import{s as rs,o as is,n as Z}from"../chunks/scheduler.25b97de1.js";import{S as ls,i as ds,g as d,s as a,r as g,A as cs,h as c,f as i,c as r,j as q,u as _,x as u,k as R,y as o,a as p,v as b,d as M,t as T,w as y}from"../chunks/index.d9030fc9.js";import{T as gt}from"../chunks/Tip.baa67368.js";import{D as H}from"../chunks/Docstring.e257edda.js";import{C as Ce}from"../chunks/CodeBlock.e6cd0d95.js";import{F as ps,M as as}from"../chunks/Markdown.7217f838.js";import{E as Ae}from"../chunks/ExampleCodeBlock.20db4b6e.js";import{P as os}from"../chunks/PipelineTag.5f100392.js";import{H as Pe,E as ms}from"../chunks/EditOnGithub.91d95064.js";function us(B){let e,m="Examples:",n,s,w;return s=new Ce({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> DistilBertConfig, DistilBertModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a DistilBERT configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = DistilBertConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function hs(B){let e,m="pair mask has the following format:",n,s,w;return s=new Ce({props:{code:"MCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMEElN0MlMjBmaXJzdCUyMHNlcXVlbmNlJTIwJTIwJTIwJTIwJTdDJTIwc2Vjb25kJTIwc2VxdWVuY2UlMjAlN0M=",highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-qjgeij"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function fs(B){let e,m="pair mask has the following format:",n,s,w;return s=new Ce({props:{code:"MCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMEElN0MlMjBmaXJzdCUyMHNlcXVlbmNlJTIwJTIwJTIwJTIwJTdDJTIwc2Vjb25kJTIwc2VxdWVuY2UlMjAlN0M=",highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-qjgeij"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function gs(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function _s(B){let e,m="Example:",n,s,w;return s=new Ce({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, DistilBertModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function bs(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function Ms(B){let e,m="Example:",n,s,w;return s=new Ce({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, DistilBertForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is [MASK].&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># retrieve index of [MASK]</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[<span class="hljs-number">0</span>].nonzero(as_tuple=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_token_id = logits[<span class="hljs-number">0</span>, mask_token_index].argmax(axis=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(<span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># mask labels of non-[MASK] tokens</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.where(inputs.input_ids == tokenizer.mask_token_id, labels, -<span class="hljs-number">100</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels)`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function Ts(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function ys(B){let e,m="Example of single-label classification:",n,s,w;return s=new Ce({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, DistilBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_id = logits.argmax().item()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, num_labels=num_labels)

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.tensor([<span class="hljs-number">1</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-ykxpe4"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function ws(B){let e,m="Example of multi-label classification:",n,s,w;return s=new Ce({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQXV0b1Rva2VuaXplciUyQyUyMERpc3RpbEJlcnRGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0Rm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIlMkMlMjBwcm9ibGVtX3R5cGUlM0QlMjJtdWx0aV9sYWJlbF9jbGFzc2lmaWNhdGlvbiUyMiklMEElMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoJTIySGVsbG8lMkMlMjBteSUyMGRvZyUyMGlzJTIwY3V0ZSUyMiUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBJTBBd2l0aCUyMHRvcmNoLm5vX2dyYWQoKSUzQSUwQSUyMCUyMCUyMCUyMGxvZ2l0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKS5sb2dpdHMlMEElMEFwcmVkaWN0ZWRfY2xhc3NfaWRzJTIwJTNEJTIwdG9yY2guYXJhbmdlKDAlMkMlMjBsb2dpdHMuc2hhcGUlNUItMSU1RCklNUJ0b3JjaC5zaWdtb2lkKGxvZ2l0cykuc3F1ZWV6ZShkaW0lM0QwKSUyMCUzRSUyMDAuNSU1RCUwQSUwQSUyMyUyMFRvJTIwdHJhaW4lMjBhJTIwbW9kZWwlMjBvbiUyMCU2MG51bV9sYWJlbHMlNjAlMjBjbGFzc2VzJTJDJTIweW91JTIwY2FuJTIwcGFzcyUyMCU2MG51bV9sYWJlbHMlM0RudW1fbGFiZWxzJTYwJTIwdG8lMjAlNjAuZnJvbV9wcmV0cmFpbmVkKC4uLiklNjAlMEFudW1fbGFiZWxzJTIwJTNEJTIwbGVuKG1vZGVsLmNvbmZpZy5pZDJsYWJlbCklMEFtb2RlbCUyMCUzRCUyMERpc3RpbEJlcnRGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMiUyQyUyMG51bV9sYWJlbHMlM0RudW1fbGFiZWxzJTJDJTIwcHJvYmxlbV90eXBlJTNEJTIybXVsdGlfbGFiZWxfY2xhc3NpZmljYXRpb24lMjIlMEEpJTBBJTBBbGFiZWxzJTIwJTNEJTIwdG9yY2guc3VtKCUwQSUyMCUyMCUyMCUyMHRvcmNoLm5uLmZ1bmN0aW9uYWwub25lX2hvdChwcmVkaWN0ZWRfY2xhc3NfaWRzJTVCTm9uZSUyQyUyMCUzQSU1RC5jbG9uZSgpJTJDJTIwbnVtX2NsYXNzZXMlM0RudW1fbGFiZWxzKSUyQyUyMGRpbSUzRDElMEEpLnRvKHRvcmNoLmZsb2F0KSUwQWxvc3MlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyUyQyUyMGxhYmVscyUzRGxhYmVscykubG9zcw==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, DistilBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, problem_type=<span class="hljs-string">&quot;multi_label_classification&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_ids = torch.arange(<span class="hljs-number">0</span>, logits.shape[-<span class="hljs-number">1</span>])[torch.sigmoid(logits).squeeze(dim=<span class="hljs-number">0</span>) &gt; <span class="hljs-number">0.5</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, num_labels=num_labels, problem_type=<span class="hljs-string">&quot;multi_label_classification&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.<span class="hljs-built_in">sum</span>(
<span class="hljs-meta">... </span>    torch.nn.functional.one_hot(predicted_class_ids[<span class="hljs-literal">None</span>, :].clone(), num_classes=num_labels), dim=<span class="hljs-number">1</span>
<span class="hljs-meta">... </span>).to(torch.<span class="hljs-built_in">float</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-1l8e32d"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function ks(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function $s(B){let e,m="Examples:",n,s,w;return s=new Ce({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, DistilBertForMultipleChoice
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-cased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;distilbert-base-cased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = torch.tensor(<span class="hljs-number">0</span>).unsqueeze(<span class="hljs-number">0</span>)  <span class="hljs-comment"># choice0 is correct (according to Wikipedia ;)), batch size 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer([[prompt, choice0], [prompt, choice1]], return_tensors=<span class="hljs-string">&quot;pt&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**{k: v.unsqueeze(<span class="hljs-number">0</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()}, labels=labels)  <span class="hljs-comment"># batch size is 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-kvfsh7"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function vs(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function xs(B){let e,m="Example:",n,s,w;return s=new Ce({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBEaXN0aWxCZXJ0Rm9yVG9rZW5DbGFzc2lmaWNhdGlvbiUwQWltcG9ydCUyMHRvcmNoJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBEaXN0aWxCZXJ0Rm9yVG9rZW5DbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIpJTBBJTBBaW5wdXRzJTIwJTNEJTIwdG9rZW5pemVyKCUwQSUyMCUyMCUyMCUyMCUyMkh1Z2dpbmdGYWNlJTIwaXMlMjBhJTIwY29tcGFueSUyMGJhc2VkJTIwaW4lMjBQYXJpcyUyMGFuZCUyME5ldyUyMFlvcmslMjIlMkMlMjBhZGRfc3BlY2lhbF90b2tlbnMlM0RGYWxzZSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIlMEEpJTBBJTBBd2l0aCUyMHRvcmNoLm5vX2dyYWQoKSUzQSUwQSUyMCUyMCUyMCUyMGxvZ2l0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKS5sb2dpdHMlMEElMEFwcmVkaWN0ZWRfdG9rZW5fY2xhc3NfaWRzJTIwJTNEJTIwbG9naXRzLmFyZ21heCgtMSklMEElMEElMjMlMjBOb3RlJTIwdGhhdCUyMHRva2VucyUyMGFyZSUyMGNsYXNzaWZpZWQlMjByYXRoZXIlMjB0aGVuJTIwaW5wdXQlMjB3b3JkcyUyMHdoaWNoJTIwbWVhbnMlMjB0aGF0JTBBJTIzJTIwdGhlcmUlMjBtaWdodCUyMGJlJTIwbW9yZSUyMHByZWRpY3RlZCUyMHRva2VuJTIwY2xhc3NlcyUyMHRoYW4lMjB3b3Jkcy4lMEElMjMlMjBNdWx0aXBsZSUyMHRva2VuJTIwY2xhc3NlcyUyMG1pZ2h0JTIwYWNjb3VudCUyMGZvciUyMHRoZSUyMHNhbWUlMjB3b3JkJTBBcHJlZGljdGVkX3Rva2Vuc19jbGFzc2VzJTIwJTNEJTIwJTVCbW9kZWwuY29uZmlnLmlkMmxhYmVsJTVCdC5pdGVtKCklNUQlMjBmb3IlMjB0JTIwaW4lMjBwcmVkaWN0ZWRfdG9rZW5fY2xhc3NfaWRzJTVCMCU1RCU1RCUwQSUwQWxhYmVscyUyMCUzRCUyMHByZWRpY3RlZF90b2tlbl9jbGFzc19pZHMlMEFsb3NzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMlMkMlMjBsYWJlbHMlM0RsYWJlbHMpLmxvc3M=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, DistilBertForTokenClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForTokenClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;HuggingFace is a company based in Paris and New York&quot;</span>, add_special_tokens=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_token_class_ids = logits.argmax(-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Note that tokens are classified rather then input words which means that</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># there might be more predicted token classes than words.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Multiple token classes might account for the same word</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_tokens_classes = [model.config.id2label[t.item()] <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> predicted_token_class_ids[<span class="hljs-number">0</span>]]

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = predicted_token_class_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function Bs(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function Fs(B){let e,m="Example:",n,s,w;return s=new Ce({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBEaXN0aWxCZXJ0Rm9yUXVlc3Rpb25BbnN3ZXJpbmclMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRGlzdGlsQmVydEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMiklMEElMEFxdWVzdGlvbiUyQyUyMHRleHQlMjAlM0QlMjAlMjJXaG8lMjB3YXMlMjBKaW0lMjBIZW5zb24lM0YlMjIlMkMlMjAlMjJKaW0lMjBIZW5zb24lMjB3YXMlMjBhJTIwbmljZSUyMHB1cHBldCUyMiUwQSUwQWlucHV0cyUyMCUzRCUyMHRva2VuaXplcihxdWVzdGlvbiUyQyUyMHRleHQlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQXdpdGglMjB0b3JjaC5ub19ncmFkKCklM0ElMEElMjAlMjAlMjAlMjBvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMpJTBBJTBBYW5zd2VyX3N0YXJ0X2luZGV4JTIwJTNEJTIwb3V0cHV0cy5zdGFydF9sb2dpdHMuYXJnbWF4KCklMEFhbnN3ZXJfZW5kX2luZGV4JTIwJTNEJTIwb3V0cHV0cy5lbmRfbG9naXRzLmFyZ21heCgpJTBBJTBBcHJlZGljdF9hbnN3ZXJfdG9rZW5zJTIwJTNEJTIwaW5wdXRzLmlucHV0X2lkcyU1QjAlMkMlMjBhbnN3ZXJfc3RhcnRfaW5kZXglMjAlM0ElMjBhbnN3ZXJfZW5kX2luZGV4JTIwJTJCJTIwMSU1RCUwQSUwQSUyMyUyMHRhcmdldCUyMGlzJTIwJTIybmljZSUyMHB1cHBldCUyMiUwQXRhcmdldF9zdGFydF9pbmRleCUyMCUzRCUyMHRvcmNoLnRlbnNvciglNUIxNCU1RCklMEF0YXJnZXRfZW5kX2luZGV4JTIwJTNEJTIwdG9yY2gudGVuc29yKCU1QjE1JTVEKSUwQSUwQW91dHB1dHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyUyQyUyMHN0YXJ0X3Bvc2l0aW9ucyUzRHRhcmdldF9zdGFydF9pbmRleCUyQyUyMGVuZF9wb3NpdGlvbnMlM0R0YXJnZXRfZW5kX2luZGV4KSUwQWxvc3MlMjAlM0QlMjBvdXRwdXRzLmxvc3M=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, DistilBertForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>answer_start_index = outputs.start_logits.argmax()
<span class="hljs-meta">&gt;&gt;&gt; </span>answer_end_index = outputs.end_logits.argmax()

<span class="hljs-meta">&gt;&gt;&gt; </span>predict_answer_tokens = inputs.input_ids[<span class="hljs-number">0</span>, answer_start_index : answer_end_index + <span class="hljs-number">1</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># target is &quot;nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_start_index = torch.tensor([<span class="hljs-number">14</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>target_end_index = torch.tensor([<span class="hljs-number">15</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function js(B){let e,m,n,s,w,t,k="The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",se,W,z=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Y,D,U=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,O,f,j,Ye,Me,Pn='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> forward method, overrides the <code>__call__</code> special method.',sn,oe,bn,Ze,_t,ot,Nt,P,Ht,Q,at,xt="DistilBert Model with a <code>masked language modeling</code> head on top.",Mn,K,Bt=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,on,rt,Yn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,an,ue,rn,it,Yt,Oe='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> forward method, overrides the <code>__call__</code> special method.',bt,lt,Lt,Tn,Ot,Je,Jn,N,Ft,Xt,Ge,In=`DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`,zn,V,Un=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Vt,Re,Zn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Dn,G,Et,Gn,ve,Kn='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',Rn,ae,En,re,qe,yn,Kt,Ke,Ln,ne,Ne,en,jt,ze=`DistilBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.`,Qt,He,ln=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,ie,St,tn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Ct,Ue,At,Le,dn,ee='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> forward method, overrides the <code>__call__</code> special method.',Mt,Te,Jt,dt,cn,he,pn,fe,ct,Qn,Tt,yt=`DistilBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.`,wn,pt,te=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,zt,mn,E=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Ut,mt,xe,On,kn,ye='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',$n,et,Dt,Wt,Wn,X,un,S,de,vn,Pt,hn=`DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers on top of the hidden-states output to compute <code>span start logits</code> and <code>span end logits</code>).`,Sn,wt,Xe=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,kt,xn,tt=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Xn,Be,Fe,je,nt,It='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',Zt,ut,Gt,Rt,Bn;return e=new Pe({props:{title:"DistilBertModel",local:"transformers.DistilBertModel",headingTag:"h2"}}),s=new H({props:{name:"class transformers.DistilBertModel",anchor:"transformers.DistilBertModel",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L688"}}),j=new H({props:{name:"forward",anchor:"transformers.DistilBertModel.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.DistilBertModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L765",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),oe=new gt({props:{$$slots:{default:[gs]},$$scope:{ctx:B}}}),Ze=new Ae({props:{anchor:"transformers.DistilBertModel.forward.example",$$slots:{default:[_s]},$$scope:{ctx:B}}}),ot=new Pe({props:{title:"DistilBertForMaskedLM",local:"transformers.DistilBertForMaskedLM",headingTag:"h2"}}),Ht=new H({props:{name:"class transformers.DistilBertForMaskedLM",anchor:"transformers.DistilBertForMaskedLM",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L820"}}),rn=new H({props:{name:"forward",anchor:"transformers.DistilBertForMaskedLM.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.DistilBertForMaskedLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForMaskedLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForMaskedLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForMaskedLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForMaskedLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForMaskedLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForMaskedLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForMaskedLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L868",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Masked language modeling (MLM) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),lt=new gt({props:{$$slots:{default:[bs]},$$scope:{ctx:B}}}),Tn=new Ae({props:{anchor:"transformers.DistilBertForMaskedLM.forward.example",$$slots:{default:[Ms]},$$scope:{ctx:B}}}),Je=new Pe({props:{title:"DistilBertForSequenceClassification",local:"transformers.DistilBertForSequenceClassification",headingTag:"h2"}}),Ft=new H({props:{name:"class transformers.DistilBertForSequenceClassification",anchor:"transformers.DistilBertForSequenceClassification",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L924"}}),Et=new H({props:{name:"forward",anchor:"transformers.DistilBertForSequenceClassification.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.DistilBertForSequenceClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForSequenceClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForSequenceClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForSequenceClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForSequenceClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForSequenceClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForSequenceClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L965",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ae=new gt({props:{$$slots:{default:[Ts]},$$scope:{ctx:B}}}),re=new Ae({props:{anchor:"transformers.DistilBertForSequenceClassification.forward.example",$$slots:{default:[ys]},$$scope:{ctx:B}}}),yn=new Ae({props:{anchor:"transformers.DistilBertForSequenceClassification.forward.example-2",$$slots:{default:[ws]},$$scope:{ctx:B}}}),Ke=new Pe({props:{title:"DistilBertForMultipleChoice",local:"transformers.DistilBertForMultipleChoice",headingTag:"h2"}}),Ne=new H({props:{name:"class transformers.DistilBertForMultipleChoice",anchor:"transformers.DistilBertForMultipleChoice",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L1253"}}),At=new H({props:{name:"forward",anchor:"transformers.DistilBertForMultipleChoice.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.DistilBertForMultipleChoice.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForMultipleChoice.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForMultipleChoice.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForMultipleChoice.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForMultipleChoice.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForMultipleChoice.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForMultipleChoice.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForMultipleChoice.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices-1]</code> where <code>num_choices</code> is the size of the second dimension of the input tensors. (See
<code>input_ids</code> above)`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L1292",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <em>(1,)</em>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>) — <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Te=new gt({props:{$$slots:{default:[ks]},$$scope:{ctx:B}}}),dt=new Ae({props:{anchor:"transformers.DistilBertForMultipleChoice.forward.example",$$slots:{default:[$s]},$$scope:{ctx:B}}}),he=new Pe({props:{title:"DistilBertForTokenClassification",local:"transformers.DistilBertForTokenClassification",headingTag:"h2"}}),ct=new H({props:{name:"class transformers.DistilBertForTokenClassification",anchor:"transformers.DistilBertForTokenClassification",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L1159"}}),xe=new H({props:{name:"forward",anchor:"transformers.DistilBertForTokenClassification.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.DistilBertForTokenClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForTokenClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForTokenClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForTokenClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForTokenClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForTokenClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForTokenClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForTokenClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L1198",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided)  — Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) — Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),et=new gt({props:{$$slots:{default:[vs]},$$scope:{ctx:B}}}),Wt=new Ae({props:{anchor:"transformers.DistilBertForTokenClassification.forward.example",$$slots:{default:[xs]},$$scope:{ctx:B}}}),X=new Pe({props:{title:"DistilBertForQuestionAnswering",local:"transformers.DistilBertForQuestionAnswering",headingTag:"h2"}}),de=new H({props:{name:"class transformers.DistilBertForQuestionAnswering",anchor:"transformers.DistilBertForQuestionAnswering",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L1041"}}),Fe=new H({props:{name:"forward",anchor:"transformers.DistilBertForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"start_positions",val:": Optional = None"},{name:"end_positions",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.DistilBertForQuestionAnswering.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.start_positions",description:`<strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.end_positions",description:`<strong>end_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_distilbert.py#L1082",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.</p>
</li>
<li>
<p><strong>start_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) — Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>) — Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ut=new gt({props:{$$slots:{default:[Bs]},$$scope:{ctx:B}}}),Rt=new Ae({props:{anchor:"transformers.DistilBertForQuestionAnswering.forward.example",$$slots:{default:[Fs]},$$scope:{ctx:B}}}),{c(){g(e.$$.fragment),m=a(),n=d("div"),g(s.$$.fragment),w=a(),t=d("p"),t.textContent=k,se=a(),W=d("p"),W.innerHTML=z,Y=a(),D=d("p"),D.innerHTML=U,O=a(),f=d("div"),g(j.$$.fragment),Ye=a(),Me=d("p"),Me.innerHTML=Pn,sn=a(),g(oe.$$.fragment),bn=a(),g(Ze.$$.fragment),_t=a(),g(ot.$$.fragment),Nt=a(),P=d("div"),g(Ht.$$.fragment),Q=a(),at=d("p"),at.innerHTML=xt,Mn=a(),K=d("p"),K.innerHTML=Bt,on=a(),rt=d("p"),rt.innerHTML=Yn,an=a(),ue=d("div"),g(rn.$$.fragment),it=a(),Yt=d("p"),Yt.innerHTML=Oe,bt=a(),g(lt.$$.fragment),Lt=a(),g(Tn.$$.fragment),Ot=a(),g(Je.$$.fragment),Jn=a(),N=d("div"),g(Ft.$$.fragment),Xt=a(),Ge=d("p"),Ge.textContent=In,zn=a(),V=d("p"),V.innerHTML=Un,Vt=a(),Re=d("p"),Re.innerHTML=Zn,Dn=a(),G=d("div"),g(Et.$$.fragment),Gn=a(),ve=d("p"),ve.innerHTML=Kn,Rn=a(),g(ae.$$.fragment),En=a(),g(re.$$.fragment),qe=a(),g(yn.$$.fragment),Kt=a(),g(Ke.$$.fragment),Ln=a(),ne=d("div"),g(Ne.$$.fragment),en=a(),jt=d("p"),jt.textContent=ze,Qt=a(),He=d("p"),He.innerHTML=ln,ie=a(),St=d("p"),St.innerHTML=tn,Ct=a(),Ue=d("div"),g(At.$$.fragment),Le=a(),dn=d("p"),dn.innerHTML=ee,Mt=a(),g(Te.$$.fragment),Jt=a(),g(dt.$$.fragment),cn=a(),g(he.$$.fragment),pn=a(),fe=d("div"),g(ct.$$.fragment),Qn=a(),Tt=d("p"),Tt.textContent=yt,wn=a(),pt=d("p"),pt.innerHTML=te,zt=a(),mn=d("p"),mn.innerHTML=E,Ut=a(),mt=d("div"),g(xe.$$.fragment),On=a(),kn=d("p"),kn.innerHTML=ye,$n=a(),g(et.$$.fragment),Dt=a(),g(Wt.$$.fragment),Wn=a(),g(X.$$.fragment),un=a(),S=d("div"),g(de.$$.fragment),vn=a(),Pt=d("p"),Pt.innerHTML=hn,Sn=a(),wt=d("p"),wt.innerHTML=Xe,kt=a(),xn=d("p"),xn.innerHTML=tt,Xn=a(),Be=d("div"),g(Fe.$$.fragment),je=a(),nt=d("p"),nt.innerHTML=It,Zt=a(),g(ut.$$.fragment),Gt=a(),g(Rt.$$.fragment),this.h()},l($){_(e.$$.fragment,$),m=r($),n=c($,"DIV",{class:!0});var C=q(n);_(s.$$.fragment,C),w=r(C),t=c(C,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ololo3"&&(t.textContent=k),se=r(C),W=c(C,"P",{"data-svelte-h":!0}),u(W)!=="svelte-qlenqi"&&(W.innerHTML=z),Y=r(C),D=c(C,"P",{"data-svelte-h":!0}),u(D)!=="svelte-hswkmf"&&(D.innerHTML=U),O=r(C),f=c(C,"DIV",{class:!0});var ce=q(f);_(j.$$.fragment,ce),Ye=r(ce),Me=c(ce,"P",{"data-svelte-h":!0}),u(Me)!=="svelte-ix37hv"&&(Me.innerHTML=Pn),sn=r(ce),_(oe.$$.fragment,ce),bn=r(ce),_(Ze.$$.fragment,ce),ce.forEach(i),C.forEach(i),_t=r($),_(ot.$$.fragment,$),Nt=r($),P=c($,"DIV",{class:!0});var De=q(P);_(Ht.$$.fragment,De),Q=r(De),at=c(De,"P",{"data-svelte-h":!0}),u(at)!=="svelte-23pii1"&&(at.innerHTML=xt),Mn=r(De),K=c(De,"P",{"data-svelte-h":!0}),u(K)!=="svelte-qlenqi"&&(K.innerHTML=Bt),on=r(De),rt=c(De,"P",{"data-svelte-h":!0}),u(rt)!=="svelte-hswkmf"&&(rt.innerHTML=Yn),an=r(De),ue=c(De,"DIV",{class:!0});var L=q(ue);_(rn.$$.fragment,L),it=r(L),Yt=c(L,"P",{"data-svelte-h":!0}),u(Yt)!=="svelte-5jbvvj"&&(Yt.innerHTML=Oe),bt=r(L),_(lt.$$.fragment,L),Lt=r(L),_(Tn.$$.fragment,L),L.forEach(i),De.forEach(i),Ot=r($),_(Je.$$.fragment,$),Jn=r($),N=c($,"DIV",{class:!0});var ge=q(N);_(Ft.$$.fragment,ge),Xt=r(ge),Ge=c(ge,"P",{"data-svelte-h":!0}),u(Ge)!=="svelte-1kr5cyc"&&(Ge.textContent=In),zn=r(ge),V=c(ge,"P",{"data-svelte-h":!0}),u(V)!=="svelte-qlenqi"&&(V.innerHTML=Un),Vt=r(ge),Re=c(ge,"P",{"data-svelte-h":!0}),u(Re)!=="svelte-hswkmf"&&(Re.innerHTML=Zn),Dn=r(ge),G=c(ge,"DIV",{class:!0});var I=q(G);_(Et.$$.fragment,I),Gn=r(I),ve=c(I,"P",{"data-svelte-h":!0}),u(ve)!=="svelte-3ncetz"&&(ve.innerHTML=Kn),Rn=r(I),_(ae.$$.fragment,I),En=r(I),_(re.$$.fragment,I),qe=r(I),_(yn.$$.fragment,I),I.forEach(i),ge.forEach(i),Kt=r($),_(Ke.$$.fragment,$),Ln=r($),ne=c($,"DIV",{class:!0});var A=q(ne);_(Ne.$$.fragment,A),en=r(A),jt=c(A,"P",{"data-svelte-h":!0}),u(jt)!=="svelte-i84iso"&&(jt.textContent=ze),Qt=r(A),He=c(A,"P",{"data-svelte-h":!0}),u(He)!=="svelte-qlenqi"&&(He.innerHTML=ln),ie=r(A),St=c(A,"P",{"data-svelte-h":!0}),u(St)!=="svelte-hswkmf"&&(St.innerHTML=tn),Ct=r(A),Ue=c(A,"DIV",{class:!0});var qt=q(Ue);_(At.$$.fragment,qt),Le=r(qt),dn=c(qt,"P",{"data-svelte-h":!0}),u(dn)!=="svelte-2rpp1j"&&(dn.innerHTML=ee),Mt=r(qt),_(Te.$$.fragment,qt),Jt=r(qt),_(dt.$$.fragment,qt),qt.forEach(i),A.forEach(i),cn=r($),_(he.$$.fragment,$),pn=r($),fe=c($,"DIV",{class:!0});var pe=q(fe);_(ct.$$.fragment,pe),Qn=r(pe),Tt=c(pe,"P",{"data-svelte-h":!0}),u(Tt)!=="svelte-14y5xfr"&&(Tt.textContent=yt),wn=r(pe),pt=c(pe,"P",{"data-svelte-h":!0}),u(pt)!=="svelte-qlenqi"&&(pt.innerHTML=te),zt=r(pe),mn=c(pe,"P",{"data-svelte-h":!0}),u(mn)!=="svelte-hswkmf"&&(mn.innerHTML=E),Ut=r(pe),mt=c(pe,"DIV",{class:!0});var ht=q(mt);_(xe.$$.fragment,ht),On=r(ht),kn=c(ht,"P",{"data-svelte-h":!0}),u(kn)!=="svelte-1nv63wb"&&(kn.innerHTML=ye),$n=r(ht),_(et.$$.fragment,ht),Dt=r(ht),_(Wt.$$.fragment,ht),ht.forEach(i),pe.forEach(i),Wn=r($),_(X.$$.fragment,$),un=r($),S=c($,"DIV",{class:!0});var ft=q(S);_(de.$$.fragment,ft),vn=r(ft),Pt=c(ft,"P",{"data-svelte-h":!0}),u(Pt)!=="svelte-18jj0o9"&&(Pt.innerHTML=hn),Sn=r(ft),wt=c(ft,"P",{"data-svelte-h":!0}),u(wt)!=="svelte-qlenqi"&&(wt.innerHTML=Xe),kt=r(ft),xn=c(ft,"P",{"data-svelte-h":!0}),u(xn)!=="svelte-hswkmf"&&(xn.innerHTML=tt),Xn=r(ft),Be=c(ft,"DIV",{class:!0});var me=q(Be);_(Fe.$$.fragment,me),je=r(me),nt=c(me,"P",{"data-svelte-h":!0}),u(nt)!=="svelte-r2bk5x"&&(nt.innerHTML=It),Zt=r(me),_(ut.$$.fragment,me),Gt=r(me),_(Rt.$$.fragment,me),me.forEach(i),ft.forEach(i),this.h()},h(){R(f,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(Ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(ne,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(mt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(fe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(Be,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m($,C){b(e,$,C),p($,m,C),p($,n,C),b(s,n,null),o(n,w),o(n,t),o(n,se),o(n,W),o(n,Y),o(n,D),o(n,O),o(n,f),b(j,f,null),o(f,Ye),o(f,Me),o(f,sn),b(oe,f,null),o(f,bn),b(Ze,f,null),p($,_t,C),b(ot,$,C),p($,Nt,C),p($,P,C),b(Ht,P,null),o(P,Q),o(P,at),o(P,Mn),o(P,K),o(P,on),o(P,rt),o(P,an),o(P,ue),b(rn,ue,null),o(ue,it),o(ue,Yt),o(ue,bt),b(lt,ue,null),o(ue,Lt),b(Tn,ue,null),p($,Ot,C),b(Je,$,C),p($,Jn,C),p($,N,C),b(Ft,N,null),o(N,Xt),o(N,Ge),o(N,zn),o(N,V),o(N,Vt),o(N,Re),o(N,Dn),o(N,G),b(Et,G,null),o(G,Gn),o(G,ve),o(G,Rn),b(ae,G,null),o(G,En),b(re,G,null),o(G,qe),b(yn,G,null),p($,Kt,C),b(Ke,$,C),p($,Ln,C),p($,ne,C),b(Ne,ne,null),o(ne,en),o(ne,jt),o(ne,Qt),o(ne,He),o(ne,ie),o(ne,St),o(ne,Ct),o(ne,Ue),b(At,Ue,null),o(Ue,Le),o(Ue,dn),o(Ue,Mt),b(Te,Ue,null),o(Ue,Jt),b(dt,Ue,null),p($,cn,C),b(he,$,C),p($,pn,C),p($,fe,C),b(ct,fe,null),o(fe,Qn),o(fe,Tt),o(fe,wn),o(fe,pt),o(fe,zt),o(fe,mn),o(fe,Ut),o(fe,mt),b(xe,mt,null),o(mt,On),o(mt,kn),o(mt,$n),b(et,mt,null),o(mt,Dt),b(Wt,mt,null),p($,Wn,C),b(X,$,C),p($,un,C),p($,S,C),b(de,S,null),o(S,vn),o(S,Pt),o(S,Sn),o(S,wt),o(S,kt),o(S,xn),o(S,Xn),o(S,Be),b(Fe,Be,null),o(Be,je),o(Be,nt),o(Be,Zt),b(ut,Be,null),o(Be,Gt),b(Rt,Be,null),Bn=!0},p($,C){const ce={};C&2&&(ce.$$scope={dirty:C,ctx:$}),oe.$set(ce);const De={};C&2&&(De.$$scope={dirty:C,ctx:$}),Ze.$set(De);const L={};C&2&&(L.$$scope={dirty:C,ctx:$}),lt.$set(L);const ge={};C&2&&(ge.$$scope={dirty:C,ctx:$}),Tn.$set(ge);const I={};C&2&&(I.$$scope={dirty:C,ctx:$}),ae.$set(I);const A={};C&2&&(A.$$scope={dirty:C,ctx:$}),re.$set(A);const qt={};C&2&&(qt.$$scope={dirty:C,ctx:$}),yn.$set(qt);const pe={};C&2&&(pe.$$scope={dirty:C,ctx:$}),Te.$set(pe);const ht={};C&2&&(ht.$$scope={dirty:C,ctx:$}),dt.$set(ht);const ft={};C&2&&(ft.$$scope={dirty:C,ctx:$}),et.$set(ft);const me={};C&2&&(me.$$scope={dirty:C,ctx:$}),Wt.$set(me);const nn={};C&2&&(nn.$$scope={dirty:C,ctx:$}),ut.$set(nn);const $t={};C&2&&($t.$$scope={dirty:C,ctx:$}),Rt.$set($t)},i($){Bn||(M(e.$$.fragment,$),M(s.$$.fragment,$),M(j.$$.fragment,$),M(oe.$$.fragment,$),M(Ze.$$.fragment,$),M(ot.$$.fragment,$),M(Ht.$$.fragment,$),M(rn.$$.fragment,$),M(lt.$$.fragment,$),M(Tn.$$.fragment,$),M(Je.$$.fragment,$),M(Ft.$$.fragment,$),M(Et.$$.fragment,$),M(ae.$$.fragment,$),M(re.$$.fragment,$),M(yn.$$.fragment,$),M(Ke.$$.fragment,$),M(Ne.$$.fragment,$),M(At.$$.fragment,$),M(Te.$$.fragment,$),M(dt.$$.fragment,$),M(he.$$.fragment,$),M(ct.$$.fragment,$),M(xe.$$.fragment,$),M(et.$$.fragment,$),M(Wt.$$.fragment,$),M(X.$$.fragment,$),M(de.$$.fragment,$),M(Fe.$$.fragment,$),M(ut.$$.fragment,$),M(Rt.$$.fragment,$),Bn=!0)},o($){T(e.$$.fragment,$),T(s.$$.fragment,$),T(j.$$.fragment,$),T(oe.$$.fragment,$),T(Ze.$$.fragment,$),T(ot.$$.fragment,$),T(Ht.$$.fragment,$),T(rn.$$.fragment,$),T(lt.$$.fragment,$),T(Tn.$$.fragment,$),T(Je.$$.fragment,$),T(Ft.$$.fragment,$),T(Et.$$.fragment,$),T(ae.$$.fragment,$),T(re.$$.fragment,$),T(yn.$$.fragment,$),T(Ke.$$.fragment,$),T(Ne.$$.fragment,$),T(At.$$.fragment,$),T(Te.$$.fragment,$),T(dt.$$.fragment,$),T(he.$$.fragment,$),T(ct.$$.fragment,$),T(xe.$$.fragment,$),T(et.$$.fragment,$),T(Wt.$$.fragment,$),T(X.$$.fragment,$),T(de.$$.fragment,$),T(Fe.$$.fragment,$),T(ut.$$.fragment,$),T(Rt.$$.fragment,$),Bn=!1},d($){$&&(i(m),i(n),i(_t),i(Nt),i(P),i(Ot),i(Jn),i(N),i(Kt),i(Ln),i(ne),i(cn),i(pn),i(fe),i(Wn),i(un),i(S)),y(e,$),y(s),y(j),y(oe),y(Ze),y(ot,$),y(Ht),y(rn),y(lt),y(Tn),y(Je,$),y(Ft),y(Et),y(ae),y(re),y(yn),y(Ke,$),y(Ne),y(At),y(Te),y(dt),y(he,$),y(ct),y(xe),y(et),y(Wt),y(X,$),y(de),y(Fe),y(ut),y(Rt)}}}function Cs(B){let e,m;return e=new as({props:{$$slots:{default:[js]},$$scope:{ctx:B}}}),{c(){g(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,s){b(e,n,s),m=!0},p(n,s){const w={};s&2&&(w.$$scope={dirty:s,ctx:n}),e.$set(w)},i(n){m||(M(e.$$.fragment,n),m=!0)},o(n){T(e.$$.fragment,n),m=!1},d(n){y(e,n)}}}function Js(B){let e,m="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",n,s,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",t,k,se=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,W,z,Y=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,D,U,O=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){e=d("p"),e.innerHTML=m,n=a(),s=d("ul"),s.innerHTML=w,t=a(),k=d("p"),k.innerHTML=se,W=a(),z=d("ul"),z.innerHTML=Y,D=a(),U=d("p"),U.innerHTML=O},l(f){e=c(f,"P",{"data-svelte-h":!0}),u(e)!=="svelte-1ajbfxg"&&(e.innerHTML=m),n=r(f),s=c(f,"UL",{"data-svelte-h":!0}),u(s)!=="svelte-qm1t26"&&(s.innerHTML=w),t=r(f),k=c(f,"P",{"data-svelte-h":!0}),u(k)!=="svelte-1v9qsc5"&&(k.innerHTML=se),W=r(f),z=c(f,"UL",{"data-svelte-h":!0}),u(z)!=="svelte-15scerc"&&(z.innerHTML=Y),D=r(f),U=c(f,"P",{"data-svelte-h":!0}),u(U)!=="svelte-1an3odd"&&(U.innerHTML=O)},m(f,j){p(f,e,j),p(f,n,j),p(f,s,j),p(f,t,j),p(f,k,j),p(f,W,j),p(f,z,j),p(f,D,j),p(f,U,j)},p:Z,d(f){f&&(i(e),i(n),i(s),i(t),i(k),i(W),i(z),i(D),i(U))}}}function zs(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function Us(B){let e,m="Example:",n,s,w;return s=new Ce({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, TFDistilBertModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function Ds(B){let e,m="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",n,s,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",t,k,se=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,W,z,Y=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,D,U,O=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){e=d("p"),e.innerHTML=m,n=a(),s=d("ul"),s.innerHTML=w,t=a(),k=d("p"),k.innerHTML=se,W=a(),z=d("ul"),z.innerHTML=Y,D=a(),U=d("p"),U.innerHTML=O},l(f){e=c(f,"P",{"data-svelte-h":!0}),u(e)!=="svelte-1ajbfxg"&&(e.innerHTML=m),n=r(f),s=c(f,"UL",{"data-svelte-h":!0}),u(s)!=="svelte-qm1t26"&&(s.innerHTML=w),t=r(f),k=c(f,"P",{"data-svelte-h":!0}),u(k)!=="svelte-1v9qsc5"&&(k.innerHTML=se),W=r(f),z=c(f,"UL",{"data-svelte-h":!0}),u(z)!=="svelte-15scerc"&&(z.innerHTML=Y),D=r(f),U=c(f,"P",{"data-svelte-h":!0}),u(U)!=="svelte-1an3odd"&&(U.innerHTML=O)},m(f,j){p(f,e,j),p(f,n,j),p(f,s,j),p(f,t,j),p(f,k,j),p(f,W,j),p(f,z,j),p(f,D,j),p(f,U,j)},p:Z,d(f){f&&(i(e),i(n),i(s),i(t),i(k),i(W),i(z),i(D),i(U))}}}function Ws(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function Is(B){let e,m="Example:",n,s,w;return s=new Ce({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, TFDistilBertForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDistilBertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is [MASK].&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># retrieve index of [MASK]</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>mask_token_index = tf.where((inputs.input_ids == tokenizer.mask_token_id)[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>selected_logits = tf.gather_nd(logits[<span class="hljs-number">0</span>], indices=mask_token_index)

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_token_id = tf.math.argmax(selected_logits, axis=-<span class="hljs-number">1</span>)`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function Zs(B){let e,m;return e=new Ce({props:{code:"bGFiZWxzJTIwJTNEJTIwdG9rZW5pemVyKCUyMlRoZSUyMGNhcGl0YWwlMjBvZiUyMEZyYW5jZSUyMGlzJTIwUGFyaXMuJTIyJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJ0ZiUyMiklNUIlMjJpbnB1dF9pZHMlMjIlNUQlMEElMjMlMjBtYXNrJTIwbGFiZWxzJTIwb2YlMjBub24tJTVCTUFTSyU1RCUyMHRva2VucyUwQWxhYmVscyUyMCUzRCUyMHRmLndoZXJlKGlucHV0cy5pbnB1dF9pZHMlMjAlM0QlM0QlMjB0b2tlbml6ZXIubWFza190b2tlbl9pZCUyQyUyMGxhYmVscyUyQyUyMC0xMDApJTBBJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzJTJDJTIwbGFiZWxzJTNEbGFiZWxzKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(<span class="hljs-string">&quot;The capital of France is Paris.&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)[<span class="hljs-string">&quot;input_ids&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># mask labels of non-[MASK] tokens</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -<span class="hljs-number">100</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels)`,wrap:!1}}),{c(){g(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,s){b(e,n,s),m=!0},p:Z,i(n){m||(M(e.$$.fragment,n),m=!0)},o(n){T(e.$$.fragment,n),m=!1},d(n){y(e,n)}}}function Gs(B){let e,m="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",n,s,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",t,k,se=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,W,z,Y=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,D,U,O=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){e=d("p"),e.innerHTML=m,n=a(),s=d("ul"),s.innerHTML=w,t=a(),k=d("p"),k.innerHTML=se,W=a(),z=d("ul"),z.innerHTML=Y,D=a(),U=d("p"),U.innerHTML=O},l(f){e=c(f,"P",{"data-svelte-h":!0}),u(e)!=="svelte-1ajbfxg"&&(e.innerHTML=m),n=r(f),s=c(f,"UL",{"data-svelte-h":!0}),u(s)!=="svelte-qm1t26"&&(s.innerHTML=w),t=r(f),k=c(f,"P",{"data-svelte-h":!0}),u(k)!=="svelte-1v9qsc5"&&(k.innerHTML=se),W=r(f),z=c(f,"UL",{"data-svelte-h":!0}),u(z)!=="svelte-15scerc"&&(z.innerHTML=Y),D=r(f),U=c(f,"P",{"data-svelte-h":!0}),u(U)!=="svelte-1an3odd"&&(U.innerHTML=O)},m(f,j){p(f,e,j),p(f,n,j),p(f,s,j),p(f,t,j),p(f,k,j),p(f,W,j),p(f,z,j),p(f,D,j),p(f,U,j)},p:Z,d(f){f&&(i(e),i(n),i(s),i(t),i(k),i(W),i(z),i(D),i(U))}}}function Rs(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function qs(B){let e,m="Example:",n,s,w;return s=new Ce({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, TFDistilBertForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(**inputs).logits

<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_id = <span class="hljs-built_in">int</span>(tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>)[<span class="hljs-number">0</span>])`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function Ns(B){let e,m;return e=new Ce({props:{code:"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",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># To train a model on `num_labels` classes, you can pass `num_labels=num_labels` to `.from_pretrained(...)`</span>\n<span class="hljs-meta">&gt;&gt;&gt; </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)\n<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>, num_labels=num_labels)\n\n<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tf.constant(<span class="hljs-number">1</span>)\n<span class="hljs-meta">&gt;&gt;&gt; </span>loss = model(**inputs, labels=labels).loss',wrap:!1}}),{c(){g(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,s){b(e,n,s),m=!0},p:Z,i(n){m||(M(e.$$.fragment,n),m=!0)},o(n){T(e.$$.fragment,n),m=!1},d(n){y(e,n)}}}function Hs(B){let e,m="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",n,s,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",t,k,se=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,W,z,Y=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,D,U,O=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){e=d("p"),e.innerHTML=m,n=a(),s=d("ul"),s.innerHTML=w,t=a(),k=d("p"),k.innerHTML=se,W=a(),z=d("ul"),z.innerHTML=Y,D=a(),U=d("p"),U.innerHTML=O},l(f){e=c(f,"P",{"data-svelte-h":!0}),u(e)!=="svelte-1ajbfxg"&&(e.innerHTML=m),n=r(f),s=c(f,"UL",{"data-svelte-h":!0}),u(s)!=="svelte-qm1t26"&&(s.innerHTML=w),t=r(f),k=c(f,"P",{"data-svelte-h":!0}),u(k)!=="svelte-1v9qsc5"&&(k.innerHTML=se),W=r(f),z=c(f,"UL",{"data-svelte-h":!0}),u(z)!=="svelte-15scerc"&&(z.innerHTML=Y),D=r(f),U=c(f,"P",{"data-svelte-h":!0}),u(U)!=="svelte-1an3odd"&&(U.innerHTML=O)},m(f,j){p(f,e,j),p(f,n,j),p(f,s,j),p(f,t,j),p(f,k,j),p(f,W,j),p(f,z,j),p(f,D,j),p(f,U,j)},p:Z,d(f){f&&(i(e),i(n),i(s),i(t),i(k),i(W),i(z),i(D),i(U))}}}function Ls(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function Xs(B){let e,m="Example:",n,s,w;return s=new Ce({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, TFDistilBertForMultipleChoice
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDistilBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors=<span class="hljs-string">&quot;tf&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = {k: tf.expand_dims(v, <span class="hljs-number">0</span>) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()}
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(inputs)  <span class="hljs-comment"># batch size is 1</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function Vs(B){let e,m="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",n,s,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",t,k,se=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,W,z,Y=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,D,U,O=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){e=d("p"),e.innerHTML=m,n=a(),s=d("ul"),s.innerHTML=w,t=a(),k=d("p"),k.innerHTML=se,W=a(),z=d("ul"),z.innerHTML=Y,D=a(),U=d("p"),U.innerHTML=O},l(f){e=c(f,"P",{"data-svelte-h":!0}),u(e)!=="svelte-1ajbfxg"&&(e.innerHTML=m),n=r(f),s=c(f,"UL",{"data-svelte-h":!0}),u(s)!=="svelte-qm1t26"&&(s.innerHTML=w),t=r(f),k=c(f,"P",{"data-svelte-h":!0}),u(k)!=="svelte-1v9qsc5"&&(k.innerHTML=se),W=r(f),z=c(f,"UL",{"data-svelte-h":!0}),u(z)!=="svelte-15scerc"&&(z.innerHTML=Y),D=r(f),U=c(f,"P",{"data-svelte-h":!0}),u(U)!=="svelte-1an3odd"&&(U.innerHTML=O)},m(f,j){p(f,e,j),p(f,n,j),p(f,s,j),p(f,t,j),p(f,k,j),p(f,W,j),p(f,z,j),p(f,D,j),p(f,U,j)},p:Z,d(f){f&&(i(e),i(n),i(s),i(t),i(k),i(W),i(z),i(D),i(U))}}}function Es(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function Qs(B){let e,m="Example:",n,s,w;return s=new Ce({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, TFDistilBertForTokenClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDistilBertForTokenClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;HuggingFace is a company based in Paris and New York&quot;</span>, add_special_tokens=<span class="hljs-literal">False</span>, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = model(**inputs).logits
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_token_class_ids = tf.math.argmax(logits, axis=-<span class="hljs-number">1</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Note that tokens are classified rather then input words which means that</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># there might be more predicted token classes than words.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Multiple token classes might account for the same word</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_tokens_classes = [model.config.id2label[t] <span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> predicted_token_class_ids[<span class="hljs-number">0</span>].numpy().tolist()]`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function Ss(B){let e,m;return e=new Ce({props:{code:"bGFiZWxzJTIwJTNEJTIwcHJlZGljdGVkX3Rva2VuX2NsYXNzX2lkcyUwQWxvc3MlMjAlM0QlMjB0Zi5tYXRoLnJlZHVjZV9tZWFuKG1vZGVsKCoqaW5wdXRzJTJDJTIwbGFiZWxzJTNEbGFiZWxzKS5sb3NzKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>labels = predicted_token_class_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = tf.math.reduce_mean(model(**inputs, labels=labels).loss)`,wrap:!1}}),{c(){g(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,s){b(e,n,s),m=!0},p:Z,i(n){m||(M(e.$$.fragment,n),m=!0)},o(n){T(e.$$.fragment,n),m=!1},d(n){y(e,n)}}}function As(B){let e,m="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",n,s,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",t,k,se=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,W,z,Y=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,D,U,O=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){e=d("p"),e.innerHTML=m,n=a(),s=d("ul"),s.innerHTML=w,t=a(),k=d("p"),k.innerHTML=se,W=a(),z=d("ul"),z.innerHTML=Y,D=a(),U=d("p"),U.innerHTML=O},l(f){e=c(f,"P",{"data-svelte-h":!0}),u(e)!=="svelte-1ajbfxg"&&(e.innerHTML=m),n=r(f),s=c(f,"UL",{"data-svelte-h":!0}),u(s)!=="svelte-qm1t26"&&(s.innerHTML=w),t=r(f),k=c(f,"P",{"data-svelte-h":!0}),u(k)!=="svelte-1v9qsc5"&&(k.innerHTML=se),W=r(f),z=c(f,"UL",{"data-svelte-h":!0}),u(z)!=="svelte-15scerc"&&(z.innerHTML=Y),D=r(f),U=c(f,"P",{"data-svelte-h":!0}),u(U)!=="svelte-1an3odd"&&(U.innerHTML=O)},m(f,j){p(f,e,j),p(f,n,j),p(f,s,j),p(f,t,j),p(f,k,j),p(f,W,j),p(f,z,j),p(f,D,j),p(f,U,j)},p:Z,d(f){f&&(i(e),i(n),i(s),i(t),i(k),i(W),i(z),i(D),i(U))}}}function Ps(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function Ys(B){let e,m="Example:",n,s,w;return s=new Ce({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, TFDistilBertForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFDistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;tf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>answer_start_index = <span class="hljs-built_in">int</span>(tf.math.argmax(outputs.start_logits, axis=-<span class="hljs-number">1</span>)[<span class="hljs-number">0</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>answer_end_index = <span class="hljs-built_in">int</span>(tf.math.argmax(outputs.end_logits, axis=-<span class="hljs-number">1</span>)[<span class="hljs-number">0</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>predict_answer_tokens = inputs.input_ids[<span class="hljs-number">0</span>, answer_start_index : answer_end_index + <span class="hljs-number">1</span>]`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function Os(B){let e,m;return e=new Ce({props:{code:"JTIzJTIwdGFyZ2V0JTIwaXMlMjAlMjJuaWNlJTIwcHVwcGV0JTIyJTBBdGFyZ2V0X3N0YXJ0X2luZGV4JTIwJTNEJTIwdGYuY29uc3RhbnQoJTVCMTQlNUQpJTBBdGFyZ2V0X2VuZF9pbmRleCUyMCUzRCUyMHRmLmNvbnN0YW50KCU1QjE1JTVEKSUwQSUwQW91dHB1dHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyUyQyUyMHN0YXJ0X3Bvc2l0aW9ucyUzRHRhcmdldF9zdGFydF9pbmRleCUyQyUyMGVuZF9wb3NpdGlvbnMlM0R0YXJnZXRfZW5kX2luZGV4KSUwQWxvc3MlMjAlM0QlMjB0Zi5tYXRoLnJlZHVjZV9tZWFuKG91dHB1dHMubG9zcyk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># target is &quot;nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_start_index = tf.constant([<span class="hljs-number">14</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>target_end_index = tf.constant([<span class="hljs-number">15</span>])

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = tf.math.reduce_mean(outputs.loss)`,wrap:!1}}),{c(){g(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,s){b(e,n,s),m=!0},p:Z,i(n){m||(M(e.$$.fragment,n),m=!0)},o(n){T(e.$$.fragment,n),m=!1},d(n){y(e,n)}}}function Ks(B){let e,m,n,s,w,t,k="The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",se,W,z=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Y,D,U=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,O,f,j,Ye,Me,Pn,sn,oe='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> forward method, overrides the <code>__call__</code> special method.',bn,Ze,_t,ot,Nt,P,Ht,Q,at,xt,Mn,K="DistilBert Model with a <code>masked language modeling</code> head on top.",Bt,on,rt=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Yn,an,ue=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,rn,it,Yt,Oe,bt,lt,Lt,Tn='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> forward method, overrides the <code>__call__</code> special method.',Ot,Je,Jn,N,Ft,Xt,Ge,In,zn,V,Un,Vt,Re,Zn=`DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`,Dn,G,Et=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Gn,ve,Kn=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,Rn,ae,En,re,qe,yn,Kt,Ke='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',Ln,ne,Ne,en,jt,ze,Qt,He,ln,ie,St,tn,Ct,Ue=`DistilBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.`,At,Le,dn=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,ee,Mt,Te=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,Jt,dt,cn,he,pn,fe,ct,Qn='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> forward method, overrides the <code>__call__</code> special method.',Tt,yt,wn,pt,te,zt,mn,E,Ut,mt,xe,On=`DistilBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.`,kn,ye,$n=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,et,Dt,Wt=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,Wn,X,un,S,de,vn,Pt,hn='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',Sn,wt,Xe,kt,xn,tt,Xn,Be,Fe,je,nt,It,Zt,ut=`DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layer on top of the hidden-states output to compute <code>span start logits</code> and <code>span end logits</code>).`,Gt,Rt,Bn=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,$,C,ce=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,De,L,ge,I,A,qt,pe,ht='The <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',ft,me,nn,$t,Vn,Fn,es;return e=new Pe({props:{title:"TFDistilBertModel",local:"transformers.TFDistilBertModel",headingTag:"h2"}}),s=new H({props:{name:"class transformers.TFDistilBertModel",anchor:"transformers.TFDistilBertModel",parameters:[{name:"config",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L575"}}),f=new gt({props:{$$slots:{default:[Js]},$$scope:{ctx:B}}}),Me=new H({props:{name:"call",anchor:"transformers.TFDistilBertModel.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertModel.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertModel.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertModel.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L584",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput"
>transformers.modeling_tf_outputs.TFBaseModelOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput"
>transformers.modeling_tf_outputs.TFBaseModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Ze=new gt({props:{$$slots:{default:[zs]},$$scope:{ctx:B}}}),ot=new Ae({props:{anchor:"transformers.TFDistilBertModel.call.example",$$slots:{default:[Us]},$$scope:{ctx:B}}}),P=new Pe({props:{title:"TFDistilBertForMaskedLM",local:"transformers.TFDistilBertForMaskedLM",headingTag:"h2"}}),at=new H({props:{name:"class transformers.TFDistilBertForMaskedLM",anchor:"transformers.TFDistilBertForMaskedLM",parameters:[{name:"config",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L663"}}),it=new gt({props:{$$slots:{default:[Ds]},$$scope:{ctx:B}}}),bt=new H({props:{name:"call",anchor:"transformers.TFDistilBertForMaskedLM.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForMaskedLM.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForMaskedLM.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForMaskedLM.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForMaskedLM.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForMaskedLM.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForMaskedLM.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForMaskedLM.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForMaskedLM.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForMaskedLM.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L687",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of non-masked labels, returned when <code>labels</code> is provided) — Masked language modeling (MLM) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Je=new gt({props:{$$slots:{default:[Ws]},$$scope:{ctx:B}}}),N=new Ae({props:{anchor:"transformers.TFDistilBertForMaskedLM.call.example",$$slots:{default:[Is]},$$scope:{ctx:B}}}),Xt=new Ae({props:{anchor:"transformers.TFDistilBertForMaskedLM.call.example-2",$$slots:{default:[Zs]},$$scope:{ctx:B}}}),In=new Pe({props:{title:"TFDistilBertForSequenceClassification",local:"transformers.TFDistilBertForSequenceClassification",headingTag:"h2"}}),Un=new H({props:{name:"class transformers.TFDistilBertForSequenceClassification",anchor:"transformers.TFDistilBertForSequenceClassification",parameters:[{name:"config",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L759"}}),ae=new gt({props:{$$slots:{default:[Gs]},$$scope:{ctx:B}}}),qe=new H({props:{name:"call",anchor:"transformers.TFDistilBertForSequenceClassification.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForSequenceClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L784",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),ne=new gt({props:{$$slots:{default:[Rs]},$$scope:{ctx:B}}}),en=new Ae({props:{anchor:"transformers.TFDistilBertForSequenceClassification.call.example",$$slots:{default:[qs]},$$scope:{ctx:B}}}),ze=new Ae({props:{anchor:"transformers.TFDistilBertForSequenceClassification.call.example-2",$$slots:{default:[Ns]},$$scope:{ctx:B}}}),He=new Pe({props:{title:"TFDistilBertForMultipleChoice",local:"transformers.TFDistilBertForMultipleChoice",headingTag:"h2"}}),St=new H({props:{name:"class transformers.TFDistilBertForMultipleChoice",anchor:"transformers.TFDistilBertForMultipleChoice",parameters:[{name:"config",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L933"}}),dt=new gt({props:{$$slots:{default:[Hs]},$$scope:{ctx:B}}}),pn=new H({props:{name:"call",anchor:"transformers.TFDistilBertForMultipleChoice.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForMultipleChoice.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices]</code>
where <code>num_choices</code> is the size of the second dimension of the input tensors. (See <code>input_ids</code> above)`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L957",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <em>(batch_size, )</em>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices)</code>) — <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),yt=new gt({props:{$$slots:{default:[Ls]},$$scope:{ctx:B}}}),pt=new Ae({props:{anchor:"transformers.TFDistilBertForMultipleChoice.call.example",$$slots:{default:[Xs]},$$scope:{ctx:B}}}),zt=new Pe({props:{title:"TFDistilBertForTokenClassification",local:"transformers.TFDistilBertForTokenClassification",headingTag:"h2"}}),Ut=new H({props:{name:"class transformers.TFDistilBertForTokenClassification",anchor:"transformers.TFDistilBertForTokenClassification",parameters:[{name:"config",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L853"}}),X=new gt({props:{$$slots:{default:[Vs]},$$scope:{ctx:B}}}),de=new H({props:{name:"call",anchor:"transformers.TFDistilBertForTokenClassification.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForTokenClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForTokenClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForTokenClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForTokenClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForTokenClassification.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForTokenClassification.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForTokenClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForTokenClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForTokenClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L872",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(n,)</code>, <em>optional</em>, where n is the number of unmasked labels, returned when <code>labels</code> is provided)  — Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) — Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),wt=new gt({props:{$$slots:{default:[Es]},$$scope:{ctx:B}}}),kt=new Ae({props:{anchor:"transformers.TFDistilBertForTokenClassification.call.example",$$slots:{default:[Qs]},$$scope:{ctx:B}}}),tt=new Ae({props:{anchor:"transformers.TFDistilBertForTokenClassification.call.example-2",$$slots:{default:[Ss]},$$scope:{ctx:B}}}),Be=new Pe({props:{title:"TFDistilBertForQuestionAnswering",local:"transformers.TFDistilBertForQuestionAnswering",headingTag:"h2"}}),nt=new H({props:{name:"class transformers.TFDistilBertForQuestionAnswering",anchor:"transformers.TFDistilBertForQuestionAnswering",parameters:[{name:"config",val:""},{name:"*inputs",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L1042"}}),L=new gt({props:{$$slots:{default:[As]},$$scope:{ctx:B}}}),A=new H({props:{name:"call",anchor:"transformers.TFDistilBertForQuestionAnswering.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"start_positions",val:": np.ndarray | tf.Tensor | None = None"},{name:"end_positions",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForQuestionAnswering.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.start_positions",description:`<strong>start_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.end_positions",description:`<strong>end_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_tf_distilbert.py#L1061",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or a tuple of <code>tf.Tensor</code> (if
<code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various elements depending on the
configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, )</code>, <em>optional</em>, returned when <code>start_positions</code> and <code>end_positions</code> are provided) — Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.</p>
</li>
<li>
<p><strong>start_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) — Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) — Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>tf.Tensor</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(tf.Tensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>tf.Tensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),me=new gt({props:{$$slots:{default:[Ps]},$$scope:{ctx:B}}}),$t=new Ae({props:{anchor:"transformers.TFDistilBertForQuestionAnswering.call.example",$$slots:{default:[Ys]},$$scope:{ctx:B}}}),Fn=new Ae({props:{anchor:"transformers.TFDistilBertForQuestionAnswering.call.example-2",$$slots:{default:[Os]},$$scope:{ctx:B}}}),{c(){g(e.$$.fragment),m=a(),n=d("div"),g(s.$$.fragment),w=a(),t=d("p"),t.textContent=k,se=a(),W=d("p"),W.innerHTML=z,Y=a(),D=d("p"),D.innerHTML=U,O=a(),g(f.$$.fragment),j=a(),Ye=d("div"),g(Me.$$.fragment),Pn=a(),sn=d("p"),sn.innerHTML=oe,bn=a(),g(Ze.$$.fragment),_t=a(),g(ot.$$.fragment),Nt=a(),g(P.$$.fragment),Ht=a(),Q=d("div"),g(at.$$.fragment),xt=a(),Mn=d("p"),Mn.innerHTML=K,Bt=a(),on=d("p"),on.innerHTML=rt,Yn=a(),an=d("p"),an.innerHTML=ue,rn=a(),g(it.$$.fragment),Yt=a(),Oe=d("div"),g(bt.$$.fragment),lt=a(),Lt=d("p"),Lt.innerHTML=Tn,Ot=a(),g(Je.$$.fragment),Jn=a(),g(N.$$.fragment),Ft=a(),g(Xt.$$.fragment),Ge=a(),g(In.$$.fragment),zn=a(),V=d("div"),g(Un.$$.fragment),Vt=a(),Re=d("p"),Re.textContent=Zn,Dn=a(),G=d("p"),G.innerHTML=Et,Gn=a(),ve=d("p"),ve.innerHTML=Kn,Rn=a(),g(ae.$$.fragment),En=a(),re=d("div"),g(qe.$$.fragment),yn=a(),Kt=d("p"),Kt.innerHTML=Ke,Ln=a(),g(ne.$$.fragment),Ne=a(),g(en.$$.fragment),jt=a(),g(ze.$$.fragment),Qt=a(),g(He.$$.fragment),ln=a(),ie=d("div"),g(St.$$.fragment),tn=a(),Ct=d("p"),Ct.textContent=Ue,At=a(),Le=d("p"),Le.innerHTML=dn,ee=a(),Mt=d("p"),Mt.innerHTML=Te,Jt=a(),g(dt.$$.fragment),cn=a(),he=d("div"),g(pn.$$.fragment),fe=a(),ct=d("p"),ct.innerHTML=Qn,Tt=a(),g(yt.$$.fragment),wn=a(),g(pt.$$.fragment),te=a(),g(zt.$$.fragment),mn=a(),E=d("div"),g(Ut.$$.fragment),mt=a(),xe=d("p"),xe.textContent=On,kn=a(),ye=d("p"),ye.innerHTML=$n,et=a(),Dt=d("p"),Dt.innerHTML=Wt,Wn=a(),g(X.$$.fragment),un=a(),S=d("div"),g(de.$$.fragment),vn=a(),Pt=d("p"),Pt.innerHTML=hn,Sn=a(),g(wt.$$.fragment),Xe=a(),g(kt.$$.fragment),xn=a(),g(tt.$$.fragment),Xn=a(),g(Be.$$.fragment),Fe=a(),je=d("div"),g(nt.$$.fragment),It=a(),Zt=d("p"),Zt.innerHTML=ut,Gt=a(),Rt=d("p"),Rt.innerHTML=Bn,$=a(),C=d("p"),C.innerHTML=ce,De=a(),g(L.$$.fragment),ge=a(),I=d("div"),g(A.$$.fragment),qt=a(),pe=d("p"),pe.innerHTML=ht,ft=a(),g(me.$$.fragment),nn=a(),g($t.$$.fragment),Vn=a(),g(Fn.$$.fragment),this.h()},l(h){_(e.$$.fragment,h),m=r(h),n=c(h,"DIV",{class:!0});var F=q(n);_(s.$$.fragment,F),w=r(F),t=c(F,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ololo3"&&(t.textContent=k),se=r(F),W=c(F,"P",{"data-svelte-h":!0}),u(W)!=="svelte-1dti1vu"&&(W.innerHTML=z),Y=r(F),D=c(F,"P",{"data-svelte-h":!0}),u(D)!=="svelte-1be7e3c"&&(D.innerHTML=U),O=r(F),_(f.$$.fragment,F),j=r(F),Ye=c(F,"DIV",{class:!0});var jn=q(Ye);_(Me.$$.fragment,jn),Pn=r(jn),sn=c(jn,"P",{"data-svelte-h":!0}),u(sn)!=="svelte-18tv57j"&&(sn.innerHTML=oe),bn=r(jn),_(Ze.$$.fragment,jn),_t=r(jn),_(ot.$$.fragment,jn),jn.forEach(i),F.forEach(i),Nt=r(h),_(P.$$.fragment,h),Ht=r(h),Q=c(h,"DIV",{class:!0});var Ve=q(Q);_(at.$$.fragment,Ve),xt=r(Ve),Mn=c(Ve,"P",{"data-svelte-h":!0}),u(Mn)!=="svelte-23pii1"&&(Mn.innerHTML=K),Bt=r(Ve),on=c(Ve,"P",{"data-svelte-h":!0}),u(on)!=="svelte-1dti1vu"&&(on.innerHTML=rt),Yn=r(Ve),an=c(Ve,"P",{"data-svelte-h":!0}),u(an)!=="svelte-1be7e3c"&&(an.innerHTML=ue),rn=r(Ve),_(it.$$.fragment,Ve),Yt=r(Ve),Oe=c(Ve,"DIV",{class:!0});var we=q(Oe);_(bt.$$.fragment,we),lt=r(we),Lt=c(we,"P",{"data-svelte-h":!0}),u(Lt)!=="svelte-17v5uar"&&(Lt.innerHTML=Tn),Ot=r(we),_(Je.$$.fragment,we),Jn=r(we),_(N.$$.fragment,we),Ft=r(we),_(Xt.$$.fragment,we),we.forEach(i),Ve.forEach(i),Ge=r(h),_(In.$$.fragment,h),zn=r(h),V=c(h,"DIV",{class:!0});var We=q(V);_(Un.$$.fragment,We),Vt=r(We),Re=c(We,"P",{"data-svelte-h":!0}),u(Re)!=="svelte-1kr5cyc"&&(Re.textContent=Zn),Dn=r(We),G=c(We,"P",{"data-svelte-h":!0}),u(G)!=="svelte-1dti1vu"&&(G.innerHTML=Et),Gn=r(We),ve=c(We,"P",{"data-svelte-h":!0}),u(ve)!=="svelte-1be7e3c"&&(ve.innerHTML=Kn),Rn=r(We),_(ae.$$.fragment,We),En=r(We),re=c(We,"DIV",{class:!0});var _e=q(re);_(qe.$$.fragment,_e),yn=r(_e),Kt=c(_e,"P",{"data-svelte-h":!0}),u(Kt)!=="svelte-nlb28v"&&(Kt.innerHTML=Ke),Ln=r(_e),_(ne.$$.fragment,_e),Ne=r(_e),_(en.$$.fragment,_e),jt=r(_e),_(ze.$$.fragment,_e),_e.forEach(i),We.forEach(i),Qt=r(h),_(He.$$.fragment,h),ln=r(h),ie=c(h,"DIV",{class:!0});var be=q(ie);_(St.$$.fragment,be),tn=r(be),Ct=c(be,"P",{"data-svelte-h":!0}),u(Ct)!=="svelte-i84iso"&&(Ct.textContent=Ue),At=r(be),Le=c(be,"P",{"data-svelte-h":!0}),u(Le)!=="svelte-1dti1vu"&&(Le.innerHTML=dn),ee=r(be),Mt=c(be,"P",{"data-svelte-h":!0}),u(Mt)!=="svelte-1be7e3c"&&(Mt.innerHTML=Te),Jt=r(be),_(dt.$$.fragment,be),cn=r(be),he=c(be,"DIV",{class:!0});var Cn=q(he);_(pn.$$.fragment,Cn),fe=r(Cn),ct=c(Cn,"P",{"data-svelte-h":!0}),u(ct)!=="svelte-1xf8y9z"&&(ct.innerHTML=Qn),Tt=r(Cn),_(yt.$$.fragment,Cn),wn=r(Cn),_(pt.$$.fragment,Cn),Cn.forEach(i),be.forEach(i),te=r(h),_(zt.$$.fragment,h),mn=r(h),E=c(h,"DIV",{class:!0});var Ee=q(E);_(Ut.$$.fragment,Ee),mt=r(Ee),xe=c(Ee,"P",{"data-svelte-h":!0}),u(xe)!=="svelte-14y5xfr"&&(xe.textContent=On),kn=r(Ee),ye=c(Ee,"P",{"data-svelte-h":!0}),u(ye)!=="svelte-1dti1vu"&&(ye.innerHTML=$n),et=r(Ee),Dt=c(Ee,"P",{"data-svelte-h":!0}),u(Dt)!=="svelte-1be7e3c"&&(Dt.innerHTML=Wt),Wn=r(Ee),_(X.$$.fragment,Ee),un=r(Ee),S=c(Ee,"DIV",{class:!0});var st=q(S);_(de.$$.fragment,st),vn=r(st),Pt=c(st,"P",{"data-svelte-h":!0}),u(Pt)!=="svelte-1quhkzr"&&(Pt.innerHTML=hn),Sn=r(st),_(wt.$$.fragment,st),Xe=r(st),_(kt.$$.fragment,st),xn=r(st),_(tt.$$.fragment,st),st.forEach(i),Ee.forEach(i),Xn=r(h),_(Be.$$.fragment,h),Fe=r(h),je=c(h,"DIV",{class:!0});var vt=q(je);_(nt.$$.fragment,vt),It=r(vt),Zt=c(vt,"P",{"data-svelte-h":!0}),u(Zt)!=="svelte-zfb4ky"&&(Zt.innerHTML=ut),Gt=r(vt),Rt=c(vt,"P",{"data-svelte-h":!0}),u(Rt)!=="svelte-1dti1vu"&&(Rt.innerHTML=Bn),$=r(vt),C=c(vt,"P",{"data-svelte-h":!0}),u(C)!=="svelte-1be7e3c"&&(C.innerHTML=ce),De=r(vt),_(L.$$.fragment,vt),ge=r(vt),I=c(vt,"DIV",{class:!0});var ke=q(I);_(A.$$.fragment,ke),qt=r(ke),pe=c(ke,"P",{"data-svelte-h":!0}),u(pe)!=="svelte-17yp7jt"&&(pe.innerHTML=ht),ft=r(ke),_(me.$$.fragment,ke),nn=r(ke),_($t.$$.fragment,ke),Vn=r(ke),_(Fn.$$.fragment,ke),ke.forEach(i),vt.forEach(i),this.h()},h(){R(Ye,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(Oe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(he,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(je,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(h,F){b(e,h,F),p(h,m,F),p(h,n,F),b(s,n,null),o(n,w),o(n,t),o(n,se),o(n,W),o(n,Y),o(n,D),o(n,O),b(f,n,null),o(n,j),o(n,Ye),b(Me,Ye,null),o(Ye,Pn),o(Ye,sn),o(Ye,bn),b(Ze,Ye,null),o(Ye,_t),b(ot,Ye,null),p(h,Nt,F),b(P,h,F),p(h,Ht,F),p(h,Q,F),b(at,Q,null),o(Q,xt),o(Q,Mn),o(Q,Bt),o(Q,on),o(Q,Yn),o(Q,an),o(Q,rn),b(it,Q,null),o(Q,Yt),o(Q,Oe),b(bt,Oe,null),o(Oe,lt),o(Oe,Lt),o(Oe,Ot),b(Je,Oe,null),o(Oe,Jn),b(N,Oe,null),o(Oe,Ft),b(Xt,Oe,null),p(h,Ge,F),b(In,h,F),p(h,zn,F),p(h,V,F),b(Un,V,null),o(V,Vt),o(V,Re),o(V,Dn),o(V,G),o(V,Gn),o(V,ve),o(V,Rn),b(ae,V,null),o(V,En),o(V,re),b(qe,re,null),o(re,yn),o(re,Kt),o(re,Ln),b(ne,re,null),o(re,Ne),b(en,re,null),o(re,jt),b(ze,re,null),p(h,Qt,F),b(He,h,F),p(h,ln,F),p(h,ie,F),b(St,ie,null),o(ie,tn),o(ie,Ct),o(ie,At),o(ie,Le),o(ie,ee),o(ie,Mt),o(ie,Jt),b(dt,ie,null),o(ie,cn),o(ie,he),b(pn,he,null),o(he,fe),o(he,ct),o(he,Tt),b(yt,he,null),o(he,wn),b(pt,he,null),p(h,te,F),b(zt,h,F),p(h,mn,F),p(h,E,F),b(Ut,E,null),o(E,mt),o(E,xe),o(E,kn),o(E,ye),o(E,et),o(E,Dt),o(E,Wn),b(X,E,null),o(E,un),o(E,S),b(de,S,null),o(S,vn),o(S,Pt),o(S,Sn),b(wt,S,null),o(S,Xe),b(kt,S,null),o(S,xn),b(tt,S,null),p(h,Xn,F),b(Be,h,F),p(h,Fe,F),p(h,je,F),b(nt,je,null),o(je,It),o(je,Zt),o(je,Gt),o(je,Rt),o(je,$),o(je,C),o(je,De),b(L,je,null),o(je,ge),o(je,I),b(A,I,null),o(I,qt),o(I,pe),o(I,ft),b(me,I,null),o(I,nn),b($t,I,null),o(I,Vn),b(Fn,I,null),es=!0},p(h,F){const jn={};F&2&&(jn.$$scope={dirty:F,ctx:h}),f.$set(jn);const Ve={};F&2&&(Ve.$$scope={dirty:F,ctx:h}),Ze.$set(Ve);const we={};F&2&&(we.$$scope={dirty:F,ctx:h}),ot.$set(we);const We={};F&2&&(We.$$scope={dirty:F,ctx:h}),it.$set(We);const _e={};F&2&&(_e.$$scope={dirty:F,ctx:h}),Je.$set(_e);const be={};F&2&&(be.$$scope={dirty:F,ctx:h}),N.$set(be);const Cn={};F&2&&(Cn.$$scope={dirty:F,ctx:h}),Xt.$set(Cn);const Ee={};F&2&&(Ee.$$scope={dirty:F,ctx:h}),ae.$set(Ee);const st={};F&2&&(st.$$scope={dirty:F,ctx:h}),ne.$set(st);const vt={};F&2&&(vt.$$scope={dirty:F,ctx:h}),en.$set(vt);const ke={};F&2&&(ke.$$scope={dirty:F,ctx:h}),ze.$set(ke);const ts={};F&2&&(ts.$$scope={dirty:F,ctx:h}),dt.$set(ts);const qn={};F&2&&(qn.$$scope={dirty:F,ctx:h}),yt.$set(qn);const Nn={};F&2&&(Nn.$$scope={dirty:F,ctx:h}),pt.$set(Nn);const v={};F&2&&(v.$$scope={dirty:F,ctx:h}),X.$set(v);const J={};F&2&&(J.$$scope={dirty:F,ctx:h}),wt.$set(J);const fn={};F&2&&(fn.$$scope={dirty:F,ctx:h}),kt.$set(fn);const Qe={};F&2&&(Qe.$$scope={dirty:F,ctx:h}),tt.$set(Qe);const gn={};F&2&&(gn.$$scope={dirty:F,ctx:h}),L.$set(gn);const l={};F&2&&(l.$$scope={dirty:F,ctx:h}),me.$set(l);const x={};F&2&&(x.$$scope={dirty:F,ctx:h}),$t.$set(x);const le={};F&2&&(le.$$scope={dirty:F,ctx:h}),Fn.$set(le)},i(h){es||(M(e.$$.fragment,h),M(s.$$.fragment,h),M(f.$$.fragment,h),M(Me.$$.fragment,h),M(Ze.$$.fragment,h),M(ot.$$.fragment,h),M(P.$$.fragment,h),M(at.$$.fragment,h),M(it.$$.fragment,h),M(bt.$$.fragment,h),M(Je.$$.fragment,h),M(N.$$.fragment,h),M(Xt.$$.fragment,h),M(In.$$.fragment,h),M(Un.$$.fragment,h),M(ae.$$.fragment,h),M(qe.$$.fragment,h),M(ne.$$.fragment,h),M(en.$$.fragment,h),M(ze.$$.fragment,h),M(He.$$.fragment,h),M(St.$$.fragment,h),M(dt.$$.fragment,h),M(pn.$$.fragment,h),M(yt.$$.fragment,h),M(pt.$$.fragment,h),M(zt.$$.fragment,h),M(Ut.$$.fragment,h),M(X.$$.fragment,h),M(de.$$.fragment,h),M(wt.$$.fragment,h),M(kt.$$.fragment,h),M(tt.$$.fragment,h),M(Be.$$.fragment,h),M(nt.$$.fragment,h),M(L.$$.fragment,h),M(A.$$.fragment,h),M(me.$$.fragment,h),M($t.$$.fragment,h),M(Fn.$$.fragment,h),es=!0)},o(h){T(e.$$.fragment,h),T(s.$$.fragment,h),T(f.$$.fragment,h),T(Me.$$.fragment,h),T(Ze.$$.fragment,h),T(ot.$$.fragment,h),T(P.$$.fragment,h),T(at.$$.fragment,h),T(it.$$.fragment,h),T(bt.$$.fragment,h),T(Je.$$.fragment,h),T(N.$$.fragment,h),T(Xt.$$.fragment,h),T(In.$$.fragment,h),T(Un.$$.fragment,h),T(ae.$$.fragment,h),T(qe.$$.fragment,h),T(ne.$$.fragment,h),T(en.$$.fragment,h),T(ze.$$.fragment,h),T(He.$$.fragment,h),T(St.$$.fragment,h),T(dt.$$.fragment,h),T(pn.$$.fragment,h),T(yt.$$.fragment,h),T(pt.$$.fragment,h),T(zt.$$.fragment,h),T(Ut.$$.fragment,h),T(X.$$.fragment,h),T(de.$$.fragment,h),T(wt.$$.fragment,h),T(kt.$$.fragment,h),T(tt.$$.fragment,h),T(Be.$$.fragment,h),T(nt.$$.fragment,h),T(L.$$.fragment,h),T(A.$$.fragment,h),T(me.$$.fragment,h),T($t.$$.fragment,h),T(Fn.$$.fragment,h),es=!1},d(h){h&&(i(m),i(n),i(Nt),i(Ht),i(Q),i(Ge),i(zn),i(V),i(Qt),i(ln),i(ie),i(te),i(mn),i(E),i(Xn),i(Fe),i(je)),y(e,h),y(s),y(f),y(Me),y(Ze),y(ot),y(P,h),y(at),y(it),y(bt),y(Je),y(N),y(Xt),y(In,h),y(Un),y(ae),y(qe),y(ne),y(en),y(ze),y(He,h),y(St),y(dt),y(pn),y(yt),y(pt),y(zt,h),y(Ut),y(X),y(de),y(wt),y(kt),y(tt),y(Be,h),y(nt),y(L),y(A),y(me),y($t),y(Fn)}}}function eo(B){let e,m;return e=new as({props:{$$slots:{default:[Ks]},$$scope:{ctx:B}}}),{c(){g(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,s){b(e,n,s),m=!0},p(n,s){const w={};s&2&&(w.$$scope={dirty:s,ctx:n}),e.$set(w)},i(n){m||(M(e.$$.fragment,n),m=!0)},o(n){T(e.$$.fragment,n),m=!1},d(n){y(e,n)}}}function to(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function no(B){let e,m="Example:",n,s,w;return s=new Ce({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, FlaxDistilBertModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxDistilBertModel.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function so(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function oo(B){let e,m="Example:",n,s,w;return s=new Ce({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, FlaxDistilBertForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxDistilBertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;The capital of France is [MASK].&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function ao(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function ro(B){let e,m="Example:",n,s,w;return s=new Ce({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, FlaxDistilBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxDistilBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function io(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function lo(B){let e,m="Example:",n,s,w;return s=new Ce({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, FlaxDistilBertForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxDistilBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice0 = <span class="hljs-string">&quot;It is eaten with a fork and a knife.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>choice1 = <span class="hljs-string">&quot;It is eaten while held in the hand.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoding = tokenizer([prompt, prompt], [choice0, choice1], return_tensors=<span class="hljs-string">&quot;jax&quot;</span>, padding=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**{k: v[<span class="hljs-literal">None</span>, :] <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items()})

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function co(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function po(B){let e,m="Example:",n,s,w;return s=new Ce({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, FlaxDistilBertForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxDistilBertForTokenClassification.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function mo(B){let e,m=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){e=d("p"),e.innerHTML=m},l(n){e=c(n,"P",{"data-svelte-h":!0}),u(e)!=="svelte-fincs2"&&(e.innerHTML=m)},m(n,s){p(n,e,s)},p:Z,d(n){n&&i(e)}}}function uo(B){let e,m="Example:",n,s,w;return s=new Ce({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, FlaxDistilBertForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxDistilBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;distilbert-base-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>question, text = <span class="hljs-string">&quot;Who was Jim Henson?&quot;</span>, <span class="hljs-string">&quot;Jim Henson was a nice puppet&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(question, text, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_scores = outputs.start_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>end_scores = outputs.end_logits`,wrap:!1}}),{c(){e=d("p"),e.textContent=m,n=a(),g(s.$$.fragment)},l(t){e=c(t,"P",{"data-svelte-h":!0}),u(e)!=="svelte-11lpom8"&&(e.textContent=m),n=r(t),_(s.$$.fragment,t)},m(t,k){p(t,e,k),p(t,n,k),b(s,t,k),w=!0},p:Z,i(t){w||(M(s.$$.fragment,t),w=!0)},o(t){T(s.$$.fragment,t),w=!1},d(t){t&&(i(e),i(n)),y(s,t)}}}function ho(B){let e,m,n,s,w,t,k="The bare DistilBert Model transformer outputting raw hidden-states without any specific head on top.",se,W,z=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,Y,D,U=`This model is also a
<a href="https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html" rel="nofollow">flax.linen.Module</a> subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.`,O,f,j="Finally, this model supports inherent JAX features such as:",Ye,Me,Pn='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',sn,oe,bn,Ze,_t,ot="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Nt,P,Ht,Q,at,xt,Mn,K,Bt,on,rt,Yn="DistilBert Model with a <code>language modeling</code> head on top.",an,ue,rn=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,it,Yt,Oe=`This model is also a
<a href="https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html" rel="nofollow">flax.linen.Module</a> subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.`,bt,lt,Lt="Finally, this model supports inherent JAX features such as:",Tn,Ot,Je='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',Jn,N,Ft,Xt,Ge,In="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",zn,V,Un,Vt,Re,Zn,Dn,G,Et,Gn,ve,Kn=`DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`,Rn,ae,En=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,re,qe,yn=`This model is also a
<a href="https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html" rel="nofollow">flax.linen.Module</a> subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.`,Kt,Ke,Ln="Finally, this model supports inherent JAX features such as:",ne,Ne,en='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',jt,ze,Qt,He,ln,ie="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",St,tn,Ct,Ue,At,Le,dn,ee,Mt,Te,Jt,dt=`DistilBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.`,cn,he,pn=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,fe,ct,Qn=`This model is also a
<a href="https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html" rel="nofollow">flax.linen.Module</a> subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.`,Tt,yt,wn="Finally, this model supports inherent JAX features such as:",pt,te,zt='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',mn,E,Ut,mt,xe,On="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",kn,ye,$n,et,Dt,Wt,Wn,X,un,S,de,vn=`DistilBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.`,Pt,hn,Sn=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,wt,Xe,kt=`This model is also a
<a href="https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html" rel="nofollow">flax.linen.Module</a> subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.`,xn,tt,Xn="Finally, this model supports inherent JAX features such as:",Be,Fe,je='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',nt,It,Zt,ut,Gt,Rt="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Bn,$,C,ce,De,L,ge,I,A,qt,pe,ht=`DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers on top of the hidden-states output to compute <code>span start logits</code> and <code>span end logits</code>).`,ft,me,nn=`This model inherits from <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,$t,Vn,Fn=`This model is also a
<a href="https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html" rel="nofollow">flax.linen.Module</a> subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.`,es,h,F="Finally, this model supports inherent JAX features such as:",jn,Ve,we='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',We,_e,be,Cn,Ee,st="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",vt,ke,ts,qn,Nn;return e=new Pe({props:{title:"FlaxDistilBertModel",local:"transformers.FlaxDistilBertModel",headingTag:"h2"}}),s=new H({props:{name:"class transformers.FlaxDistilBertModel",anchor:"transformers.FlaxDistilBertModel",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L529"}}),bn=new H({props:{name:"__call__",anchor:"transformers.FlaxDistilBertModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertModel.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertModel.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L456"}}),P=new gt({props:{$$slots:{default:[to]},$$scope:{ctx:B}}}),Q=new Ae({props:{anchor:"transformers.FlaxDistilBertModel.__call__.example",$$slots:{default:[no]},$$scope:{ctx:B}}}),xt=new Pe({props:{title:"FlaxDistilBertForMaskedLM",local:"transformers.FlaxDistilBertForMaskedLM",headingTag:"h2"}}),Bt=new H({props:{name:"class transformers.FlaxDistilBertForMaskedLM",anchor:"transformers.FlaxDistilBertForMaskedLM",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L605"}}),Ft=new H({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForMaskedLM.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L456",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),V=new gt({props:{$$slots:{default:[so]},$$scope:{ctx:B}}}),Vt=new Ae({props:{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.example",$$slots:{default:[oo]},$$scope:{ctx:B}}}),Zn=new Pe({props:{title:"FlaxDistilBertForSequenceClassification",local:"transformers.FlaxDistilBertForSequenceClassification",headingTag:"h2"}}),Et=new H({props:{name:"class transformers.FlaxDistilBertForSequenceClassification",anchor:"transformers.FlaxDistilBertForSequenceClassification",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L666"}}),Qt=new H({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L456",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),tn=new gt({props:{$$slots:{default:[ao]},$$scope:{ctx:B}}}),Ue=new Ae({props:{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.example",$$slots:{default:[ro]},$$scope:{ctx:B}}}),Le=new Pe({props:{title:"FlaxDistilBertForMultipleChoice",local:"transformers.FlaxDistilBertForMultipleChoice",headingTag:"h2"}}),Mt=new H({props:{name:"class transformers.FlaxDistilBertForMultipleChoice",anchor:"transformers.FlaxDistilBertForMultipleChoice",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L745"}}),Ut=new H({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L456",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, num_choices)</code>) — <em>num_choices</em> is the second dimension of the input tensors. (see <em>input_ids</em> above).</p>
<p>Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ye=new gt({props:{$$slots:{default:[io]},$$scope:{ctx:B}}}),et=new Ae({props:{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.example",$$slots:{default:[lo]},$$scope:{ctx:B}}}),Wt=new Pe({props:{title:"FlaxDistilBertForTokenClassification",local:"transformers.FlaxDistilBertForTokenClassification",headingTag:"h2"}}),un=new H({props:{name:"class transformers.FlaxDistilBertForTokenClassification",anchor:"transformers.FlaxDistilBertForTokenClassification",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L810"}}),Zt=new H({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForTokenClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L456",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) — Classification scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),$=new gt({props:{$$slots:{default:[co]},$$scope:{ctx:B}}}),ce=new Ae({props:{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.example",$$slots:{default:[po]},$$scope:{ctx:B}}}),L=new Pe({props:{title:"FlaxDistilBertForQuestionAnswering",local:"transformers.FlaxDistilBertForQuestionAnswering",headingTag:"h2"}}),A=new H({props:{name:"class transformers.FlaxDistilBertForQuestionAnswering",anchor:"transformers.FlaxDistilBertForQuestionAnswering",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_28071/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L879"}}),be=new H({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_28071/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_28071/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_28071/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/modeling_flax_distilbert.py#L456",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"
>transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertConfig"
>DistilBertConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>start_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) — Span-start scores (before SoftMax).</p>
</li>
<li>
<p><strong>end_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) — Span-end scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_28071/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"
>transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ke=new gt({props:{$$slots:{default:[mo]},$$scope:{ctx:B}}}),qn=new Ae({props:{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.example",$$slots:{default:[uo]},$$scope:{ctx:B}}}),{c(){g(e.$$.fragment),m=a(),n=d("div"),g(s.$$.fragment),w=a(),t=d("p"),t.textContent=k,se=a(),W=d("p"),W.innerHTML=z,Y=a(),D=d("p"),D.innerHTML=U,O=a(),f=d("p"),f.textContent=j,Ye=a(),Me=d("ul"),Me.innerHTML=Pn,sn=a(),oe=d("div"),g(bn.$$.fragment),Ze=a(),_t=d("p"),_t.innerHTML=ot,Nt=a(),g(P.$$.fragment),Ht=a(),g(Q.$$.fragment),at=a(),g(xt.$$.fragment),Mn=a(),K=d("div"),g(Bt.$$.fragment),on=a(),rt=d("p"),rt.innerHTML=Yn,an=a(),ue=d("p"),ue.innerHTML=rn,it=a(),Yt=d("p"),Yt.innerHTML=Oe,bt=a(),lt=d("p"),lt.textContent=Lt,Tn=a(),Ot=d("ul"),Ot.innerHTML=Je,Jn=a(),N=d("div"),g(Ft.$$.fragment),Xt=a(),Ge=d("p"),Ge.innerHTML=In,zn=a(),g(V.$$.fragment),Un=a(),g(Vt.$$.fragment),Re=a(),g(Zn.$$.fragment),Dn=a(),G=d("div"),g(Et.$$.fragment),Gn=a(),ve=d("p"),ve.textContent=Kn,Rn=a(),ae=d("p"),ae.innerHTML=En,re=a(),qe=d("p"),qe.innerHTML=yn,Kt=a(),Ke=d("p"),Ke.textContent=Ln,ne=a(),Ne=d("ul"),Ne.innerHTML=en,jt=a(),ze=d("div"),g(Qt.$$.fragment),He=a(),ln=d("p"),ln.innerHTML=ie,St=a(),g(tn.$$.fragment),Ct=a(),g(Ue.$$.fragment),At=a(),g(Le.$$.fragment),dn=a(),ee=d("div"),g(Mt.$$.fragment),Te=a(),Jt=d("p"),Jt.textContent=dt,cn=a(),he=d("p"),he.innerHTML=pn,fe=a(),ct=d("p"),ct.innerHTML=Qn,Tt=a(),yt=d("p"),yt.textContent=wn,pt=a(),te=d("ul"),te.innerHTML=zt,mn=a(),E=d("div"),g(Ut.$$.fragment),mt=a(),xe=d("p"),xe.innerHTML=On,kn=a(),g(ye.$$.fragment),$n=a(),g(et.$$.fragment),Dt=a(),g(Wt.$$.fragment),Wn=a(),X=d("div"),g(un.$$.fragment),S=a(),de=d("p"),de.textContent=vn,Pt=a(),hn=d("p"),hn.innerHTML=Sn,wt=a(),Xe=d("p"),Xe.innerHTML=kt,xn=a(),tt=d("p"),tt.textContent=Xn,Be=a(),Fe=d("ul"),Fe.innerHTML=je,nt=a(),It=d("div"),g(Zt.$$.fragment),ut=a(),Gt=d("p"),Gt.innerHTML=Rt,Bn=a(),g($.$$.fragment),C=a(),g(ce.$$.fragment),De=a(),g(L.$$.fragment),ge=a(),I=d("div"),g(A.$$.fragment),qt=a(),pe=d("p"),pe.innerHTML=ht,ft=a(),me=d("p"),me.innerHTML=nn,$t=a(),Vn=d("p"),Vn.innerHTML=Fn,es=a(),h=d("p"),h.textContent=F,jn=a(),Ve=d("ul"),Ve.innerHTML=we,We=a(),_e=d("div"),g(be.$$.fragment),Cn=a(),Ee=d("p"),Ee.innerHTML=st,vt=a(),g(ke.$$.fragment),ts=a(),g(qn.$$.fragment),this.h()},l(v){_(e.$$.fragment,v),m=r(v),n=c(v,"DIV",{class:!0});var J=q(n);_(s.$$.fragment,J),w=r(J),t=c(J,"P",{"data-svelte-h":!0}),u(t)!=="svelte-3lz6al"&&(t.textContent=k),se=r(J),W=c(J,"P",{"data-svelte-h":!0}),u(W)!=="svelte-1u1va5b"&&(W.innerHTML=z),Y=r(J),D=c(J,"P",{"data-svelte-h":!0}),u(D)!=="svelte-10nfsf3"&&(D.innerHTML=U),O=r(J),f=c(J,"P",{"data-svelte-h":!0}),u(f)!=="svelte-1pplc4a"&&(f.textContent=j),Ye=r(J),Me=c(J,"UL",{"data-svelte-h":!0}),u(Me)!=="svelte-1w7z84m"&&(Me.innerHTML=Pn),sn=r(J),oe=c(J,"DIV",{class:!0});var fn=q(oe);_(bn.$$.fragment,fn),Ze=r(fn),_t=c(fn,"P",{"data-svelte-h":!0}),u(_t)!=="svelte-16oef0h"&&(_t.innerHTML=ot),Nt=r(fn),_(P.$$.fragment,fn),Ht=r(fn),_(Q.$$.fragment,fn),fn.forEach(i),J.forEach(i),at=r(v),_(xt.$$.fragment,v),Mn=r(v),K=c(v,"DIV",{class:!0});var Qe=q(K);_(Bt.$$.fragment,Qe),on=r(Qe),rt=c(Qe,"P",{"data-svelte-h":!0}),u(rt)!=="svelte-5bwii0"&&(rt.innerHTML=Yn),an=r(Qe),ue=c(Qe,"P",{"data-svelte-h":!0}),u(ue)!=="svelte-1u1va5b"&&(ue.innerHTML=rn),it=r(Qe),Yt=c(Qe,"P",{"data-svelte-h":!0}),u(Yt)!=="svelte-10nfsf3"&&(Yt.innerHTML=Oe),bt=r(Qe),lt=c(Qe,"P",{"data-svelte-h":!0}),u(lt)!=="svelte-1pplc4a"&&(lt.textContent=Lt),Tn=r(Qe),Ot=c(Qe,"UL",{"data-svelte-h":!0}),u(Ot)!=="svelte-1w7z84m"&&(Ot.innerHTML=Je),Jn=r(Qe),N=c(Qe,"DIV",{class:!0});var gn=q(N);_(Ft.$$.fragment,gn),Xt=r(gn),Ge=c(gn,"P",{"data-svelte-h":!0}),u(Ge)!=="svelte-16oef0h"&&(Ge.innerHTML=In),zn=r(gn),_(V.$$.fragment,gn),Un=r(gn),_(Vt.$$.fragment,gn),gn.forEach(i),Qe.forEach(i),Re=r(v),_(Zn.$$.fragment,v),Dn=r(v),G=c(v,"DIV",{class:!0});var l=q(G);_(Et.$$.fragment,l),Gn=r(l),ve=c(l,"P",{"data-svelte-h":!0}),u(ve)!=="svelte-1kr5cyc"&&(ve.textContent=Kn),Rn=r(l),ae=c(l,"P",{"data-svelte-h":!0}),u(ae)!=="svelte-1u1va5b"&&(ae.innerHTML=En),re=r(l),qe=c(l,"P",{"data-svelte-h":!0}),u(qe)!=="svelte-10nfsf3"&&(qe.innerHTML=yn),Kt=r(l),Ke=c(l,"P",{"data-svelte-h":!0}),u(Ke)!=="svelte-1pplc4a"&&(Ke.textContent=Ln),ne=r(l),Ne=c(l,"UL",{"data-svelte-h":!0}),u(Ne)!=="svelte-1w7z84m"&&(Ne.innerHTML=en),jt=r(l),ze=c(l,"DIV",{class:!0});var x=q(ze);_(Qt.$$.fragment,x),He=r(x),ln=c(x,"P",{"data-svelte-h":!0}),u(ln)!=="svelte-16oef0h"&&(ln.innerHTML=ie),St=r(x),_(tn.$$.fragment,x),Ct=r(x),_(Ue.$$.fragment,x),x.forEach(i),l.forEach(i),At=r(v),_(Le.$$.fragment,v),dn=r(v),ee=c(v,"DIV",{class:!0});var le=q(ee);_(Mt.$$.fragment,le),Te=r(le),Jt=c(le,"P",{"data-svelte-h":!0}),u(Jt)!=="svelte-i84iso"&&(Jt.textContent=dt),cn=r(le),he=c(le,"P",{"data-svelte-h":!0}),u(he)!=="svelte-1u1va5b"&&(he.innerHTML=pn),fe=r(le),ct=c(le,"P",{"data-svelte-h":!0}),u(ct)!=="svelte-10nfsf3"&&(ct.innerHTML=Qn),Tt=r(le),yt=c(le,"P",{"data-svelte-h":!0}),u(yt)!=="svelte-1pplc4a"&&(yt.textContent=wn),pt=r(le),te=c(le,"UL",{"data-svelte-h":!0}),u(te)!=="svelte-1w7z84m"&&(te.innerHTML=zt),mn=r(le),E=c(le,"DIV",{class:!0});var $e=q(E);_(Ut.$$.fragment,$e),mt=r($e),xe=c($e,"P",{"data-svelte-h":!0}),u(xe)!=="svelte-16oef0h"&&(xe.innerHTML=On),kn=r($e),_(ye.$$.fragment,$e),$n=r($e),_(et.$$.fragment,$e),$e.forEach(i),le.forEach(i),Dt=r(v),_(Wt.$$.fragment,v),Wn=r(v),X=c(v,"DIV",{class:!0});var Ie=q(X);_(un.$$.fragment,Ie),S=r(Ie),de=c(Ie,"P",{"data-svelte-h":!0}),u(de)!=="svelte-14y5xfr"&&(de.textContent=vn),Pt=r(Ie),hn=c(Ie,"P",{"data-svelte-h":!0}),u(hn)!=="svelte-1u1va5b"&&(hn.innerHTML=Sn),wt=r(Ie),Xe=c(Ie,"P",{"data-svelte-h":!0}),u(Xe)!=="svelte-10nfsf3"&&(Xe.innerHTML=kt),xn=r(Ie),tt=c(Ie,"P",{"data-svelte-h":!0}),u(tt)!=="svelte-1pplc4a"&&(tt.textContent=Xn),Be=r(Ie),Fe=c(Ie,"UL",{"data-svelte-h":!0}),u(Fe)!=="svelte-1w7z84m"&&(Fe.innerHTML=je),nt=r(Ie),It=c(Ie,"DIV",{class:!0});var _n=q(It);_(Zt.$$.fragment,_n),ut=r(_n),Gt=c(_n,"P",{"data-svelte-h":!0}),u(Gt)!=="svelte-16oef0h"&&(Gt.innerHTML=Rt),Bn=r(_n),_($.$$.fragment,_n),C=r(_n),_(ce.$$.fragment,_n),_n.forEach(i),Ie.forEach(i),De=r(v),_(L.$$.fragment,v),ge=r(v),I=c(v,"DIV",{class:!0});var Se=q(I);_(A.$$.fragment,Se),qt=r(Se),pe=c(Se,"P",{"data-svelte-h":!0}),u(pe)!=="svelte-18jj0o9"&&(pe.innerHTML=ht),ft=r(Se),me=c(Se,"P",{"data-svelte-h":!0}),u(me)!=="svelte-1u1va5b"&&(me.innerHTML=nn),$t=r(Se),Vn=c(Se,"P",{"data-svelte-h":!0}),u(Vn)!=="svelte-10nfsf3"&&(Vn.innerHTML=Fn),es=r(Se),h=c(Se,"P",{"data-svelte-h":!0}),u(h)!=="svelte-1pplc4a"&&(h.textContent=F),jn=r(Se),Ve=c(Se,"UL",{"data-svelte-h":!0}),u(Ve)!=="svelte-1w7z84m"&&(Ve.innerHTML=we),We=r(Se),_e=c(Se,"DIV",{class:!0});var Hn=q(_e);_(be.$$.fragment,Hn),Cn=r(Hn),Ee=c(Hn,"P",{"data-svelte-h":!0}),u(Ee)!=="svelte-16oef0h"&&(Ee.innerHTML=st),vt=r(Hn),_(ke.$$.fragment,Hn),ts=r(Hn),_(qn.$$.fragment,Hn),Hn.forEach(i),Se.forEach(i),this.h()},h(){R(oe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(ze,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(It,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(_e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(v,J){b(e,v,J),p(v,m,J),p(v,n,J),b(s,n,null),o(n,w),o(n,t),o(n,se),o(n,W),o(n,Y),o(n,D),o(n,O),o(n,f),o(n,Ye),o(n,Me),o(n,sn),o(n,oe),b(bn,oe,null),o(oe,Ze),o(oe,_t),o(oe,Nt),b(P,oe,null),o(oe,Ht),b(Q,oe,null),p(v,at,J),b(xt,v,J),p(v,Mn,J),p(v,K,J),b(Bt,K,null),o(K,on),o(K,rt),o(K,an),o(K,ue),o(K,it),o(K,Yt),o(K,bt),o(K,lt),o(K,Tn),o(K,Ot),o(K,Jn),o(K,N),b(Ft,N,null),o(N,Xt),o(N,Ge),o(N,zn),b(V,N,null),o(N,Un),b(Vt,N,null),p(v,Re,J),b(Zn,v,J),p(v,Dn,J),p(v,G,J),b(Et,G,null),o(G,Gn),o(G,ve),o(G,Rn),o(G,ae),o(G,re),o(G,qe),o(G,Kt),o(G,Ke),o(G,ne),o(G,Ne),o(G,jt),o(G,ze),b(Qt,ze,null),o(ze,He),o(ze,ln),o(ze,St),b(tn,ze,null),o(ze,Ct),b(Ue,ze,null),p(v,At,J),b(Le,v,J),p(v,dn,J),p(v,ee,J),b(Mt,ee,null),o(ee,Te),o(ee,Jt),o(ee,cn),o(ee,he),o(ee,fe),o(ee,ct),o(ee,Tt),o(ee,yt),o(ee,pt),o(ee,te),o(ee,mn),o(ee,E),b(Ut,E,null),o(E,mt),o(E,xe),o(E,kn),b(ye,E,null),o(E,$n),b(et,E,null),p(v,Dt,J),b(Wt,v,J),p(v,Wn,J),p(v,X,J),b(un,X,null),o(X,S),o(X,de),o(X,Pt),o(X,hn),o(X,wt),o(X,Xe),o(X,xn),o(X,tt),o(X,Be),o(X,Fe),o(X,nt),o(X,It),b(Zt,It,null),o(It,ut),o(It,Gt),o(It,Bn),b($,It,null),o(It,C),b(ce,It,null),p(v,De,J),b(L,v,J),p(v,ge,J),p(v,I,J),b(A,I,null),o(I,qt),o(I,pe),o(I,ft),o(I,me),o(I,$t),o(I,Vn),o(I,es),o(I,h),o(I,jn),o(I,Ve),o(I,We),o(I,_e),b(be,_e,null),o(_e,Cn),o(_e,Ee),o(_e,vt),b(ke,_e,null),o(_e,ts),b(qn,_e,null),Nn=!0},p(v,J){const fn={};J&2&&(fn.$$scope={dirty:J,ctx:v}),P.$set(fn);const Qe={};J&2&&(Qe.$$scope={dirty:J,ctx:v}),Q.$set(Qe);const gn={};J&2&&(gn.$$scope={dirty:J,ctx:v}),V.$set(gn);const l={};J&2&&(l.$$scope={dirty:J,ctx:v}),Vt.$set(l);const x={};J&2&&(x.$$scope={dirty:J,ctx:v}),tn.$set(x);const le={};J&2&&(le.$$scope={dirty:J,ctx:v}),Ue.$set(le);const $e={};J&2&&($e.$$scope={dirty:J,ctx:v}),ye.$set($e);const Ie={};J&2&&(Ie.$$scope={dirty:J,ctx:v}),et.$set(Ie);const _n={};J&2&&(_n.$$scope={dirty:J,ctx:v}),$.$set(_n);const Se={};J&2&&(Se.$$scope={dirty:J,ctx:v}),ce.$set(Se);const Hn={};J&2&&(Hn.$$scope={dirty:J,ctx:v}),ke.$set(Hn);const An={};J&2&&(An.$$scope={dirty:J,ctx:v}),qn.$set(An)},i(v){Nn||(M(e.$$.fragment,v),M(s.$$.fragment,v),M(bn.$$.fragment,v),M(P.$$.fragment,v),M(Q.$$.fragment,v),M(xt.$$.fragment,v),M(Bt.$$.fragment,v),M(Ft.$$.fragment,v),M(V.$$.fragment,v),M(Vt.$$.fragment,v),M(Zn.$$.fragment,v),M(Et.$$.fragment,v),M(Qt.$$.fragment,v),M(tn.$$.fragment,v),M(Ue.$$.fragment,v),M(Le.$$.fragment,v),M(Mt.$$.fragment,v),M(Ut.$$.fragment,v),M(ye.$$.fragment,v),M(et.$$.fragment,v),M(Wt.$$.fragment,v),M(un.$$.fragment,v),M(Zt.$$.fragment,v),M($.$$.fragment,v),M(ce.$$.fragment,v),M(L.$$.fragment,v),M(A.$$.fragment,v),M(be.$$.fragment,v),M(ke.$$.fragment,v),M(qn.$$.fragment,v),Nn=!0)},o(v){T(e.$$.fragment,v),T(s.$$.fragment,v),T(bn.$$.fragment,v),T(P.$$.fragment,v),T(Q.$$.fragment,v),T(xt.$$.fragment,v),T(Bt.$$.fragment,v),T(Ft.$$.fragment,v),T(V.$$.fragment,v),T(Vt.$$.fragment,v),T(Zn.$$.fragment,v),T(Et.$$.fragment,v),T(Qt.$$.fragment,v),T(tn.$$.fragment,v),T(Ue.$$.fragment,v),T(Le.$$.fragment,v),T(Mt.$$.fragment,v),T(Ut.$$.fragment,v),T(ye.$$.fragment,v),T(et.$$.fragment,v),T(Wt.$$.fragment,v),T(un.$$.fragment,v),T(Zt.$$.fragment,v),T($.$$.fragment,v),T(ce.$$.fragment,v),T(L.$$.fragment,v),T(A.$$.fragment,v),T(be.$$.fragment,v),T(ke.$$.fragment,v),T(qn.$$.fragment,v),Nn=!1},d(v){v&&(i(m),i(n),i(at),i(Mn),i(K),i(Re),i(Dn),i(G),i(At),i(dn),i(ee),i(Dt),i(Wn),i(X),i(De),i(ge),i(I)),y(e,v),y(s),y(bn),y(P),y(Q),y(xt,v),y(Bt),y(Ft),y(V),y(Vt),y(Zn,v),y(Et),y(Qt),y(tn),y(Ue),y(Le,v),y(Mt),y(Ut),y(ye),y(et),y(Wt,v),y(un),y(Zt),y($),y(ce),y(L,v),y(A),y(be),y(ke),y(qn)}}}function fo(B){let e,m;return e=new as({props:{$$slots:{default:[ho]},$$scope:{ctx:B}}}),{c(){g(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,s){b(e,n,s),m=!0},p(n,s){const w={};s&2&&(w.$$scope={dirty:s,ctx:n}),e.$set(w)},i(n){m||(M(e.$$.fragment,n),m=!0)},o(n){T(e.$$.fragment,n),m=!1},d(n){y(e,n)}}}function go(B){let e,m,n,s,w,t,k,se='<a href="https://huggingface.co/models?filter=distilbert"><img alt="Models" src="https://img.shields.io/badge/All_model_pages-distilbert-blueviolet"/></a> <a href="https://huggingface.co/spaces/docs-demos/distilbert-base-uncased"><img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a> <a href="https://huggingface.co/papers/1910.01108"><img alt="Paper page" src="https://img.shields.io/badge/Paper%20page-1910.01108-green"/></a>',W,z,Y,D,U=`The DistilBERT model was proposed in the blog post <a href="https://medium.com/huggingface/distilbert-8cf3380435b5" rel="nofollow">Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
distilled version of BERT</a>, and the paper <a href="https://arxiv.org/abs/1910.01108" rel="nofollow">DistilBERT, a
distilled version of BERT: smaller, faster, cheaper and lighter</a>. DistilBERT is a
small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
<em>google-bert/bert-base-uncased</em>, runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language
understanding benchmark.`,O,f,j="The abstract from the paper is the following:",Ye,Me,Pn=`<em>As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP),
operating these large models in on-the-edge and/or under constrained computational training or inference budgets
remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
study.</em>`,sn,oe,bn=`This model was contributed by <a href="https://huggingface.co/victorsanh" rel="nofollow">victorsanh</a>. This model jax version was
contributed by <a href="https://huggingface.co/kamalkraj" rel="nofollow">kamalkraj</a>. The original code can be found <a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation" rel="nofollow">here</a>.`,Ze,_t,ot,Nt,P=`<li><p>DistilBERT doesn’t have <code>token_type_ids</code>, you don’t need to indicate which token belongs to which segment. Just
separate your segments with the separation token <code>tokenizer.sep_token</code> (or <code>[SEP]</code>).</p></li> <li><p>DistilBERT doesn’t have options to select the input positions (<code>position_ids</code> input). This could be added if
necessary though, just let us know if you need this option.</p></li> <li><p>Same as BERT but smaller. Trained by distillation of the pretrained BERT model, meaning it’s been trained to predict the same probabilities as the larger model. The actual objective is a combination of:</p> <ul><li>finding the same probabilities as the teacher model</li> <li>predicting the masked tokens correctly (but no next-sentence objective)</li> <li>a cosine similarity between the hidden states of the student and the teacher model</li></ul></li>`,Ht,Q,at,xt,Mn="A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DistilBERT. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.",K,Bt,on,rt,Yn='<li>A blog post on <a href="https://huggingface.co/blog/sentiment-analysis-python" rel="nofollow">Getting Started with Sentiment Analysis using Python</a> with DistilBERT.</li> <li>A blog post on how to <a href="https://huggingface.co/blog/fastai" rel="nofollow">train DistilBERT with Blurr for sequence classification</a>.</li> <li>A blog post on how to use <a href="https://huggingface.co/blog/ray-tune" rel="nofollow">Ray to tune DistilBERT hyperparameters</a>.</li> <li>A blog post on how to <a href="https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face" rel="nofollow">train DistilBERT with Hugging Face and Amazon SageMaker</a>.</li> <li>A notebook on how to <a href="https://colab.research.google.com/github/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb" rel="nofollow">finetune DistilBERT for multi-label classification</a>. 🌎</li> <li>A notebook on how to <a href="https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb" rel="nofollow">finetune DistilBERT for multiclass classification with PyTorch</a>. 🌎</li> <li>A notebook on how to <a href="https://colab.research.google.com/github/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb" rel="nofollow">finetune DistilBERT for text classification in TensorFlow</a>. 🌎</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/flax/text-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_flax.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="../tasks/sequence_classification">Text classification task guide</a></li>',an,ue,rn,it,Yt='<li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/flax/token-classification" rel="nofollow">example script</a>.</li> <li><a href="https://huggingface.co/course/chapter7/2?fw=pt" rel="nofollow">Token classification</a> chapter of the 🤗 Hugging Face Course.</li> <li><a href="../tasks/token_classification">Token classification task guide</a></li>',Oe,bt,lt,Lt,Tn='<li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#robertabertdistilbert-and-masked-language-modeling" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_mlmpy" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#masked-language-modeling" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="https://huggingface.co/course/chapter7/3?fw=pt" rel="nofollow">Masked language modeling</a> chapter of the 🤗 Hugging Face Course.</li> <li><a href="../tasks/masked_language_modeling">Masked language modeling task guide</a></li>',Ot,Je,Jn,N,Ft='<li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/flax/question-answering" rel="nofollow">example script</a>.</li> <li><a href="https://huggingface.co/course/chapter7/7?fw=pt" rel="nofollow">Question answering</a> chapter of the 🤗 Hugging Face Course.</li> <li><a href="../tasks/question_answering">Question answering task guide</a></li>',Xt,Ge,In="<strong>Multiple choice</strong>",zn,V,Un='<li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="../tasks/multiple_choice">Multiple choice task guide</a></li>',Vt,Re,Zn="⚗️ Optimization",Dn,G,Et='<li>A blog post on how to <a href="https://huggingface.co/blog/intel" rel="nofollow">quantize DistilBERT with 🤗 Optimum and Intel</a>.</li> <li>A blog post on how <a href="https://www.philschmid.de/optimizing-transformers-with-optimum-gpu" rel="nofollow">Optimizing Transformers for GPUs with 🤗 Optimum</a>.</li> <li>A blog post on <a href="https://www.philschmid.de/optimizing-transformers-with-optimum" rel="nofollow">Optimizing Transformers with Hugging Face Optimum</a>.</li>',Gn,ve,Kn="⚡️ Inference",Rn,ae,En='<li>A blog post on how to <a href="https://huggingface.co/blog/bert-inferentia-sagemaker" rel="nofollow">Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia</a> with DistilBERT.</li> <li>A blog post on <a href="https://www.philschmid.de/sagemaker-serverless-huggingface-distilbert" rel="nofollow">Serverless Inference with Hugging Face’s Transformers, DistilBERT and Amazon SageMaker</a>.</li>',re,qe,yn="🚀 Deploy",Kt,Ke,Ln='<li>A blog post on how to <a href="https://huggingface.co/blog/how-to-deploy-a-pipeline-to-google-clouds" rel="nofollow">deploy DistilBERT on Google Cloud</a>.</li> <li>A blog post on how to <a href="https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker" rel="nofollow">deploy DistilBERT with Amazon SageMaker</a>.</li> <li>A blog post on how to <a href="https://www.philschmid.de/terraform-huggingface-amazon-sagemaker" rel="nofollow">Deploy BERT with Hugging Face Transformers, Amazon SageMaker and Terraform module</a>.</li>',ne,Ne,en,jt,ze="First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.",Qt,He,ln,ie,St="Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your model in half-precision (e.g. <code>torch.float16</code>)",tn,Ct,Ue="To load and run a model using Flash Attention 2, refer to the snippet below:",At,Le,dn,ee,Mt,Te,Jt,dt,cn,he=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> or a <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a>. It
is used to instantiate a DistilBERT model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the DistilBERT
<a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">distilbert-base-uncased</a> architecture.`,pn,fe,ct=`Configuration objects inherit from <a href="/docs/transformers/pr_28071/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/pr_28071/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Qn,Tt,yt,wn,pt,te,zt,mn,E,Ut="Construct a DistilBERT tokenizer. Based on WordPiece.",mt,xe,On=`This tokenizer inherits from <a href="/docs/transformers/pr_28071/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`,kn,ye,$n,et,Dt,Wt=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:`,Wn,X,un="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",S,de,vn,Pt,hn,Sn="Converts a sequence of tokens (string) in a single string.",wt,Xe,kt,xn,tt,Xn="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",Be,Fe,je,nt,It="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",Zt,ut,Gt,Rt,Bn,$=`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> method.`,C,ce,De,L,ge,I,A,qt="Construct a “fast” DistilBERT tokenizer (backed by HuggingFace’s <em>tokenizers</em> library). Based on WordPiece.",pe,ht,ft=`This tokenizer inherits from <a href="/docs/transformers/pr_28071/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`,me,nn,$t,Vn,Fn,es=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:`,h,F,jn="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",Ve,we,We,_e,be,Cn="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",Ee,st,vt,ke,ts="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",qn,Nn,v,J,fn,Qe,gn;return w=new Pe({props:{title:"DistilBERT",local:"distilbert",headingTag:"h1"}}),z=new Pe({props:{title:"Overview",local:"overview",headingTag:"h2"}}),_t=new Pe({props:{title:"Usage tips",local:"usage-tips",headingTag:"h2"}}),Q=new Pe({props:{title:"Resources",local:"resources",headingTag:"h2"}}),Bt=new os({props:{pipeline:"text-classification"}}),ue=new os({props:{pipeline:"token-classification"}}),bt=new os({props:{pipeline:"fill-mask"}}),Je=new os({props:{pipeline:"question-answering"}}),Ne=new Pe({props:{title:"Combining DistilBERT and Flash Attention 2",local:"combining-distilbert-and-flash-attention-2",headingTag:"h2"}}),He=new Ce({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1VJTIwZmxhc2gtYXR0biUyMC0tbm8tYnVpbGQtaXNvbGF0aW9u",highlighted:"pip install -U flash-attn --no-build-isolation",wrap:!1}}),Le=new Ce({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModel

<span class="hljs-meta">&gt;&gt;&gt; </span>device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-comment"># the device to load the model onto</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&#x27;distilbert/distilbert-base-uncased&#x27;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;distilbert/distilbert-base-uncased&quot;</span>, torch_dtype=torch.float16, attn_implementation=<span class="hljs-string">&quot;flash_attention_2&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;Replace me by any text you&#x27;d like.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_input = tokenizer(text, return_tensors=<span class="hljs-string">&#x27;pt&#x27;</span>).to(device)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.to(device)

<span class="hljs-meta">&gt;&gt;&gt; </span>output = model(**encoded_input)`,wrap:!1}}),ee=new Pe({props:{title:"DistilBertConfig",local:"transformers.DistilBertConfig",headingTag:"h2"}}),Jt=new H({props:{name:"class transformers.DistilBertConfig",anchor:"transformers.DistilBertConfig",parameters:[{name:"vocab_size",val:" = 30522"},{name:"max_position_embeddings",val:" = 512"},{name:"sinusoidal_pos_embds",val:" = False"},{name:"n_layers",val:" = 6"},{name:"n_heads",val:" = 12"},{name:"dim",val:" = 768"},{name:"hidden_dim",val:" = 3072"},{name:"dropout",val:" = 0.1"},{name:"attention_dropout",val:" = 0.1"},{name:"activation",val:" = 'gelu'"},{name:"initializer_range",val:" = 0.02"},{name:"qa_dropout",val:" = 0.1"},{name:"seq_classif_dropout",val:" = 0.2"},{name:"pad_token_id",val:" = 0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DistilBertConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the DistilBERT model. Defines the number of different tokens that can be represented by
the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> or <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a>.`,name:"vocab_size"},{anchor:"transformers.DistilBertConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.DistilBertConfig.sinusoidal_pos_embds",description:`<strong>sinusoidal_pos_embds</strong> (<code>boolean</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use sinusoidal positional embeddings.`,name:"sinusoidal_pos_embds"},{anchor:"transformers.DistilBertConfig.n_layers",description:`<strong>n_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"n_layers"},{anchor:"transformers.DistilBertConfig.n_heads",description:`<strong>n_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"n_heads"},{anchor:"transformers.DistilBertConfig.dim",description:`<strong>dim</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"dim"},{anchor:"transformers.DistilBertConfig.hidden_dim",description:`<strong>hidden_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
The size of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the Transformer encoder.`,name:"hidden_dim"},{anchor:"transformers.DistilBertConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.DistilBertConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.DistilBertConfig.activation",description:`<strong>activation</strong> (<code>str</code> or <code>Callable</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"activation"},{anchor:"transformers.DistilBertConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.DistilBertConfig.qa_dropout",description:`<strong>qa_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilities used in the question answering model <a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a>.`,name:"qa_dropout"},{anchor:"transformers.DistilBertConfig.seq_classif_dropout",description:`<strong>seq_classif_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.2) &#x2014;
The dropout probabilities used in the sequence classification and the multiple choice model
<a href="/docs/transformers/pr_28071/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a>.`,name:"seq_classif_dropout"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/configuration_distilbert.py#L28"}}),Tt=new Ae({props:{anchor:"transformers.DistilBertConfig.example",$$slots:{default:[us]},$$scope:{ctx:B}}}),wn=new Pe({props:{title:"DistilBertTokenizer",local:"transformers.DistilBertTokenizer",headingTag:"h2"}}),zt=new H({props:{name:"class transformers.DistilBertTokenizer",anchor:"transformers.DistilBertTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DistilBertTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.DistilBertTokenizer.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.DistilBertTokenizer.do_basic_tokenize",description:`<strong>do_basic_tokenize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to do basic tokenization before WordPiece.`,name:"do_basic_tokenize"},{anchor:"transformers.DistilBertTokenizer.never_split",description:`<strong>never_split</strong> (<code>Iterable</code>, <em>optional</em>) &#x2014;
Collection of tokens which will never be split during tokenization. Only has an effect when
<code>do_basic_tokenize=True</code>`,name:"never_split"},{anchor:"transformers.DistilBertTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.DistilBertTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.DistilBertTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.DistilBertTokenizer.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.`,name:"cls_token"},{anchor:"transformers.DistilBertTokenizer.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"},{anchor:"transformers.DistilBertTokenizer.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters.</p>
<p>This should likely be deactivated for Japanese (see this
<a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.DistilBertTokenizer.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original BERT).`,name:"strip_accents"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/tokenization_distilbert.py#L53"}}),$n=new H({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.DistilBertTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/tokenization_distilbert.py#L191",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),vn=new H({props:{name:"convert_tokens_to_string",anchor:"transformers.DistilBertTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/tokenization_distilbert.py#L185"}}),kt=new H({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.DistilBertTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/tokenization_distilbert.py#L246",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),Fe=new Ae({props:{anchor:"transformers.DistilBertTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[hs]},$$scope:{ctx:B}}}),Gt=new H({props:{name:"get_special_tokens_mask",anchor:"transformers.DistilBertTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.DistilBertTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/tokenization_distilbert.py#L217",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),ce=new Pe({props:{title:"DistilBertTokenizerFast",local:"transformers.DistilBertTokenizerFast",headingTag:"h2"}}),ge=new H({props:{name:"class transformers.DistilBertTokenizerFast",anchor:"transformers.DistilBertTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DistilBertTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.DistilBertTokenizerFast.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.DistilBertTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.DistilBertTokenizerFast.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.DistilBertTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.DistilBertTokenizerFast.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.`,name:"cls_token"},{anchor:"transformers.DistilBertTokenizerFast.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"},{anchor:"transformers.DistilBertTokenizerFast.clean_text",description:`<strong>clean_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.`,name:"clean_text"},{anchor:"transformers.DistilBertTokenizerFast.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see <a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">this
issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.DistilBertTokenizerFast.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original BERT).`,name:"strip_accents"},{anchor:"transformers.DistilBertTokenizerFast.wordpieces_prefix",description:`<strong>wordpieces_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;##&quot;</code>) &#x2014;
The prefix for subwords.`,name:"wordpieces_prefix"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/tokenization_distilbert_fast.py#L32"}}),$t=new H({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.DistilBertTokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:""},{name:"token_ids_1",val:" = None"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizerFast.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizerFast.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/tokenization_distilbert_fast.py#L119",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),We=new H({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.DistilBertTokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_28071/src/transformers/models/distilbert/tokenization_distilbert_fast.py#L144",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),st=new Ae({props:{anchor:"transformers.DistilBertTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[fs]},$$scope:{ctx:B}}}),Nn=new ps({props:{pytorch:!0,tensorflow:!0,jax:!0,$$slots:{jax:[fo],tensorflow:[eo],pytorch:[Cs]},$$scope:{ctx:B}}}),J=new ms({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/distilbert.md"}}),{c(){e=d("meta"),m=a(),n=d("p"),s=a(),g(w.$$.fragment),t=a(),k=d("div"),k.innerHTML=se,W=a(),g(z.$$.fragment),Y=a(),D=d("p"),D.innerHTML=U,O=a(),f=d("p"),f.textContent=j,Ye=a(),Me=d("p"),Me.innerHTML=Pn,sn=a(),oe=d("p"),oe.innerHTML=bn,Ze=a(),g(_t.$$.fragment),ot=a(),Nt=d("ul"),Nt.innerHTML=P,Ht=a(),g(Q.$$.fragment),at=a(),xt=d("p"),xt.textContent=Mn,K=a(),g(Bt.$$.fragment),on=a(),rt=d("ul"),rt.innerHTML=Yn,an=a(),g(ue.$$.fragment),rn=a(),it=d("ul"),it.innerHTML=Yt,Oe=a(),g(bt.$$.fragment),lt=a(),Lt=d("ul"),Lt.innerHTML=Tn,Ot=a(),g(Je.$$.fragment),Jn=a(),N=d("ul"),N.innerHTML=Ft,Xt=a(),Ge=d("p"),Ge.innerHTML=In,zn=a(),V=d("ul"),V.innerHTML=Un,Vt=a(),Re=d("p"),Re.textContent=Zn,Dn=a(),G=d("ul"),G.innerHTML=Et,Gn=a(),ve=d("p"),ve.textContent=Kn,Rn=a(),ae=d("ul"),ae.innerHTML=En,re=a(),qe=d("p"),qe.textContent=yn,Kt=a(),Ke=d("ul"),Ke.innerHTML=Ln,ne=a(),g(Ne.$$.fragment),en=a(),jt=d("p"),jt.textContent=ze,Qt=a(),g(He.$$.fragment),ln=a(),ie=d("p"),ie.innerHTML=St,tn=a(),Ct=d("p"),Ct.textContent=Ue,At=a(),g(Le.$$.fragment),dn=a(),g(ee.$$.fragment),Mt=a(),Te=d("div"),g(Jt.$$.fragment),dt=a(),cn=d("p"),cn.innerHTML=he,pn=a(),fe=d("p"),fe.innerHTML=ct,Qn=a(),g(Tt.$$.fragment),yt=a(),g(wn.$$.fragment),pt=a(),te=d("div"),g(zt.$$.fragment),mn=a(),E=d("p"),E.textContent=Ut,mt=a(),xe=d("p"),xe.innerHTML=On,kn=a(),ye=d("div"),g($n.$$.fragment),et=a(),Dt=d("p"),Dt.textContent=Wt,Wn=a(),X=d("ul"),X.innerHTML=un,S=a(),de=d("div"),g(vn.$$.fragment),Pt=a(),hn=d("p"),hn.textContent=Sn,wt=a(),Xe=d("div"),g(kt.$$.fragment),xn=a(),tt=d("p"),tt.textContent=Xn,Be=a(),g(Fe.$$.fragment),je=a(),nt=d("p"),nt.innerHTML=It,Zt=a(),ut=d("div"),g(Gt.$$.fragment),Rt=a(),Bn=d("p"),Bn.innerHTML=$,C=a(),g(ce.$$.fragment),De=a(),L=d("div"),g(ge.$$.fragment),I=a(),A=d("p"),A.innerHTML=qt,pe=a(),ht=d("p"),ht.innerHTML=ft,me=a(),nn=d("div"),g($t.$$.fragment),Vn=a(),Fn=d("p"),Fn.textContent=es,h=a(),F=d("ul"),F.innerHTML=jn,Ve=a(),we=d("div"),g(We.$$.fragment),_e=a(),be=d("p"),be.textContent=Cn,Ee=a(),g(st.$$.fragment),vt=a(),ke=d("p"),ke.innerHTML=ts,qn=a(),g(Nn.$$.fragment),v=a(),g(J.$$.fragment),fn=a(),Qe=d("p"),this.h()},l(l){const x=cs("svelte-u9bgzb",document.head);e=c(x,"META",{name:!0,content:!0}),x.forEach(i),m=r(l),n=c(l,"P",{}),q(n).forEach(i),s=r(l),_(w.$$.fragment,l),t=r(l),k=c(l,"DIV",{class:!0,"data-svelte-h":!0}),u(k)!=="svelte-19va6f1"&&(k.innerHTML=se),W=r(l),_(z.$$.fragment,l),Y=r(l),D=c(l,"P",{"data-svelte-h":!0}),u(D)!=="svelte-wyvay1"&&(D.innerHTML=U),O=r(l),f=c(l,"P",{"data-svelte-h":!0}),u(f)!=="svelte-vfdo9a"&&(f.textContent=j),Ye=r(l),Me=c(l,"P",{"data-svelte-h":!0}),u(Me)!=="svelte-yrk2ws"&&(Me.innerHTML=Pn),sn=r(l),oe=c(l,"P",{"data-svelte-h":!0}),u(oe)!=="svelte-d89fny"&&(oe.innerHTML=bn),Ze=r(l),_(_t.$$.fragment,l),ot=r(l),Nt=c(l,"UL",{"data-svelte-h":!0}),u(Nt)!=="svelte-1pw0ah6"&&(Nt.innerHTML=P),Ht=r(l),_(Q.$$.fragment,l),at=r(l),xt=c(l,"P",{"data-svelte-h":!0}),u(xt)!=="svelte-1204ph7"&&(xt.textContent=Mn),K=r(l),_(Bt.$$.fragment,l),on=r(l),rt=c(l,"UL",{"data-svelte-h":!0}),u(rt)!=="svelte-n849n2"&&(rt.innerHTML=Yn),an=r(l),_(ue.$$.fragment,l),rn=r(l),it=c(l,"UL",{"data-svelte-h":!0}),u(it)!=="svelte-85zxgv"&&(it.innerHTML=Yt),Oe=r(l),_(bt.$$.fragment,l),lt=r(l),Lt=c(l,"UL",{"data-svelte-h":!0}),u(Lt)!=="svelte-bjdaqy"&&(Lt.innerHTML=Tn),Ot=r(l),_(Je.$$.fragment,l),Jn=r(l),N=c(l,"UL",{"data-svelte-h":!0}),u(N)!=="svelte-2eqcl0"&&(N.innerHTML=Ft),Xt=r(l),Ge=c(l,"P",{"data-svelte-h":!0}),u(Ge)!=="svelte-cplu6u"&&(Ge.innerHTML=In),zn=r(l),V=c(l,"UL",{"data-svelte-h":!0}),u(V)!=="svelte-8jayq5"&&(V.innerHTML=Un),Vt=r(l),Re=c(l,"P",{"data-svelte-h":!0}),u(Re)!=="svelte-liq2q7"&&(Re.textContent=Zn),Dn=r(l),G=c(l,"UL",{"data-svelte-h":!0}),u(G)!=="svelte-1i3rzd0"&&(G.innerHTML=Et),Gn=r(l),ve=c(l,"P",{"data-svelte-h":!0}),u(ve)!=="svelte-1wntqpp"&&(ve.textContent=Kn),Rn=r(l),ae=c(l,"UL",{"data-svelte-h":!0}),u(ae)!=="svelte-1hcjkg2"&&(ae.innerHTML=En),re=r(l),qe=c(l,"P",{"data-svelte-h":!0}),u(qe)!=="svelte-lk14e4"&&(qe.textContent=yn),Kt=r(l),Ke=c(l,"UL",{"data-svelte-h":!0}),u(Ke)!=="svelte-ii18nw"&&(Ke.innerHTML=Ln),ne=r(l),_(Ne.$$.fragment,l),en=r(l),jt=c(l,"P",{"data-svelte-h":!0}),u(jt)!=="svelte-o3pzzu"&&(jt.textContent=ze),Qt=r(l),_(He.$$.fragment,l),ln=r(l),ie=c(l,"P",{"data-svelte-h":!0}),u(ie)!=="svelte-awcq03"&&(ie.innerHTML=St),tn=r(l),Ct=c(l,"P",{"data-svelte-h":!0}),u(Ct)!=="svelte-14hchid"&&(Ct.textContent=Ue),At=r(l),_(Le.$$.fragment,l),dn=r(l),_(ee.$$.fragment,l),Mt=r(l),Te=c(l,"DIV",{class:!0});var le=q(Te);_(Jt.$$.fragment,le),dt=r(le),cn=c(le,"P",{"data-svelte-h":!0}),u(cn)!=="svelte-69jqnq"&&(cn.innerHTML=he),pn=r(le),fe=c(le,"P",{"data-svelte-h":!0}),u(fe)!=="svelte-1b4xab"&&(fe.innerHTML=ct),Qn=r(le),_(Tt.$$.fragment,le),le.forEach(i),yt=r(l),_(wn.$$.fragment,l),pt=r(l),te=c(l,"DIV",{class:!0});var $e=q(te);_(zt.$$.fragment,$e),mn=r($e),E=c($e,"P",{"data-svelte-h":!0}),u(E)!=="svelte-10o6nt5"&&(E.textContent=Ut),mt=r($e),xe=c($e,"P",{"data-svelte-h":!0}),u(xe)!=="svelte-o3cm0g"&&(xe.innerHTML=On),kn=r($e),ye=c($e,"DIV",{class:!0});var Ie=q(ye);_($n.$$.fragment,Ie),et=r(Ie),Dt=c(Ie,"P",{"data-svelte-h":!0}),u(Dt)!=="svelte-t7qurq"&&(Dt.textContent=Wt),Wn=r(Ie),X=c(Ie,"UL",{"data-svelte-h":!0}),u(X)!=="svelte-xi6653"&&(X.innerHTML=un),Ie.forEach(i),S=r($e),de=c($e,"DIV",{class:!0});var _n=q(de);_(vn.$$.fragment,_n),Pt=r(_n),hn=c(_n,"P",{"data-svelte-h":!0}),u(hn)!=="svelte-b3k2yi"&&(hn.textContent=Sn),_n.forEach(i),wt=r($e),Xe=c($e,"DIV",{class:!0});var Se=q(Xe);_(kt.$$.fragment,Se),xn=r(Se),tt=c(Se,"P",{"data-svelte-h":!0}),u(tt)!=="svelte-gn6wi7"&&(tt.textContent=Xn),Be=r(Se),_(Fe.$$.fragment,Se),je=r(Se),nt=c(Se,"P",{"data-svelte-h":!0}),u(nt)!=="svelte-owoxgn"&&(nt.innerHTML=It),Se.forEach(i),Zt=r($e),ut=c($e,"DIV",{class:!0});var Hn=q(ut);_(Gt.$$.fragment,Hn),Rt=r(Hn),Bn=c(Hn,"P",{"data-svelte-h":!0}),u(Bn)!=="svelte-1f4f5kp"&&(Bn.innerHTML=$),Hn.forEach(i),$e.forEach(i),C=r(l),_(ce.$$.fragment,l),De=r(l),L=c(l,"DIV",{class:!0});var An=q(L);_(ge.$$.fragment,An),I=r(An),A=c(An,"P",{"data-svelte-h":!0}),u(A)!=="svelte-88fhae"&&(A.innerHTML=qt),pe=r(An),ht=c(An,"P",{"data-svelte-h":!0}),u(ht)!=="svelte-1az1iz0"&&(ht.innerHTML=ft),me=r(An),nn=c(An,"DIV",{class:!0});var ss=q(nn);_($t.$$.fragment,ss),Vn=r(ss),Fn=c(ss,"P",{"data-svelte-h":!0}),u(Fn)!=="svelte-t7qurq"&&(Fn.textContent=es),h=r(ss),F=c(ss,"UL",{"data-svelte-h":!0}),u(F)!=="svelte-xi6653"&&(F.innerHTML=jn),ss.forEach(i),Ve=r(An),we=c(An,"DIV",{class:!0});var ns=q(we);_(We.$$.fragment,ns),_e=r(ns),be=c(ns,"P",{"data-svelte-h":!0}),u(be)!=="svelte-gn6wi7"&&(be.textContent=Cn),Ee=r(ns),_(st.$$.fragment,ns),vt=r(ns),ke=c(ns,"P",{"data-svelte-h":!0}),u(ke)!=="svelte-owoxgn"&&(ke.innerHTML=ts),ns.forEach(i),An.forEach(i),qn=r(l),_(Nn.$$.fragment,l),v=r(l),_(J.$$.fragment,l),fn=r(l),Qe=c(l,"P",{}),q(Qe).forEach(i),this.h()},h(){R(e,"name","hf:doc:metadata"),R(e,"content",_o),R(k,"class","flex flex-wrap space-x-1"),R(Te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(ye,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(Xe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(ut,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(nn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(we,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),R(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(l,x){o(document.head,e),p(l,m,x),p(l,n,x),p(l,s,x),b(w,l,x),p(l,t,x),p(l,k,x),p(l,W,x),b(z,l,x),p(l,Y,x),p(l,D,x),p(l,O,x),p(l,f,x),p(l,Ye,x),p(l,Me,x),p(l,sn,x),p(l,oe,x),p(l,Ze,x),b(_t,l,x),p(l,ot,x),p(l,Nt,x),p(l,Ht,x),b(Q,l,x),p(l,at,x),p(l,xt,x),p(l,K,x),b(Bt,l,x),p(l,on,x),p(l,rt,x),p(l,an,x),b(ue,l,x),p(l,rn,x),p(l,it,x),p(l,Oe,x),b(bt,l,x),p(l,lt,x),p(l,Lt,x),p(l,Ot,x),b(Je,l,x),p(l,Jn,x),p(l,N,x),p(l,Xt,x),p(l,Ge,x),p(l,zn,x),p(l,V,x),p(l,Vt,x),p(l,Re,x),p(l,Dn,x),p(l,G,x),p(l,Gn,x),p(l,ve,x),p(l,Rn,x),p(l,ae,x),p(l,re,x),p(l,qe,x),p(l,Kt,x),p(l,Ke,x),p(l,ne,x),b(Ne,l,x),p(l,en,x),p(l,jt,x),p(l,Qt,x),b(He,l,x),p(l,ln,x),p(l,ie,x),p(l,tn,x),p(l,Ct,x),p(l,At,x),b(Le,l,x),p(l,dn,x),b(ee,l,x),p(l,Mt,x),p(l,Te,x),b(Jt,Te,null),o(Te,dt),o(Te,cn),o(Te,pn),o(Te,fe),o(Te,Qn),b(Tt,Te,null),p(l,yt,x),b(wn,l,x),p(l,pt,x),p(l,te,x),b(zt,te,null),o(te,mn),o(te,E),o(te,mt),o(te,xe),o(te,kn),o(te,ye),b($n,ye,null),o(ye,et),o(ye,Dt),o(ye,Wn),o(ye,X),o(te,S),o(te,de),b(vn,de,null),o(de,Pt),o(de,hn),o(te,wt),o(te,Xe),b(kt,Xe,null),o(Xe,xn),o(Xe,tt),o(Xe,Be),b(Fe,Xe,null),o(Xe,je),o(Xe,nt),o(te,Zt),o(te,ut),b(Gt,ut,null),o(ut,Rt),o(ut,Bn),p(l,C,x),b(ce,l,x),p(l,De,x),p(l,L,x),b(ge,L,null),o(L,I),o(L,A),o(L,pe),o(L,ht),o(L,me),o(L,nn),b($t,nn,null),o(nn,Vn),o(nn,Fn),o(nn,h),o(nn,F),o(L,Ve),o(L,we),b(We,we,null),o(we,_e),o(we,be),o(we,Ee),b(st,we,null),o(we,vt),o(we,ke),p(l,qn,x),b(Nn,l,x),p(l,v,x),b(J,l,x),p(l,fn,x),p(l,Qe,x),gn=!0},p(l,[x]){const le={};x&2&&(le.$$scope={dirty:x,ctx:l}),Tt.$set(le);const $e={};x&2&&($e.$$scope={dirty:x,ctx:l}),Fe.$set($e);const Ie={};x&2&&(Ie.$$scope={dirty:x,ctx:l}),st.$set(Ie);const _n={};x&2&&(_n.$$scope={dirty:x,ctx:l}),Nn.$set(_n)},i(l){gn||(M(w.$$.fragment,l),M(z.$$.fragment,l),M(_t.$$.fragment,l),M(Q.$$.fragment,l),M(Bt.$$.fragment,l),M(ue.$$.fragment,l),M(bt.$$.fragment,l),M(Je.$$.fragment,l),M(Ne.$$.fragment,l),M(He.$$.fragment,l),M(Le.$$.fragment,l),M(ee.$$.fragment,l),M(Jt.$$.fragment,l),M(Tt.$$.fragment,l),M(wn.$$.fragment,l),M(zt.$$.fragment,l),M($n.$$.fragment,l),M(vn.$$.fragment,l),M(kt.$$.fragment,l),M(Fe.$$.fragment,l),M(Gt.$$.fragment,l),M(ce.$$.fragment,l),M(ge.$$.fragment,l),M($t.$$.fragment,l),M(We.$$.fragment,l),M(st.$$.fragment,l),M(Nn.$$.fragment,l),M(J.$$.fragment,l),gn=!0)},o(l){T(w.$$.fragment,l),T(z.$$.fragment,l),T(_t.$$.fragment,l),T(Q.$$.fragment,l),T(Bt.$$.fragment,l),T(ue.$$.fragment,l),T(bt.$$.fragment,l),T(Je.$$.fragment,l),T(Ne.$$.fragment,l),T(He.$$.fragment,l),T(Le.$$.fragment,l),T(ee.$$.fragment,l),T(Jt.$$.fragment,l),T(Tt.$$.fragment,l),T(wn.$$.fragment,l),T(zt.$$.fragment,l),T($n.$$.fragment,l),T(vn.$$.fragment,l),T(kt.$$.fragment,l),T(Fe.$$.fragment,l),T(Gt.$$.fragment,l),T(ce.$$.fragment,l),T(ge.$$.fragment,l),T($t.$$.fragment,l),T(We.$$.fragment,l),T(st.$$.fragment,l),T(Nn.$$.fragment,l),T(J.$$.fragment,l),gn=!1},d(l){l&&(i(m),i(n),i(s),i(t),i(k),i(W),i(Y),i(D),i(O),i(f),i(Ye),i(Me),i(sn),i(oe),i(Ze),i(ot),i(Nt),i(Ht),i(at),i(xt),i(K),i(on),i(rt),i(an),i(rn),i(it),i(Oe),i(lt),i(Lt),i(Ot),i(Jn),i(N),i(Xt),i(Ge),i(zn),i(V),i(Vt),i(Re),i(Dn),i(G),i(Gn),i(ve),i(Rn),i(ae),i(re),i(qe),i(Kt),i(Ke),i(ne),i(en),i(jt),i(Qt),i(ln),i(ie),i(tn),i(Ct),i(At),i(dn),i(Mt),i(Te),i(yt),i(pt),i(te),i(C),i(De),i(L),i(qn),i(v),i(fn),i(Qe)),i(e),y(w,l),y(z,l),y(_t,l),y(Q,l),y(Bt,l),y(ue,l),y(bt,l),y(Je,l),y(Ne,l),y(He,l),y(Le,l),y(ee,l),y(Jt),y(Tt),y(wn,l),y(zt),y($n),y(vn),y(kt),y(Fe),y(Gt),y(ce,l),y(ge),y($t),y(We),y(st),y(Nn,l),y(J,l)}}}const _o='{"title":"DistilBERT","local":"distilbert","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Usage tips","local":"usage-tips","sections":[],"depth":2},{"title":"Resources","local":"resources","sections":[],"depth":2},{"title":"Combining DistilBERT and Flash Attention 2","local":"combining-distilbert-and-flash-attention-2","sections":[],"depth":2},{"title":"DistilBertConfig","local":"transformers.DistilBertConfig","sections":[],"depth":2},{"title":"DistilBertTokenizer","local":"transformers.DistilBertTokenizer","sections":[],"depth":2},{"title":"DistilBertTokenizerFast","local":"transformers.DistilBertTokenizerFast","sections":[],"depth":2},{"title":"DistilBertModel","local":"transformers.DistilBertModel","sections":[],"depth":2},{"title":"DistilBertForMaskedLM","local":"transformers.DistilBertForMaskedLM","sections":[],"depth":2},{"title":"DistilBertForSequenceClassification","local":"transformers.DistilBertForSequenceClassification","sections":[],"depth":2},{"title":"DistilBertForMultipleChoice","local":"transformers.DistilBertForMultipleChoice","sections":[],"depth":2},{"title":"DistilBertForTokenClassification","local":"transformers.DistilBertForTokenClassification","sections":[],"depth":2},{"title":"DistilBertForQuestionAnswering","local":"transformers.DistilBertForQuestionAnswering","sections":[],"depth":2},{"title":"TFDistilBertModel","local":"transformers.TFDistilBertModel","sections":[],"depth":2},{"title":"TFDistilBertForMaskedLM","local":"transformers.TFDistilBertForMaskedLM","sections":[],"depth":2},{"title":"TFDistilBertForSequenceClassification","local":"transformers.TFDistilBertForSequenceClassification","sections":[],"depth":2},{"title":"TFDistilBertForMultipleChoice","local":"transformers.TFDistilBertForMultipleChoice","sections":[],"depth":2},{"title":"TFDistilBertForTokenClassification","local":"transformers.TFDistilBertForTokenClassification","sections":[],"depth":2},{"title":"TFDistilBertForQuestionAnswering","local":"transformers.TFDistilBertForQuestionAnswering","sections":[],"depth":2},{"title":"FlaxDistilBertModel","local":"transformers.FlaxDistilBertModel","sections":[],"depth":2},{"title":"FlaxDistilBertForMaskedLM","local":"transformers.FlaxDistilBertForMaskedLM","sections":[],"depth":2},{"title":"FlaxDistilBertForSequenceClassification","local":"transformers.FlaxDistilBertForSequenceClassification","sections":[],"depth":2},{"title":"FlaxDistilBertForMultipleChoice","local":"transformers.FlaxDistilBertForMultipleChoice","sections":[],"depth":2},{"title":"FlaxDistilBertForTokenClassification","local":"transformers.FlaxDistilBertForTokenClassification","sections":[],"depth":2},{"title":"FlaxDistilBertForQuestionAnswering","local":"transformers.FlaxDistilBertForQuestionAnswering","sections":[],"depth":2}],"depth":1}';function bo(B){return is(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Fo extends ls{constructor(e){super(),ds(this,e,bo,go,rs,{})}}export{Fo as component};
