l(Cs,"P",{"data-svelte-h":!0}),f(yy)!=="svelte-1mjzf2l"&&(yy.textContent=f5),XS=a(Cs),vy=l(Cs,"P",{"data-svelte-h":!0}),f(vy)!=="svelte-1enn9p2"&&(vy.innerHTML=g5),DS=a(Cs),wy=l(Cs,"UL",{"data-svelte-h":!0}),f(wy)!=="svelte-fjtit3"&&(wy.innerHTML=p5),QS=a(Cs),h(Ol.$$.fragment,Cs),Cs.forEach(d),Fs.forEach(d),Ew=a(o),h(fh.$$.fragment,o),Iw=a(o),xy=l(o,"P",{}),v(xy).forEach(d),this.h()},h(){w(t,"name","hf:doc:metadata"),w(t,"content",Y4),w(Eo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ws,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Io,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ls,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Bs,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(fe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(A,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Zs,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ge,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ps,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(pe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Gt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(he,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Pt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Uo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(St,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(No,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(_e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Wt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(be,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Jt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(qo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Vt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Xo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Et,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Fe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(It,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Do,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ut,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Qo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ye,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Nt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ve,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(qt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ho,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(we,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Xt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Yo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(xe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Om,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(oc,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Dt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Qt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(zo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ke,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ht,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Oo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w($e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Yt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Be,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(zt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ko,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(je,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ot,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(er,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ae,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Kt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ze,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(en,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(or,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(on,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(rr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ge,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(rn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Pe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(tn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(tr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Se,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(nn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(nr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(We,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(an,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Je,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(sn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ar,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ve,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ln,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(sr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(dn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(U,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(mn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ir,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(cn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(lr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ne,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(yf,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(xf,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(fn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(qe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(gn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Xe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(pn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(dr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(De,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(hn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(mr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Qe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(un,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(He,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(zf,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(_n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ye,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(bn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(cr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ze,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Mn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Oe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Tn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ke,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ug,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Fn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(eo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Cn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(fr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(oo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(yn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ro,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(vn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(to,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(wn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(no,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(xn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(gr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ao,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Ln,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(so,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(kn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(oe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(io,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w($n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(pr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(lo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Bn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(mo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(jn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(co,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(An,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ne,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(fo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Zn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(hr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(go,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Rn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ur,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(po,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Gn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ae,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ho,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Lp,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Bp,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Pn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(se,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(uo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Sn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(_r,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(_o,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Wn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(bo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Jn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(br,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Mo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Vn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(To,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(En,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Fo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(In,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Mr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Co,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Un,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Tr,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(yo,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(o,g){r(document.head,t),T(o,C,g),T(o,m,g),T(o,s,g),u(c,o,g),T(o,e,g),T(o,F,g),T(o,Ay,g),T(o,yd,g),T(o,Zy,g),u(vd,o,g),T(o,Ry,g),T(o,wd,g),T(o,Gy,g),T(o,xd,g),T(o,Py,g),u(Ld,o,g),T(o,Sy,g),T(o,kd,g),T(o,Wy,g),u($d,o,g),T(o,Jy,g),T(o,Bd,g),T(o,Vy,g),u(ys,o,g),T(o,Ey,g),u(jd,o,g),T(o,Iy,g),T(o,me,g),u(Ad,me,null),r(me,qw),r(me,bh),r(me,Xw),r(me,Mh),r(me,Dw),r(me,Eo),u(Zd,Eo,null),r(Eo,Qw),r(Eo,Th),r(Eo,Hw),r(Eo,Fh),r(Eo,Yw),r(Eo,Ch),r(Eo,zw),u(vs,Eo,null),r(me,Ow),r(me,ws),u(Rd,ws,null),r(ws,Kw),r(ws,yh),T(o,Uy,g),u(Gd,o,g),T(o,Ny,g),T(o,ce,g),u(Pd,ce,null),r(ce,e3),r(ce,vh),r(ce,o3),r(ce,wh),r(ce,r3),r(ce,Io),u(Sd,Io,null),r(Io,t3),r(Io,xh),r(Io,n3),r(Io,Lh),r(Io,a3),r(Io,kh),r(Io,s3),u(xs,Io,null),r(ce,i3),r(ce,Ls),u(Wd,Ls,null),r(Ls,l3),r(Ls,$h),T(o,qy,g),u(Jd,o,g),T(o,Xy,g),T(o,fe,g),u(Vd,fe,null),r(fe,d3),r(fe,Bh),r(fe,m3),r(fe,jh),r(fe,c3),r(fe,j),u(Ed,j,null),r(j,f3),r(j,Ah),r(j,g3),r(j,Zh),r(j,p3),r(j,Rh),r(j,h3),u(ks,j,null),r(j,u3),u($s,j,null),r(fe,_3),r(fe,Bs),u(Id,Bs,null),r(Bs,b3),r(Bs,Gh),T(o,Dy,g),u(Ud,o,g),T(o,Qy,g),T(o,ge,g),u(Nd,ge,null),r(ge,M3),r(ge,Ph),r(ge,T3),r(ge,Sh),r(ge,F3),r(ge,A),u(qd,A,null),r(A,C3),r(A,Wh),r(A,y3),r(A,Jh),r(A,v3),r(A,Vh),r(A,w3),u(js,A,null),r(A,x3),u(As,A,null),r(ge,L3),r(ge,Zs),u(Xd,Zs,null),r(Zs,k3),r(Zs,Eh),T(o,Hy,g),u(Dd,o,g),T(o,Yy,g),T(o,pe,g),u(Qd,pe,null),r(pe,$3),r(pe,Ih),r(pe,B3),r(pe,Uh),r(pe,j3),r(pe,Z),u(Hd,Z,null),r(Z,A3),r(Z,Nh),r(Z,Z3),r(Z,qh),r(Z,R3),r(Z,Xh),r(Z,G3),u(Rs,Z,null),r(Z,P3),u(Gs,Z,null),r(pe,S3),r(pe,Ps),u(Yd,Ps,null),r(Ps,W3),r(Ps,Dh),T(o,zy,g),u(zd,o,g),T(o,Oy,g),T(o,Od,g),T(o,Ky,g),u(Kd,o,g),T(o,ev,g),T(o,he,g),u(em,he,null),r(he,J3),r(he,Qh),r(he,V3),r(he,Hh),r(he,E3),r(he,Gt),u(om,Gt,null),r(Gt,I3),r(Gt,Yh),r(Gt,U3),r(Gt,zh),r(Gt,N3),u(Ss,Gt,null),r(he,q3),r(he,R),u(rm,R,null),r(R,X3),r(R,Oh),r(R,D3),r(R,Kh),r(R,Q3),r(R,eu),r(R,H3),r(R,ou),r(R,Y3),u(Ws,R,null),T(o,ov,g),u(tm,o,g),T(o,rv,g),T(o,ue,g),u(nm,ue,null),r(ue,z3),r(ue,ru),r(ue,O3),r(ue,tu),r(ue,K3),r(ue,Pt),u(am,Pt,null),r(Pt,ex),r(Pt,nu),r(Pt,ox),r(Pt,au),r(Pt,rx),u(Js,Pt,null),r(ue,tx),r(ue,Uo),u(sm,Uo,null),r(Uo,nx),r(Uo,su),r(Uo,ax),r(Uo,iu),r(Uo,sx),r(Uo,lu),r(Uo,ix),u(Vs,Uo,null),T(o,tv,g),u(im,o,g),T(o,nv,g),T(o,_e,g),u(lm,_e,null),r(_e,lx),r(_e,du),r(_e,dx),r(_e,mu),r(_e,mx),r(_e,St),u(dm,St,null),r(St,cx),r(St,cu),r(St,fx),r(St,fu),r(St,gx),u(Es,St,null),r(_e,px),r(_e,No),u(mm,No,null),r(No,hx),r(No,gu),r(No,ux),r(No,pu),r(No,_x),r(No,hu),r(No,bx),u(Is,No,null),T(o,av,g),u(cm,o,g),T(o,sv,g),T(o,fm,g),T(o,iv,g),u(gm,o,g),T(o,lv,g),T(o,be,g),u(pm,be,null),r(be,Mx),r(be,uu),r(be,Tx),r(be,_u),r(be,Fx),r(be,Wt),u(hm,Wt,null),r(Wt,Cx),r(Wt,bu),r(Wt,yx),r(Wt,Mu),r(Wt,vx),u(Us,Wt,null),r(be,wx),r(be,G),u(um,G,null),r(G,xx),r(G,Tu),r(G,Lx),r(G,Fu),r(G,kx),r(G,Cu),r(G,$x),r(G,yu),r(G,Bx),u(Ns,G,null),T(o,dv,g),u(_m,o,g),T(o,mv,g),T(o,Me,g),u(bm,Me,null),r(Me,jx),r(Me,vu),r(Me,Ax),r(Me,wu),r(Me,Zx),r(Me,Jt),u(Mm,Jt,null),r(Jt,Rx),r(Jt,xu),r(Jt,Gx),r(Jt,Lu),r(Jt,Px),u(qs,Jt,null),r(Me,Sx),r(Me,qo),u(Tm,qo,null),r(qo,Wx),r(qo,ku),r(qo,Jx),r(qo,$u),r(qo,Vx),r(qo,Bu),r(qo,Ex),u(Xs,qo,null),T(o,cv,g),u(Fm,o,g),T(o,fv,g),T(o,Te,g),u(Cm,Te,null),r(Te,Ix),r(Te,ju),r(Te,Ux),r(Te,Au),r(Te,Nx),r(Te,Vt),u(ym,Vt,null),r(Vt,qx),r(Vt,Zu),r(Vt,Xx),r(Vt,Ru),r(Vt,Dx),u(Ds,Vt,null),r(Te,Qx),r(Te,Xo),u(vm,Xo,null),r(Xo,Hx),r(Xo,Gu),r(Xo,Yx),r(Xo,Pu),r(Xo,zx),r(Xo,Su),r(Xo,Ox),u(Qs,Xo,null),T(o,gv,g),u(wm,o,g),T(o,pv,g),T(o,xm,g),T(o,hv,g),u(Lm,o,g),T(o,uv,g),T(o,Fe,g),u(km,Fe,null),r(Fe,Kx),r(Fe,Wu),r(Fe,e6),r(Fe,Ju),r(Fe,o6),r(Fe,Et),u($m,Et,null),r(Et,r6),r(Et,Vu),r(Et,t6),r(Et,Eu),r(Et,n6),u(Hs,Et,null),r(Fe,a6),r(Fe,P),u(Bm,P,null),r(P,s6),r(P,Iu),r(P,i6),r(P,Uu),r(P,l6),r(P,Nu),r(P,d6),r(P,qu),r(P,m6),u(Ys,P,null),T(o,_v,g),u(jm,o,g),T(o,bv,g),T(o,Ce,g),u(Am,Ce,null),r(Ce,c6),r(Ce,Xu),r(Ce,f6),r(Ce,Du),r(Ce,g6),r(Ce,It),u(Zm,It,null),r(It,p6),r(It,Qu),r(It,h6),r(It,Hu),r(It,u6),u(zs,It,null),r(Ce,_6),r(Ce,Do),u(Rm,Do,null),r(Do,b6),r(Do,Yu),r(Do,M6),r(Do,zu),r(Do,T6),r(Do,Ou),r(Do,F6),u(Os,Do,null),T(o,Mv,g),u(Gm,o,g),T(o,Tv,g),T(o,ye,g),u(Pm,ye,null),r(ye,C6),r(ye,Ku),r(ye,y6),r(ye,e_),r(ye,v6),r(ye,Ut),u(Sm,Ut,null),r(Ut,w6),r(Ut,o_),r(Ut,x6),r(Ut,r_),r(Ut,L6),u(Ks,Ut,null),r(ye,k6),r(ye,Qo),u(Wm,Qo,null),r(Qo,$6),r(Qo,t_),r(Qo,B6),r(Qo,n_),r(Qo,j6),r(Qo,a_),r(Qo,A6),u(ei,Qo,null),T(o,Fv,g),u(Jm,o,g),T(o,Cv,g),T(o,ve,g),u(Vm,ve,null),r(ve,Z6),r(ve,s_),r(ve,R6),r(ve,i_),r(ve,G6),r(ve,Nt),u(Em,Nt,null),r(Nt,P6),r(Nt,l_),r(Nt,S6),r(Nt,d_),r(Nt,W6),u(oi,Nt,null),r(ve,J6),r(ve,S),u(Im,S,null),r(S,V6),r(S,m_),r(S,E6),r(S,c_),r(S,I6),r(S,f_),r(S,U6),r(S,g_),r(S,N6),u(ri,S,null),T(o,yv,g),u(Um,o,g),T(o,vv,g),T(o,we,g),u(Nm,we,null),r(we,q6),r(we,p_),r(we,X6),r(we,h_),r(we,D6),r(we,qt),u(qm,qt,null),r(qt,Q6),r(qt,u_),r(qt,H6),r(qt,__),r(qt,Y6),u(ti,qt,null),r(we,z6),r(we,Ho),u(Xm,Ho,null),r(Ho,O6),r(Ho,b_),r(Ho,K6),r(Ho,M_),r(Ho,eL),r(Ho,T_),r(Ho,oL),u(ni,Ho,null),T(o,wv,g),u(Dm,o,g),T(o,xv,g),T(o,xe,g),u(Qm,xe,null),r(xe,rL),r(xe,F_),r(xe,tL),r(xe,C_),r(xe,nL),r(xe,Xt),u(Hm,Xt,null),r(Xt,aL),r(Xt,y_),r(Xt,sL),r(Xt,v_),r(Xt,iL),u(ai,Xt,null),r(xe,lL),r(xe,Yo),u(Ym,Yo,null),r(Yo,dL),r(Yo,w_),r(Yo,mL),r(Yo,x_),r(Yo,cL),r(Yo,L_),r(Yo,fL),u(si,Yo,null),T(o,Lv,g),u(zm,o,g),T(o,kv,g),T(o,Om,g),u(Km,Om,null),T(o,$v,g),u(ec,o,g),T(o,Bv,g),T(o,oc,g),u(rc,oc,null),T(o,jv,g),u(tc,o,g),T(o,Av,g),T(o,Le,g),u(nc,Le,null),r(Le,gL),r(Le,k_),r(Le,pL),r(Le,$_),r(Le,hL),r(Le,Dt),u(ac,Dt,null),r(Dt,uL),r(Dt,B_),r(Dt,_L),r(Dt,j_),r(Dt,bL),u(ii,Dt,null),r(Le,ML),r(Le,W),u(sc,W,null),r(W,TL),r(W,A_),r(W,FL),r(W,Z_),r(W,CL),r(W,R_),r(W,yL),r(W,G_),r(W,vL),u(li,W,null),T(o,Zv,g),u(ic,o,g),T(o,Rv,g),T(o,ke,g),u(lc,ke,null),r(ke,wL),r(ke,P_),r(ke,xL),r(ke,S_),r(ke,LL),r(ke,Qt),u(dc,Qt,null),r(Qt,kL),r(Qt,W_),r(Qt,$L),r(Qt,J_),r(Qt,BL),u(di,Qt,null),r(ke,jL),r(ke,zo),u(mc,zo,null),r(zo,AL),r(zo,V_),r(zo,ZL),r(zo,E_),r(zo,RL),r(zo,I_),r(zo,GL),u(mi,zo,null),T(o,Gv,g),u(cc,o,g),T(o,Pv,g),T(o,$e,g),u(fc,$e,null),r($e,PL),r($e,U_),r($e,SL),r($e,N_),r($e,WL),r($e,Ht),u(gc,Ht,null),r(Ht,JL),r(Ht,q_),r(Ht,VL),r(Ht,X_),r(Ht,EL),u(ci,Ht,null),r($e,IL),r($e,Oo),u(pc,Oo,null),r(Oo,UL),r(Oo,D_),r(Oo,NL),r(Oo,Q_),r(Oo,qL),r(Oo,H_),r(Oo,XL),u(fi,Oo,null),T(o,Sv,g),u(hc,o,g),T(o,Wv,g),T(o,Be,g),u(uc,Be,null),r(Be,DL),r(Be,Y_),r(Be,QL),r(Be,z_),r(Be,HL),r(Be,Yt),u(_c,Yt,null),r(Yt,YL),r(Yt,O_),r(Yt,zL),r(Yt,K_),r(Yt,OL),u(gi,Yt,null),r(Be,KL),r(Be,J),u(bc,J,null),r(J,ek),r(J,e1),r(J,ok),r(J,o1),r(J,rk),r(J,r1),r(J,tk),r(J,t1),r(J,nk),u(pi,J,null),T(o,Jv,g),u(Mc,o,g),T(o,Vv,g),T(o,je,g),u(Tc,je,null),r(je,ak),r(je,n1),r(je,sk),r(je,a1),r(je,ik),r(je,zt),u(Fc,zt,null),r(zt,lk),r(zt,s1),r(zt,dk),r(zt,i1),r(zt,mk),u(hi,zt,null),r(je,ck),r(je,Ko),u(Cc,Ko,null),r(Ko,fk),r(Ko,l1),r(Ko,gk),r(Ko,d1),r(Ko,pk),r(Ko,m1),r(Ko,hk),u(ui,Ko,null),T(o,Ev,g),u(yc,o,g),T(o,Iv,g),T(o,Ae,g),u(vc,Ae,null),r(Ae,uk),r(Ae,c1),r(Ae,_k),r(Ae,f1),r(Ae,bk),r(Ae,Ot),u(wc,Ot,null),r(Ot,Mk),r(Ot,g1),r(Ot,Tk),r(Ot,p1),r(Ot,Fk),u(_i,Ot,null),r(Ae,Ck),r(Ae,er),u(xc,er,null),r(er,yk),r(er,h1),r(er,vk),r(er,u1),r(er,wk),r(er,_1),r(er,xk),u(bi,er,null),T(o,Uv,g),u(Lc,o,g),T(o,Nv,g),T(o,Ze,g),u(kc,Ze,null),r(Ze,Lk),r(Ze,b1),r(Ze,kk),r(Ze,M1),r(Ze,$k),r(Ze,Kt),u($c,Kt,null),r(Kt,Bk),r(Kt,T1),r(Kt,jk),r(Kt,F1),r(Kt,Ak),u(Mi,Kt,null),r(Ze,Zk),r(Ze,V),u(Bc,V,null),r(V,Rk),r(V,C1),r(V,Gk),r(V,y1),r(V,Pk),r(V,v1),r(V,Sk),r(V,w1),r(V,Wk),u(Ti,V,null),T(o,qv,g),u(jc,o,g),T(o,Xv,g),T(o,Re,g),u(Ac,Re,null),r(Re,Jk),r(Re,x1),r(Re,Vk),r(Re,L1),r(Re,Ek),r(Re,en),u(Zc,en,null),r(en,Ik),r(en,k1),r(en,Uk),r(en,$1),r(en,Nk),u(Fi,en,null),r(Re,qk),r(Re,or),u(Rc,or,null),r(or,Xk),r(or,B1),r(or,Dk),r(or,j1),r(or,Qk),r(or,A1),r(or,Hk),u(Ci,or,null),T(o,Dv,g),u(Gc,o,g),T(o,Qv,g),T(o,Ge,g),u(Pc,Ge,null),r(Ge,Yk),r(Ge,Z1),r(Ge,zk),r(Ge,R1),r(Ge,Ok),r(Ge,on),u(Sc,on,null),r(on,Kk),r(on,G1),r(on,e$),r(on,P1),r(on,o$),u(yi,on,null),r(Ge,r$),r(Ge,rr),u(Wc,rr,null),r(rr,t$),r(rr,S1),r(rr,n$),r(rr,W1),r(rr,a$),r(rr,J1),r(rr,s$),u(vi,rr,null),T(o,Hv,g),u(Jc,o,g),T(o,Yv,g),T(o,Pe,g),u(Vc,Pe,null),r(Pe,i$),r(Pe,V1),r(Pe,l$),r(Pe,E1),r(Pe,d$),r(Pe,rn),u(Ec,rn,null),r(rn,m$),r(rn,I1),r(rn,c$),r(rn,U1),r(rn,f$),u(wi,rn,null),r(Pe,g$),r(Pe,E),u(Ic,E,null),r(E,p$),r(E,N1),r(E,h$),r(E,q1),r(E,u$),r(E,X1),r(E,_$),r(E,D1),r(E,b$),u(xi,E,null),T(o,zv,g),u(Uc,o,g),T(o,Ov,g),T(o,Se,g),u(Nc,Se,null),r(Se,M$),r(Se,Q1),r(Se,T$),r(Se,H1),r(Se,F$),r(Se,tn),u(qc,tn,null),r(tn,C$),r(tn,Y1),r(tn,y$),r(tn,z1),r(tn,v$),u(Li,tn,null),r(Se,w$),r(Se,tr),u(Xc,tr,null),r(tr,x$),r(tr,O1),r(tr,L$),r(tr,K1),r(tr,k$),r(tr,eb),r(tr,$$),u(ki,tr,null),T(o,Kv,g),u(Dc,o,g),T(o,e2,g),T(o,We,g),u(Qc,We,null),r(We,B$),r(We,ob),r(We,j$),r(We,rb),r(We,A$),r(We,nn),u(Hc,nn,null),r(nn,Z$),r(nn,tb),r(nn,R$),r(nn,nb),r(nn,G$),u($i,nn,null),r(We,P$),r(We,nr),u(Yc,nr,null),r(nr,S$),r(nr,ab),r(nr,W$),r(nr,sb),r(nr,J$),r(nr,ib),r(nr,V$),u(Bi,nr,null),T(o,o2,g),u(zc,o,g),T(o,r2,g),T(o,Je,g),u(Oc,Je,null),r(Je,E$),r(Je,lb),r(Je,I$),r(Je,db),r(Je,U$),r(Je,an),u(Kc,an,null),r(an,N$),r(an,mb),r(an,q$),r(an,cb),r(an,X$),u(ji,an,null),r(Je,D$),r(Je,I),u(ef,I,null),r(I,Q$),r(I,fb),r(I,H$),r(I,gb),r(I,Y$),r(I,pb),r(I,z$),r(I,hb),r(I,O$),u(Ai,I,null),T(o,t2,g),u(of,o,g),T(o,n2,g),T(o,Ve,g),u(rf,Ve,null),r(Ve,K$),r(Ve,ub),r(Ve,eB),r(Ve,_b),r(Ve,oB),r(Ve,sn),u(tf,sn,null),r(sn,rB),r(sn,bb),r(sn,tB),r(sn,Mb),r(sn,nB),u(Zi,sn,null),r(Ve,aB),r(Ve,ar),u(nf,ar,null),r(ar,sB),r(ar,Tb),r(ar,iB),r(ar,Fb),r(ar,lB),r(ar,Cb),r(ar,dB),u(Ri,ar,null),T(o,a2,g),u(af,o,g),T(o,s2,g),T(o,Ee,g),u(sf,Ee,null),r(Ee,mB),r(Ee,yb),r(Ee,cB),r(Ee,vb),r(Ee,fB),r(Ee,ln),u(lf,ln,null),r(ln,gB),r(ln,wb),r(ln,pB),r(ln,xb),r(ln,hB),u(Gi,ln,null),r(Ee,uB),r(Ee,sr),u(df,sr,null),r(sr,_B),r(sr,Lb),r(sr,bB),r(sr,kb),r(sr,MB),r(sr,$b),r(sr,TB),u(Pi,sr,null),T(o,i2,g),u(mf,o,g),T(o,l2,g),T(o,Ie,g),u(cf,Ie,null),r(Ie,FB),r(Ie,Bb),r(Ie,CB),r(Ie,jb),r(Ie,yB),r(Ie,dn),u(ff,dn,null),r(dn,vB),r(dn,Ab),r(dn,wB),r(dn,Zb),r(dn,xB),u(Si,dn,null),r(Ie,LB),r(Ie,U),u(gf,U,null),r(U,kB),r(U,Rb),r(U,$B),r(U,Gb),r(U,BB),r(U,Pb),r(U,jB),r(U,Sb),r(U,AB),u(Wi,U,null),T(o,d2,g),u(pf,o,g),T(o,m2,g),T(o,Ue,g),u(hf,Ue,null),r(Ue,ZB),r(Ue,Wb),r(Ue,RB),r(Ue,Jb),r(Ue,GB),r(Ue,mn),u(uf,mn,null),r(mn,PB),r(mn,Vb),r(mn,SB),r(mn,Eb),r(mn,WB),u(Ji,mn,null),r(Ue,JB),r(Ue,ir),u(_f,ir,null),r(ir,VB),r(ir,Ib),r(ir,EB),r(ir,Ub),r(ir,IB),r(ir,Nb),r(ir,UB),u(Vi,ir,null),T(o,c2,g),u(bf,o,g),T(o,f2,g),T(o,Ne,g),u(Mf,Ne,null),r(Ne,NB),r(Ne,qb),r(Ne,qB),r(Ne,Xb),r(Ne,XB),r(Ne,cn),u(Tf,cn,null),r(cn,DB),r(cn,Db),r(cn,QB),r(cn,Qb),r(cn,HB),u(Ei,cn,null),r(Ne,YB),r(Ne,lr),u(Ff,lr,null),r(lr,zB),r(lr,Hb),r(lr,OB),r(lr,Yb),r(lr,KB),r(lr,zb),r(lr,ej),u(Ii,lr,null),T(o,g2,g),u(Cf,o,g),T(o,p2,g),T(o,yf,g),u(vf,yf,null),T(o,h2,g),u(wf,o,g),T(o,u2,g),T(o,xf,g),u(Lf,xf,null),T(o,_2,g),u(kf,o,g),T(o,b2,g),T(o,$f,g),T(o,M2,g),u(Bf,o,g),T(o,T2,g),T(o,qe,g),u(jf,qe,null),r(qe,oj),r(qe,Ob),r(qe,rj),r(qe,Kb),r(qe,tj),r(qe,fn),u(Af,fn,null),r(fn,nj),r(fn,eM),r(fn,aj),r(fn,oM),r(fn,sj),u(Ui,fn,null),r(qe,ij),r(qe,N),u(Zf,N,null),r(N,lj),r(N,rM),r(N,dj),r(N,tM),r(N,mj),r(N,nM),r(N,cj),r(N,aM),r(N,fj),u(Ni,N,null),T(o,F2,g),u(Rf,o,g),T(o,C2,g),T(o,Xe,g),u(Gf,Xe,null),r(Xe,gj),r(Xe,sM),r(Xe,pj),r(Xe,iM),r(Xe,hj),r(Xe,gn),u(Pf,gn,null),r(gn,uj),r(gn,lM),r(gn,_j),r(gn,dM),r(gn,bj),u(qi,gn,null),r(Xe,Mj),r(Xe,q),u(Sf,q,null),r(q,Tj),r(q,mM),r(q,Fj),r(q,cM),r(q,Cj),r(q,fM),r(q,yj),r(q,gM),r(q,vj),u(Xi,q,null),T(o,y2,g),u(Wf,o,g),T(o,v2,g),T(o,De,g),u(Jf,De,null),r(De,wj),r(De,pM),r(De,xj),r(De,hM),r(De,Lj),r(De,pn),u(Vf,pn,null),r(pn,kj),r(pn,uM),r(pn,$j),r(pn,_M),r(pn,Bj),u(Di,pn,null),r(De,jj),r(De,dr),u(Ef,dr,null),r(dr,Aj),r(dr,bM),r(dr,Zj),r(dr,MM),r(dr,Rj),r(dr,TM),r(dr,Gj),u(Qi,dr,null),T(o,w2,g),u(If,o,g),T(o,x2,g),T(o,Qe,g),u(Uf,Qe,null),r(Qe,Pj),r(Qe,FM),r(Qe,Sj),r(Qe,CM),r(Qe,Wj),r(Qe,hn),u(Nf,hn,null),r(hn,Jj),r(hn,yM),r(hn,Vj),r(hn,vM),r(hn,Ej),u(Hi,hn,null),r(Qe,Ij),r(Qe,mr),u(qf,mr,null),r(mr,Uj),r(mr,wM),r(mr,Nj),r(mr,xM),r(mr,qj),r(mr,LM),r(mr,Xj),u(Yi,mr,null),T(o,L2,g),u(Xf,o,g),T(o,k2,g),T(o,He,g),u(Df,He,null),r(He,Dj),r(He,kM),r(He,Qj),r(He,$M),r(He,Hj),r(He,un),u(Qf,un,null),r(un,Yj),r(un,BM),r(un,zj),r(un,jM),r(un,Oj),u(zi,un,null),r(He,Kj),r(He,X),u(Hf,X,null),r(X,eA),r(X,AM),r(X,oA),r(X,ZM),r(X,rA),r(X,RM),r(X,tA),r(X,GM),r(X,nA),u(Oi,X,null),T(o,$2,g),u(Yf,o,g),T(o,B2,g),T(o,zf,g),u(Of,zf,null),T(o,j2,g),u(Kf,o,g),T(o,A2,g),T(o,Ye,g),u(eg,Ye,null),r(Ye,aA),r(Ye,PM),r(Ye,sA),r(Ye,SM),r(Ye,iA),r(Ye,_n),u(og,_n,null),r(_n,lA),r(_n,WM),r(_n,dA),r(_n,JM),r(_n,mA),u(Ki,_n,null),r(Ye,cA),r(Ye,D),u(rg,D,null),r(D,fA),r(D,VM),r(D,gA),r(D,EM),r(D,pA),r(D,IM),r(D,hA),r(D,UM),r(D,uA),u(el,D,null),T(o,Z2,g),u(tg,o,g),T(o,R2,g),T(o,ze,g),u(ng,ze,null),r(ze,_A),r(ze,NM),r(ze,bA),r(ze,qM),r(ze,MA),r(ze,bn),u(ag,bn,null),r(bn,TA),r(bn,XM),r(bn,FA),r(bn,DM),r(bn,CA),u(ol,bn,null),r(ze,yA),r(ze,cr),u(sg,cr,null),r(cr,vA),r(cr,QM),r(cr,wA),r(cr,HM),r(cr,xA),r(cr,YM),r(cr,LA),u(rl,cr,null),T(o,G2,g),u(ig,o,g),T(o,P2,g),T(o,Oe,g),u(lg,Oe,null),r(Oe,kA),r(Oe,zM),r(Oe,$A),r(Oe,OM),r(Oe,BA),r(Oe,Mn),u(dg,Mn,null),r(Mn,jA),r(Mn,KM),r(Mn,AA),r(Mn,eT),r(Mn,ZA),u(tl,Mn,null),r(Oe,RA),r(Oe,Q),u(mg,Q,null),r(Q,GA),r(Q,oT),r(Q,PA),r(Q,rT),r(Q,SA),r(Q,tT),r(Q,WA),r(Q,nT),r(Q,JA),u(nl,Q,null),T(o,S2,g),u(cg,o,g),T(o,W2,g),T(o,Ke,g),u(fg,Ke,null),r(Ke,VA),r(Ke,aT),r(Ke,EA),r(Ke,sT),r(Ke,IA),r(Ke,Tn),u(gg,Tn,null),r(Tn,UA),r(Tn,iT),r(Tn,NA),r(Tn,lT),r(Tn,qA),u(al,Tn,null),r(Ke,XA),r(Ke,H),u(pg,H,null),r(H,DA),r(H,dT),r(H,QA),r(H,mT),r(H,HA),r(H,cT),r(H,YA),r(H,fT),r(H,zA),u(sl,H,null),T(o,J2,g),u(hg,o,g),T(o,V2,g),T(o,ug,g),u(_g,ug,null),T(o,E2,g),u(bg,o,g),T(o,I2,g),T(o,eo,g),u(Mg,eo,null),r(eo,OA),r(eo,gT),r(eo,KA),r(eo,pT),r(eo,eZ),r(eo,Fn),u(Tg,Fn,null),r(Fn,oZ),r(Fn,hT),r(Fn,rZ),r(Fn,uT),r(Fn,tZ),u(il,Fn,null),r(eo,nZ),r(eo,Y),u(Fg,Y,null),r(Y,aZ),r(Y,_T),r(Y,sZ),r(Y,bT),r(Y,iZ),r(Y,MT),r(Y,lZ),r(Y,TT),r(Y,dZ),u(ll,Y,null),T(o,U2,g),u(Cg,o,g),T(o,N2,g),T(o,oo,g),u(yg,oo,null),r(oo,mZ),r(oo,FT),r(oo,cZ),r(oo,CT),r(oo,fZ),r(oo,Cn),u(vg,Cn,null),r(Cn,gZ),r(Cn,yT),r(Cn,pZ),r(Cn,vT),r(Cn,hZ),u(dl,Cn,null),r(oo,uZ),r(oo,fr),u(wg,fr,null),r(fr,_Z),r(fr,wT),r(fr,bZ),r(fr,xT),r(fr,MZ),r(fr,LT),r(fr,TZ),u(ml,fr,null),T(o,q2,g),u(xg,o,g),T(o,X2,g),T(o,ro,g),u(Lg,ro,null),r(ro,FZ),r(ro,kT),r(ro,CZ),r(ro,$T),r(ro,yZ),r(ro,yn),u(kg,yn,null),r(yn,vZ),r(yn,BT),r(yn,wZ),r(yn,jT),r(yn,xZ),u(cl,yn,null),r(ro,LZ),r(ro,z),u($g,z,null),r(z,kZ),r(z,AT),r(z,$Z),r(z,ZT),r(z,BZ),r(z,RT),r(z,jZ),r(z,GT),r(z,AZ),u(fl,z,null),T(o,D2,g),u(Bg,o,g),T(o,Q2,g),T(o,to,g),u(jg,to,null),r(to,ZZ),r(to,PT),r(to,RZ),r(to,ST),r(to,GZ),r(to,vn),u(Ag,vn,null),r(vn,PZ),r(vn,WT),r(vn,SZ),r(vn,JT),r(vn,WZ),u(gl,vn,null),r(to,JZ),r(to,O),u(Zg,O,null),r(O,VZ),r(O,VT),r(O,EZ),r(O,ET),r(O,IZ),r(O,IT),r(O,UZ),r(O,UT),r(O,NZ),u(pl,O,null),T(o,H2,g),u(Rg,o,g),T(o,Y2,g),T(o,no,g),u(Gg,no,null),r(no,qZ),r(no,NT),r(no,XZ),r(no,qT),r(no,DZ),r(no,wn),u(Pg,wn,null),r(wn,QZ),r(wn,XT),r(wn,HZ),r(wn,DT),r(wn,YZ),u(hl,wn,null),r(no,zZ),r(no,K),u(Sg,K,null),r(K,OZ),r(K,QT),r(K,KZ),r(K,HT),r(K,eR),r(K,YT),r(K,oR),r(K,zT),r(K,rR),u(ul,K,null),T(o,z2,g),u(Wg,o,g),T(o,O2,g),T(o,ao,g),u(Jg,ao,null),r(ao,tR),r(ao,OT),r(ao,nR),r(ao,KT),r(ao,aR),r(ao,xn),u(Vg,xn,null),r(xn,sR),r(xn,eF),r(xn,iR),r(xn,oF),r(xn,lR),u(_l,xn,null),r(ao,dR),r(ao,gr),u(Eg,gr,null),r(gr,mR),r(gr,rF),r(gr,cR),r(gr,tF),r(gr,fR),r(gr,nF),r(gr,gR),u(bl,gr,null),T(o,K2,g),u(Ig,o,g),T(o,ew,g),T(o,so,g),u(Ug,so,null),r(so,pR),r(so,aF),r(so,hR),r(so,sF),r(so,uR),r(so,Ln),u(Ng,Ln,null),r(Ln,_R),r(Ln,iF),r(Ln,bR),r(Ln,lF),r(Ln,MR),u(Ml,Ln,null),r(so,TR),r(so,ee),u(qg,ee,null),r(ee,FR),r(ee,dF),r(ee,CR),r(ee,mF),r(ee,yR),r(ee,cF),r(ee,vR),r(ee,fF),r(ee,wR),u(Tl,ee,null),T(o,ow,g),u(Xg,o,g),T(o,rw,g),T(o,Dg,g),T(o,tw,g),u(Qg,o,g),T(o,nw,g),T(o,io,g),u(Hg,io,null),r(io,xR),r(io,gF),r(io,LR),r(io,pF),r(io,kR),r(io,kn),u(Yg,kn,null),r(kn,$R),r(kn,hF),r(kn,BR),r(kn,uF),r(kn,jR),u(Fl,kn,null),r(io,AR),r(io,oe),u(zg,oe,null),r(oe,ZR),r(oe,_F),r(oe,RR),r(oe,bF),r(oe,GR),r(oe,MF),r(oe,PR),r(oe,TF),r(oe,SR),u(Cl,oe,null),T(o,aw,g),u(Og,o,g),T(o,sw,g),T(o,lo,g),u(Kg,lo,null),r(lo,WR),r(lo,FF),r(lo,JR),r(lo,CF),r(lo,VR),r(lo,$n),u(ep,$n,null),r($n,ER),r($n,yF),r($n,IR),r($n,vF),r($n,UR),u(yl,$n,null),r(lo,NR),r(lo,pr),u(op,pr,null),r(pr,qR),r(pr,wF),r(pr,XR),r(pr,xF),r(pr,DR),r(pr,LF),r(pr,QR),u(vl,pr,null),T(o,iw,g),u(rp,o,g),T(o,lw,g),T(o,mo,g),u(tp,mo,null),r(mo,HR),r(mo,kF),r(mo,YR),r(mo,$F),r(mo,zR),r(mo,Bn),u(np,Bn,null),r(Bn,OR),r(Bn,BF),r(Bn,KR),r(Bn,jF),r(Bn,eG),u(wl,Bn,null),r(mo,oG),r(mo,re),u(ap,re,null),r(re,rG),r(re,AF),r(re,tG),r(re,ZF),r(re,nG),r(re,RF),r(re,aG),r(re,GF),r(re,sG),u(xl,re,null),T(o,dw,g),u(sp,o,g),T(o,mw,g),T(o,co,g),u(ip,co,null),r(co,iG),r(co,PF),r(co,lG),r(co,SF),r(co,dG),r(co,jn),u(lp,jn,null),r(jn,mG),r(jn,WF),r(jn,cG),r(jn,JF),r(jn,fG),u(Ll,jn,null),r(co,gG),r(co,te),u(dp,te,null),r(te,pG),r(te,VF),r(te,hG),r(te,EF),r(te,uG),r(te,IF),r(te,_G),r(te,UF),r(te,bG),u(kl,te,null),T(o,cw,g),u(mp,o,g),T(o,fw,g),T(o,fo,g),u(cp,fo,null),r(fo,MG),r(fo,NF),r(fo,TG),r(fo,qF),r(fo,FG),r(fo,An),u(fp,An,null),r(An,CG),r(An,XF),r(An,yG),r(An,DF),r(An,vG),u($l,An,null),r(fo,wG),r(fo,ne),u(gp,ne,null),r(ne,xG),r(ne,QF),r(ne,LG),r(ne,HF),r(ne,kG),r(ne,YF),r(ne,$G),r(ne,zF),r(ne,BG),u(Bl,ne,null),T(o,gw,g),u(pp,o,g),T(o,pw,g),T(o,go,g),u(hp,go,null),r(go,jG),r(go,OF),r(go,AG),r(go,KF),r(go,ZG),r(go,Zn),u(up,Zn,null),r(Zn,RG),r(Zn,eC),r(Zn,GG),r(Zn,oC),r(Zn,PG),u(jl,Zn,null),r(go,SG),r(go,hr),u(_p,hr,null),r(hr,WG),r(hr,rC),r(hr,JG),r(hr,tC),r(hr,VG),r(hr,nC),r(hr,EG),u(Al,hr,null),T(o,hw,g),u(bp,o,g),T(o,uw,g),T(o,po,g),u(Mp,po,null),r(po,IG),r(po,aC),r(po,UG),r(po,sC),r(po,NG),r(po,Rn),u(Tp,Rn,null),r(Rn,qG),r(Rn,iC),r(Rn,XG),r(Rn,lC),r(Rn,DG),u(Zl,Rn,null),r(po,QG),r(po,ur),u(Fp,ur,null),r(ur,HG),r(ur,dC),r(ur,YG),r(ur,mC),r(ur,zG),r(ur,cC),r(ur,OG),u(Rl,ur,null),T(o,_w,g),u(Cp,o,g),T(o,bw,g),T(o,ho,g),u(yp,ho,null),r(ho,KG),r(ho,fC),r(ho,eP),r(ho,gC),r(ho,oP),r(ho,Gn),u(vp,Gn,null),r(Gn,rP),r(Gn,pC),r(Gn,tP),r(Gn,hC),r(Gn,nP),u(Gl,Gn,null),r(ho,aP),r(ho,ae),u(wp,ae,null),r(ae,sP),r(ae,uC),r(ae,iP),r(ae,_C),r(ae,lP),r(ae,bC),r(ae,dP),r(ae,MC),r(ae,mP),u(Pl,ae,null),T(o,Mw,g),u(xp,o,g),T(o,Tw,g),T(o,Lp,g),u(kp,Lp,null),T(o,Fw,g),u($p,o,g),T(o,Cw,g),T(o,Bp,g),u(jp,Bp,null),T(o,yw,g),u(Ap,o,g),T(o,vw,g),T(o,Zp,g),T(o,ww,g),u(Rp,o,g),T(o,xw,g),T(o,uo,g),u(Gp,uo,null),r(uo,cP),r(uo,TC),r(uo,fP),r(uo,FC),r(uo,gP),r(uo,Pn),u(Pp,Pn,null),r(Pn,pP),r(Pn,CC),r(Pn,hP),r(Pn,yC),r(Pn,uP),u(Sl,Pn,null),r(uo,_P),r(uo,se),u(Sp,se,null),r(se,bP),r(se,vC),r(se,MP),r(se,wC),r(se,TP),r(se,xC),r(se,FP),r(se,LC),r(se,CP),u(Wl,se,null),T(o,Lw,g),u(Wp,o,g),T(o,kw,g),T(o,_o,g),u(Jp,_o,null),r(_o,yP),r(_o,kC),r(_o,vP),r(_o,$C),r(_o,wP),r(_o,Sn),u(Vp,Sn,null),r(Sn,xP),r(Sn,BC),r(Sn,LP),r(Sn,jC),r(Sn,kP),u(Jl,Sn,null),r(_o,$P),r(_o,_r),u(Ep,_r,null),r(_r,BP),r(_r,AC),r(_r,jP),r(_r,ZC),r(_r,AP),r(_r,RC),r(_r,ZP),u(Vl,_r,null),T(o,$w,g),u(Ip,o,g),T(o,Bw,g),T(o,bo,g),u(Up,bo,null),r(bo,RP),r(bo,GC),r(bo,GP),r(bo,PC),r(bo,PP),r(bo,Wn),u(Np,Wn,null),r(Wn,SP),r(Wn,SC),r(Wn,WP),r(Wn,WC),r(Wn,JP),u(El,Wn,null),r(bo,VP),r(bo,ie),u(qp,ie,null),r(ie,EP),r(ie,JC),r(ie,IP),r(ie,VC),r(ie,UP),r(ie,EC),r(ie,NP),r(ie,IC),r(ie,qP),u(Il,ie,null),T(o,jw,g),u(Xp,o,g),T(o,Aw,g),T(o,Mo,g),u(Dp,Mo,null),r(Mo,XP),r(Mo,UC),r(Mo,DP),r(Mo,NC),r(Mo,QP),r(Mo,Jn),u(Qp,Jn,null),r(Jn,HP),r(Jn,qC),r(Jn,YP),r(Jn,XC),r(Jn,zP),u(Ul,Jn,null),r(Mo,OP),r(Mo,br),u(Hp,br,null),r(br,KP),r(br,DC),r(br,eS),r(br,QC),r(br,oS),r(br,HC),r(br,rS),u(Nl,br,null),T(o,Zw,g),u(Yp,o,g),T(o,Rw,g),T(o,To,g),u(zp,To,null),r(To,tS),r(To,YC),r(To,nS),r(To,zC),r(To,aS),r(To,Vn),u(Op,Vn,null),r(Vn,sS),r(Vn,OC),r(Vn,iS),r(Vn,KC),r(Vn,lS),u(ql,Vn,null),r(To,dS),r(To,le),u(Kp,le,null),r(le,mS),r(le,ey),r(le,cS),r(le,oy),r(le,fS),r(le,ry),r(le,gS),r(le,ty),r(le,pS),u(Xl,le,null),T(o,Gw,g),u(eh,o,g),T(o,Pw,g),T(o,Fo,g),u(oh,Fo,null),r(Fo,hS),r(Fo,ny),r(Fo,uS),r(Fo,ay),r(Fo,_S),r(Fo,En),u(rh,En,null),r(En,bS),r(En,sy),r(En,MS),r(En,iy),r(En,TS),u(Dl,En,null),r(Fo,FS),r(Fo,de),u(th,de,null),r(de,CS),r(de,ly),r(de,yS),r(de,dy),r(de,vS),r(de,my),r(de,wS),r(de,cy),r(de,xS),u(Ql,de,null),T(o,Sw,g),u(nh,o,g),T(o,Ww,g),T(o,Co,g),u(ah,Co,null),r(Co,LS),r(Co,fy),r(Co,kS),r(Co,gy),r(Co,$S),r(Co,In),u(sh,In,null),r(In,BS),r(In,py),r(In,jS),r(In,hy),r(In,AS),u(Hl,In,null),r(Co,ZS),r(Co,Mr),u(ih,Mr,null),r(Mr,RS),r(Mr,uy),r(Mr,GS),r(Mr,_y),r(Mr,PS),r(Mr,by),r(Mr,SS),u(Yl,Mr,null),T(o,Jw,g),u(lh,o,g),T(o,Vw,g),T(o,yo,g),u(dh,yo,null),r(yo,WS),r(yo,My),r(yo,JS),r(yo,Ty),r(yo,VS),r(yo,Un),u(mh,Un,null),r(Un,ES),r(Un,Fy),r(Un,IS),r(Un,Cy),r(Un,US),u(zl,Un,null),r(yo,NS),r(yo,Tr),u(ch,Tr,null),r(Tr,qS),r(Tr,yy),r(Tr,XS),r(Tr,vy),r(Tr,DS),r(Tr,wy),r(Tr,QS),u(Ol,Tr,null),T(o,Ew,g),u(fh,o,g),T(o,Iw,g),T(o,xy,g),Uw=!0},p(o,[g]){const Fr={};g&2&&(Fr.$$scope={dirty:g,ctx:o}),ys.$set(Fr);const Cr={};g&2&&(Cr.$$scope={dirty:g,ctx:o}),vs.$set(Cr);const gh={};g&2&&(gh.$$scope={dirty:g,ctx:o}),xs.$set(gh);const yr={};g&2&&(yr.$$scope={dirty:g,ctx:o}),ks.$set(yr);const vr={};g&2&&(vr.$$scope={dirty:g,ctx:o}),$s.$set(vr);const ph={};g&2&&(ph.$$scope={dirty:g,ctx:o}),js.$set(ph);const wr={};g&2&&(wr.$$scope={dirty:g,ctx:o}),As.$set(wr);const vo={};g&2&&(vo.$$scope={dirty:g,ctx:o}),Rs.$set(vo);const hh={};g&2&&(hh.$$scope={dirty:g,ctx:o}),Gs.$set(hh);const xr={};g&2&&(xr.$$scope={dirty:g,ctx:o}),Ss.$set(xr);const wo={};g&2&&(wo.$$scope={dirty:g,ctx:o}),Ws.$set(wo);const uh={};g&2&&(uh.$$scope={dirty:g,ctx:o}),Js.$set(uh);const Lr={};g&2&&(Lr.$$scope={dirty:g,ctx:o}),Vs.$set(Lr);const xo={};g&2&&(xo.$$scope={dirty:g,ctx:o}),Es.$set(xo);const _h={};g&2&&(_h.$$scope={dirty:g,ctx:o}),Is.$set(_h);const kr={};g&2&&(kr.$$scope={dirty:g,ctx:o}),Us.$set(kr);const sa={};g&2&&(sa.$$scope={dirty:g,ctx:o}),Ns.$set(sa);const Lo={};g&2&&(Lo.$$scope={dirty:g,ctx:o}),qs.$set(Lo);const $r={};g&2&&($r.$$scope={dirty:g,ctx:o}),Xs.$set($r);const ia={};g&2&&(ia.$$scope={dirty:g,ctx:o}),Ds.$set(ia);const Br={};g&2&&(Br.$$scope={dirty:g,ctx:o}),Qs.$set(Br);const jr={};g&2&&(jr.$$scope={dirty:g,ctx:o}),Hs.$set(jr);const la={};g&2&&(la.$$scope={dirty:g,ctx:o}),Ys.$set(la);const Ar={};g&2&&(Ar.$$scope={dirty:g,ctx:o}),zs.$set(Ar);const Zr={};g&2&&(Zr.$$scope={dirty:g,ctx:o}),Os.$set(Zr);const da={};g&2&&(da.$$scope={dirty:g,ctx:o}),Ks.$set(da);const ko={};g&2&&(ko.$$scope={dirty:g,ctx:o}),ei.$set(ko);const Rr={};g&2&&(Rr.$$scope={dirty:g,ctx:o}),oi.$set(Rr);const ma={};g&2&&(ma.$$scope={dirty:g,ctx:o}),ri.$set(ma);const Gr={};g&2&&(Gr.$$scope={dirty:g,ctx:o}),ti.$set(Gr);const Pr={};g&2&&(Pr.$$scope={dirty:g,ctx:o}),ni.$set(Pr);const ca={};g&2&&(ca.$$scope={dirty:g,ctx:o}),ai.$set(ca);const Sr={};g&2&&(Sr.$$scope={dirty:g,ctx:o}),si.$set(Sr);const Wr={};g&2&&(Wr.$$scope={dirty:g,ctx:o}),ii.$set(Wr);const fa={};g&2&&(fa.$$scope={dirty:g,ctx:o}),li.$set(fa);const $o={};g&2&&($o.$$scope={dirty:g,ctx:o}),di.$set($o);const Jr={};g&2&&(Jr.$$scope={dirty:g,ctx:o}),mi.$set(Jr);const ga={};g&2&&(ga.$$scope={dirty:g,ctx:o}),ci.$set(ga);const Vr={};g&2&&(Vr.$$scope={dirty:g,ctx:o}),fi.$set(Vr);const Er={};g&2&&(Er.$$scope={dirty:g,ctx:o}),gi.$set(Er);const pa={};g&2&&(pa.$$scope={dirty:g,ctx:o}),pi.$set(pa);const Ir={};g&2&&(Ir.$$scope={dirty:g,ctx:o}),hi.$set(Ir);const Ur={};g&2&&(Ur.$$scope={dirty:g,ctx:o}),ui.$set(Ur);const ha={};g&2&&(ha.$$scope={dirty:g,ctx:o}),_i.$set(ha);const Bo={};g&2&&(Bo.$$scope={dirty:g,ctx:o}),bi.$set(Bo);const Nr={};g&2&&(Nr.$$scope={dirty:g,ctx:o}),Mi.$set(Nr);const ua={};g&2&&(ua.$$scope={dirty:g,ctx:o}),Ti.$set(ua);const qr={};g&2&&(qr.$$scope={dirty:g,ctx:o}),Fi.$set(qr);const Xr={};g&2&&(Xr.$$scope={dirty:g,ctx:o}),Ci.$set(Xr);const _a={};g&2&&(_a.$$scope={dirty:g,ctx:o}),yi.$set(_a);const Dr={};g&2&&(Dr.$$scope={dirty:g,ctx:o}),vi.$set(Dr);const Ly={};g&2&&(Ly.$$scope={dirty:g,ctx:o}),wi.$set(Ly);const ky={};g&2&&(ky.$$scope={dirty:g,ctx:o}),xi.$set(ky);const Qr={};g&2&&(Qr.$$scope={dirty:g,ctx:o}),Li.$set(Qr);const ba={};g&2&&(ba.$$scope={dirty:g,ctx:o}),ki.$set(ba);const jo={};g&2&&(jo.$$scope={dirty:g,ctx:o}),$i.$set(jo);const Hr={};g&2&&(Hr.$$scope={dirty:g,ctx:o}),Bi.$set(Hr);const Ma={};g&2&&(Ma.$$scope={dirty:g,ctx:o}),ji.$set(Ma);const Yr={};g&2&&(Yr.$$scope={dirty:g,ctx:o}),Ai.$set(Yr);const zr={};g&2&&(zr.$$scope={dirty:g,ctx:o}),Zi.$set(zr);const Ta={};g&2&&(Ta.$$scope={dirty:g,ctx:o}),Ri.$set(Ta);const Or={};g&2&&(Or.$$scope={dirty:g,ctx:o}),Gi.$set(Or);const Kr={};g&2&&(Kr.$$scope={dirty:g,ctx:o}),Pi.$set(Kr);const Fa={};g&2&&(Fa.$$scope={dirty:g,ctx:o}),Si.$set(Fa);const Ao={};g&2&&(Ao.$$scope={dirty:g,ctx:o}),Wi.$set(Ao);const et={};g&2&&(et.$$scope={dirty:g,ctx:o}),Ji.$set(et);const Ca={};g&2&&(Ca.$$scope={dirty:g,ctx:o}),Vi.$set(Ca);const ot={};g&2&&(ot.$$scope={dirty:g,ctx:o}),Ei.$set(ot);const rt={};g&2&&(rt.$$scope={dirty:g,ctx:o}),Ii.$set(rt);const ya={};g&2&&(ya.$$scope={dirty:g,ctx:o}),Ui.$set(ya);const tt={};g&2&&(tt.$$scope={dirty:g,ctx:o}),Ni.$set(tt);const nt={};g&2&&(nt.$$scope={dirty:g,ctx:o}),qi.$set(nt);const va={};g&2&&(va.$$scope={dirty:g,ctx:o}),Xi.$set(va);const Zo={};g&2&&(Zo.$$scope={dirty:g,ctx:o}),Di.$set(Zo);const at={};g&2&&(at.$$scope={dirty:g,ctx:o}),Qi.$set(at);const wa={};g&2&&(wa.$$scope={dirty:g,ctx:o}),Hi.$set(wa);const st={};g&2&&(st.$$scope={dirty:g,ctx:o}),Yi.$set(st);const it={};g&2&&(it.$$scope={dirty:g,ctx:o}),zi.$set(it);const xa={};g&2&&(xa.$$scope={dirty:g,ctx:o}),Oi.$set(xa);const lt={};g&2&&(lt.$$scope={dirty:g,ctx:o}),Ki.$set(lt);const dt={};g&2&&(dt.$$scope={dirty:g,ctx:o}),el.$set(dt);const La={};g&2&&(La.$$scope={dirty:g,ctx:o}),ol.$set(La);const Ro={};g&2&&(Ro.$$scope={dirty:g,ctx:o}),rl.$set(Ro);const mt={};g&2&&(mt.$$scope={dirty:g,ctx:o}),tl.$set(mt);const ka={};g&2&&(ka.$$scope={dirty:g,ctx:o}),nl.$set(ka);const ct={};g&2&&(ct.$$scope={dirty:g,ctx:o}),al.$set(ct);const ft={};g&2&&(ft.$$scope={dirty:g,ctx:o}),sl.$set(ft);const $a={};g&2&&($a.$$scope={dirty:g,ctx:o}),il.$set($a);const gt={};g&2&&(gt.$$scope={dirty:g,ctx:o}),ll.$set(gt);const pt={};g&2&&(pt.$$scope={dirty:g,ctx:o}),dl.$set(pt);const Ba={};g&2&&(Ba.$$scope={dirty:g,ctx:o}),ml.$set(Ba);const Go={};g&2&&(Go.$$scope={dirty:g,ctx:o}),cl.$set(Go);const ht={};g&2&&(ht.$$scope={dirty:g,ctx:o}),fl.$set(ht);const ja={};g&2&&(ja.$$scope={dirty:g,ctx:o}),gl.$set(ja);const ut={};g&2&&(ut.$$scope={dirty:g,ctx:o}),pl.$set(ut);const _t={};g&2&&(_t.$$scope={dirty:g,ctx:o}),hl.$set(_t);const Aa={};g&2&&(Aa.$$scope={dirty:g,ctx:o}),ul.$set(Aa);const bt={};g&2&&(bt.$$scope={dirty:g,ctx:o}),_l.$set(bt);const Mt={};g&2&&(Mt.$$scope={dirty:g,ctx:o}),bl.$set(Mt);const Za={};g&2&&(Za.$$scope={dirty:g,ctx:o}),Ml.$set(Za);const Po={};g&2&&(Po.$$scope={dirty:g,ctx:o}),Tl.$set(Po);const Tt={};g&2&&(Tt.$$scope={dirty:g,ctx:o}),Fl.$set(Tt);const Ra={};g&2&&(Ra.$$scope={dirty:g,ctx:o}),Cl.$set(Ra);const Ft={};g&2&&(Ft.$$scope={dirty:g,ctx:o}),yl.$set(Ft);const Ct={};g&2&&(Ct.$$scope={dirty:g,ctx:o}),vl.$set(Ct);const Ga={};g&2&&(Ga.$$scope={dirty:g,ctx:o}),wl.$set(Ga);const yt={};g&2&&(yt.$$scope={dirty:g,ctx:o}),xl.$set(yt);const $y={};g&2&&($y.$$scope={dirty:g,ctx:o}),Ll.$set($y);const By={};g&2&&(By.$$scope={dirty:g,ctx:o}),kl.$set(By);const vt={};g&2&&(vt.$$scope={dirty:g,ctx:o}),$l.$set(vt);const Pa={};g&2&&(Pa.$$scope={dirty:g,ctx:o}),Bl.$set(Pa);const So={};g&2&&(So.$$scope={dirty:g,ctx:o}),jl.$set(So);const wt={};g&2&&(wt.$$scope={dirty:g,ctx:o}),Al.$set(wt);const Sa={};g&2&&(Sa.$$scope={dirty:g,ctx:o}),Zl.$set(Sa);const Wo={};g&2&&(Wo.$$scope={dirty:g,ctx:o}),Rl.$set(Wo);const xt={};g&2&&(xt.$$scope={dirty:g,ctx:o}),Gl.$set(xt);const Wa={};g&2&&(Wa.$$scope={dirty:g,ctx:o}),Pl.$set(Wa);const Lt={};g&2&&(Lt.$$scope={dirty:g,ctx:o}),Sl.$set(Lt);const kt={};g&2&&(kt.$$scope={dirty:g,ctx:o}),Wl.$set(kt);const Ja={};g&2&&(Ja.$$scope={dirty:g,ctx:o}),Jl.$set(Ja);const $t={};g&2&&($t.$$scope={dirty:g,ctx:o}),Vl.$set($t);const Bt={};g&2&&(Bt.$$scope={dirty:g,ctx:o}),El.$set(Bt);const Va={};g&2&&(Va.$$scope={dirty:g,ctx:o}),Il.$set(Va);const Jo={};g&2&&(Jo.$$scope={dirty:g,ctx:o}),Ul.$set(Jo);const jy={};g&2&&(jy.$$scope={dirty:g,ctx:o}),Nl.$set(jy);const jt={};g&2&&(jt.$$scope={dirty:g,ctx:o}),ql.$set(jt);const Ea={};g&2&&(Ea.$$scope={dirty:g,ctx:o}),Xl.$set(Ea);const Vo={};g&2&&(Vo.$$scope={dirty:g,ctx:o}),Dl.$set(Vo);const At={};g&2&&(At.$$scope={dirty:g,ctx:o}),Ql.$set(At);const Ia={};g&2&&(Ia.$$scope={dirty:g,ctx:o}),Hl.$set(Ia);const Zt={};g&2&&(Zt.$$scope={dirty:g,ctx:o}),Yl.$set(Zt);const Rt={};g&2&&(Rt.$$scope={dirty:g,ctx:o}),zl.$set(Rt);const Ua={};g&2&&(Ua.$$scope={dirty:g,ctx:o}),Ol.$set(Ua)},i(o){Uw||(_(c.$$.fragment,o),_(vd.$$.fragment,o),_(Ld.$$.fragment,o),_($d.$$.fragment,o),_(ys.$$.fragment,o),_(jd.$$.fragment,o),_(Ad.$$.fragment,o),_(Zd.$$.fragment,o),_(vs.$$.fragment,o),_(Rd.$$.fragment,o),_(Gd.$$.fragment,o),_(Pd.$$.fragment,o),_(Sd.$$.fragment,o),_(xs.$$.fragment,o),_(Wd.$$.fragment,o),_(Jd.$$.fragment,o),_(Vd.$$.fragment,o),_(Ed.$$.fragment,o),_(ks.$$.fragment,o),_($s.$$.fragment,o),_(Id.$$.fragment,o),_(Ud.$$.fragment,o),_(Nd.$$.fragment,o),_(qd.$$.fragment,o),_(js.$$.fragment,o),_(As.$$.fragment,o),_(Xd.$$.fragment,o),_(Dd.$$.fragment,o),_(Qd.$$.fragment,o),_(Hd.$$.fragment,o),_(Rs.$$.fragment,o),_(Gs.$$.fragment,o),_(Yd.$$.fragment,o),_(zd.$$.fragment,o),_(Kd.$$.fragment,o),_(em.$$.fragment,o),_(om.$$.fragment,o),_(Ss.$$.fragment,o),_(rm.$$.fragment,o),_(Ws.$$.fragment,o),_(tm.$$.fragment,o),_(nm.$$.fragment,o),_(am.$$.fragment,o),_(Js.$$.fragment,o),_(sm.$$.fragment,o),_(Vs.$$.fragment,o),_(im.$$.fragment,o),_(lm.$$.fragment,o),_(dm.$$.fragment,o),_(Es.$$.fragment,o),_(mm.$$.fragment,o),_(Is.$$.fragment,o),_(cm.$$.fragment,o),_(gm.$$.fragment,o),_(pm.$$.fragment,o),_(hm.$$.fragment,o),_(Us.$$.fragment,o),_(um.$$.fragment,o),_(Ns.$$.fragment,o),_(_m.$$.fragment,o),_(bm.$$.fragment,o),_(Mm.$$.fragment,o),_(qs.$$.fragment,o),_(Tm.$$.fragment,o),_(Xs.$$.fragment,o),_(Fm.$$.fragment,o),_(Cm.$$.fragment,o),_(ym.$$.fragment,o),_(Ds.$$.fragment,o),_(vm.$$.fragment,o),_(Qs.$$.fragment,o),_(wm.$$.fragment,o),_(Lm.$$.fragment,o),_(km.$$.fragment,o),_($m.$$.fragment,o),_(Hs.$$.fragment,o),_(Bm.$$.fragment,o),_(Ys.$$.fragment,o),_(jm.$$.fragment,o),_(Am.$$.fragment,o),_(Zm.$$.fragment,o),_(zs.$$.fragment,o),_(Rm.$$.fragment,o),_(Os.$$.fragment,o),_(Gm.$$.fragment,o),_(Pm.$$.fragment,o),_(Sm.$$.fragment,o),_(Ks.$$.fragment,o),_(Wm.$$.fragment,o),_(ei.$$.fragment,o),_(Jm.$$.fragment,o),_(Vm.$$.fragment,o),_(Em.$$.fragment,o),_(oi.$$.fragment,o),_(Im.$$.fragment,o),_(ri.$$.fragment,o),_(Um.$$.fragment,o),_(Nm.$$.fragment,o),_(qm.$$.fragment,o),_(ti.$$.fragment,o),_(Xm.$$.fragment,o),_(ni.$$.fragment,o),_(Dm.$$.fragment,o),_(Qm.$$.fragment,o),_(Hm.$$.fragment,o),_(ai.$$.fragment,o),_(Ym.$$.fragment,o),_(si.$$.fragment,o),_(zm.$$.fragment,o),_(Km.$$.fragment,o),_(ec.$$.fragment,o),_(rc.$$.fragment,o),_(tc.$$.fragment,o),_(nc.$$.fragment,o),_(ac.$$.fragment,o),_(ii.$$.fragment,o),_(sc.$$.fragment,o),_(li.$$.fragment,o),_(ic.$$.fragment,o),_(lc.$$.fragment,o),_(dc.$$.fragment,o),_(di.$$.fragment,o),_(mc.$$.fragment,o),_(mi.$$.fragment,o),_(cc.$$.fragment,o),_(fc.$$.fragment,o),_(gc.$$.fragment,o),_(ci.$$.fragment,o),_(pc.$$.fragment,o),_(fi.$$.fragment,o),_(hc.$$.fragment,o),_(uc.$$.fragment,o),_(_c.$$.fragment,o),_(gi.$$.fragment,o),_(bc.$$.fragment,o),_(pi.$$.fragment,o),_(Mc.$$.fragment,o),_(Tc.$$.fragment,o),_(Fc.$$.fragment,o),_(hi.$$.fragment,o),_(Cc.$$.fragment,o),_(ui.$$.fragment,o),_(yc.$$.fragment,o),_(vc.$$.fragment,o),_(wc.$$.fragment,o),_(_i.$$.fragment,o),_(xc.$$.fragment,o),_(bi.$$.fragment,o),_(Lc.$$.fragment,o),_(kc.$$.fragment,o),_($c.$$.fragment,o),_(Mi.$$.fragment,o),_(Bc.$$.fragment,o),_(Ti.$$.fragment,o),_(jc.$$.fragment,o),_(Ac.$$.fragment,o),_(Zc.$$.fragment,o),_(Fi.$$.fragment,o),_(Rc.$$.fragment,o),_(Ci.$$.fragment,o),_(Gc.$$.fragment,o),_(Pc.$$.fragment,o),_(Sc.$$.fragment,o),_(yi.$$.fragment,o),_(Wc.$$.fragment,o),_(vi.$$.fragment,o),_(Jc.$$.fragment,o),_(Vc.$$.fragment,o),_(Ec.$$.fragment,o),_(wi.$$.fragment,o),_(Ic.$$.fragment,o),_(xi.$$.fragment,o),_(Uc.$$.fragment,o),_(Nc.$$.fragment,o),_(qc.$$.fragment,o),_(Li.$$.fragment,o),_(Xc.$$.fragment,o),_(ki.$$.fragment,o),_(Dc.$$.fragment,o),_(Qc.$$.fragment,o),_(Hc.$$.fragment,o),_($i.$$.fragment,o),_(Yc.$$.fragment,o),_(Bi.$$.fragment,o),_(zc.$$.fragment,o),_(Oc.$$.fragment,o),_(Kc.$$.fragment,o),_(ji.$$.fragment,o),_(ef.$$.fragment,o),_(Ai.$$.fragment,o),_(of.$$.fragment,o),_(rf.$$.fragment,o),_(tf.$$.fragment,o),_(Zi.$$.fragment,o),_(nf.$$.fragment,o),_(Ri.$$.fragment,o),_(af.$$.fragment,o),_(sf.$$.fragment,o),_(lf.$$.fragment,o),_(Gi.$$.fragment,o),_(df.$$.fragment,o),_(Pi.$$.fragment,o),_(mf.$$.fragment,o),_(cf.$$.fragment,o),_(ff.$$.fragment,o),_(Si.$$.fragment,o),_(gf.$$.fragment,o),_(Wi.$$.fragment,o),_(pf.$$.fragment,o),_(hf.$$.fragment,o),_(uf.$$.fragment,o),_(Ji.$$.fragment,o),_(_f.$$.fragment,o),_(Vi.$$.fragment,o),_(bf.$$.fragment,o),_(Mf.$$.fragment,o),_(Tf.$$.fragment,o),_(Ei.$$.fragment,o),_(Ff.$$.fragment,o),_(Ii.$$.fragment,o),_(Cf.$$.fragment,o),_(vf.$$.fragment,o),_(wf.$$.fragment,o),_(Lf.$$.fragment,o),_(kf.$$.fragment,o),_(Bf.$$.fragment,o),_(jf.$$.fragment,o),_(Af.$$.fragment,o),_(Ui.$$.fragment,o),_(Zf.$$.fragment,o),_(Ni.$$.fragment,o),_(Rf.$$.fragment,o),_(Gf.$$.fragment,o),_(Pf.$$.fragment,o),_(qi.$$.fragment,o),_(Sf.$$.fragment,o),_(Xi.$$.fragment,o),_(Wf.$$.fragment,o),_(Jf.$$.fragment,o),_(Vf.$$.fragment,o),_(Di.$$.fragment,o),_(Ef.$$.fragment,o),_(Qi.$$.fragment,o),_(If.$$.fragment,o),_(Uf.$$.fragment,o),_(Nf.$$.fragment,o),_(Hi.$$.fragment,o),_(qf.$$.fragment,o),_(Yi.$$.fragment,o),_(Xf.$$.fragment,o),_(Df.$$.fragment,o),_(Qf.$$.fragment,o),_(zi.$$.fragment,o),_(Hf.$$.fragment,o),_(Oi.$$.fragment,o),_(Yf.$$.fragment,o),_(Of.$$.fragment,o),_(Kf.$$.fragment,o),_(eg.$$.fragment,o),_(og.$$.fragment,o),_(Ki.$$.fragment,o),_(rg.$$.fragment,o),_(el.$$.fragment,o),_(tg.$$.fragment,o),_(ng.$$.fragment,o),_(ag.$$.fragment,o),_(ol.$$.fragment,o),_(sg.$$.fragment,o),_(rl.$$.fragment,o),_(ig.$$.fragment,o),_(lg.$$.fragment,o),_(dg.$$.fragment,o),_(tl.$$.fragment,o),_(mg.$$.fragment,o),_(nl.$$.fragment,o),_(cg.$$.fragment,o),_(fg.$$.fragment,o),_(gg.$$.fragment,o),_(al.$$.fragment,o),_(pg.$$.fragment,o),_(sl.$$.fragment,o),_(hg.$$.fragment,o),_(_g.$$.fragment,o),_(bg.$$.fragment,o),_(Mg.$$.fragment,o),_(Tg.$$.fragment,o),_(il.$$.fragment,o),_(Fg.$$.fragment,o),_(ll.$$.fragment,o),_(Cg.$$.fragment,o),_(yg.$$.fragment,o),_(vg.$$.fragment,o),_(dl.$$.fragment,o),_(wg.$$.fragment,o),_(ml.$$.fragment,o),_(xg.$$.fragment,o),_(Lg.$$.fragment,o),_(kg.$$.fragment,o),_(cl.$$.fragment,o),_($g.$$.fragment,o),_(fl.$$.fragment,o),_(Bg.$$.fragment,o),_(jg.$$.fragment,o),_(Ag.$$.fragment,o),_(gl.$$.fragment,o),_(Zg.$$.fragment,o),_(pl.$$.fragment,o),_(Rg.$$.fragment,o),_(Gg.$$.fragment,o),_(Pg.$$.fragment,o),_(hl.$$.fragment,o),_(Sg.$$.fragment,o),_(ul.$$.fragment,o),_(Wg.$$.fragment,o),_(Jg.$$.fragment,o),_(Vg.$$.fragment,o),_(_l.$$.fragment,o),_(Eg.$$.fragment,o),_(bl.$$.fragment,o),_(Ig.$$.fragment,o),_(Ug.$$.fragment,o),_(Ng.$$.fragment,o),_(Ml.$$.fragment,o),_(qg.$$.fragment,o),_(Tl.$$.fragment,o),_(Xg.$$.fragment,o),_(Qg.$$.fragment,o),_(Hg.$$.fragment,o),_(Yg.$$.fragment,o),_(Fl.$$.fragment,o),_(zg.$$.fragment,o),_(Cl.$$.fragment,o),_(Og.$$.fragment,o),_(Kg.$$.fragment,o),_(ep.$$.fragment,o),_(yl.$$.fragment,o),_(op.$$.fragment,o),_(vl.$$.fragment,o),_(rp.$$.fragment,o),_(tp.$$.fragment,o),_(np.$$.fragment,o),_(wl.$$.fragment,o),_(ap.$$.fragment,o),_(xl.$$.fragment,o),_(sp.$$.fragment,o),_(ip.$$.fragment,o),_(lp.$$.fragment,o),_(Ll.$$.fragment,o),_(dp.$$.fragment,o),_(kl.$$.fragment,o),_(mp.$$.fragment,o),_(cp.$$.fragment,o),_(fp.$$.fragment,o),_($l.$$.fragment,o),_(gp.$$.fragment,o),_(Bl.$$.fragment,o),_(pp.$$.fragment,o),_(hp.$$.fragment,o),_(up.$$.fragment,o),_(jl.$$.fragment,o),_(_p.$$.fragment,o),_(Al.$$.fragment,o),_(bp.$$.fragment,o),_(Mp.$$.fragment,o),_(Tp.$$.fragment,o),_(Zl.$$.fragment,o),_(Fp.$$.fragment,o),_(Rl.$$.fragment,o),_(Cp.$$.fragment,o),_(yp.$$.fragment,o),_(vp.$$.fragment,o),_(Gl.$$.fragment,o),_(wp.$$.fragment,o),_(Pl.$$.fragment,o),_(xp.$$.fragment,o),_(kp.$$.fragment,o),_($p.$$.fragment,o),_(jp.$$.fragment,o),_(Ap.$$.fragment,o),_(Rp.$$.fragment,o),_(Gp.$$.fragment,o),_(Pp.$$.fragment,o),_(Sl.$$.fragment,o),_(Sp.$$.fragment,o),_(Wl.$$.fragment,o),_(Wp.$$.fragment,o),_(Jp.$$.fragment,o),_(Vp.$$.fragment,o),_(Jl.$$.fragment,o),_(Ep.$$.fragment,o),_(Vl.$$.fragment,o),_(Ip.$$.fragment,o),_(Up.$$.fragment,o),_(Np.$$.fragment,o),_(El.$$.fragment,o),_(qp.$$.fragment,o),_(Il.$$.fragment,o),_(Xp.$$.fragment,o),_(Dp.$$.fragment,o),_(Qp.$$.fragment,o),_(Ul.$$.fragment,o),_(Hp.$$.fragment,o),_(Nl.$$.fragment,o),_(Yp.$$.fragment,o),_(zp.$$.fragment,o),_(Op.$$.fragment,o),_(ql.$$.fragment,o),_(Kp.$$.fragment,o),_(Xl.$$.fragment,o),_(eh.$$.fragment,o),_(oh.$$.fragment,o),_(rh.$$.fragment,o),_(Dl.$$.fragment,o),_(th.$$.fragment,o),_(Ql.$$.fragment,o),_(nh.$$.fragment,o),_(ah.$$.fragment,o),_(sh.$$.fragment,o),_(Hl.$$.fragment,o),_(ih.$$.fragment,o),_(Yl.$$.fragment,o),_(lh.$$.fragment,o),_(dh.$$.fragment,o),_(mh.$$.fragment,o),_(zl.$$.fragment,o),_(ch.$$.fragment,o),_(Ol.$$.fragment,o),_(fh.$$.fragment,o),Uw=!0)},o(o){b(c.$$.fragment,o),b(vd.$$.fragment,o),b(Ld.$$.fragment,o),b($d.$$.fragment,o),b(ys.$$.fragment,o),b(jd.$$.fragment,o),b(Ad.$$.fragment,o),b(Zd.$$.fragment,o),b(vs.$$.fragment,o),b(Rd.$$.fragment,o),b(Gd.$$.fragment,o),b(Pd.$$.fragment,o),b(Sd.$$.fragment,o),b(xs.$$.fragment,o),b(Wd.$$.fragment,o),b(Jd.$$.fragment,o),b(Vd.$$.fragment,o),b(Ed.$$.fragment,o),b(ks.$$.fragment,o),b($s.$$.fragment,o),b(Id.$$.fragment,o),b(Ud.$$.fragment,o),b(Nd.$$.fragment,o),b(qd.$$.fragment,o),b(js.$$.fragment,o),b(As.$$.fragment,o),b(Xd.$$.fragment,o),b(Dd.$$.fragment,o),b(Qd.$$.fragment,o),b(Hd.$$.fragment,o),b(Rs.$$.fragment,o),b(Gs.$$.fragment,o),b(Yd.$$.fragment,o),b(zd.$$.fragment,o),b(Kd.$$.fragment,o),b(em.$$.fragment,o),b(om.$$.fragment,o),b(Ss.$$.fragment,o),b(rm.$$.fragment,o),b(Ws.$$.fragment,o),b(tm.$$.fragment,o),b(nm.$$.fragment,o),b(am.$$.fragment,o),b(Js.$$.fragment,o),b(sm.$$.fragment,o),b(Vs.$$.fragment,o),b(im.$$.fragment,o),b(lm.$$.fragment,o),b(dm.$$.fragment,o),b(Es.$$.fragment,o),b(mm.$$.fragment,o),b(Is.$$.fragment,o),b(cm.$$.fragment,o),b(gm.$$.fragment,o),b(pm.$$.fragment,o),b(hm.$$.fragment,o),b(Us.$$.fragment,o),b(um.$$.fragment,o),b(Ns.$$.fragment,o),b(_m.$$.fragment,o),b(bm.$$.fragment,o),b(Mm.$$.fragment,o),b(qs.$$.fragment,o),b(Tm.$$.fragment,o),b(Xs.$$.fragment,o),b(Fm.$$.fragment,o),b(Cm.$$.fragment,o),b(ym.$$.fragment,o),b(Ds.$$.fragment,o),b(vm.$$.fragment,o),b(Qs.$$.fragment,o),b(wm.$$.fragment,o),b(Lm.$$.fragment,o),b(km.$$.fragment,o),b($m.$$.fragment,o),b(Hs.$$.fragment,o),b(Bm.$$.fragment,o),b(Ys.$$.fragment,o),b(jm.$$.fragment,o),b(Am.$$.fragment,o),b(Zm.$$.fragment,o),b(zs.$$.fragment,o),b(Rm.$$.fragment,o),b(Os.$$.fragment,o),b(Gm.$$.fragment,o),b(Pm.$$.fragment,o),b(Sm.$$.fragment,o),b(Ks.$$.fragment,o),b(Wm.$$.fragment,o),b(ei.$$.fragment,o),b(Jm.$$.fragment,o),b(Vm.$$.fragment,o),b(Em.$$.fragment,o),b(oi.$$.fragment,o),b(Im.$$.fragment,o),b(ri.$$.fragment,o),b(Um.$$.fragment,o),b(Nm.$$.fragment,o),b(qm.$$.fragment,o),b(ti.$$.fragment,o),b(Xm.$$.fragment,o),b(ni.$$.fragment,o),b(Dm.$$.fragment,o),b(Qm.$$.fragment,o),b(Hm.$$.fragment,o),b(ai.$$.fragment,o),b(Ym.$$.fragment,o),b(si.$$.fragment,o),b(zm.$$.fragment,o),b(Km.$$.fragment,o),b(ec.$$.fragment,o),b(rc.$$.fragment,o),b(tc.$$.fragment,o),b(nc.$$.fragment,o),b(ac.$$.fragment,o),b(ii.$$.fragment,o),b(sc.$$.fragment,o),b(li.$$.fragment,o),b(ic.$$.fragment,o),b(lc.$$.fragment,o),b(dc.$$.fragment,o),b(di.$$.fragment,o),b(mc.$$.fragment,o),b(mi.$$.fragment,o),b(cc.$$.fragment,o),b(fc.$$.fragment,o),b(gc.$$.fragment,o),b(ci.$$.fragment,o),b(pc.$$.fragment,o),b(fi.$$.fragment,o),b(hc.$$.fragment,o),b(uc.$$.fragment,o),b(_c.$$.fragment,o),b(gi.$$.fragment,o),b(bc.$$.fragment,o),b(pi.$$.fragment,o),b(Mc.$$.fragment,o),b(Tc.$$.fragment,o),b(Fc.$$.fragment,o),b(hi.$$.fragment,o),b(Cc.$$.fragment,o),b(ui.$$.fragment,o),b(yc.$$.fragment,o),b(vc.$$.fragment,o),b(wc.$$.fragment,o),b(_i.$$.fragment,o),b(xc.$$.fragment,o),b(bi.$$.fragment,o),b(Lc.$$.fragment,o),b(kc.$$.fragment,o),b($c.$$.fragment,o),b(Mi.$$.fragment,o),b(Bc.$$.fragment,o),b(Ti.$$.fragment,o),b(jc.$$.fragment,o),b(Ac.$$.fragment,o),b(Zc.$$.fragment,o),b(Fi.$$.fragment,o),b(Rc.$$.fragment,o),b(Ci.$$.fragment,o),b(Gc.$$.fragment,o),b(Pc.$$.fragment,o),b(Sc.$$.fragment,o),b(yi.$$.fragment,o),b(Wc.$$.fragment,o),b(vi.$$.fragment,o),b(Jc.$$.fragment,o),b(Vc.$$.fragment,o),b(Ec.$$.fragment,o),b(wi.$$.fragment,o),b(Ic.$$.fragment,o),b(xi.$$.fragment,o),b(Uc.$$.fragment,o),b(Nc.$$.fragment,o),b(qc.$$.fragment,o),b(Li.$$.fragment,o),b(Xc.$$.fragment,o),b(ki.$$.fragment,o),b(Dc.$$.fragment,o),b(Qc.$$.fragment,o),b(Hc.$$.fragment,o),b($i.$$.fragment,o),b(Yc.$$.fragment,o),b(Bi.$$.fragment,o),b(zc.$$.fragment,o),b(Oc.$$.fragment,o),b(Kc.$$.fragment,o),b(ji.$$.fragment,o),b(ef.$$.fragment,o),b(Ai.$$.fragment,o),b(of.$$.fragment,o),b(rf.$$.fragment,o),b(tf.$$.fragment,o),b(Zi.$$.fragment,o),b(nf.$$.fragment,o),b(Ri.$$.fragment,o),b(af.$$.fragment,o),b(sf.$$.fragment,o),b(lf.$$.fragment,o),b(Gi.$$.fragment,o),b(df.$$.fragment,o),b(Pi.$$.fragment,o),b(mf.$$.fragment,o),b(cf.$$.fragment,o),b(ff.$$.fragment,o),b(Si.$$.fragment,o),b(gf.$$.fragment,o),b(Wi.$$.fragment,o),b(pf.$$.fragment,o),b(hf.$$.fragment,o),b(uf.$$.fragment,o),b(Ji.$$.fragment,o),b(_f.$$.fragment,o),b(Vi.$$.fragment,o),b(bf.$$.fragment,o),b(Mf.$$.fragment,o),b(Tf.$$.fragment,o),b(Ei.$$.fragment,o),b(Ff.$$.fragment,o),b(Ii.$$.fragment,o),b(Cf.$$.fragment,o),b(vf.$$.fragment,o),b(wf.$$.fragment,o),b(Lf.$$.fragment,o),b(kf.$$.fragment,o),b(Bf.$$.fragment,o),b(jf.$$.fragment,o),b(Af.$$.fragment,o),b(Ui.$$.fragment,o),b(Zf.$$.fragment,o),b(Ni.$$.fragment,o),b(Rf.$$.fragment,o),b(Gf.$$.fragment,o),b(Pf.$$.fragment,o),b(qi.$$.fragment,o),b(Sf.$$.fragment,o),b(Xi.$$.fragment,o),b(Wf.$$.fragment,o),b(Jf.$$.fragment,o),b(Vf.$$.fragment,o),b(Di.$$.fragment,o),b(Ef.$$.fragment,o),b(Qi.$$.fragment,o),b(If.$$.fragment,o),b(Uf.$$.fragment,o),b(Nf.$$.fragment,o),b(Hi.$$.fragment,o),b(qf.$$.fragment,o),b(Yi.$$.fragment,o),b(Xf.$$.fragment,o),b(Df.$$.fragment,o),b(Qf.$$.fragment,o),b(zi.$$.fragment,o),b(Hf.$$.fragment,o),b(Oi.$$.fragment,o),b(Yf.$$.fragment,o),b(Of.$$.fragment,o),b(Kf.$$.fragment,o),b(eg.$$.fragment,o),b(og.$$.fragment,o),b(Ki.$$.fragment,o),b(rg.$$.fragment,o),b(el.$$.fragment,o),b(tg.$$.fragment,o),b(ng.$$.fragment,o),b(ag.$$.fragment,o),b(ol.$$.fragment,o),b(sg.$$.fragment,o),b(rl.$$.fragment,o),b(ig.$$.fragment,o),b(lg.$$.fragment,o),b(dg.$$.fragment,o),b(tl.$$.fragment,o),b(mg.$$.fragment,o),b(nl.$$.fragment,o),b(cg.$$.fragment,o),b(fg.$$.fragment,o),b(gg.$$.fragment,o),b(al.$$.fragment,o),b(pg.$$.fragment,o),b(sl.$$.fragment,o),b(hg.$$.fragment,o),b(_g.$$.fragment,o),b(bg.$$.fragment,o),b(Mg.$$.fragment,o),b(Tg.$$.fragment,o),b(il.$$.fragment,o),b(Fg.$$.fragment,o),b(ll.$$.fragment,o),b(Cg.$$.fragment,o),b(yg.$$.fragment,o),b(vg.$$.fragment,o),b(dl.$$.fragment,o),b(wg.$$.fragment,o),b(ml.$$.fragment,o),b(xg.$$.fragment,o),b(Lg.$$.fragment,o),b(kg.$$.fragment,o),b(cl.$$.fragment,o),b($g.$$.fragment,o),b(fl.$$.fragment,o),b(Bg.$$.fragment,o),b(jg.$$.fragment,o),b(Ag.$$.fragment,o),b(gl.$$.fragment,o),b(Zg.$$.fragment,o),b(pl.$$.fragment,o),b(Rg.$$.fragment,o),b(Gg.$$.fragment,o),b(Pg.$$.fragment,o),b(hl.$$.fragment,o),b(Sg.$$.fragment,o),b(ul.$$.fragment,o),b(Wg.$$.fragment,o),b(Jg.$$.fragment,o),b(Vg.$$.fragment,o),b(_l.$$.fragment,o),b(Eg.$$.fragment,o),b(bl.$$.fragment,o),b(Ig.$$.fragment,o),b(Ug.$$.fragment,o),b(Ng.$$.fragment,o),b(Ml.$$.fragment,o),b(qg.$$.fragment,o),b(Tl.$$.fragment,o),b(Xg.$$.fragment,o),b(Qg.$$.fragment,o),b(Hg.$$.fragment,o),b(Yg.$$.fragment,o),b(Fl.$$.fragment,o),b(zg.$$.fragment,o),b(Cl.$$.fragment,o),b(Og.$$.fragment,o),b(Kg.$$.fragment,o),b(ep.$$.fragment,o),b(yl.$$.fragment,o),b(op.$$.fragment,o),b(vl.$$.fragment,o),b(rp.$$.fragment,o),b(tp.$$.fragment,o),b(np.$$.fragment,o),b(wl.$$.fragment,o),b(ap.$$.fragment,o),b(xl.$$.fragment,o),b(sp.$$.fragment,o),b(ip.$$.fragment,o),b(lp.$$.fragment,o),b(Ll.$$.fragment,o),b(dp.$$.fragment,o),b(kl.$$.fragment,o),b(mp.$$.fragment,o),b(cp.$$.fragment,o),b(fp.$$.fragment,o),b($l.$$.fragment,o),b(gp.$$.fragment,o),b(Bl.$$.fragment,o),b(pp.$$.fragment,o),b(hp.$$.fragment,o),b(up.$$.fragment,o),b(jl.$$.fragment,o),b(_p.$$.fragment,o),b(Al.$$.fragment,o),b(bp.$$.fragment,o),b(Mp.$$.fragment,o),b(Tp.$$.fragment,o),b(Zl.$$.fragment,o),b(Fp.$$.fragment,o),b(Rl.$$.fragment,o),b(Cp.$$.fragment,o),b(yp.$$.fragment,o),b(vp.$$.fragment,o),b(Gl.$$.fragment,o),b(wp.$$.fragment,o),b(Pl.$$.fragment,o),b(xp.$$.fragment,o),b(kp.$$.fragment,o),b($p.$$.fragment,o),b(jp.$$.fragment,o),b(Ap.$$.fragment,o),b(Rp.$$.fragment,o),b(Gp.$$.fragment,o),b(Pp.$$.fragment,o),b(Sl.$$.fragment,o),b(Sp.$$.fragment,o),b(Wl.$$.fragment,o),b(Wp.$$.fragment,o),b(Jp.$$.fragment,o),b(Vp.$$.fragment,o),b(Jl.$$.fragment,o),b(Ep.$$.fragment,o),b(Vl.$$.fragment,o),b(Ip.$$.fragment,o),b(Up.$$.fragment,o),b(Np.$$.fragment,o),b(El.$$.fragment,o),b(qp.$$.fragment,o),b(Il.$$.fragment,o),b(Xp.$$.fragment,o),b(Dp.$$.fragment,o),b(Qp.$$.fragment,o),b(Ul.$$.fragment,o),b(Hp.$$.fragment,o),b(Nl.$$.fragment,o),b(Yp.$$.fragment,o),b(zp.$$.fragment,o),b(Op.$$.fragment,o),b(ql.$$.fragment,o),b(Kp.$$.fragment,o),b(Xl.$$.fragment,o),b(eh.$$.fragment,o),b(oh.$$.fragment,o),b(rh.$$.fragment,o),b(Dl.$$.fragment,o),b(th.$$.fragment,o),b(Ql.$$.fragment,o),b(nh.$$.fragment,o),b(ah.$$.fragment,o),b(sh.$$.fragment,o),b(Hl.$$.fragment,o),b(ih.$$.fragment,o),b(Yl.$$.fragment,o),b(lh.$$.fragment,o),b(dh.$$.fragment,o),b(mh.$$.fragment,o),b(zl.$$.fragment,o),b(ch.$$.fragment,o),b(Ol.$$.fragment,o),b(fh.$$.fragment,o),Uw=!1},d(o){o&&(d(C),d(m),d(s),d(e),d(F),d(Ay),d(yd),d(Zy),d(Ry),d(wd),d(Gy),d(xd),d(Py),d(Sy),d(kd),d(Wy),d(Jy),d(Bd),d(Vy),d(Ey),d(Iy),d(me),d(Uy),d(Ny),d(ce),d(qy),d(Xy),d(fe),d(Dy),d(Qy),d(ge),d(Hy),d(Yy),d(pe),d(zy),d(Oy),d(Od),d(Ky),d(ev),d(he),d(ov),d(rv),d(ue),d(tv),d(nv),d(_e),d(av),d(sv),d(fm),d(iv),d(lv),d(be),d(dv),d(mv),d(Me),d(cv),d(fv),d(Te),d(gv),d(pv),d(xm),d(hv),d(uv),d(Fe),d(_v),d(bv),d(Ce),d(Mv),d(Tv),d(ye),d(Fv),d(Cv),d(ve),d(yv),d(vv),d(we),d(wv),d(xv),d(xe),d(Lv),d(kv),d(Om),d($v),d(Bv),d(oc),d(jv),d(Av),d(Le),d(Zv),d(Rv),d(ke),d(Gv),d(Pv),d($e),d(Sv),d(Wv),d(Be),d(Jv),d(Vv),d(je),d(Ev),d(Iv),d(Ae),d(Uv),d(Nv),d(Ze),d(qv),d(Xv),d(Re),d(Dv),d(Qv),d(Ge),d(Hv),d(Yv),d(Pe),d(zv),d(Ov),d(Se),d(Kv),d(e2),d(We),d(o2),d(r2),d(Je),d(t2),d(n2),d(Ve),d(a2),d(s2),d(Ee),d(i2),d(l2),d(Ie),d(d2),d(m2),d(Ue),d(c2),d(f2),d(Ne),d(g2),d(p2),d(yf),d(h2),d(u2),d(xf),d(_2),d(b2),d($f),d(M2),d(T2),d(qe),d(F2),d(C2),d(Xe),d(y2),d(v2),d(De),d(w2),d(x2),d(Qe),d(L2),d(k2),d(He),d($2),d(B2),d(zf),d(j2),d(A2),d(Ye),d(Z2),d(R2),d(ze),d(G2),d(P2),d(Oe),d(S2),d(W2),d(Ke),d(J2),d(V2),d(ug),d(E2),d(I2),d(eo),d(U2),d(N2),d(oo),d(q2),d(X2),d(ro),d(D2),d(Q2),d(to),d(H2),d(Y2),d(no),d(z2),d(O2),d(ao),d(K2),d(ew),d(so),d(ow),d(rw),d(Dg),d(tw),d(nw),d(io),d(aw),d(sw),d(lo),d(iw),d(lw),d(mo),d(dw),d(mw),d(co),d(cw),d(fw),d(fo),d(gw),d(pw),d(go),d(hw),d(uw),d(po),d(_w),d(bw),d(ho),d(Mw),d(Tw),d(Lp),d(Fw),d(Cw),d(Bp),d(yw),d(vw),d(Zp),d(ww),d(xw),d(uo),d(Lw),d(kw),d(_o),d($w),d(Bw),d(bo),d(jw),d(Aw),d(Mo),d(Zw),d(Rw),d(To),d(Gw),d(Pw),d(Fo),d(Sw),d(Ww),d(Co),d(Jw),d(Vw),d(yo),d(Ew),d(Iw),d(xy)),d(t),M(c,o),M(vd,o),M(Ld,o),M($d,o),M(ys,o),M(jd,o),M(Ad),M(Zd),M(vs),M(Rd),M(Gd,o),M(Pd),M(Sd),M(xs),M(Wd),M(Jd,o),M(Vd),M(Ed),M(ks),M($s),M(Id),M(Ud,o),M(Nd),M(qd),M(js),M(As),M(Xd),M(Dd,o),M(Qd),M(Hd),M(Rs),M(Gs),M(Yd),M(zd,o),M(Kd,o),M(em),M(om),M(Ss),M(rm),M(Ws),M(tm,o),M(nm),M(am),M(Js),M(sm),M(Vs),M(im,o),M(lm),M(dm),M(Es),M(mm),M(Is),M(cm,o),M(gm,o),M(pm),M(hm),M(Us),M(um),M(Ns),M(_m,o),M(bm),M(Mm),M(qs),M(Tm),M(Xs),M(Fm,o),M(Cm),M(ym),M(Ds),M(vm),M(Qs),M(wm,o),M(Lm,o),M(km),M($m),M(Hs),M(Bm),M(Ys),M(jm,o),M(Am),M(Zm),M(zs),M(Rm),M(Os),M(Gm,o),M(Pm),M(Sm),M(Ks),M(Wm),M(ei),M(Jm,o),M(Vm),M(Em),M(oi),M(Im),M(ri),M(Um,o),M(Nm),M(qm),M(ti),M(Xm),M(ni),M(Dm,o),M(Qm),M(Hm),M(ai),M(Ym),M(si),M(zm,o),M(Km),M(ec,o),M(rc),M(tc,o),M(nc),M(ac),M(ii),M(sc),M(li),M(ic,o),M(lc),M(dc),M(di),M(mc),M(mi),M(cc,o),M(fc),M(gc),M(ci),M(pc),M(fi),M(hc,o),M(uc),M(_c),M(gi),M(bc),M(pi),M(Mc,o),M(Tc),M(Fc),M(hi),M(Cc),M(ui),M(yc,o),M(vc),M(wc),M(_i),M(xc),M(bi),M(Lc,o),M(kc),M($c),M(Mi),M(Bc),M(Ti),M(jc,o),M(Ac),M(Zc),M(Fi),M(Rc),M(Ci),M(Gc,o),M(Pc),M(Sc),M(yi),M(Wc),M(vi),M(Jc,o),M(Vc),M(Ec),M(wi),M(Ic),M(xi),M(Uc,o),M(Nc),M(qc),M(Li),M(Xc),M(ki),M(Dc,o),M(Qc),M(Hc),M($i),M(Yc),M(Bi),M(zc,o),M(Oc),M(Kc),M(ji),M(ef),M(Ai),M(of,o),M(rf),M(tf),M(Zi),M(nf),M(Ri),M(af,o),M(sf),M(lf),M(Gi),M(df),M(Pi),M(mf,o),M(cf),M(ff),M(Si),M(gf),M(Wi),M(pf,o),M(hf),M(uf),M(Ji),M(_f),M(Vi),M(bf,o),M(Mf),M(Tf),M(Ei),M(Ff),M(Ii),M(Cf,o),M(vf),M(wf,o),M(Lf),M(kf,o),M(Bf,o),M(jf),M(Af),M(Ui),M(Zf),M(Ni),M(Rf,o),M(Gf),M(Pf),M(qi),M(Sf),M(Xi),M(Wf,o),M(Jf),M(Vf),M(Di),M(Ef),M(Qi),M(If,o),M(Uf),M(Nf),M(Hi),M(qf),M(Yi),M(Xf,o),M(Df),M(Qf),M(zi),M(Hf),M(Oi),M(Yf,o),M(Of),M(Kf,o),M(eg),M(og),M(Ki),M(rg),M(el),M(tg,o),M(ng),M(ag),M(ol),M(sg),M(rl),M(ig,o),M(lg),M(dg),M(tl),M(mg),M(nl),M(cg,o),M(fg),M(gg),M(al),M(pg),M(sl),M(hg,o),M(_g),M(bg,o),M(Mg),M(Tg),M(il),M(Fg),M(ll),M(Cg,o),M(yg),M(vg),M(dl),M(wg),M(ml),M(xg,o),M(Lg),M(kg),M(cl),M($g),M(fl),M(Bg,o),M(jg),M(Ag),M(gl),M(Zg),M(pl),M(Rg,o),M(Gg),M(Pg),M(hl),M(Sg),M(ul),M(Wg,o),M(Jg),M(Vg),M(_l),M(Eg),M(bl),M(Ig,o),M(Ug),M(Ng),M(Ml),M(qg),M(Tl),M(Xg,o),M(Qg,o),M(Hg),M(Yg),M(Fl),M(zg),M(Cl),M(Og,o),M(Kg),M(ep),M(yl),M(op),M(vl),M(rp,o),M(tp),M(np),M(wl),M(ap),M(xl),M(sp,o),M(ip),M(lp),M(Ll),M(dp),M(kl),M(mp,o),M(cp),M(fp),M($l),M(gp),M(Bl),M(pp,o),M(hp),M(up),M(jl),M(_p),M(Al),M(bp,o),M(Mp),M(Tp),M(Zl),M(Fp),M(Rl),M(Cp,o),M(yp),M(vp),M(Gl),M(wp),M(Pl),M(xp,o),M(kp),M($p,o),M(jp),M(Ap,o),M(Rp,o),M(Gp),M(Pp),M(Sl),M(Sp),M(Wl),M(Wp,o),M(Jp),M(Vp),M(Jl),M(Ep),M(Vl),M(Ip,o),M(Up),M(Np),M(El),M(qp),M(Il),M(Xp,o),M(Dp),M(Qp),M(Ul),M(Hp),M(Nl),M(Yp,o),M(zp),M(Op),M(ql),M(Kp),M(Xl),M(eh,o),M(oh),M(rh),M(Dl),M(th),M(Ql),M(nh,o),M(ah),M(sh),M(Hl),M(ih),M(Yl),M(lh,o),M(dh),M(mh),M(zl),M(ch),M(Ol),M(fh,o)}}}const Y4='{"title":"Auto Classes","local":"auto-classes","sections":[{"title":"Extending the Auto Classes","local":"extending-the-auto-classes","sections":[],"depth":2},{"title":"AutoConfig","local":"transformers.AutoConfig","sections":[],"depth":2},{"title":"AutoTokenizer","local":"transformers.AutoTokenizer","sections":[],"depth":2},{"title":"AutoFeatureExtractor","local":"transformers.AutoFeatureExtractor","sections":[],"depth":2},{"title":"AutoImageProcessor","local":"transformers.AutoImageProcessor","sections":[],"depth":2},{"title":"AutoProcessor","local":"transformers.AutoProcessor","sections":[],"depth":2},{"title":"Generic model classes","local":"generic-model-classes","sections":[{"title":"AutoModel","local":"transformers.AutoModel","sections":[],"depth":3},{"title":"TFAutoModel","local":"transformers.TFAutoModel","sections":[],"depth":3},{"title":"FlaxAutoModel","local":"transformers.FlaxAutoModel","sections":[],"depth":3}],"depth":2},{"title":"Generic pretraining classes","local":"generic-pretraining-classes","sections":[{"title":"AutoModelForPreTraining","local":"transformers.AutoModelForPreTraining","sections":[],"depth":3},{"title":"TFAutoModelForPreTraining","local":"transformers.TFAutoModelForPreTraining","sections":[],"depth":3},{"title":"FlaxAutoModelForPreTraining","local":"transformers.FlaxAutoModelForPreTraining","sections":[],"depth":3}],"depth":2},{"title":"Natural Language Processing","local":"natural-language-processing","sections":[{"title":"AutoModelForCausalLM","local":"transformers.AutoModelForCausalLM","sections":[],"depth":3},{"title":"TFAutoModelForCausalLM","local":"transformers.TFAutoModelForCausalLM","sections":[],"depth":3},{"title":"FlaxAutoModelForCausalLM","local":"transformers.FlaxAutoModelForCausalLM","sections":[],"depth":3},{"title":"AutoModelForMaskedLM","local":"transformers.AutoModelForMaskedLM","sections":[],"depth":3},{"title":"TFAutoModelForMaskedLM","local":"transformers.TFAutoModelForMaskedLM","sections":[],"depth":3},{"title":"FlaxAutoModelForMaskedLM","local":"transformers.FlaxAutoModelForMaskedLM","sections":[],"depth":3},{"title":"AutoModelForMaskGeneration","local":"transformers.AutoModelForMaskGeneration","sections":[],"depth":3},{"title":"TFAutoModelForMaskGeneration","local":"transformers.TFAutoModelForMaskGeneration","sections":[],"depth":3},{"title":"AutoModelForSeq2SeqLM","local":"transformers.AutoModelForSeq2SeqLM","sections":[],"depth":3},{"title":"TFAutoModelForSeq2SeqLM","local":"transformers.TFAutoModelForSeq2SeqLM","sections":[],"depth":3},{"title":"FlaxAutoModelForSeq2SeqLM","local":"transformers.FlaxAutoModelForSeq2SeqLM","sections":[],"depth":3},{"title":"AutoModelForSequenceClassification","local":"transformers.AutoModelForSequenceClassification","sections":[],"depth":3},{"title":"TFAutoModelForSequenceClassification","local":"transformers.TFAutoModelForSequenceClassification","sections":[],"depth":3},{"title":"FlaxAutoModelForSequenceClassification","local":"transformers.FlaxAutoModelForSequenceClassification","sections":[],"depth":3},{"title":"AutoModelForMultipleChoice","local":"transformers.AutoModelForMultipleChoice","sections":[],"depth":3},{"title":"TFAutoModelForMultipleChoice","local":"transformers.TFAutoModelForMultipleChoice","sections":[],"depth":3},{"title":"FlaxAutoModelForMultipleChoice","local":"transformers.FlaxAutoModelForMultipleChoice","sections":[],"depth":3},{"title":"AutoModelForNextSentencePrediction","local":"transformers.AutoModelForNextSentencePrediction","sections":[],"depth":3},{"title":"TFAutoModelForNextSentencePrediction","local":"transformers.TFAutoModelForNextSentencePrediction","sections":[],"depth":3},{"title":"FlaxAutoModelForNextSentencePrediction","local":"transformers.FlaxAutoModelForNextSentencePrediction","sections":[],"depth":3},{"title":"AutoModelForTokenClassification","local":"transformers.AutoModelForTokenClassification","sections":[],"depth":3},{"title":"TFAutoModelForTokenClassification","local":"transformers.TFAutoModelForTokenClassification","sections":[],"depth":3},{"title":"FlaxAutoModelForTokenClassification","local":"transformers.FlaxAutoModelForTokenClassification","sections":[],"depth":3},{"title":"AutoModelForQuestionAnswering","local":"transformers.AutoModelForQuestionAnswering","sections":[],"depth":3},{"title":"TFAutoModelForQuestionAnswering","local":"transformers.TFAutoModelForQuestionAnswering","sections":[],"depth":3},{"title":"FlaxAutoModelForQuestionAnswering","local":"transformers.FlaxAutoModelForQuestionAnswering","sections":[],"depth":3},{"title":"AutoModelForTextEncoding","local":"transformers.AutoModelForTextEncoding","sections":[],"depth":3},{"title":"TFAutoModelForTextEncoding","local":"transformers.TFAutoModelForTextEncoding","sections":[],"depth":3}],"depth":2},{"title":"Computer vision","local":"computer-vision","sections":[{"title":"AutoModelForDepthEstimation","local":"transformers.AutoModelForDepthEstimation","sections":[],"depth":3},{"title":"AutoModelForImageClassification","local":"transformers.AutoModelForImageClassification","sections":[],"depth":3},{"title":"TFAutoModelForImageClassification","local":"transformers.TFAutoModelForImageClassification","sections":[],"depth":3},{"title":"FlaxAutoModelForImageClassification","local":"transformers.FlaxAutoModelForImageClassification","sections":[],"depth":3},{"title":"AutoModelForVideoClassification","local":"transformers.AutoModelForVideoClassification","sections":[],"depth":3},{"title":"AutoModelForKeypointDetection","local":"transformers.AutoModelForKeypointDetection","sections":[],"depth":3},{"title":"AutoModelForMaskedImageModeling","local":"transformers.AutoModelForMaskedImageModeling","sections":[],"depth":3},{"title":"TFAutoModelForMaskedImageModeling","local":"transformers.TFAutoModelForMaskedImageModeling","sections":[],"depth":3},{"title":"AutoModelForObjectDetection","local":"transformers.AutoModelForObjectDetection","sections":[],"depth":3},{"title":"AutoModelForImageSegmentation","local":"transformers.AutoModelForImageSegmentation","sections":[],"depth":3},{"title":"AutoModelForImageToImage","local":"transformers.AutoModelForImageToImage","sections":[],"depth":3},{"title":"AutoModelForSemanticSegmentation","local":"transformers.AutoModelForSemanticSegmentation","sections":[],"depth":3},{"title":"TFAutoModelForSemanticSegmentation","local":"transformers.TFAutoModelForSemanticSegmentation","sections":[],"depth":3},{"title":"AutoModelForInstanceSegmentation","local":"transformers.AutoModelForInstanceSegmentation","sections":[],"depth":3},{"title":"AutoModelForUniversalSegmentation","local":"transformers.AutoModelForUniversalSegmentation","sections":[],"depth":3},{"title":"AutoModelForZeroShotImageClassification","local":"transformers.AutoModelForZeroShotImageClassification","sections":[],"depth":3},{"title":"TFAutoModelForZeroShotImageClassification","local":"transformers.TFAutoModelForZeroShotImageClassification","sections":[],"depth":3},{"title":"AutoModelForZeroShotObjectDetection","local":"transformers.AutoModelForZeroShotObjectDetection","sections":[],"depth":3}],"depth":2},{"title":"Audio","local":"audio","sections":[{"title":"AutoModelForAudioClassification","local":"transformers.AutoModelForAudioClassification","sections":[],"depth":3},{"title":"AutoModelForAudioFrameClassification","local":"transformers.TFAutoModelForAudioClassification","sections":[],"depth":3},{"title":"TFAutoModelForAudioFrameClassification","local":"transformers.AutoModelForAudioFrameClassification","sections":[],"depth":3},{"title":"AutoModelForCTC","local":"transformers.AutoModelForCTC","sections":[],"depth":3},{"title":"AutoModelForSpeechSeq2Seq","local":"transformers.AutoModelForSpeechSeq2Seq","sections":[],"depth":3},{"title":"TFAutoModelForSpeechSeq2Seq","local":"transformers.TFAutoModelForSpeechSeq2Seq","sections":[],"depth":3},{"title":"FlaxAutoModelForSpeechSeq2Seq","local":"transformers.FlaxAutoModelForSpeechSeq2Seq","sections":[],"depth":3},{"title":"AutoModelForAudioXVector","local":"transformers.AutoModelForAudioXVector","sections":[],"depth":3},{"title":"AutoModelForTextToSpectrogram","local":"transformers.AutoModelForTextToSpectrogram","sections":[],"depth":3},{"title":"AutoModelForTextToWaveform","local":"transformers.AutoModelForTextToWaveform","sections":[],"depth":3}],"depth":2},{"title":"Multimodal","local":"multimodal","sections":[{"title":"AutoModelForTableQuestionAnswering","local":"transformers.AutoModelForTableQuestionAnswering","sections":[],"depth":3},{"title":"TFAutoModelForTableQuestionAnswering","local":"transformers.TFAutoModelForTableQuestionAnswering","sections":[],"depth":3},{"title":"AutoModelForDocumentQuestionAnswering","local":"transformers.AutoModelForDocumentQuestionAnswering","sections":[],"depth":3},{"title":"TFAutoModelForDocumentQuestionAnswering","local":"transformers.TFAutoModelForDocumentQuestionAnswering","sections":[],"depth":3},{"title":"AutoModelForVisualQuestionAnswering","local":"transformers.AutoModelForVisualQuestionAnswering","sections":[],"depth":3},{"title":"AutoModelForVision2Seq","local":"transformers.AutoModelForVision2Seq","sections":[],"depth":3},{"title":"TFAutoModelForVision2Seq","local":"transformers.TFAutoModelForVision2Seq","sections":[],"depth":3},{"title":"FlaxAutoModelForVision2Seq","local":"transformers.FlaxAutoModelForVision2Seq","sections":[],"depth":3}],"depth":2}],"depth":1}';function z4(y){return M5(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class aX extends T5{constructor(t){super(),F5(this,t,z4,H4,b5,{})}}export{aX as component};
