import{s as bs,o as ys,n as z}from"../chunks/scheduler.9bc65507.js";import{S as ks,i as Ts,g as c,s,r as f,A as Ms,h as p,f as l,c as a,j,u as _,x as g,k as F,y as o,a as h,v as b,d as y,t as k,w as T}from"../chunks/index.707bf1b6.js";import{T as Mt}from"../chunks/Tip.c2ecdbf4.js";import{D as C}from"../chunks/Docstring.86474e80.js";import{C as ge}from"../chunks/CodeBlock.54a9f38d.js";import{F as Bs,M as _s}from"../chunks/Markdown.8ab98a13.js";import{E as he}from"../chunks/ExampleCodeBlock.034f3a73.js";import{H as Ie,E as vs}from"../chunks/EditOnGithub.922df6ba.js";function ws(w){let e,u="Example:",n,r,M;return r=new ge({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEJpZ0JpcmRDb25maWclMkMlMjBCaWdCaXJkTW9kZWwlMEElMEElMjMlMjBJbml0aWFsaXppbmclMjBhJTIwQmlnQmlyZCUyMGdvb2dsZSUyRmJpZ2JpcmQtcm9iZXJ0YS1iYXNlJTIwc3R5bGUlMjBjb25maWd1cmF0aW9uJTBBY29uZmlndXJhdGlvbiUyMCUzRCUyMEJpZ0JpcmRDb25maWcoKSUwQSUwQSUyMyUyMEluaXRpYWxpemluZyUyMGElMjBtb2RlbCUyMCh3aXRoJTIwcmFuZG9tJTIwd2VpZ2h0cyklMjBmcm9tJTIwdGhlJTIwZ29vZ2xlJTJGYmlnYmlyZC1yb2JlcnRhLWJhc2UlMjBzdHlsZSUyMGNvbmZpZ3VyYXRpb24lMEFtb2RlbCUyMCUzRCUyMEJpZ0JpcmRNb2RlbChjb25maWd1cmF0aW9uKSUwQSUwQSUyMyUyMEFjY2Vzc2luZyUyMHRoZSUyMG1vZGVsJTIwY29uZmlndXJhdGlvbiUwQWNvbmZpZ3VyYXRpb24lMjAlM0QlMjBtb2RlbC5jb25maWc=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> BigBirdConfig, BigBirdModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a BigBird google/bigbird-roberta-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = BigBirdConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the google/bigbird-roberta-base style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdModel(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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function xs(w){let e,u="sequence pair mask has the following format:",n,r,M;return r=new ge({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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-16klr56"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function $s(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function js(w){let e,u="Example:",n,r,M;return r=new ge({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, BigBirdModel
<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;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdModel.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function Fs(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Cs(w){let e,u="Example:",n,r,M;return r=new ge({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, BigBirdForPreTraining
<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;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForPreTraining.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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>prediction_logits = outputs.prediction_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>seq_relationship_logits = outputs.seq_relationship_logits`,wrap:!1}}),{c(){e=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function zs(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Js(w){let e,u="Example:",n,r,M;return r=new ge({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, BigBirdForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForCausalLM.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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, labels=inputs[<span class="hljs-string">&quot;input_ids&quot;</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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function Us(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function qs(w){let e,u="Example:",n,r,M;return r=new ge({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, BigBirdForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>squad_ds = load_dataset(<span class="hljs-string">&quot;squad_v2&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># select random long article</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE_TARGET = squad_ds[<span class="hljs-number">81514</span>][<span class="hljs-string">&quot;context&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># select random sentence</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE_TARGET[<span class="hljs-number">332</span>:<span class="hljs-number">398</span>]
<span class="hljs-string">&#x27;the highest values are very close to the theoretical maximum value&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># add mask_token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE_TO_MASK = LONG_ARTICLE_TARGET.replace(<span class="hljs-string">&quot;maximum&quot;</span>, <span class="hljs-string">&quot;[MASK]&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(LONG_ARTICLE_TO_MASK, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># long article input</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">919</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>tokenizer.decode(predicted_token_id)
<span class="hljs-string">&#x27;maximum&#x27;</span>`,wrap:!1}}),{c(){e=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function Is(w){let e,u;return e=new ge({props:{code:"bGFiZWxzJTIwJTNEJTIwdG9rZW5pemVyKExPTkdfQVJUSUNMRV9UQVJHRVQlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSU1QiUyMmlucHV0X2lkcyUyMiU1RCUwQWxhYmVscyUyMCUzRCUyMHRvcmNoLndoZXJlKGlucHV0cy5pbnB1dF9pZHMlMjAlM0QlM0QlMjB0b2tlbml6ZXIubWFza190b2tlbl9pZCUyQyUyMGxhYmVscyUyQyUyMC0xMDApJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzJTJDJTIwbGFiZWxzJTNEbGFiZWxzKSUwQXJvdW5kKG91dHB1dHMubG9zcy5pdGVtKCklMkMlMjAyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(LONG_ARTICLE_TARGET, 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>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)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(outputs.loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">1.99</span>`,wrap:!1}}),{c(){f(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,r){b(e,n,r),u=!0},p:z,i(n){u||(y(e.$$.fragment,n),u=!0)},o(n){k(e.$$.fragment,n),u=!1},d(n){T(e,n)}}}function Zs(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Ws(w){let e,u="Example:",n,r,M;return r=new ge({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, BigBirdForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;l-yohai/bigbird-roberta-base-mnli&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;l-yohai/bigbird-roberta-base-mnli&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>squad_ds = load_dataset(<span class="hljs-string">&quot;squad_v2&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE = squad_ds[<span class="hljs-number">81514</span>][<span class="hljs-string">&quot;context&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(LONG_ARTICLE, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># long input article</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">919</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>model.config.id2label[predicted_class_id]
<span class="hljs-string">&#x27;LABEL_0&#x27;</span>`,wrap:!1}}),{c(){e=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function Ls(w){let e,u;return e=new ge({props:{code:"bnVtX2xhYmVscyUyMCUzRCUyMGxlbihtb2RlbC5jb25maWcuaWQybGFiZWwpJTBBbW9kZWwlMjAlM0QlMjBCaWdCaXJkRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIybC15b2hhaSUyRmJpZ2JpcmQtcm9iZXJ0YS1iYXNlLW1ubGklMjIlMkMlMjBudW1fbGFiZWxzJTNEbnVtX2xhYmVscyUwQSklMEFsYWJlbHMlMjAlM0QlMjB0b3JjaC50ZW5zb3IoMSklMEFsb3NzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMlMkMlMjBsYWJlbHMlM0RsYWJlbHMpLmxvc3MlMEFyb3VuZChsb3NzLml0ZW0oKSUyQyUyMDIp",highlighted:`<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 = BigBirdForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;l-yohai/bigbird-roberta-base-mnli&quot;</span>, num_labels=num_labels
<span class="hljs-meta">... </span>)
<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
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
<span class="hljs-number">1.13</span>`,wrap:!1}}),{c(){f(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,r){b(e,n,r),u=!0},p:z,i(n){u||(y(e.$$.fragment,n),u=!0)},o(n){k(e.$$.fragment,n),u=!1},d(n){T(e,n)}}}function Ns(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Gs(w){let e,u="Example:",n,r,M;return r=new ge({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, BigBirdForMultipleChoice
<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;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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, prompt], [choice0, 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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function Ps(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Hs(w){let e,u="Example:",n,r,M;return r=new ge({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, BigBirdForTokenClassification
<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;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function Vs(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Es(w){let e,u="Example:",n,r,M;return r=new ge({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, BigBirdForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BigBirdForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>squad_ds = load_dataset(<span class="hljs-string">&quot;squad_v2&quot;</span>, split=<span class="hljs-string">&quot;train&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># select random article and question</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>LONG_ARTICLE = squad_ds[<span class="hljs-number">81514</span>][<span class="hljs-string">&quot;context&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>QUESTION = squad_ds[<span class="hljs-number">81514</span>][<span class="hljs-string">&quot;question&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>QUESTION
<span class="hljs-string">&#x27;During daytime how high can the temperatures reach?&#x27;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(QUESTION, LONG_ARTICLE, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># long article and question input</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(inputs[<span class="hljs-string">&quot;input_ids&quot;</span>].shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">929</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_token_ids = 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>predict_answer_token = tokenizer.decode(predict_answer_token_ids)`,wrap:!1}}),{c(){e=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function Rs(w){let e,u;return e=new ge({props:{code:"dGFyZ2V0X3N0YXJ0X2luZGV4JTJDJTIwdGFyZ2V0X2VuZF9pbmRleCUyMCUzRCUyMHRvcmNoLnRlbnNvciglNUIxMzAlNUQpJTJDJTIwdG9yY2gudGVuc29yKCU1QjEzMiU1RCklMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMlMkMlMjBzdGFydF9wb3NpdGlvbnMlM0R0YXJnZXRfc3RhcnRfaW5kZXglMkMlMjBlbmRfcG9zaXRpb25zJTNEdGFyZ2V0X2VuZF9pbmRleCklMEFsb3NzJTIwJTNEJTIwb3V0cHV0cy5sb3Nz",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>target_start_index, target_end_index = torch.tensor([<span class="hljs-number">130</span>]), torch.tensor([<span class="hljs-number">132</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(){f(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,r){b(e,n,r),u=!0},p:z,i(n){u||(y(e.$$.fragment,n),u=!0)},o(n){k(e.$$.fragment,n),u=!1},d(n){T(e,n)}}}function Xs(w){let e,u,n,r,M,t,B=`The bare BigBird Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,bn,ne,to=`The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in <a href="https://arxiv.org/abs/1706.03762" rel="nofollow">Attention is
all you need</a> by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.`,yn,oe,no=`To behave as an decoder the model needs to be initialized with the <code>is_decoder</code> argument of the configuration set
to <code>True</code>. To be used in a Seq2Seq model, the model needs to initialized with both <code>is_decoder</code> argument and
<code>add_cross_attention</code> set to <code>True</code>; an <code>encoder_hidden_states</code> is then expected as an input to the forward pass.`,kn,W,an,Tn,se,oo='The <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> forward method, overrides the <code>__call__</code> special method.',Mn,q,Vt,Ae,we,Bt,ht,ae,gt,fe,Y,I,Et,vt,Z='The <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> forward method, overrides the <code>__call__</code> special method.',Rt,ft,Oe,Xt,Bn,R,vn,X,xe,P,Ze,rn=`BigBird Model with a <code>language modeling</code> head on top for CLM fine-tuning.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,ln,$e,We,dn,cn,je='The <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> forward method, overrides the <code>__call__</code> special method.',D,_e,St,Le,zn,be,wt,S,_t,ye,Fe,H=`BigBird Model with a <code>language modeling</code> head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Ne,re,Ge,xt,Pe,pn='The <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> forward method, overrides the <code>__call__</code> special method.',mn,$t,Qt,He,jt,ke,Ft,bt,L,E,Ct,Q,Ye,Rn=`BigBird Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`,ie,De,Xn=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Ce,le,Te,zt,Ke,Dn='The <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',de,J,Jt,ze,un,Ut,et,Ve,At,Me,Ee,qt,Re,hn=`BigBird 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.`,gn,fn,Ot=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Yt,ce,U,Je,Dt,yt='The <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> forward method, overrides the <code>__call__</code> special method.',Kn,tt,pe,nt,me,ot,Xe,A,d,$,K,N=`BigBird Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.`,kt,It,Kt=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,eo,G,ue,Zt,Wt,en='The <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',ko,wn,Sn,Jn,so,Be,xn,st,tn,To,Qn,Un=`BigBird Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute <code>span start logits</code> and <code>span end logits</code>).`,co,Lt,po=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,qn,at,$n,O,nn,Mo='The <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',An,In,mo,Nt,Bo,jn,Zn;return e=new Ie({props:{title:"BigBirdModel",local:"transformers.BigBirdModel",headingTag:"h2"}}),r=new C({props:{name:"class transformers.BigBirdModel",anchor:"transformers.BigBirdModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.BigBirdModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L1903"}}),an=new C({props:{name:"forward",anchor:"transformers.BigBirdModel.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"encoder_hidden_states",val:": Optional = None"},{name:"encoder_attention_mask",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"use_cache",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.BigBirdModel.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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.BigBirdModel.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.BigBirdModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.BigBirdModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BigBirdModel.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.BigBirdModel.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BigBirdModel.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.BigBirdModel.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.BigBirdModel.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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdModel.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.BigBirdModel.forward.encoder_attention_mask",description:`<strong>encoder_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 the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. 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>`,name:"encoder_attention_mask"},{anchor:"transformers.BigBirdModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.BigBirdModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_big_bird.py#L1964",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) after further processing
through the layers used for the auxiliary pretraining task. E.g. for BERT-family of models, this returns
the classification token after processing through a linear layer and a tanh activation function. The linear
layer weights are trained from the next sentence prediction (classification) objective during pretraining.</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>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=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 of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_30621/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),q=new Mt({props:{$$slots:{default:[$s]},$$scope:{ctx:w}}}),Ae=new he({props:{anchor:"transformers.BigBirdModel.forward.example",$$slots:{default:[js]},$$scope:{ctx:w}}}),Bt=new Ie({props:{title:"BigBirdForPreTraining",local:"transformers.BigBirdForPreTraining",headingTag:"h2"}}),gt=new C({props:{name:"class transformers.BigBirdForPreTraining",anchor:"transformers.BigBirdForPreTraining",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2246"}}),I=new C({props:{name:"forward",anchor:"transformers.BigBirdForPreTraining.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"next_sentence_label",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.BigBirdForPreTraining.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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.BigBirdForPreTraining.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.BigBirdForPreTraining.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.BigBirdForPreTraining.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BigBirdForPreTraining.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.BigBirdForPreTraining.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BigBirdForPreTraining.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.BigBirdForPreTraining.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.BigBirdForPreTraining.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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForPreTraining.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"},{anchor:"transformers.BigBirdForPreTraining.forward.next_sentence_label",description:`<strong>next_sentence_label</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the next sequence prediction (classification) loss. If specified, nsp loss will be
added to masked_lm loss. Input should be a sequence pair (see <code>input_ids</code> docstring) Indices should be in
<code>[0, 1]</code>:</p>
<ul>
<li>0 indicates sequence B is a continuation of sequence A,</li>
<li>1 indicates sequence B is a random sequence.</li>
</ul>`,name:"next_sentence_label"},{anchor:"transformers.BigBirdForPreTraining.forward.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, any]</code>, optional, defaults to <em>{}</em>) &#x2014;
Used to hide legacy arguments that have been deprecated.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2265",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput"
>transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput</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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<em>optional</em>, returned when <code>labels</code> is provided, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) — Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.</p>
</li>
<li>
<p><strong>prediction_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>seq_relationship_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>) — Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
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 + 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(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_30621/ja/model_doc/big_bird#transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput"
>transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ft=new Mt({props:{$$slots:{default:[Fs]},$$scope:{ctx:w}}}),Xt=new he({props:{anchor:"transformers.BigBirdForPreTraining.forward.example",$$slots:{default:[Cs]},$$scope:{ctx:w}}}),R=new Ie({props:{title:"BigBirdForCausalLM",local:"transformers.BigBirdForCausalLM",headingTag:"h2"}}),xe=new C({props:{name:"class transformers.BigBirdForCausalLM",anchor:"transformers.BigBirdForCausalLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2495"}}),We=new C({props:{name:"forward",anchor:"transformers.BigBirdForCausalLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"encoder_hidden_states",val:": Optional = None"},{name:"encoder_attention_mask",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"use_cache",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.BigBirdForCausalLM.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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.BigBirdForCausalLM.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.BigBirdForCausalLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.BigBirdForCausalLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BigBirdForCausalLM.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.BigBirdForCausalLM.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BigBirdForCausalLM.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.BigBirdForCausalLM.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.BigBirdForCausalLM.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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForCausalLM.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.BigBirdForCausalLM.forward.encoder_attention_mask",description:`<strong>encoder_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 the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. 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>`,name:"encoder_attention_mask"},{anchor:"transformers.BigBirdForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.BigBirdForCausalLM.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 left-to-right language modeling loss (next word prediction). 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 n <code>[0, ..., config.vocab_size]</code>.`,name:"labels"},{anchor:"transformers.BigBirdForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2520",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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) — Language modeling loss (for next-token prediction).</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>
<li>
<p><strong>cross_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>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_30621/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),_e=new Mt({props:{$$slots:{default:[zs]},$$scope:{ctx:w}}}),Le=new he({props:{anchor:"transformers.BigBirdForCausalLM.forward.example",$$slots:{default:[Js]},$$scope:{ctx:w}}}),be=new Ie({props:{title:"BigBirdForMaskedLM",local:"transformers.BigBirdForMaskedLM",headingTag:"h2"}}),_t=new C({props:{name:"class transformers.BigBirdForMaskedLM",anchor:"transformers.BigBirdForMaskedLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2352"}}),Ge=new C({props:{name:"forward",anchor:"transformers.BigBirdForMaskedLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"encoder_hidden_states",val:": Optional = None"},{name:"encoder_attention_mask",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.BigBirdForMaskedLM.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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.BigBirdForMaskedLM.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.BigBirdForMaskedLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.BigBirdForMaskedLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BigBirdForMaskedLM.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.BigBirdForMaskedLM.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BigBirdForMaskedLM.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.BigBirdForMaskedLM.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.BigBirdForMaskedLM.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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForMaskedLM.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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2378",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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_30621/ja/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),$t=new Mt({props:{$$slots:{default:[Us]},$$scope:{ctx:w}}}),He=new he({props:{anchor:"transformers.BigBirdForMaskedLM.forward.example",$$slots:{default:[qs]},$$scope:{ctx:w}}}),ke=new he({props:{anchor:"transformers.BigBirdForMaskedLM.forward.example-2",$$slots:{default:[Is]},$$scope:{ctx:w}}}),bt=new Ie({props:{title:"BigBirdForSequenceClassification",local:"transformers.BigBirdForSequenceClassification",headingTag:"h2"}}),Ct=new C({props:{name:"class transformers.BigBirdForSequenceClassification",anchor:"transformers.BigBirdForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2664"}}),Te=new C({props:{name:"forward",anchor:"transformers.BigBirdForSequenceClassification.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",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.BigBirdForSequenceClassification.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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.BigBirdForSequenceClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.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.BigBirdForSequenceClassification.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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForSequenceClassification.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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2682",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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_30621/ja/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),J=new Mt({props:{$$slots:{default:[Zs]},$$scope:{ctx:w}}}),ze=new he({props:{anchor:"transformers.BigBirdForSequenceClassification.forward.example",$$slots:{default:[Ws]},$$scope:{ctx:w}}}),Ut=new he({props:{anchor:"transformers.BigBirdForSequenceClassification.forward.example-2",$$slots:{default:[Ls]},$$scope:{ctx:w}}}),Ve=new Ie({props:{title:"BigBirdForMultipleChoice",local:"transformers.BigBirdForMultipleChoice",headingTag:"h2"}}),Ee=new C({props:{name:"class transformers.BigBirdForMultipleChoice",anchor:"transformers.BigBirdForMultipleChoice",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2792"}}),U=new C({props:{name:"forward",anchor:"transformers.BigBirdForMultipleChoice.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",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.BigBirdForMultipleChoice.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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.BigBirdForMultipleChoice.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.BigBirdForMultipleChoice.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.BigBirdForMultipleChoice.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BigBirdForMultipleChoice.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.BigBirdForMultipleChoice.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BigBirdForMultipleChoice.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.BigBirdForMultipleChoice.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.BigBirdForMultipleChoice.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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForMultipleChoice.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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2810",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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_30621/ja/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),tt=new Mt({props:{$$slots:{default:[Ns]},$$scope:{ctx:w}}}),nt=new he({props:{anchor:"transformers.BigBirdForMultipleChoice.forward.example",$$slots:{default:[Gs]},$$scope:{ctx:w}}}),ot=new Ie({props:{title:"BigBirdForTokenClassification",local:"transformers.BigBirdForTokenClassification",headingTag:"h2"}}),d=new C({props:{name:"class transformers.BigBirdForTokenClassification",anchor:"transformers.BigBirdForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BigBirdForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2885"}}),ue=new C({props:{name:"forward",anchor:"transformers.BigBirdForTokenClassification.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",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.BigBirdForTokenClassification.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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.BigBirdForTokenClassification.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.BigBirdForTokenClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.BigBirdForTokenClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BigBirdForTokenClassification.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.BigBirdForTokenClassification.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BigBirdForTokenClassification.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.BigBirdForTokenClassification.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.BigBirdForTokenClassification.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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForTokenClassification.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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2907",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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_30621/ja/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),wn=new Mt({props:{$$slots:{default:[Ps]},$$scope:{ctx:w}}}),Jn=new he({props:{anchor:"transformers.BigBirdForTokenClassification.forward.example",$$slots:{default:[Hs]},$$scope:{ctx:w}}}),Be=new Ie({props:{title:"BigBirdForQuestionAnswering",local:"transformers.BigBirdForQuestionAnswering",headingTag:"h2"}}),tn=new C({props:{name:"class transformers.BigBirdForQuestionAnswering",anchor:"transformers.BigBirdForQuestionAnswering",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = False"}],parametersDescription:[{anchor:"transformers.BigBirdForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L2984"}}),$n=new C({props:{name:"forward",anchor:"transformers.BigBirdForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"question_lengths",val:": Optional = None"},{name:"token_type_ids",val:": Optional = None"},{name:"position_ids",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.BigBirdForQuestionAnswering.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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.BigBirdForQuestionAnswering.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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 <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BigBirdForQuestionAnswering.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.BigBirdForQuestionAnswering.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_30621/src/transformers/models/big_bird/modeling_big_bird.py#L3005",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.big_bird.modeling_big_bird.BigBirdForQuestionAnsweringModelOutput</code> 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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 1)</code>) — pooler output from BigBigModel</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 + 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(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><code>transformers.models.big_bird.modeling_big_bird.BigBirdForQuestionAnsweringModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),In=new Mt({props:{$$slots:{default:[Vs]},$$scope:{ctx:w}}}),Nt=new he({props:{anchor:"transformers.BigBirdForQuestionAnswering.forward.example",$$slots:{default:[Es]},$$scope:{ctx:w}}}),jn=new he({props:{anchor:"transformers.BigBirdForQuestionAnswering.forward.example-2",$$slots:{default:[Rs]},$$scope:{ctx:w}}}),{c(){f(e.$$.fragment),u=s(),n=c("div"),f(r.$$.fragment),M=s(),t=c("p"),t.innerHTML=B,bn=s(),ne=c("p"),ne.innerHTML=to,yn=s(),oe=c("p"),oe.innerHTML=no,kn=s(),W=c("div"),f(an.$$.fragment),Tn=s(),se=c("p"),se.innerHTML=oo,Mn=s(),f(q.$$.fragment),Vt=s(),f(Ae.$$.fragment),we=s(),f(Bt.$$.fragment),ht=s(),ae=c("div"),f(gt.$$.fragment),fe=s(),Y=c("div"),f(I.$$.fragment),Et=s(),vt=c("p"),vt.innerHTML=Z,Rt=s(),f(ft.$$.fragment),Oe=s(),f(Xt.$$.fragment),Bn=s(),f(R.$$.fragment),vn=s(),X=c("div"),f(xe.$$.fragment),P=s(),Ze=c("p"),Ze.innerHTML=rn,ln=s(),$e=c("div"),f(We.$$.fragment),dn=s(),cn=c("p"),cn.innerHTML=je,D=s(),f(_e.$$.fragment),St=s(),f(Le.$$.fragment),zn=s(),f(be.$$.fragment),wt=s(),S=c("div"),f(_t.$$.fragment),ye=s(),Fe=c("p"),Fe.innerHTML=H,Ne=s(),re=c("div"),f(Ge.$$.fragment),xt=s(),Pe=c("p"),Pe.innerHTML=pn,mn=s(),f($t.$$.fragment),Qt=s(),f(He.$$.fragment),jt=s(),f(ke.$$.fragment),Ft=s(),f(bt.$$.fragment),L=s(),E=c("div"),f(Ct.$$.fragment),Q=s(),Ye=c("p"),Ye.textContent=Rn,ie=s(),De=c("p"),De.innerHTML=Xn,Ce=s(),le=c("div"),f(Te.$$.fragment),zt=s(),Ke=c("p"),Ke.innerHTML=Dn,de=s(),f(J.$$.fragment),Jt=s(),f(ze.$$.fragment),un=s(),f(Ut.$$.fragment),et=s(),f(Ve.$$.fragment),At=s(),Me=c("div"),f(Ee.$$.fragment),qt=s(),Re=c("p"),Re.textContent=hn,gn=s(),fn=c("p"),fn.innerHTML=Ot,Yt=s(),ce=c("div"),f(U.$$.fragment),Je=s(),Dt=c("p"),Dt.innerHTML=yt,Kn=s(),f(tt.$$.fragment),pe=s(),f(nt.$$.fragment),me=s(),f(ot.$$.fragment),Xe=s(),A=c("div"),f(d.$$.fragment),$=s(),K=c("p"),K.textContent=N,kt=s(),It=c("p"),It.innerHTML=Kt,eo=s(),G=c("div"),f(ue.$$.fragment),Zt=s(),Wt=c("p"),Wt.innerHTML=en,ko=s(),f(wn.$$.fragment),Sn=s(),f(Jn.$$.fragment),so=s(),f(Be.$$.fragment),xn=s(),st=c("div"),f(tn.$$.fragment),To=s(),Qn=c("p"),Qn.innerHTML=Un,co=s(),Lt=c("p"),Lt.innerHTML=po,qn=s(),at=c("div"),f($n.$$.fragment),O=s(),nn=c("p"),nn.innerHTML=Mo,An=s(),f(In.$$.fragment),mo=s(),f(Nt.$$.fragment),Bo=s(),f(jn.$$.fragment),this.h()},l(i){_(e.$$.fragment,i),u=a(i),n=p(i,"DIV",{class:!0});var v=j(n);_(r.$$.fragment,v),M=a(v),t=p(v,"P",{"data-svelte-h":!0}),g(t)!=="svelte-y21dxp"&&(t.innerHTML=B),bn=a(v),ne=p(v,"P",{"data-svelte-h":!0}),g(ne)!=="svelte-1du13oj"&&(ne.innerHTML=to),yn=a(v),oe=p(v,"P",{"data-svelte-h":!0}),g(oe)!=="svelte-174erte"&&(oe.innerHTML=no),kn=a(v),W=p(v,"DIV",{class:!0});var Se=j(W);_(an.$$.fragment,Se),Tn=a(Se),se=p(Se,"P",{"data-svelte-h":!0}),g(se)!=="svelte-1wukctf"&&(se.innerHTML=oo),Mn=a(Se),_(q.$$.fragment,Se),Vt=a(Se),_(Ae.$$.fragment,Se),Se.forEach(l),v.forEach(l),we=a(i),_(Bt.$$.fragment,i),ht=a(i),ae=p(i,"DIV",{class:!0});var On=j(ae);_(gt.$$.fragment,On),fe=a(On),Y=p(On,"DIV",{class:!0});var Gt=j(Y);_(I.$$.fragment,Gt),Et=a(Gt),vt=p(Gt,"P",{"data-svelte-h":!0}),g(vt)!=="svelte-npp8w7"&&(vt.innerHTML=Z),Rt=a(Gt),_(ft.$$.fragment,Gt),Oe=a(Gt),_(Xt.$$.fragment,Gt),Gt.forEach(l),On.forEach(l),Bn=a(i),_(R.$$.fragment,i),vn=a(i),X=p(i,"DIV",{class:!0});var Tt=j(X);_(xe.$$.fragment,Tt),P=a(Tt),Ze=p(Tt,"P",{"data-svelte-h":!0}),g(Ze)!=="svelte-1bxx5tz"&&(Ze.innerHTML=rn),ln=a(Tt),$e=p(Tt,"DIV",{class:!0});var on=j($e);_(We.$$.fragment,on),dn=a(on),cn=p(on,"P",{"data-svelte-h":!0}),g(cn)!=="svelte-18z59xv"&&(cn.innerHTML=je),D=a(on),_(_e.$$.fragment,on),St=a(on),_(Le.$$.fragment,on),on.forEach(l),Tt.forEach(l),zn=a(i),_(be.$$.fragment,i),wt=a(i),S=p(i,"DIV",{class:!0});var _n=j(S);_(_t.$$.fragment,_n),ye=a(_n),Fe=p(_n,"P",{"data-svelte-h":!0}),g(Fe)!=="svelte-xdrvf0"&&(Fe.innerHTML=H),Ne=a(_n),re=p(_n,"DIV",{class:!0});var V=j(re);_(Ge.$$.fragment,V),xt=a(V),Pe=p(V,"P",{"data-svelte-h":!0}),g(Pe)!=="svelte-azi3jn"&&(Pe.innerHTML=pn),mn=a(V),_($t.$$.fragment,V),Qt=a(V),_(He.$$.fragment,V),jt=a(V),_(ke.$$.fragment,V),V.forEach(l),_n.forEach(l),Ft=a(i),_(bt.$$.fragment,i),L=a(i),E=p(i,"DIV",{class:!0});var Ue=j(E);_(Ct.$$.fragment,Ue),Q=a(Ue),Ye=p(Ue,"P",{"data-svelte-h":!0}),g(Ye)!=="svelte-ip8mlz"&&(Ye.textContent=Rn),ie=a(Ue),De=p(Ue,"P",{"data-svelte-h":!0}),g(De)!=="svelte-68lg8f"&&(De.innerHTML=Xn),Ce=a(Ue),le=p(Ue,"DIV",{class:!0});var rt=j(le);_(Te.$$.fragment,rt),zt=a(rt),Ke=p(rt,"P",{"data-svelte-h":!0}),g(Ke)!=="svelte-520bj5"&&(Ke.innerHTML=Dn),de=a(rt),_(J.$$.fragment,rt),Jt=a(rt),_(ze.$$.fragment,rt),un=a(rt),_(Ut.$$.fragment,rt),rt.forEach(l),Ue.forEach(l),et=a(i),_(Ve.$$.fragment,i),At=a(i),Me=p(i,"DIV",{class:!0});var Qe=j(Me);_(Ee.$$.fragment,Qe),qt=a(Qe),Re=p(Qe,"P",{"data-svelte-h":!0}),g(Re)!=="svelte-1u97t9z"&&(Re.textContent=hn),gn=a(Qe),fn=p(Qe,"P",{"data-svelte-h":!0}),g(fn)!=="svelte-68lg8f"&&(fn.innerHTML=Ot),Yt=a(Qe),ce=p(Qe,"DIV",{class:!0});var sn=j(ce);_(U.$$.fragment,sn),Je=a(sn),Dt=p(sn,"P",{"data-svelte-h":!0}),g(Dt)!=="svelte-po7b71"&&(Dt.innerHTML=yt),Kn=a(sn),_(tt.$$.fragment,sn),pe=a(sn),_(nt.$$.fragment,sn),sn.forEach(l),Qe.forEach(l),me=a(i),_(ot.$$.fragment,i),Xe=a(i),A=p(i,"DIV",{class:!0});var Pt=j(A);_(d.$$.fragment,Pt),$=a(Pt),K=p(Pt,"P",{"data-svelte-h":!0}),g(K)!=="svelte-1stsyjq"&&(K.textContent=N),kt=a(Pt),It=p(Pt,"P",{"data-svelte-h":!0}),g(It)!=="svelte-68lg8f"&&(It.innerHTML=Kt),eo=a(Pt),G=p(Pt,"DIV",{class:!0});var ve=j(G);_(ue.$$.fragment,ve),Zt=a(ve),Wt=p(ve,"P",{"data-svelte-h":!0}),g(Wt)!=="svelte-1ojxzej"&&(Wt.innerHTML=en),ko=a(ve),_(wn.$$.fragment,ve),Sn=a(ve),_(Jn.$$.fragment,ve),ve.forEach(l),Pt.forEach(l),so=a(i),_(Be.$$.fragment,i),xn=a(i),st=p(i,"DIV",{class:!0});var Ht=j(st);_(tn.$$.fragment,Ht),To=a(Ht),Qn=p(Ht,"P",{"data-svelte-h":!0}),g(Qn)!=="svelte-1s4517o"&&(Qn.innerHTML=Un),co=a(Ht),Lt=p(Ht,"P",{"data-svelte-h":!0}),g(Lt)!=="svelte-68lg8f"&&(Lt.innerHTML=po),qn=a(Ht),at=p(Ht,"DIV",{class:!0});var ee=j(at);_($n.$$.fragment,ee),O=a(ee),nn=p(ee,"P",{"data-svelte-h":!0}),g(nn)!=="svelte-6lnyxn"&&(nn.innerHTML=Mo),An=a(ee),_(In.$$.fragment,ee),mo=a(ee),_(Nt.$$.fragment,ee),Bo=a(ee),_(jn.$$.fragment,ee),ee.forEach(l),Ht.forEach(l),this.h()},h(){F(W,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(ae,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F($e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(A,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(at,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(st,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(i,v){b(e,i,v),h(i,u,v),h(i,n,v),b(r,n,null),o(n,M),o(n,t),o(n,bn),o(n,ne),o(n,yn),o(n,oe),o(n,kn),o(n,W),b(an,W,null),o(W,Tn),o(W,se),o(W,Mn),b(q,W,null),o(W,Vt),b(Ae,W,null),h(i,we,v),b(Bt,i,v),h(i,ht,v),h(i,ae,v),b(gt,ae,null),o(ae,fe),o(ae,Y),b(I,Y,null),o(Y,Et),o(Y,vt),o(Y,Rt),b(ft,Y,null),o(Y,Oe),b(Xt,Y,null),h(i,Bn,v),b(R,i,v),h(i,vn,v),h(i,X,v),b(xe,X,null),o(X,P),o(X,Ze),o(X,ln),o(X,$e),b(We,$e,null),o($e,dn),o($e,cn),o($e,D),b(_e,$e,null),o($e,St),b(Le,$e,null),h(i,zn,v),b(be,i,v),h(i,wt,v),h(i,S,v),b(_t,S,null),o(S,ye),o(S,Fe),o(S,Ne),o(S,re),b(Ge,re,null),o(re,xt),o(re,Pe),o(re,mn),b($t,re,null),o(re,Qt),b(He,re,null),o(re,jt),b(ke,re,null),h(i,Ft,v),b(bt,i,v),h(i,L,v),h(i,E,v),b(Ct,E,null),o(E,Q),o(E,Ye),o(E,ie),o(E,De),o(E,Ce),o(E,le),b(Te,le,null),o(le,zt),o(le,Ke),o(le,de),b(J,le,null),o(le,Jt),b(ze,le,null),o(le,un),b(Ut,le,null),h(i,et,v),b(Ve,i,v),h(i,At,v),h(i,Me,v),b(Ee,Me,null),o(Me,qt),o(Me,Re),o(Me,gn),o(Me,fn),o(Me,Yt),o(Me,ce),b(U,ce,null),o(ce,Je),o(ce,Dt),o(ce,Kn),b(tt,ce,null),o(ce,pe),b(nt,ce,null),h(i,me,v),b(ot,i,v),h(i,Xe,v),h(i,A,v),b(d,A,null),o(A,$),o(A,K),o(A,kt),o(A,It),o(A,eo),o(A,G),b(ue,G,null),o(G,Zt),o(G,Wt),o(G,ko),b(wn,G,null),o(G,Sn),b(Jn,G,null),h(i,so,v),b(Be,i,v),h(i,xn,v),h(i,st,v),b(tn,st,null),o(st,To),o(st,Qn),o(st,co),o(st,Lt),o(st,qn),o(st,at),b($n,at,null),o(at,O),o(at,nn),o(at,An),b(In,at,null),o(at,mo),b(Nt,at,null),o(at,Bo),b(jn,at,null),Zn=!0},p(i,v){const Se={};v&2&&(Se.$$scope={dirty:v,ctx:i}),q.$set(Se);const On={};v&2&&(On.$$scope={dirty:v,ctx:i}),Ae.$set(On);const Gt={};v&2&&(Gt.$$scope={dirty:v,ctx:i}),ft.$set(Gt);const Tt={};v&2&&(Tt.$$scope={dirty:v,ctx:i}),Xt.$set(Tt);const on={};v&2&&(on.$$scope={dirty:v,ctx:i}),_e.$set(on);const _n={};v&2&&(_n.$$scope={dirty:v,ctx:i}),Le.$set(_n);const V={};v&2&&(V.$$scope={dirty:v,ctx:i}),$t.$set(V);const Ue={};v&2&&(Ue.$$scope={dirty:v,ctx:i}),He.$set(Ue);const rt={};v&2&&(rt.$$scope={dirty:v,ctx:i}),ke.$set(rt);const Qe={};v&2&&(Qe.$$scope={dirty:v,ctx:i}),J.$set(Qe);const sn={};v&2&&(sn.$$scope={dirty:v,ctx:i}),ze.$set(sn);const Pt={};v&2&&(Pt.$$scope={dirty:v,ctx:i}),Ut.$set(Pt);const ve={};v&2&&(ve.$$scope={dirty:v,ctx:i}),tt.$set(ve);const Ht={};v&2&&(Ht.$$scope={dirty:v,ctx:i}),nt.$set(Ht);const ee={};v&2&&(ee.$$scope={dirty:v,ctx:i}),wn.$set(ee);const uo={};v&2&&(uo.$$scope={dirty:v,ctx:i}),Jn.$set(uo);const Yn={};v&2&&(Yn.$$scope={dirty:v,ctx:i}),In.$set(Yn);const ho={};v&2&&(ho.$$scope={dirty:v,ctx:i}),Nt.$set(ho);const te={};v&2&&(te.$$scope={dirty:v,ctx:i}),jn.$set(te)},i(i){Zn||(y(e.$$.fragment,i),y(r.$$.fragment,i),y(an.$$.fragment,i),y(q.$$.fragment,i),y(Ae.$$.fragment,i),y(Bt.$$.fragment,i),y(gt.$$.fragment,i),y(I.$$.fragment,i),y(ft.$$.fragment,i),y(Xt.$$.fragment,i),y(R.$$.fragment,i),y(xe.$$.fragment,i),y(We.$$.fragment,i),y(_e.$$.fragment,i),y(Le.$$.fragment,i),y(be.$$.fragment,i),y(_t.$$.fragment,i),y(Ge.$$.fragment,i),y($t.$$.fragment,i),y(He.$$.fragment,i),y(ke.$$.fragment,i),y(bt.$$.fragment,i),y(Ct.$$.fragment,i),y(Te.$$.fragment,i),y(J.$$.fragment,i),y(ze.$$.fragment,i),y(Ut.$$.fragment,i),y(Ve.$$.fragment,i),y(Ee.$$.fragment,i),y(U.$$.fragment,i),y(tt.$$.fragment,i),y(nt.$$.fragment,i),y(ot.$$.fragment,i),y(d.$$.fragment,i),y(ue.$$.fragment,i),y(wn.$$.fragment,i),y(Jn.$$.fragment,i),y(Be.$$.fragment,i),y(tn.$$.fragment,i),y($n.$$.fragment,i),y(In.$$.fragment,i),y(Nt.$$.fragment,i),y(jn.$$.fragment,i),Zn=!0)},o(i){k(e.$$.fragment,i),k(r.$$.fragment,i),k(an.$$.fragment,i),k(q.$$.fragment,i),k(Ae.$$.fragment,i),k(Bt.$$.fragment,i),k(gt.$$.fragment,i),k(I.$$.fragment,i),k(ft.$$.fragment,i),k(Xt.$$.fragment,i),k(R.$$.fragment,i),k(xe.$$.fragment,i),k(We.$$.fragment,i),k(_e.$$.fragment,i),k(Le.$$.fragment,i),k(be.$$.fragment,i),k(_t.$$.fragment,i),k(Ge.$$.fragment,i),k($t.$$.fragment,i),k(He.$$.fragment,i),k(ke.$$.fragment,i),k(bt.$$.fragment,i),k(Ct.$$.fragment,i),k(Te.$$.fragment,i),k(J.$$.fragment,i),k(ze.$$.fragment,i),k(Ut.$$.fragment,i),k(Ve.$$.fragment,i),k(Ee.$$.fragment,i),k(U.$$.fragment,i),k(tt.$$.fragment,i),k(nt.$$.fragment,i),k(ot.$$.fragment,i),k(d.$$.fragment,i),k(ue.$$.fragment,i),k(wn.$$.fragment,i),k(Jn.$$.fragment,i),k(Be.$$.fragment,i),k(tn.$$.fragment,i),k($n.$$.fragment,i),k(In.$$.fragment,i),k(Nt.$$.fragment,i),k(jn.$$.fragment,i),Zn=!1},d(i){i&&(l(u),l(n),l(we),l(ht),l(ae),l(Bn),l(vn),l(X),l(zn),l(wt),l(S),l(Ft),l(L),l(E),l(et),l(At),l(Me),l(me),l(Xe),l(A),l(so),l(xn),l(st)),T(e,i),T(r),T(an),T(q),T(Ae),T(Bt,i),T(gt),T(I),T(ft),T(Xt),T(R,i),T(xe),T(We),T(_e),T(Le),T(be,i),T(_t),T(Ge),T($t),T(He),T(ke),T(bt,i),T(Ct),T(Te),T(J),T(ze),T(Ut),T(Ve,i),T(Ee),T(U),T(tt),T(nt),T(ot,i),T(d),T(ue),T(wn),T(Jn),T(Be,i),T(tn),T($n),T(In),T(Nt),T(jn)}}}function Ss(w){let e,u;return e=new _s({props:{$$slots:{default:[Xs]},$$scope:{ctx:w}}}),{c(){f(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,r){b(e,n,r),u=!0},p(n,r){const M={};r&2&&(M.$$scope={dirty:r,ctx:n}),e.$set(M)},i(n){u||(y(e.$$.fragment,n),u=!0)},o(n){k(e.$$.fragment,n),u=!1},d(n){T(e,n)}}}function Qs(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function As(w){let e,u="Example:",n,r,M;return r=new ge({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBGbGF4QmlnQmlyZE1vZGVsJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGYmlnYmlyZC1yb2JlcnRhLWJhc2UlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4QmlnQmlyZE1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZiaWdiaXJkLXJvYmVydGEtYmFzZSUyMiklMEElMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoJTIySGVsbG8lMkMlMjBteSUyMGRvZyUyMGlzJTIwY3V0ZSUyMiUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIyamF4JTIyKSUwQW91dHB1dHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyklMEElMEFsYXN0X2hpZGRlbl9zdGF0ZXMlMjAlM0QlMjBvdXRwdXRzLmxhc3RfaGlkZGVuX3N0YXRl",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxBigBirdModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdModel.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function Os(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Ys(w){let e,u="Example:",n,r,M;return r=new ge({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, FlaxBigBirdForPreTraining

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForPreTraining.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>prediction_logits = outputs.prediction_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>seq_relationship_logits = outputs.seq_relationship_logits`,wrap:!1}}),{c(){e=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function Ds(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Ks(w){let e,u="Example:",n,r,M;return r=new ge({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, FlaxBigBirdForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForCausalLM.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># retrieve logts for next token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>next_token_logits = outputs.logits[:, -<span class="hljs-number">1</span>]`,wrap:!1}}),{c(){e=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function ea(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function ta(w){let e,u="Example:",n,r,M;return r=new ge({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, FlaxBigBirdForMaskedLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function na(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function oa(w){let e,u="Example:",n,r,M;return r=new ge({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, FlaxBigBirdForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function sa(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function aa(w){let e,u="Example:",n,r,M;return r=new ge({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, FlaxBigBirdForMultipleChoice

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function ra(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function ia(w){let e,u="Example:",n,r,M;return r=new ge({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, FlaxBigBirdForTokenClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function la(w){let e,u=`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=c("p"),e.innerHTML=u},l(n){e=p(n,"P",{"data-svelte-h":!0}),g(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function da(w){let e,u="Example:",n,r,M;return r=new ge({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, FlaxBigBirdForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxBigBirdForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/bigbird-roberta-base&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=c("p"),e.textContent=u,n=s(),f(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),g(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),_(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),b(r,t,B),M=!0},p:z,i(t){M||(y(r.$$.fragment,t),M=!0)},o(t){k(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),T(r,t)}}}function ca(w){let e,u,n,r,M,t,B="The bare BigBird Model transformer outputting raw hidden-states without any specific head on top.",bn,ne,to=`This model inherits from <a href="/docs/transformers/pr_30621/ja/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)`,yn,oe,no=`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.`,kn,W,an="Finally, this model supports inherent JAX features such as:",Tn,se,oo='<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,q,Vt,Ae,we,Bt="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",ht,ae,gt,fe,Y,I,Et="## FlaxBigBirdForPreTraining[[transformers.FlaxBigBirdForPreTraining]]",vt,Z,Rt,ft,Oe,Xt="BigBird Model with two heads on top as done during the pretraining: a <code>masked language modeling</code> head and a <code>next sentence prediction (classification)</code> head.",Bn,R,vn=`This model inherits from <a href="/docs/transformers/pr_30621/ja/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)`,X,xe,P=`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.`,Ze,rn,ln="Finally, this model supports inherent JAX features such as:",$e,We,dn='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',cn,je,D,_e,St,Le="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",zn,be,wt,S,_t,ye,Fe,H,Ne,re,Ge,xt=`BigBird Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
autoregressive tasks.`,Pe,pn,mn=`This model inherits from <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,$t,Qt,He=`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.`,jt,ke,Ft="Finally, this model supports inherent JAX features such as:",bt,L,E='<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>',Ct,Q,Ye,Rn,ie,De="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Xn,Ce,le,Te,zt,Ke,Dn="## FlaxBigBirdForMaskedLM[[transformers.FlaxBigBirdForMaskedLM]]",de,J,Jt,ze,un,Ut="BigBird Model with a <code>language modeling</code> head on top.",et,Ve,At=`This model inherits from <a href="/docs/transformers/pr_30621/ja/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)`,Me,Ee,qt=`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.`,Re,hn,gn="Finally, this model supports inherent JAX features such as:",fn,Ot,Yt='<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>',ce,U,Je,Dt,yt,Kn="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",tt,pe,nt,me,ot,Xe,A,d,$,K,N,kt=`BigBird Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`,It,Kt,eo=`This model inherits from <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,G,ue,Zt=`This model is also a
<a href="https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html" rel="nofollow">flax.linen.Module</a> subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.`,Wt,en,ko="Finally, this model supports inherent JAX features such as:",wn,Sn,Jn='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',so,Be,xn,st,tn,To="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Qn,Un,co,Lt,po,qn,at="## FlaxBigBirdForMultipleChoice[[transformers.FlaxBigBirdForMultipleChoice]]",$n,O,nn,Mo,An,In=`BigBird 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.`,mo,Nt,Bo=`This model inherits from <a href="/docs/transformers/pr_30621/ja/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)`,jn,Zn,i=`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.`,v,Se,On="Finally, this model supports inherent JAX features such as:",Gt,Tt,on='<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>',_n,V,Ue,rt,Qe,sn="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Pt,ve,Ht,ee,uo,Yn,ho,te,go,No,vo,os=`BigBird 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.`,Go,wo,ss=`This model inherits from <a href="/docs/transformers/pr_30621/ja/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)`,Po,xo,as=`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.`,Ho,$o,rs="Finally, this model supports inherent JAX features such as:",Vo,jo,is='<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>',Eo,Fn,fo,Ro,Fo,ls="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Xo,ao,So,ro,Zo,_o,ds="## FlaxBigBirdForQuestionAnswering[[transformers.FlaxBigBirdForQuestionAnswering]]",Wo,qe,bo,Qo,Co,cs=`BigBird 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>).`,Ao,zo,ps=`This model inherits from <a href="/docs/transformers/pr_30621/ja/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)`,Oo,Jo,ms=`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.`,Yo,Uo,us="Finally, this model supports inherent JAX features such as:",Do,qo,hs='<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>',Ko,Cn,yo,es,Io,gs='The <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',ts,io,ns,lo,Lo;return e=new Ie({props:{title:"FlaxBigBirdModel",local:"transformers.FlaxBigBirdModel",headingTag:"h2"}}),r=new C({props:{name:"class transformers.FlaxBigBirdModel",anchor:"transformers.FlaxBigBirdModel",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": Optional = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdModel.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1886"}}),Vt=new C({props:{name:"__call__",anchor:"transformers.FlaxBigBirdModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": Optional = None"},{name:"indices_rng",val:": Optional = 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"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdModel.__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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.FlaxBigBirdModel.__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.FlaxBigBirdModel.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdModel.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxBigBirdModel.__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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1718",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</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>pooler_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) — Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</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_30621/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ae=new Mt({props:{$$slots:{default:[Qs]},$$scope:{ctx:w}}}),fe=new he({props:{anchor:"transformers.FlaxBigBirdModel.__call__.example",$$slots:{default:[As]},$$scope:{ctx:w}}}),Rt=new C({props:{name:"class transformers.FlaxBigBirdForPreTraining",anchor:"transformers.FlaxBigBirdForPreTraining",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": Optional = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForPreTraining.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForPreTraining.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1960"}}),D=new C({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForPreTraining.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": Optional = None"},{name:"indices_rng",val:": Optional = 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"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForPreTraining.__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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.FlaxBigBirdForPreTraining.__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.FlaxBigBirdForPreTraining.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxBigBirdForPreTraining.__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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1718",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.big_bird.modeling_flax_big_bird.FlaxBigBirdForPreTrainingOutput</code> 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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>prediction_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>seq_relationship_logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, 2)</code>) — Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
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><code>transformers.models.big_bird.modeling_flax_big_bird.FlaxBigBirdForPreTrainingOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),be=new Mt({props:{$$slots:{default:[Os]},$$scope:{ctx:w}}}),S=new he({props:{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.example",$$slots:{default:[Ys]},$$scope:{ctx:w}}}),ye=new Ie({props:{title:"FlaxBigBirdForCausalLM",local:"transformers.FlaxBigBirdForCausalLM",headingTag:"h2"}}),Ne=new C({props:{name:"class transformers.FlaxBigBirdForCausalLM",anchor:"transformers.FlaxBigBirdForCausalLM",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": Optional = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForCausalLM.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2592"}}),Ye=new C({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForCausalLM.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": Optional = None"},{name:"indices_rng",val:": Optional = 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"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForCausalLM.__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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.FlaxBigBirdForCausalLM.__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.FlaxBigBirdForCausalLM.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxBigBirdForCausalLM.__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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1718",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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>
<li>
<p><strong>cross_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>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(jnp.ndarray))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — Tuple of <code>jnp.ndarray</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key, value
states of the self-attention and the cross-attention layers if model is used in encoder-decoder setting.
Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_30621/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ce=new Mt({props:{$$slots:{default:[Ds]},$$scope:{ctx:w}}}),Te=new he({props:{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.example",$$slots:{default:[Ks]},$$scope:{ctx:w}}}),Jt=new C({props:{name:"class transformers.FlaxBigBirdForMaskedLM",anchor:"transformers.FlaxBigBirdForMaskedLM",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": Optional = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForMaskedLM.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2059"}}),Je=new C({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForMaskedLM.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": Optional = None"},{name:"indices_rng",val:": Optional = 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"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForMaskedLM.__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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.FlaxBigBirdForMaskedLM.__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.FlaxBigBirdForMaskedLM.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxBigBirdForMaskedLM.__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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1718",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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_30621/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),pe=new Mt({props:{$$slots:{default:[ea]},$$scope:{ctx:w}}}),me=new he({props:{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.example",$$slots:{default:[ta]},$$scope:{ctx:w}}}),Xe=new Ie({props:{title:"FlaxBigBirdForSequenceClassification",local:"transformers.FlaxBigBirdForSequenceClassification",headingTag:"h2"}}),$=new C({props:{name:"class transformers.FlaxBigBirdForSequenceClassification",anchor:"transformers.FlaxBigBirdForSequenceClassification",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": Optional = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForSequenceClassification.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2143"}}),xn=new C({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": Optional = None"},{name:"indices_rng",val:": Optional = 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"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForSequenceClassification.__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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.FlaxBigBirdForSequenceClassification.__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.FlaxBigBirdForSequenceClassification.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxBigBirdForSequenceClassification.__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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1718",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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_30621/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Un=new Mt({props:{$$slots:{default:[na]},$$scope:{ctx:w}}}),Lt=new he({props:{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.example",$$slots:{default:[oa]},$$scope:{ctx:w}}}),nn=new C({props:{name:"class transformers.FlaxBigBirdForMultipleChoice",anchor:"transformers.FlaxBigBirdForMultipleChoice",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": Optional = None"},{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.FlaxBigBirdForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForMultipleChoice.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2225"}}),Ue=new C({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": Optional = None"},{name:"indices_rng",val:": Optional = 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"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForMultipleChoice.__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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.FlaxBigBirdForMultipleChoice.__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.FlaxBigBirdForMultipleChoice.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxBigBirdForMultipleChoice.__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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1718",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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_30621/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ve=new Mt({props:{$$slots:{default:[sa]},$$scope:{ctx:w}}}),ee=new he({props:{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.example",$$slots:{default:[aa]},$$scope:{ctx:w}}}),Yn=new Ie({props:{title:"FlaxBigBirdForTokenClassification",local:"transformers.FlaxBigBirdForTokenClassification",headingTag:"h2"}}),go=new C({props:{name:"class transformers.FlaxBigBirdForTokenClassification",anchor:"transformers.FlaxBigBirdForTokenClassification",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": Optional = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForTokenClassification.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2322"}}),fo=new C({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForTokenClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"encoder_hidden_states",val:" = None"},{name:"encoder_attention_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": Optional = None"},{name:"indices_rng",val:": Optional = 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"},{name:"past_key_values",val:": dict = None"}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForTokenClassification.__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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.FlaxBigBirdForTokenClassification.__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.FlaxBigBirdForTokenClassification.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxBigBirdForTokenClassification.__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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1718",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30621/ja/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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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_30621/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ao=new Mt({props:{$$slots:{default:[ra]},$$scope:{ctx:w}}}),ro=new he({props:{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.example",$$slots:{default:[ia]},$$scope:{ctx:w}}}),bo=new C({props:{name:"class transformers.FlaxBigBirdForQuestionAnswering",anchor:"transformers.FlaxBigBirdForQuestionAnswering",parameters:[{name:"config",val:": BigBirdConfig"},{name:"input_shape",val:": Optional = None"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"gradient_checkpointing",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxBigBirdForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdConfig">BigBirdConfig</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_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxBigBirdForQuestionAnswering.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code> (on GPUs) and
<code>jax.numpy.bfloat16</code> (on TPUs).</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_30621/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2426"}}),yo=new C({props:{name:"__call__",anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"token_type_ids",val:" = None"},{name:"position_ids",val:" = None"},{name:"head_mask",val:" = None"},{name:"question_lengths",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": Optional = None"},{name:"indices_rng",val:": Optional = 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.FlaxBigBirdForQuestionAnswering.__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_30621/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30621/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_30621/ja/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.FlaxBigBirdForQuestionAnswering.__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.FlaxBigBirdForQuestionAnswering.__call__.token_type_ids",description:`<strong>token_type_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.`,name:"position_ids"},{anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__.head_mask",description:`<strong>head_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <code>optional) -- Mask to nullify selected heads of the attention modules. Mask values selected in </code>[0, 1]\`:</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.FlaxBigBirdForQuestionAnswering.__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_30621/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2436",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.big_bird.modeling_flax_big_bird.FlaxBigBirdForQuestionAnsweringModelOutput</code> 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_30621/ja/model_doc/big_bird#transformers.BigBirdConfig"
>BigBirdConfig</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>pooled_output</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, hidden_size)</code>) — pooled_output returned by FlaxBigBirdModel.</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><code>transformers.models.big_bird.modeling_flax_big_bird.FlaxBigBirdForQuestionAnsweringModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),io=new Mt({props:{$$slots:{default:[la]},$$scope:{ctx:w}}}),lo=new he({props:{anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__.example",$$slots:{default:[da]},$$scope:{ctx:w}}}),{c(){f(e.$$.fragment),u=s(),n=c("div"),f(r.$$.fragment),M=s(),t=c("p"),t.textContent=B,bn=s(),ne=c("p"),ne.innerHTML=to,yn=s(),oe=c("p"),oe.innerHTML=no,kn=s(),W=c("p"),W.textContent=an,Tn=s(),se=c("ul"),se.innerHTML=oo,Mn=s(),q=c("div"),f(Vt.$$.fragment),Ae=s(),we=c("p"),we.innerHTML=Bt,ht=s(),f(ae.$$.fragment),gt=s(),f(fe.$$.fragment),Y=s(),I=c("p"),I.textContent=Et,vt=s(),Z=c("div"),f(Rt.$$.fragment),ft=s(),Oe=c("p"),Oe.innerHTML=Xt,Bn=s(),R=c("p"),R.innerHTML=vn,X=s(),xe=c("p"),xe.innerHTML=P,Ze=s(),rn=c("p"),rn.textContent=ln,$e=s(),We=c("ul"),We.innerHTML=dn,cn=s(),je=c("div"),f(D.$$.fragment),_e=s(),St=c("p"),St.innerHTML=Le,zn=s(),f(be.$$.fragment),wt=s(),f(S.$$.fragment),_t=s(),f(ye.$$.fragment),Fe=s(),H=c("div"),f(Ne.$$.fragment),re=s(),Ge=c("p"),Ge.textContent=xt,Pe=s(),pn=c("p"),pn.innerHTML=mn,$t=s(),Qt=c("p"),Qt.innerHTML=He,jt=s(),ke=c("p"),ke.textContent=Ft,bt=s(),L=c("ul"),L.innerHTML=E,Ct=s(),Q=c("div"),f(Ye.$$.fragment),Rn=s(),ie=c("p"),ie.innerHTML=De,Xn=s(),f(Ce.$$.fragment),le=s(),f(Te.$$.fragment),zt=s(),Ke=c("p"),Ke.textContent=Dn,de=s(),J=c("div"),f(Jt.$$.fragment),ze=s(),un=c("p"),un.innerHTML=Ut,et=s(),Ve=c("p"),Ve.innerHTML=At,Me=s(),Ee=c("p"),Ee.innerHTML=qt,Re=s(),hn=c("p"),hn.textContent=gn,fn=s(),Ot=c("ul"),Ot.innerHTML=Yt,ce=s(),U=c("div"),f(Je.$$.fragment),Dt=s(),yt=c("p"),yt.innerHTML=Kn,tt=s(),f(pe.$$.fragment),nt=s(),f(me.$$.fragment),ot=s(),f(Xe.$$.fragment),A=s(),d=c("div"),f($.$$.fragment),K=s(),N=c("p"),N.textContent=kt,It=s(),Kt=c("p"),Kt.innerHTML=eo,G=s(),ue=c("p"),ue.innerHTML=Zt,Wt=s(),en=c("p"),en.textContent=ko,wn=s(),Sn=c("ul"),Sn.innerHTML=Jn,so=s(),Be=c("div"),f(xn.$$.fragment),st=s(),tn=c("p"),tn.innerHTML=To,Qn=s(),f(Un.$$.fragment),co=s(),f(Lt.$$.fragment),po=s(),qn=c("p"),qn.textContent=at,$n=s(),O=c("div"),f(nn.$$.fragment),Mo=s(),An=c("p"),An.textContent=In,mo=s(),Nt=c("p"),Nt.innerHTML=Bo,jn=s(),Zn=c("p"),Zn.innerHTML=i,v=s(),Se=c("p"),Se.textContent=On,Gt=s(),Tt=c("ul"),Tt.innerHTML=on,_n=s(),V=c("div"),f(Ue.$$.fragment),rt=s(),Qe=c("p"),Qe.innerHTML=sn,Pt=s(),f(ve.$$.fragment),Ht=s(),f(ee.$$.fragment),uo=s(),f(Yn.$$.fragment),ho=s(),te=c("div"),f(go.$$.fragment),No=s(),vo=c("p"),vo.textContent=os,Go=s(),wo=c("p"),wo.innerHTML=ss,Po=s(),xo=c("p"),xo.innerHTML=as,Ho=s(),$o=c("p"),$o.textContent=rs,Vo=s(),jo=c("ul"),jo.innerHTML=is,Eo=s(),Fn=c("div"),f(fo.$$.fragment),Ro=s(),Fo=c("p"),Fo.innerHTML=ls,Xo=s(),f(ao.$$.fragment),So=s(),f(ro.$$.fragment),Zo=s(),_o=c("p"),_o.textContent=ds,Wo=s(),qe=c("div"),f(bo.$$.fragment),Qo=s(),Co=c("p"),Co.innerHTML=cs,Ao=s(),zo=c("p"),zo.innerHTML=ps,Oo=s(),Jo=c("p"),Jo.innerHTML=ms,Yo=s(),Uo=c("p"),Uo.textContent=us,Do=s(),qo=c("ul"),qo.innerHTML=hs,Ko=s(),Cn=c("div"),f(yo.$$.fragment),es=s(),Io=c("p"),Io.innerHTML=gs,ts=s(),f(io.$$.fragment),ns=s(),f(lo.$$.fragment),this.h()},l(m){_(e.$$.fragment,m),u=a(m),n=p(m,"DIV",{class:!0});var x=j(n);_(r.$$.fragment,x),M=a(x),t=p(x,"P",{"data-svelte-h":!0}),g(t)!=="svelte-1lmko6o"&&(t.textContent=B),bn=a(x),ne=p(x,"P",{"data-svelte-h":!0}),g(ne)!=="svelte-1utnyad"&&(ne.innerHTML=to),yn=a(x),oe=p(x,"P",{"data-svelte-h":!0}),g(oe)!=="svelte-10nfsf3"&&(oe.innerHTML=no),kn=a(x),W=p(x,"P",{"data-svelte-h":!0}),g(W)!=="svelte-1pplc4a"&&(W.textContent=an),Tn=a(x),se=p(x,"UL",{"data-svelte-h":!0}),g(se)!=="svelte-1w7z84m"&&(se.innerHTML=oo),Mn=a(x),q=p(x,"DIV",{class:!0});var Wn=j(q);_(Vt.$$.fragment,Wn),Ae=a(Wn),we=p(Wn,"P",{"data-svelte-h":!0}),g(we)!=="svelte-1qm0bay"&&(we.innerHTML=Bt),ht=a(Wn),_(ae.$$.fragment,Wn),gt=a(Wn),_(fe.$$.fragment,Wn),Wn.forEach(l),x.forEach(l),Y=a(m),I=p(m,"P",{"data-svelte-h":!0}),g(I)!=="svelte-tbv0as"&&(I.textContent=Et),vt=a(m),Z=p(m,"DIV",{class:!0});var it=j(Z);_(Rt.$$.fragment,it),ft=a(it),Oe=p(it,"P",{"data-svelte-h":!0}),g(Oe)!=="svelte-1hro5a3"&&(Oe.innerHTML=Xt),Bn=a(it),R=p(it,"P",{"data-svelte-h":!0}),g(R)!=="svelte-1b888nv"&&(R.innerHTML=vn),X=a(it),xe=p(it,"P",{"data-svelte-h":!0}),g(xe)!=="svelte-10nfsf3"&&(xe.innerHTML=P),Ze=a(it),rn=p(it,"P",{"data-svelte-h":!0}),g(rn)!=="svelte-1pplc4a"&&(rn.textContent=ln),$e=a(it),We=p(it,"UL",{"data-svelte-h":!0}),g(We)!=="svelte-m6h3rq"&&(We.innerHTML=dn),cn=a(it),je=p(it,"DIV",{class:!0});var Ln=j(je);_(D.$$.fragment,Ln),_e=a(Ln),St=p(Ln,"P",{"data-svelte-h":!0}),g(St)!=="svelte-1qm0bay"&&(St.innerHTML=Le),zn=a(Ln),_(be.$$.fragment,Ln),wt=a(Ln),_(S.$$.fragment,Ln),Ln.forEach(l),it.forEach(l),_t=a(m),_(ye.$$.fragment,m),Fe=a(m),H=p(m,"DIV",{class:!0});var lt=j(H);_(Ne.$$.fragment,lt),re=a(lt),Ge=p(lt,"P",{"data-svelte-h":!0}),g(Ge)!=="svelte-15formz"&&(Ge.textContent=xt),Pe=a(lt),pn=p(lt,"P",{"data-svelte-h":!0}),g(pn)!=="svelte-1utnyad"&&(pn.innerHTML=mn),$t=a(lt),Qt=p(lt,"P",{"data-svelte-h":!0}),g(Qt)!=="svelte-10nfsf3"&&(Qt.innerHTML=He),jt=a(lt),ke=p(lt,"P",{"data-svelte-h":!0}),g(ke)!=="svelte-1pplc4a"&&(ke.textContent=Ft),bt=a(lt),L=p(lt,"UL",{"data-svelte-h":!0}),g(L)!=="svelte-1w7z84m"&&(L.innerHTML=E),Ct=a(lt),Q=p(lt,"DIV",{class:!0});var Nn=j(Q);_(Ye.$$.fragment,Nn),Rn=a(Nn),ie=p(Nn,"P",{"data-svelte-h":!0}),g(ie)!=="svelte-1qm0bay"&&(ie.innerHTML=De),Xn=a(Nn),_(Ce.$$.fragment,Nn),le=a(Nn),_(Te.$$.fragment,Nn),Nn.forEach(l),lt.forEach(l),zt=a(m),Ke=p(m,"P",{"data-svelte-h":!0}),g(Ke)!=="svelte-1p0e46e"&&(Ke.textContent=Dn),de=a(m),J=p(m,"DIV",{class:!0});var dt=j(J);_(Jt.$$.fragment,dt),ze=a(dt),un=p(dt,"P",{"data-svelte-h":!0}),g(un)!=="svelte-1pm8ai3"&&(un.innerHTML=Ut),et=a(dt),Ve=p(dt,"P",{"data-svelte-h":!0}),g(Ve)!=="svelte-1b888nv"&&(Ve.innerHTML=At),Me=a(dt),Ee=p(dt,"P",{"data-svelte-h":!0}),g(Ee)!=="svelte-10nfsf3"&&(Ee.innerHTML=qt),Re=a(dt),hn=p(dt,"P",{"data-svelte-h":!0}),g(hn)!=="svelte-1pplc4a"&&(hn.textContent=gn),fn=a(dt),Ot=p(dt,"UL",{"data-svelte-h":!0}),g(Ot)!=="svelte-m6h3rq"&&(Ot.innerHTML=Yt),ce=a(dt),U=p(dt,"DIV",{class:!0});var Gn=j(U);_(Je.$$.fragment,Gn),Dt=a(Gn),yt=p(Gn,"P",{"data-svelte-h":!0}),g(yt)!=="svelte-1qm0bay"&&(yt.innerHTML=Kn),tt=a(Gn),_(pe.$$.fragment,Gn),nt=a(Gn),_(me.$$.fragment,Gn),Gn.forEach(l),dt.forEach(l),ot=a(m),_(Xe.$$.fragment,m),A=a(m),d=p(m,"DIV",{class:!0});var ct=j(d);_($.$$.fragment,ct),K=a(ct),N=p(ct,"P",{"data-svelte-h":!0}),g(N)!=="svelte-ip8mlz"&&(N.textContent=kt),It=a(ct),Kt=p(ct,"P",{"data-svelte-h":!0}),g(Kt)!=="svelte-1utnyad"&&(Kt.innerHTML=eo),G=a(ct),ue=p(ct,"P",{"data-svelte-h":!0}),g(ue)!=="svelte-10nfsf3"&&(ue.innerHTML=Zt),Wt=a(ct),en=p(ct,"P",{"data-svelte-h":!0}),g(en)!=="svelte-1pplc4a"&&(en.textContent=ko),wn=a(ct),Sn=p(ct,"UL",{"data-svelte-h":!0}),g(Sn)!=="svelte-1w7z84m"&&(Sn.innerHTML=Jn),so=a(ct),Be=p(ct,"DIV",{class:!0});var Pn=j(Be);_(xn.$$.fragment,Pn),st=a(Pn),tn=p(Pn,"P",{"data-svelte-h":!0}),g(tn)!=="svelte-1qm0bay"&&(tn.innerHTML=To),Qn=a(Pn),_(Un.$$.fragment,Pn),co=a(Pn),_(Lt.$$.fragment,Pn),Pn.forEach(l),ct.forEach(l),po=a(m),qn=p(m,"P",{"data-svelte-h":!0}),g(qn)!=="svelte-jlkxgg"&&(qn.textContent=at),$n=a(m),O=p(m,"DIV",{class:!0});var pt=j(O);_(nn.$$.fragment,pt),Mo=a(pt),An=p(pt,"P",{"data-svelte-h":!0}),g(An)!=="svelte-1u97t9z"&&(An.textContent=In),mo=a(pt),Nt=p(pt,"P",{"data-svelte-h":!0}),g(Nt)!=="svelte-1b888nv"&&(Nt.innerHTML=Bo),jn=a(pt),Zn=p(pt,"P",{"data-svelte-h":!0}),g(Zn)!=="svelte-10nfsf3"&&(Zn.innerHTML=i),v=a(pt),Se=p(pt,"P",{"data-svelte-h":!0}),g(Se)!=="svelte-1pplc4a"&&(Se.textContent=On),Gt=a(pt),Tt=p(pt,"UL",{"data-svelte-h":!0}),g(Tt)!=="svelte-m6h3rq"&&(Tt.innerHTML=on),_n=a(pt),V=p(pt,"DIV",{class:!0});var Hn=j(V);_(Ue.$$.fragment,Hn),rt=a(Hn),Qe=p(Hn,"P",{"data-svelte-h":!0}),g(Qe)!=="svelte-1qm0bay"&&(Qe.innerHTML=sn),Pt=a(Hn),_(ve.$$.fragment,Hn),Ht=a(Hn),_(ee.$$.fragment,Hn),Hn.forEach(l),pt.forEach(l),uo=a(m),_(Yn.$$.fragment,m),ho=a(m),te=p(m,"DIV",{class:!0});var mt=j(te);_(go.$$.fragment,mt),No=a(mt),vo=p(mt,"P",{"data-svelte-h":!0}),g(vo)!=="svelte-1stsyjq"&&(vo.textContent=os),Go=a(mt),wo=p(mt,"P",{"data-svelte-h":!0}),g(wo)!=="svelte-1utnyad"&&(wo.innerHTML=ss),Po=a(mt),xo=p(mt,"P",{"data-svelte-h":!0}),g(xo)!=="svelte-10nfsf3"&&(xo.innerHTML=as),Ho=a(mt),$o=p(mt,"P",{"data-svelte-h":!0}),g($o)!=="svelte-1pplc4a"&&($o.textContent=rs),Vo=a(mt),jo=p(mt,"UL",{"data-svelte-h":!0}),g(jo)!=="svelte-1w7z84m"&&(jo.innerHTML=is),Eo=a(mt),Fn=p(mt,"DIV",{class:!0});var Vn=j(Fn);_(fo.$$.fragment,Vn),Ro=a(Vn),Fo=p(Vn,"P",{"data-svelte-h":!0}),g(Fo)!=="svelte-1qm0bay"&&(Fo.innerHTML=ls),Xo=a(Vn),_(ao.$$.fragment,Vn),So=a(Vn),_(ro.$$.fragment,Vn),Vn.forEach(l),mt.forEach(l),Zo=a(m),_o=p(m,"P",{"data-svelte-h":!0}),g(_o)!=="svelte-vsg8f0"&&(_o.textContent=ds),Wo=a(m),qe=p(m,"DIV",{class:!0});var ut=j(qe);_(bo.$$.fragment,ut),Qo=a(ut),Co=p(ut,"P",{"data-svelte-h":!0}),g(Co)!=="svelte-1s4517o"&&(Co.innerHTML=cs),Ao=a(ut),zo=p(ut,"P",{"data-svelte-h":!0}),g(zo)!=="svelte-1b888nv"&&(zo.innerHTML=ps),Oo=a(ut),Jo=p(ut,"P",{"data-svelte-h":!0}),g(Jo)!=="svelte-10nfsf3"&&(Jo.innerHTML=ms),Yo=a(ut),Uo=p(ut,"P",{"data-svelte-h":!0}),g(Uo)!=="svelte-1pplc4a"&&(Uo.textContent=us),Do=a(ut),qo=p(ut,"UL",{"data-svelte-h":!0}),g(qo)!=="svelte-m6h3rq"&&(qo.innerHTML=hs),Ko=a(ut),Cn=p(ut,"DIV",{class:!0});var En=j(Cn);_(yo.$$.fragment,En),es=a(En),Io=p(En,"P",{"data-svelte-h":!0}),g(Io)!=="svelte-urcgbv"&&(Io.innerHTML=gs),ts=a(En),_(io.$$.fragment,En),ns=a(En),_(lo.$$.fragment,En),En.forEach(l),ut.forEach(l),this.h()},h(){F(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(n,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(je,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(U,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Be,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(d,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Fn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Cn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(qe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(m,x){b(e,m,x),h(m,u,x),h(m,n,x),b(r,n,null),o(n,M),o(n,t),o(n,bn),o(n,ne),o(n,yn),o(n,oe),o(n,kn),o(n,W),o(n,Tn),o(n,se),o(n,Mn),o(n,q),b(Vt,q,null),o(q,Ae),o(q,we),o(q,ht),b(ae,q,null),o(q,gt),b(fe,q,null),h(m,Y,x),h(m,I,x),h(m,vt,x),h(m,Z,x),b(Rt,Z,null),o(Z,ft),o(Z,Oe),o(Z,Bn),o(Z,R),o(Z,X),o(Z,xe),o(Z,Ze),o(Z,rn),o(Z,$e),o(Z,We),o(Z,cn),o(Z,je),b(D,je,null),o(je,_e),o(je,St),o(je,zn),b(be,je,null),o(je,wt),b(S,je,null),h(m,_t,x),b(ye,m,x),h(m,Fe,x),h(m,H,x),b(Ne,H,null),o(H,re),o(H,Ge),o(H,Pe),o(H,pn),o(H,$t),o(H,Qt),o(H,jt),o(H,ke),o(H,bt),o(H,L),o(H,Ct),o(H,Q),b(Ye,Q,null),o(Q,Rn),o(Q,ie),o(Q,Xn),b(Ce,Q,null),o(Q,le),b(Te,Q,null),h(m,zt,x),h(m,Ke,x),h(m,de,x),h(m,J,x),b(Jt,J,null),o(J,ze),o(J,un),o(J,et),o(J,Ve),o(J,Me),o(J,Ee),o(J,Re),o(J,hn),o(J,fn),o(J,Ot),o(J,ce),o(J,U),b(Je,U,null),o(U,Dt),o(U,yt),o(U,tt),b(pe,U,null),o(U,nt),b(me,U,null),h(m,ot,x),b(Xe,m,x),h(m,A,x),h(m,d,x),b($,d,null),o(d,K),o(d,N),o(d,It),o(d,Kt),o(d,G),o(d,ue),o(d,Wt),o(d,en),o(d,wn),o(d,Sn),o(d,so),o(d,Be),b(xn,Be,null),o(Be,st),o(Be,tn),o(Be,Qn),b(Un,Be,null),o(Be,co),b(Lt,Be,null),h(m,po,x),h(m,qn,x),h(m,$n,x),h(m,O,x),b(nn,O,null),o(O,Mo),o(O,An),o(O,mo),o(O,Nt),o(O,jn),o(O,Zn),o(O,v),o(O,Se),o(O,Gt),o(O,Tt),o(O,_n),o(O,V),b(Ue,V,null),o(V,rt),o(V,Qe),o(V,Pt),b(ve,V,null),o(V,Ht),b(ee,V,null),h(m,uo,x),b(Yn,m,x),h(m,ho,x),h(m,te,x),b(go,te,null),o(te,No),o(te,vo),o(te,Go),o(te,wo),o(te,Po),o(te,xo),o(te,Ho),o(te,$o),o(te,Vo),o(te,jo),o(te,Eo),o(te,Fn),b(fo,Fn,null),o(Fn,Ro),o(Fn,Fo),o(Fn,Xo),b(ao,Fn,null),o(Fn,So),b(ro,Fn,null),h(m,Zo,x),h(m,_o,x),h(m,Wo,x),h(m,qe,x),b(bo,qe,null),o(qe,Qo),o(qe,Co),o(qe,Ao),o(qe,zo),o(qe,Oo),o(qe,Jo),o(qe,Yo),o(qe,Uo),o(qe,Do),o(qe,qo),o(qe,Ko),o(qe,Cn),b(yo,Cn,null),o(Cn,es),o(Cn,Io),o(Cn,ts),b(io,Cn,null),o(Cn,ns),b(lo,Cn,null),Lo=!0},p(m,x){const Wn={};x&2&&(Wn.$$scope={dirty:x,ctx:m}),ae.$set(Wn);const it={};x&2&&(it.$$scope={dirty:x,ctx:m}),fe.$set(it);const Ln={};x&2&&(Ln.$$scope={dirty:x,ctx:m}),be.$set(Ln);const lt={};x&2&&(lt.$$scope={dirty:x,ctx:m}),S.$set(lt);const Nn={};x&2&&(Nn.$$scope={dirty:x,ctx:m}),Ce.$set(Nn);const dt={};x&2&&(dt.$$scope={dirty:x,ctx:m}),Te.$set(dt);const Gn={};x&2&&(Gn.$$scope={dirty:x,ctx:m}),pe.$set(Gn);const ct={};x&2&&(ct.$$scope={dirty:x,ctx:m}),me.$set(ct);const Pn={};x&2&&(Pn.$$scope={dirty:x,ctx:m}),Un.$set(Pn);const pt={};x&2&&(pt.$$scope={dirty:x,ctx:m}),Lt.$set(pt);const Hn={};x&2&&(Hn.$$scope={dirty:x,ctx:m}),ve.$set(Hn);const mt={};x&2&&(mt.$$scope={dirty:x,ctx:m}),ee.$set(mt);const Vn={};x&2&&(Vn.$$scope={dirty:x,ctx:m}),ao.$set(Vn);const ut={};x&2&&(ut.$$scope={dirty:x,ctx:m}),ro.$set(ut);const En={};x&2&&(En.$$scope={dirty:x,ctx:m}),io.$set(En);const fs={};x&2&&(fs.$$scope={dirty:x,ctx:m}),lo.$set(fs)},i(m){Lo||(y(e.$$.fragment,m),y(r.$$.fragment,m),y(Vt.$$.fragment,m),y(ae.$$.fragment,m),y(fe.$$.fragment,m),y(Rt.$$.fragment,m),y(D.$$.fragment,m),y(be.$$.fragment,m),y(S.$$.fragment,m),y(ye.$$.fragment,m),y(Ne.$$.fragment,m),y(Ye.$$.fragment,m),y(Ce.$$.fragment,m),y(Te.$$.fragment,m),y(Jt.$$.fragment,m),y(Je.$$.fragment,m),y(pe.$$.fragment,m),y(me.$$.fragment,m),y(Xe.$$.fragment,m),y($.$$.fragment,m),y(xn.$$.fragment,m),y(Un.$$.fragment,m),y(Lt.$$.fragment,m),y(nn.$$.fragment,m),y(Ue.$$.fragment,m),y(ve.$$.fragment,m),y(ee.$$.fragment,m),y(Yn.$$.fragment,m),y(go.$$.fragment,m),y(fo.$$.fragment,m),y(ao.$$.fragment,m),y(ro.$$.fragment,m),y(bo.$$.fragment,m),y(yo.$$.fragment,m),y(io.$$.fragment,m),y(lo.$$.fragment,m),Lo=!0)},o(m){k(e.$$.fragment,m),k(r.$$.fragment,m),k(Vt.$$.fragment,m),k(ae.$$.fragment,m),k(fe.$$.fragment,m),k(Rt.$$.fragment,m),k(D.$$.fragment,m),k(be.$$.fragment,m),k(S.$$.fragment,m),k(ye.$$.fragment,m),k(Ne.$$.fragment,m),k(Ye.$$.fragment,m),k(Ce.$$.fragment,m),k(Te.$$.fragment,m),k(Jt.$$.fragment,m),k(Je.$$.fragment,m),k(pe.$$.fragment,m),k(me.$$.fragment,m),k(Xe.$$.fragment,m),k($.$$.fragment,m),k(xn.$$.fragment,m),k(Un.$$.fragment,m),k(Lt.$$.fragment,m),k(nn.$$.fragment,m),k(Ue.$$.fragment,m),k(ve.$$.fragment,m),k(ee.$$.fragment,m),k(Yn.$$.fragment,m),k(go.$$.fragment,m),k(fo.$$.fragment,m),k(ao.$$.fragment,m),k(ro.$$.fragment,m),k(bo.$$.fragment,m),k(yo.$$.fragment,m),k(io.$$.fragment,m),k(lo.$$.fragment,m),Lo=!1},d(m){m&&(l(u),l(n),l(Y),l(I),l(vt),l(Z),l(_t),l(Fe),l(H),l(zt),l(Ke),l(de),l(J),l(ot),l(A),l(d),l(po),l(qn),l($n),l(O),l(uo),l(ho),l(te),l(Zo),l(_o),l(Wo),l(qe)),T(e,m),T(r),T(Vt),T(ae),T(fe),T(Rt),T(D),T(be),T(S),T(ye,m),T(Ne),T(Ye),T(Ce),T(Te),T(Jt),T(Je),T(pe),T(me),T(Xe,m),T($),T(xn),T(Un),T(Lt),T(nn),T(Ue),T(ve),T(ee),T(Yn,m),T(go),T(fo),T(ao),T(ro),T(bo),T(yo),T(io),T(lo)}}}function pa(w){let e,u;return e=new _s({props:{$$slots:{default:[ca]},$$scope:{ctx:w}}}),{c(){f(e.$$.fragment)},l(n){_(e.$$.fragment,n)},m(n,r){b(e,n,r),u=!0},p(n,r){const M={};r&2&&(M.$$scope={dirty:r,ctx:n}),e.$set(M)},i(n){u||(y(e.$$.fragment,n),u=!0)},o(n){k(e.$$.fragment,n),u=!1},d(n){T(e,n)}}}function ma(w){let e,u,n,r,M,t,B,bn,ne,to=`BigBird モデルは、<a href="https://arxiv.org/abs/2007.14062" rel="nofollow">Big Bird: Transformers for Longer Sequences</a> で提案されました。
ザヒール、マンジルとグルガネシュ、グルとダベイ、クマール・アヴィナヴァとエインズリー、ジョシュアとアルベルティ、クリスとオンタノン、
サンティアゴとファム、フィリップとラブラ、アニルードとワン、キーファンとヤン、リーなど。 BigBird は注目度が低い
BERT などの Transformer ベースのモデルをさらに長いシーケンスに拡張する、Transformer ベースのモデル。まばらに加えて
アテンションと同様に、BigBird は入力シーケンスにランダム アテンションだけでなくグローバル アテンションも適用します。理論的には、
まばらで全体的でランダムな注意を適用すると、完全な注意に近づくことが示されていますが、
長いシーケンスでは計算効率が大幅に向上します。より長いコンテキストを処理できる機能の結果として、
BigBird は、質問応答や
BERT または RoBERTa と比較した要約。`,yn,oe,no="論文の要約は次のとおりです。",kn,W,an=`<em>BERT などのトランスフォーマーベースのモデルは、NLP で最も成功した深層学習モデルの 1 つです。
残念ながら、それらの中核的な制限の 1 つは、シーケンスに対する二次依存性 (主にメモリに関する) です。
完全な注意メカニズムによる長さです。これを解決するために、BigBird は、まばらな注意メカニズムを提案します。
この二次依存関係を線形に削減します。 BigBird がシーケンス関数の汎用近似器であることを示します。
チューリングは完全であるため、二次完全注意モデルのこれらの特性が保存されます。途中、私たちの
理論分析により、O(1) 個のグローバル トークン (CLS など) を持つ利点の一部が明らかになり、
スパース注意メカニズムの一部としてのシーケンス。提案されたスパース アテンションは、次の長さのシーケンスを処理できます。
同様のハードウェアを使用して以前に可能であったものの 8 倍。より長いコンテキストを処理できる機能の結果として、
BigBird は、質問応答や要約などのさまざまな NLP タスクのパフォーマンスを大幅に向上させます。私達も
ゲノミクスデータへの新しいアプリケーションを提案します。</em>`,Tn,se,oo="チップ：",Mn,q,Vt=`<li><p>BigBird の注意がどのように機能するかについての詳細な説明については、<a href="https://huggingface.co/blog/big-bird" rel="nofollow">このブログ投稿</a> を参照してください。</p></li> <li><p>BigBird には、<strong>original_full</strong> と <strong>block_sparse</strong> の 2 つの実装が付属しています。シーケンス長が 1024 未満の場合、次を使用します。
<strong>block_sparse</strong> を使用してもメリットがないため、<strong>original_full</strong> を使用することをお勧めします。</p></li> <li><p>コードは現在、3 ブロックと 2 グローバル ブロックのウィンドウ サイズを使用しています。</p></li> <li><p>シーケンスの長さはブロック サイズで割り切れる必要があります。</p></li> <li><p>現在の実装では <strong>ITC</strong> のみがサポートされています。</p></li> <li><p>現在の実装では <strong>num_random_blocks = 0</strong> はサポートされていません</p></li> <li><p>BigBird は絶対位置埋め込みを備えたモデルであるため、通常は入力を右側にパディングすることをお勧めします。
左。</p> <p>このモデルは、<a href="https://huggingface.co/vasudevgupta" rel="nofollow">vasudevgupta</a> によって提供されました。元のコードが見つかる
<a href="https://github.com/google-research/bigbird" rel="nofollow">こちら</a>。</p></li>`,Ae,we,Bt,ht,ae='<li><a href="../tasks/sequence_classification">テキスト分類タスクガイド</a></li> <li><a href="../tasks/token_classification">トークン分類タスクガイド</a></li> <li><a href="../tasks/question_answering">質問回答タスク ガイド</a></li> <li><a href="../tasks/language_modeling">因果言語モデリング タスク ガイド</a></li> <li><a href="../tasks/masked_lang_modeling">マスクされた言語モデリング タスク ガイド</a></li> <li><a href="../tasks/multiple_choice">多肢選択タスク ガイド</a></li>',gt,fe,Y,I,Et,vt,Z,Rt=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a>. It is used to instantiate an
BigBird 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 BigBird
<a href="https://huggingface.co/google/bigbird-roberta-base" rel="nofollow">google/bigbird-roberta-base</a> architecture.`,ft,Oe,Xt=`Configuration objects inherit from <a href="/docs/transformers/pr_30621/ja/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_30621/ja/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Bn,R,vn,X,xe,P,Ze,rn,ln,$e='Construct a BigBird tokenizer. Based on <a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a>.',We,dn,cn=`This tokenizer inherits from <a href="/docs/transformers/pr_30621/ja/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.`,je,D,_e,St,Le,zn=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Big Bird sequence has the following format:`,be,wt,S="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",_t,ye,Fe,H,Ne,re=`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.`,Ge,xt,Pe,pn,mn,$t=`Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence
pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second
sequence | If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).`,Qt,He,jt,ke,Ft,bt,L,E,Ct,Q,Ye=`Construct a “fast” BigBird tokenizer (backed by HuggingFace’s <em>tokenizers</em> library). Based on
<a href="https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models" rel="nofollow">Unigram</a>. This
tokenizer inherits from <a href="/docs/transformers/pr_30621/ja/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`,Rn,ie,De,Xn,Ce,le=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An BigBird sequence has the following format:`,Te,zt,Ke="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",Dn,de,J,Jt,ze,un="Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT",Ut,et,Ve,At,Me="if token_ids_1 is None, only returns the first portion of the mask (0s).",Ee,qt,Re,hn,gn,fn=`Retrieves 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.`,Ot,Yt,ce,U,Je,Dt,yt,Kn='Output type of <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a>.',tt,pe,nt,me,ot,Xe,A;return M=new Ie({props:{title:"BigBird",local:"bigbird",headingTag:"h1"}}),B=new Ie({props:{title:"Overview",local:"overview",headingTag:"h2"}}),we=new Ie({props:{title:"ドキュメント リソース",local:"ドキュメント-リソース",headingTag:"h2"}}),fe=new Ie({props:{title:"BigBirdConfig",local:"transformers.BigBirdConfig",headingTag:"h2"}}),Et=new C({props:{name:"class transformers.BigBirdConfig",anchor:"transformers.BigBirdConfig",parameters:[{name:"vocab_size",val:" = 50358"},{name:"hidden_size",val:" = 768"},{name:"num_hidden_layers",val:" = 12"},{name:"num_attention_heads",val:" = 12"},{name:"intermediate_size",val:" = 3072"},{name:"hidden_act",val:" = 'gelu_new'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 4096"},{name:"type_vocab_size",val:" = 2"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"use_cache",val:" = True"},{name:"pad_token_id",val:" = 0"},{name:"bos_token_id",val:" = 1"},{name:"eos_token_id",val:" = 2"},{name:"sep_token_id",val:" = 66"},{name:"attention_type",val:" = 'block_sparse'"},{name:"use_bias",val:" = True"},{name:"rescale_embeddings",val:" = False"},{name:"block_size",val:" = 64"},{name:"num_random_blocks",val:" = 3"},{name:"classifier_dropout",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BigBirdConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 50358) &#x2014;
Vocabulary size of the BigBird 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_30621/ja/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a>.`,name:"vocab_size"},{anchor:"transformers.BigBirdConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimension of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.BigBirdConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.BigBirdConfig.num_attention_heads",description:`<strong>num_attention_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:"num_attention_heads"},{anchor:"transformers.BigBirdConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.BigBirdConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu_new&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;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.BigBirdConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</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:"hidden_dropout_prob"},{anchor:"transformers.BigBirdConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.BigBirdConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 1024 or 2048 or 4096).`,name:"max_position_embeddings"},{anchor:"transformers.BigBirdConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/pr_30621/ja/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.BigBirdConfig.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.BigBirdConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.BigBirdConfig.is_decoder",description:`<strong>is_decoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether the model is used as a decoder or not. If <code>False</code>, the model is used as an encoder.`,name:"is_decoder"},{anchor:"transformers.BigBirdConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if <code>config.is_decoder=True</code>.`,name:"use_cache"},{anchor:"transformers.BigBirdConfig.attention_type",description:`<strong>attention_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;block_sparse&quot;</code>) &#x2014;
Whether to use block sparse attention (with n complexity) as introduced in paper or original attention
layer (with n^2 complexity). Possible values are <code>&quot;original_full&quot;</code> and <code>&quot;block_sparse&quot;</code>.`,name:"attention_type"},{anchor:"transformers.BigBirdConfig.use_bias",description:`<strong>use_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use bias in query, key, value.`,name:"use_bias"},{anchor:"transformers.BigBirdConfig.rescale_embeddings",description:`<strong>rescale_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to rescale embeddings with (hidden_size ** 0.5).`,name:"rescale_embeddings"},{anchor:"transformers.BigBirdConfig.block_size",description:`<strong>block_size</strong> (<code>int</code>, <em>optional</em>, defaults to 64) &#x2014;
Size of each block. Useful only when <code>attention_type == &quot;block_sparse&quot;</code>.`,name:"block_size"},{anchor:"transformers.BigBirdConfig.num_random_blocks",description:`<strong>num_random_blocks</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
Each query is going to attend these many number of random blocks. Useful only when <code>attention_type == &quot;block_sparse&quot;</code>.`,name:"num_random_blocks"},{anchor:"transformers.BigBirdConfig.classifier_dropout",description:`<strong>classifier_dropout</strong> (<code>float</code>, <em>optional</em>) &#x2014;
The dropout ratio for the classification head.`,name:"classifier_dropout"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/configuration_big_bird.py#L28"}}),R=new he({props:{anchor:"transformers.BigBirdConfig.example",$$slots:{default:[ws]},$$scope:{ctx:w}}}),X=new Ie({props:{title:"BigBirdTokenizer",local:"transformers.BigBirdTokenizer",headingTag:"h2"}}),Ze=new C({props:{name:"class transformers.BigBirdTokenizer",anchor:"transformers.BigBirdTokenizer",parameters:[{name:"vocab_file",val:""},{name:"unk_token",val:" = '<unk>'"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"pad_token",val:" = '<pad>'"},{name:"sep_token",val:" = '[SEP]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"sp_model_kwargs",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BigBirdTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.BigBirdTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&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.BigBirdTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The begin of sequence token.`,name:"bos_token"},{anchor:"transformers.BigBirdTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.BigBirdTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.BigBirdTokenizer.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.BigBirdTokenizer.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.BigBirdTokenizer.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.BigBirdTokenizer.sp_model_kwargs",description:`<strong>sp_model_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Will be passed to the <code>SentencePieceProcessor.__init__()</code> method. The <a href="https://github.com/google/sentencepiece/tree/master/python" rel="nofollow">Python wrapper for
SentencePiece</a> can be used, among other things,
to set:</p>
<ul>
<li>
<p><code>enable_sampling</code>: Enable subword regularization.</p>
</li>
<li>
<p><code>nbest_size</code>: Sampling parameters for unigram. Invalid for BPE-Dropout.</p>
<ul>
<li><code>nbest_size = {0,1}</code>: No sampling is performed.</li>
<li><code>nbest_size &gt; 1</code>: samples from the nbest_size results.</li>
<li><code>nbest_size &lt; 0</code>: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.</li>
</ul>
</li>
<li>
<p><code>alpha</code>: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.</p>
</li>
</ul>`,name:"sp_model_kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/tokenization_big_bird.py#L33"}}),_e=new C({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.BigBirdTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BigBirdTokenizer.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.BigBirdTokenizer.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_30621/src/transformers/models/big_bird/tokenization_big_bird.py#L248",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>
`}}),Fe=new C({props:{name:"get_special_tokens_mask",anchor:"transformers.BigBirdTokenizer.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.BigBirdTokenizer.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.BigBirdTokenizer.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.BigBirdTokenizer.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_30621/src/transformers/models/big_bird/tokenization_big_bird.py#L273",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>
`}}),Pe=new C({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.BigBirdTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BigBirdTokenizer.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.BigBirdTokenizer.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_30621/src/transformers/models/big_bird/tokenization_big_bird.py#L300",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>
`}}),jt=new C({props:{name:"save_vocabulary",anchor:"transformers.BigBirdTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": Optional = None"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/tokenization_big_bird.py#L231"}}),Ft=new Ie({props:{title:"BigBirdTokenizerFast",local:"transformers.BigBirdTokenizerFast",headingTag:"h2"}}),E=new C({props:{name:"class transformers.BigBirdTokenizerFast",anchor:"transformers.BigBirdTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"unk_token",val:" = '<unk>'"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"pad_token",val:" = '<pad>'"},{name:"sep_token",val:" = '[SEP]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BigBirdTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.BigBirdTokenizerFast.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the <code>cls_token</code>.</p>

					</div>`,name:"bos_token"},{anchor:"transformers.BigBirdTokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the <code>sep_token</code>.`,name:"eos_token"},{anchor:"transformers.BigBirdTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&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.BigBirdTokenizerFast.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.BigBirdTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/tokenization_big_bird_fast.py#L38"}}),De=new C({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.BigBirdTokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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_30621/src/transformers/models/big_bird/tokenization_big_bird_fast.py#L126",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>
`}}),J=new C({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.BigBirdTokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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_30621/src/transformers/models/big_bird/tokenization_big_bird_fast.py#L182",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>
`}}),et=new he({props:{anchor:"transformers.BigBirdTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[xs]},$$scope:{ctx:w}}}),Re=new C({props:{name:"get_special_tokens_mask",anchor:"transformers.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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.BigBirdTokenizerFast.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;
Set to True if 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_30621/src/transformers/models/big_bird/tokenization_big_bird_fast.py#L151",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>
`}}),Yt=new Ie({props:{title:"BigBird specific outputs",local:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput",headingTag:"h2"}}),Je=new C({props:{name:"class transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput",anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput",parameters:[{name:"loss",val:": Optional = None"},{name:"prediction_logits",val:": FloatTensor = None"},{name:"seq_relationship_logits",val:": FloatTensor = None"},{name:"hidden_states",val:": Optional = None"},{name:"attentions",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.loss",description:`<strong>loss</strong> (<em>optional</em>, returned when <code>labels</code> is provided, <code>torch.FloatTensor</code> of shape <code>(1,)</code>) &#x2014;
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.`,name:"loss"},{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.prediction_logits",description:`<strong>prediction_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) &#x2014;
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).`,name:"prediction_logits"},{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.seq_relationship_logits",description:`<strong>seq_relationship_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, 2)</code>) &#x2014;
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).`,name:"seq_relationship_logits"},{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.hidden_states",description:`<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>) &#x2014;
Tuple of <code>torch.FloatTensor</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.`,name:"hidden_states"},{anchor:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput.attentions",description:`<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>) &#x2014;
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.`,name:"attentions"}],source:"https://github.com/huggingface/transformers/blob/vr_30621/src/transformers/models/big_bird/modeling_big_bird.py#L1834"}}),pe=new Bs({props:{pytorch:!0,tensorflow:!1,jax:!0,$$slots:{jax:[pa],pytorch:[Ss]},$$scope:{ctx:w}}}),me=new vs({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/ja/model_doc/big_bird.md"}}),{c(){e=c("meta"),u=s(),n=c("p"),r=s(),f(M.$$.fragment),t=s(),f(B.$$.fragment),bn=s(),ne=c("p"),ne.innerHTML=to,yn=s(),oe=c("p"),oe.textContent=no,kn=s(),W=c("p"),W.innerHTML=an,Tn=s(),se=c("p"),se.textContent=oo,Mn=s(),q=c("ul"),q.innerHTML=Vt,Ae=s(),f(we.$$.fragment),Bt=s(),ht=c("ul"),ht.innerHTML=ae,gt=s(),f(fe.$$.fragment),Y=s(),I=c("div"),f(Et.$$.fragment),vt=s(),Z=c("p"),Z.innerHTML=Rt,ft=s(),Oe=c("p"),Oe.innerHTML=Xt,Bn=s(),f(R.$$.fragment),vn=s(),f(X.$$.fragment),xe=s(),P=c("div"),f(Ze.$$.fragment),rn=s(),ln=c("p"),ln.innerHTML=$e,We=s(),dn=c("p"),dn.innerHTML=cn,je=s(),D=c("div"),f(_e.$$.fragment),St=s(),Le=c("p"),Le.textContent=zn,be=s(),wt=c("ul"),wt.innerHTML=S,_t=s(),ye=c("div"),f(Fe.$$.fragment),H=s(),Ne=c("p"),Ne.innerHTML=re,Ge=s(),xt=c("div"),f(Pe.$$.fragment),pn=s(),mn=c("p"),mn.innerHTML=$t,Qt=s(),He=c("div"),f(jt.$$.fragment),ke=s(),f(Ft.$$.fragment),bt=s(),L=c("div"),f(E.$$.fragment),Ct=s(),Q=c("p"),Q.innerHTML=Ye,Rn=s(),ie=c("div"),f(De.$$.fragment),Xn=s(),Ce=c("p"),Ce.textContent=le,Te=s(),zt=c("ul"),zt.innerHTML=Ke,Dn=s(),de=c("div"),f(J.$$.fragment),Jt=s(),ze=c("p"),ze.textContent=un,Ut=s(),f(et.$$.fragment),Ve=s(),At=c("p"),At.textContent=Me,Ee=s(),qt=c("div"),f(Re.$$.fragment),hn=s(),gn=c("p"),gn.innerHTML=fn,Ot=s(),f(Yt.$$.fragment),ce=s(),U=c("div"),f(Je.$$.fragment),Dt=s(),yt=c("p"),yt.innerHTML=Kn,tt=s(),f(pe.$$.fragment),nt=s(),f(me.$$.fragment),ot=s(),Xe=c("p"),this.h()},l(d){const $=Ms("svelte-u9bgzb",document.head);e=p($,"META",{name:!0,content:!0}),$.forEach(l),u=a(d),n=p(d,"P",{}),j(n).forEach(l),r=a(d),_(M.$$.fragment,d),t=a(d),_(B.$$.fragment,d),bn=a(d),ne=p(d,"P",{"data-svelte-h":!0}),g(ne)!=="svelte-17xuljh"&&(ne.innerHTML=to),yn=a(d),oe=p(d,"P",{"data-svelte-h":!0}),g(oe)!=="svelte-1cv3nri"&&(oe.textContent=no),kn=a(d),W=p(d,"P",{"data-svelte-h":!0}),g(W)!=="svelte-1n7i5w4"&&(W.innerHTML=an),Tn=a(d),se=p(d,"P",{"data-svelte-h":!0}),g(se)!=="svelte-fhhf9b"&&(se.textContent=oo),Mn=a(d),q=p(d,"UL",{"data-svelte-h":!0}),g(q)!=="svelte-2igudh"&&(q.innerHTML=Vt),Ae=a(d),_(we.$$.fragment,d),Bt=a(d),ht=p(d,"UL",{"data-svelte-h":!0}),g(ht)!=="svelte-hsgc6s"&&(ht.innerHTML=ae),gt=a(d),_(fe.$$.fragment,d),Y=a(d),I=p(d,"DIV",{class:!0});var K=j(I);_(Et.$$.fragment,K),vt=a(K),Z=p(K,"P",{"data-svelte-h":!0}),g(Z)!=="svelte-1bncaub"&&(Z.innerHTML=Rt),ft=a(K),Oe=p(K,"P",{"data-svelte-h":!0}),g(Oe)!=="svelte-1jtjhnb"&&(Oe.innerHTML=Xt),Bn=a(K),_(R.$$.fragment,K),K.forEach(l),vn=a(d),_(X.$$.fragment,d),xe=a(d),P=p(d,"DIV",{class:!0});var N=j(P);_(Ze.$$.fragment,N),rn=a(N),ln=p(N,"P",{"data-svelte-h":!0}),g(ln)!=="svelte-icdx2b"&&(ln.innerHTML=$e),We=a(N),dn=p(N,"P",{"data-svelte-h":!0}),g(dn)!=="svelte-cgmeqq"&&(dn.innerHTML=cn),je=a(N),D=p(N,"DIV",{class:!0});var kt=j(D);_(_e.$$.fragment,kt),St=a(kt),Le=p(kt,"P",{"data-svelte-h":!0}),g(Le)!=="svelte-1wtdd6g"&&(Le.textContent=zn),be=a(kt),wt=p(kt,"UL",{"data-svelte-h":!0}),g(wt)!=="svelte-xi6653"&&(wt.innerHTML=S),kt.forEach(l),_t=a(N),ye=p(N,"DIV",{class:!0});var It=j(ye);_(Fe.$$.fragment,It),H=a(It),Ne=p(It,"P",{"data-svelte-h":!0}),g(Ne)!=="svelte-1f4f5kp"&&(Ne.innerHTML=re),It.forEach(l),Ge=a(N),xt=p(N,"DIV",{class:!0});var Kt=j(xt);_(Pe.$$.fragment,Kt),pn=a(Kt),mn=p(Kt,"P",{"data-svelte-h":!0}),g(mn)!=="svelte-nvbrp2"&&(mn.innerHTML=$t),Kt.forEach(l),Qt=a(N),He=p(N,"DIV",{class:!0});var eo=j(He);_(jt.$$.fragment,eo),eo.forEach(l),N.forEach(l),ke=a(d),_(Ft.$$.fragment,d),bt=a(d),L=p(d,"DIV",{class:!0});var G=j(L);_(E.$$.fragment,G),Ct=a(G),Q=p(G,"P",{"data-svelte-h":!0}),g(Q)!=="svelte-141ybcy"&&(Q.innerHTML=Ye),Rn=a(G),ie=p(G,"DIV",{class:!0});var ue=j(ie);_(De.$$.fragment,ue),Xn=a(ue),Ce=p(ue,"P",{"data-svelte-h":!0}),g(Ce)!=="svelte-1of3j8a"&&(Ce.textContent=le),Te=a(ue),zt=p(ue,"UL",{"data-svelte-h":!0}),g(zt)!=="svelte-xi6653"&&(zt.innerHTML=Ke),ue.forEach(l),Dn=a(G),de=p(G,"DIV",{class:!0});var Zt=j(de);_(J.$$.fragment,Zt),Jt=a(Zt),ze=p(Zt,"P",{"data-svelte-h":!0}),g(ze)!=="svelte-13bfd60"&&(ze.textContent=un),Ut=a(Zt),_(et.$$.fragment,Zt),Ve=a(Zt),At=p(Zt,"P",{"data-svelte-h":!0}),g(At)!=="svelte-wtrslu"&&(At.textContent=Me),Zt.forEach(l),Ee=a(G),qt=p(G,"DIV",{class:!0});var Wt=j(qt);_(Re.$$.fragment,Wt),hn=a(Wt),gn=p(Wt,"P",{"data-svelte-h":!0}),g(gn)!=="svelte-1l6i2y2"&&(gn.innerHTML=fn),Wt.forEach(l),G.forEach(l),Ot=a(d),_(Yt.$$.fragment,d),ce=a(d),U=p(d,"DIV",{class:!0});var en=j(U);_(Je.$$.fragment,en),Dt=a(en),yt=p(en,"P",{"data-svelte-h":!0}),g(yt)!=="svelte-jzj16z"&&(yt.innerHTML=Kn),en.forEach(l),tt=a(d),_(pe.$$.fragment,d),nt=a(d),_(me.$$.fragment,d),ot=a(d),Xe=p(d,"P",{}),j(Xe).forEach(l),this.h()},h(){F(e,"name","hf:doc:metadata"),F(e,"content",ua),F(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(ye,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(xt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(He,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(qt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(U,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(d,$){o(document.head,e),h(d,u,$),h(d,n,$),h(d,r,$),b(M,d,$),h(d,t,$),b(B,d,$),h(d,bn,$),h(d,ne,$),h(d,yn,$),h(d,oe,$),h(d,kn,$),h(d,W,$),h(d,Tn,$),h(d,se,$),h(d,Mn,$),h(d,q,$),h(d,Ae,$),b(we,d,$),h(d,Bt,$),h(d,ht,$),h(d,gt,$),b(fe,d,$),h(d,Y,$),h(d,I,$),b(Et,I,null),o(I,vt),o(I,Z),o(I,ft),o(I,Oe),o(I,Bn),b(R,I,null),h(d,vn,$),b(X,d,$),h(d,xe,$),h(d,P,$),b(Ze,P,null),o(P,rn),o(P,ln),o(P,We),o(P,dn),o(P,je),o(P,D),b(_e,D,null),o(D,St),o(D,Le),o(D,be),o(D,wt),o(P,_t),o(P,ye),b(Fe,ye,null),o(ye,H),o(ye,Ne),o(P,Ge),o(P,xt),b(Pe,xt,null),o(xt,pn),o(xt,mn),o(P,Qt),o(P,He),b(jt,He,null),h(d,ke,$),b(Ft,d,$),h(d,bt,$),h(d,L,$),b(E,L,null),o(L,Ct),o(L,Q),o(L,Rn),o(L,ie),b(De,ie,null),o(ie,Xn),o(ie,Ce),o(ie,Te),o(ie,zt),o(L,Dn),o(L,de),b(J,de,null),o(de,Jt),o(de,ze),o(de,Ut),b(et,de,null),o(de,Ve),o(de,At),o(L,Ee),o(L,qt),b(Re,qt,null),o(qt,hn),o(qt,gn),h(d,Ot,$),b(Yt,d,$),h(d,ce,$),h(d,U,$),b(Je,U,null),o(U,Dt),o(U,yt),h(d,tt,$),b(pe,d,$),h(d,nt,$),b(me,d,$),h(d,ot,$),h(d,Xe,$),A=!0},p(d,[$]){const K={};$&2&&(K.$$scope={dirty:$,ctx:d}),R.$set(K);const N={};$&2&&(N.$$scope={dirty:$,ctx:d}),et.$set(N);const kt={};$&2&&(kt.$$scope={dirty:$,ctx:d}),pe.$set(kt)},i(d){A||(y(M.$$.fragment,d),y(B.$$.fragment,d),y(we.$$.fragment,d),y(fe.$$.fragment,d),y(Et.$$.fragment,d),y(R.$$.fragment,d),y(X.$$.fragment,d),y(Ze.$$.fragment,d),y(_e.$$.fragment,d),y(Fe.$$.fragment,d),y(Pe.$$.fragment,d),y(jt.$$.fragment,d),y(Ft.$$.fragment,d),y(E.$$.fragment,d),y(De.$$.fragment,d),y(J.$$.fragment,d),y(et.$$.fragment,d),y(Re.$$.fragment,d),y(Yt.$$.fragment,d),y(Je.$$.fragment,d),y(pe.$$.fragment,d),y(me.$$.fragment,d),A=!0)},o(d){k(M.$$.fragment,d),k(B.$$.fragment,d),k(we.$$.fragment,d),k(fe.$$.fragment,d),k(Et.$$.fragment,d),k(R.$$.fragment,d),k(X.$$.fragment,d),k(Ze.$$.fragment,d),k(_e.$$.fragment,d),k(Fe.$$.fragment,d),k(Pe.$$.fragment,d),k(jt.$$.fragment,d),k(Ft.$$.fragment,d),k(E.$$.fragment,d),k(De.$$.fragment,d),k(J.$$.fragment,d),k(et.$$.fragment,d),k(Re.$$.fragment,d),k(Yt.$$.fragment,d),k(Je.$$.fragment,d),k(pe.$$.fragment,d),k(me.$$.fragment,d),A=!1},d(d){d&&(l(u),l(n),l(r),l(t),l(bn),l(ne),l(yn),l(oe),l(kn),l(W),l(Tn),l(se),l(Mn),l(q),l(Ae),l(Bt),l(ht),l(gt),l(Y),l(I),l(vn),l(xe),l(P),l(ke),l(bt),l(L),l(Ot),l(ce),l(U),l(tt),l(nt),l(ot),l(Xe)),l(e),T(M,d),T(B,d),T(we,d),T(fe,d),T(Et),T(R),T(X,d),T(Ze),T(_e),T(Fe),T(Pe),T(jt),T(Ft,d),T(E),T(De),T(J),T(et),T(Re),T(Yt,d),T(Je),T(pe,d),T(me,d)}}}const ua='{"title":"BigBird","local":"bigbird","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"ドキュメント リソース","local":"ドキュメント-リソース","sections":[],"depth":2},{"title":"BigBirdConfig","local":"transformers.BigBirdConfig","sections":[],"depth":2},{"title":"BigBirdTokenizer","local":"transformers.BigBirdTokenizer","sections":[],"depth":2},{"title":"BigBirdTokenizerFast","local":"transformers.BigBirdTokenizerFast","sections":[],"depth":2},{"title":"BigBird specific outputs","local":"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput","sections":[],"depth":2},{"title":"BigBirdModel","local":"transformers.BigBirdModel","sections":[],"depth":2},{"title":"BigBirdForPreTraining","local":"transformers.BigBirdForPreTraining","sections":[],"depth":2},{"title":"BigBirdForCausalLM","local":"transformers.BigBirdForCausalLM","sections":[],"depth":2},{"title":"BigBirdForMaskedLM","local":"transformers.BigBirdForMaskedLM","sections":[],"depth":2},{"title":"BigBirdForSequenceClassification","local":"transformers.BigBirdForSequenceClassification","sections":[],"depth":2},{"title":"BigBirdForMultipleChoice","local":"transformers.BigBirdForMultipleChoice","sections":[],"depth":2},{"title":"BigBirdForTokenClassification","local":"transformers.BigBirdForTokenClassification","sections":[],"depth":2},{"title":"BigBirdForQuestionAnswering","local":"transformers.BigBirdForQuestionAnswering","sections":[],"depth":2},{"title":"FlaxBigBirdModel","local":"transformers.FlaxBigBirdModel","sections":[],"depth":2},{"title":"FlaxBigBirdForCausalLM","local":"transformers.FlaxBigBirdForCausalLM","sections":[],"depth":2},{"title":"FlaxBigBirdForSequenceClassification","local":"transformers.FlaxBigBirdForSequenceClassification","sections":[],"depth":2},{"title":"FlaxBigBirdForTokenClassification","local":"transformers.FlaxBigBirdForTokenClassification","sections":[],"depth":2}],"depth":1}';function ha(w){return ys(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ba extends ks{constructor(e){super(),Ts(this,e,ha,ma,bs,{})}}export{Ba as component};
