import{s as hs,o as gs,n as z}from"../chunks/scheduler.9bc65507.js";import{S as fs,i as _s,g as c,s,r as g,A as bs,h as p,f as l,c as a,j,u as f,x as T,k as F,y as o,a as h,v as _,d as b,t as y,w as k}from"../chunks/index.707bf1b6.js";import{T as jt}from"../chunks/Tip.c2ecdbf4.js";import{D as C}from"../chunks/Docstring.86474e80.js";import{C as be}from"../chunks/CodeBlock.54a9f38d.js";import{F as ys,M as us}from"../chunks/Markdown.8ab98a13.js";import{E as _e}from"../chunks/ExampleCodeBlock.034f3a73.js";import{H as ee,E as ks}from"../chunks/EditOnGithub.922df6ba.js";function Ts(w){let e,u="Example:",n,r,M;return r=new be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function Ms(w){let e,u="sequence pair mask has the following format:",n,r,M;return r=new be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-16klr56"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function Bs(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}),T(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function vs(w){let e,u="Example:",n,r,M;return r=new be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function ws(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}),T(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function $s(w){let e,u="Example:",n,r,M;return r=new be({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBCaWdCaXJkRm9yUHJlVHJhaW5pbmclMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZSUyRmJpZ2JpcmQtcm9iZXJ0YS1iYXNlJTIyKSUwQW1vZGVsJTIwJTNEJTIwQmlnQmlyZEZvclByZVRyYWluaW5nLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZiaWdiaXJkLXJvYmVydGEtYmFzZSUyMiklMEElMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoJTIySGVsbG8lMkMlMjBteSUyMGRvZyUyMGlzJTIwY3V0ZSUyMiUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQSUwQXByZWRpY3Rpb25fbG9naXRzJTIwJTNEJTIwb3V0cHV0cy5wcmVkaWN0aW9uX2xvZ2l0cyUwQXNlcV9yZWxhdGlvbnNoaXBfbG9naXRzJTIwJTNEJTIwb3V0cHV0cy5zZXFfcmVsYXRpb25zaGlwX2xvZ2l0cw==",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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function xs(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}),T(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 be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(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}),T(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 be({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;rajpurkar/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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function zs(w){let e,u;return e=new be({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(){g(e.$$.fragment)},l(n){f(e.$$.fragment,n)},m(n,r){_(e,n,r),u=!0},p:z,i(n){u||(b(e.$$.fragment,n),u=!0)},o(n){y(e.$$.fragment,n),u=!1},d(n){k(e,n)}}}function Js(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}),T(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Us(w){let e,u="Example:",n,r,M;return r=new be({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwQXV0b1Rva2VuaXplciUyQyUyMEJpZ0JpcmRGb3JTZXF1ZW5jZUNsYXNzaWZpY2F0aW9uJTBBZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIybC15b2hhaSUyRmJpZ2JpcmQtcm9iZXJ0YS1iYXNlLW1ubGklMjIpJTBBbW9kZWwlMjAlM0QlMjBCaWdCaXJkRm9yU2VxdWVuY2VDbGFzc2lmaWNhdGlvbi5mcm9tX3ByZXRyYWluZWQoJTIybC15b2hhaSUyRmJpZ2JpcmQtcm9iZXJ0YS1iYXNlLW1ubGklMjIpJTBBc3F1YWRfZHMlMjAlM0QlMjBsb2FkX2RhdGFzZXQoJTIycmFqcHVya2FyJTJGc3F1YWRfdjIlMjIlMkMlMjBzcGxpdCUzRCUyMnRyYWluJTIyKSUwQUxPTkdfQVJUSUNMRSUyMCUzRCUyMHNxdWFkX2RzJTVCODE1MTQlNUQlNUIlMjJjb250ZXh0JTIyJTVEJTBBaW5wdXRzJTIwJTNEJTIwdG9rZW5pemVyKExPTkdfQVJUSUNMRSUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBJTIzJTIwbG9uZyUyMGlucHV0JTIwYXJ0aWNsZSUwQWxpc3QoaW5wdXRzJTVCJTIyaW5wdXRfaWRzJTIyJTVELnNoYXBlKSUwQSUwQXdpdGglMjB0b3JjaC5ub19ncmFkKCklM0ElMEElMjAlMjAlMjAlMjBsb2dpdHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cykubG9naXRzJTBBcHJlZGljdGVkX2NsYXNzX2lkJTIwJTNEJTIwbG9naXRzLmFyZ21heCgpLml0ZW0oKSUwQW1vZGVsLmNvbmZpZy5pZDJsYWJlbCU1QnByZWRpY3RlZF9jbGFzc19pZCU1RA==",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;rajpurkar/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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function qs(w){let e,u;return e=new be({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(){g(e.$$.fragment)},l(n){f(e.$$.fragment,n)},m(n,r){_(e,n,r),u=!0},p:z,i(n){u||(b(e.$$.fragment,n),u=!0)},o(n){y(e.$$.fragment,n),u=!1},d(n){k(e,n)}}}function Is(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}),T(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Zs(w){let e,u="Example:",n,r,M;return r=new be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function Ws(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}),T(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Ls(w){let e,u="Example:",n,r,M;return r=new be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}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}),T(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 be({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;rajpurkar/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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function Ps(w){let e,u;return e=new be({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(){g(e.$$.fragment)},l(n){f(e.$$.fragment,n)},m(n,r){_(e,n,r),u=!0},p:z,i(n){u||(b(e.$$.fragment,n),u=!0)},o(n){y(e.$$.fragment,n),u=!1},d(n){k(e,n)}}}function Hs(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.`,un,ie,so=`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.`,hn,le,ao=`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.`,gn,W,on,fn,de,ro='The <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.BigBirdModel">BigBirdModel</a> forward method, overrides the <code>__call__</code> special method.',_n,I,Pt,tt,xe,Ft,_t,ce,bt,ye,te,Z,yt,U,nt='The <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a> forward method, overrides the <code>__call__</code> special method.',Vn,je,sn,Ct,zt,Fe,an,N,Jt,L,pe,eo=`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.`,Ht,ne,Vt,Et,oe,Xt='The <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.BigBirdForCausalLM">BigBirdForCausalLM</a> forward method, overrides the <code>__call__</code> special method.',He,Y,En,Ce,kt,Ut,ke,ze,Je,ot,J,Rt=`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.`,St,R,Qt,Tt,me,to='The <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.BigBirdForMaskedLM">BigBirdForMaskedLM</a> forward method, overrides the <code>__call__</code> special method.',At,Ve,Xn,Ue,Ee,Mt,Bt,qe,D,E,S,Xe,rn,ln=`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.`,st,Re,qt=`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.`,Ot,G,vt,Se,Yt,P='The <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.BigBirdForSequenceClassification">BigBirdForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',Q,Te,Dt,Qe,Rn,Ie,wt,It,Ae,Ze,Zt,We,at,Sn=`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.`,rt,bn,vn=`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.`,A,X,ue,Oe,dn,cn='The <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.BigBirdForMultipleChoice">BigBirdForMultipleChoice</a> forward method, overrides the <code>__call__</code> special method.',Wt,it,he,Ye,Me,lt,q,H,m,x,ge,O=`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.`,Be,Kt,wn=`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.`,pn,V,De,Le,en,$n='The <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.BigBirdForTokenClassification">BigBirdForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',Qn,xn,uo,fe,yn,jn,Fn,$t,Cn,kn,An,zn=`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>).`,io,tn,ho=`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.`,K,se,Jn,On,no,ko='The <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.BigBirdForQuestionAnswering">BigBirdForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',Yn,Un,go,Lt,To,Tn,qn;return e=new ee({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_31161/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_31161/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_31161/src/transformers/models/big_bird/modeling_big_bird.py#L1903"}}),on=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPoolingAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),I=new jt({props:{$$slots:{default:[Bs]},$$scope:{ctx:w}}}),tt=new _e({props:{anchor:"transformers.BigBirdModel.forward.example",$$slots:{default:[vs]},$$scope:{ctx:w}}}),Ft=new ee({props:{title:"BigBirdForPreTraining",local:"transformers.BigBirdForPreTraining",headingTag:"h2"}}),bt=new C({props:{name:"class transformers.BigBirdForPreTraining",anchor:"transformers.BigBirdForPreTraining",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/big_bird/modeling_big_bird.py#L2246"}}),Z=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/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>
`}}),je=new jt({props:{$$slots:{default:[ws]},$$scope:{ctx:w}}}),Ct=new _e({props:{anchor:"transformers.BigBirdForPreTraining.forward.example",$$slots:{default:[$s]},$$scope:{ctx:w}}}),Fe=new ee({props:{title:"BigBirdForCausalLM",local:"transformers.BigBirdForCausalLM",headingTag:"h2"}}),Jt=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_31161/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_31161/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_31161/src/transformers/models/big_bird/modeling_big_bird.py#L2495"}}),Vt=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Y=new jt({props:{$$slots:{default:[xs]},$$scope:{ctx:w}}}),Ce=new _e({props:{anchor:"transformers.BigBirdForCausalLM.forward.example",$$slots:{default:[js]},$$scope:{ctx:w}}}),Ut=new ee({props:{title:"BigBirdForMaskedLM",local:"transformers.BigBirdForMaskedLM",headingTag:"h2"}}),Je=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_31161/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_31161/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_31161/src/transformers/models/big_bird/modeling_big_bird.py#L2352"}}),Qt=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ve=new jt({props:{$$slots:{default:[Fs]},$$scope:{ctx:w}}}),Ue=new _e({props:{anchor:"transformers.BigBirdForMaskedLM.forward.example",$$slots:{default:[Cs]},$$scope:{ctx:w}}}),Mt=new _e({props:{anchor:"transformers.BigBirdForMaskedLM.forward.example-2",$$slots:{default:[zs]},$$scope:{ctx:w}}}),qe=new ee({props:{title:"BigBirdForSequenceClassification",local:"transformers.BigBirdForSequenceClassification",headingTag:"h2"}}),S=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_31161/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_31161/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_31161/src/transformers/models/big_bird/modeling_big_bird.py#L2664"}}),vt=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Te=new jt({props:{$$slots:{default:[Js]},$$scope:{ctx:w}}}),Qe=new _e({props:{anchor:"transformers.BigBirdForSequenceClassification.forward.example",$$slots:{default:[Us]},$$scope:{ctx:w}}}),Ie=new _e({props:{anchor:"transformers.BigBirdForSequenceClassification.forward.example-2",$$slots:{default:[qs]},$$scope:{ctx:w}}}),It=new ee({props:{title:"BigBirdForMultipleChoice",local:"transformers.BigBirdForMultipleChoice",headingTag:"h2"}}),Zt=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_31161/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_31161/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_31161/src/transformers/models/big_bird/modeling_big_bird.py#L2792"}}),ue=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),it=new jt({props:{$$slots:{default:[Is]},$$scope:{ctx:w}}}),Ye=new _e({props:{anchor:"transformers.BigBirdForMultipleChoice.forward.example",$$slots:{default:[Zs]},$$scope:{ctx:w}}}),lt=new ee({props:{title:"BigBirdForTokenClassification",local:"transformers.BigBirdForTokenClassification",headingTag:"h2"}}),m=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_31161/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_31161/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_31161/src/transformers/models/big_bird/modeling_big_bird.py#L2885"}}),De=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),xn=new jt({props:{$$slots:{default:[Ws]},$$scope:{ctx:w}}}),fe=new _e({props:{anchor:"transformers.BigBirdForTokenClassification.forward.example",$$slots:{default:[Ls]},$$scope:{ctx:w}}}),jn=new ee({props:{title:"BigBirdForQuestionAnswering",local:"transformers.BigBirdForQuestionAnswering",headingTag:"h2"}}),Cn=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_31161/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_31161/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_31161/src/transformers/models/big_bird/modeling_big_bird.py#L2984"}}),Jn=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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>
`}}),Un=new jt({props:{$$slots:{default:[Ns]},$$scope:{ctx:w}}}),Lt=new _e({props:{anchor:"transformers.BigBirdForQuestionAnswering.forward.example",$$slots:{default:[Gs]},$$scope:{ctx:w}}}),Tn=new _e({props:{anchor:"transformers.BigBirdForQuestionAnswering.forward.example-2",$$slots:{default:[Ps]},$$scope:{ctx:w}}}),{c(){g(e.$$.fragment),u=s(),n=c("div"),g(r.$$.fragment),M=s(),t=c("p"),t.innerHTML=B,un=s(),ie=c("p"),ie.innerHTML=so,hn=s(),le=c("p"),le.innerHTML=ao,gn=s(),W=c("div"),g(on.$$.fragment),fn=s(),de=c("p"),de.innerHTML=ro,_n=s(),g(I.$$.fragment),Pt=s(),g(tt.$$.fragment),xe=s(),g(Ft.$$.fragment),_t=s(),ce=c("div"),g(bt.$$.fragment),ye=s(),te=c("div"),g(Z.$$.fragment),yt=s(),U=c("p"),U.innerHTML=nt,Vn=s(),g(je.$$.fragment),sn=s(),g(Ct.$$.fragment),zt=s(),g(Fe.$$.fragment),an=s(),N=c("div"),g(Jt.$$.fragment),L=s(),pe=c("p"),pe.innerHTML=eo,Ht=s(),ne=c("div"),g(Vt.$$.fragment),Et=s(),oe=c("p"),oe.innerHTML=Xt,He=s(),g(Y.$$.fragment),En=s(),g(Ce.$$.fragment),kt=s(),g(Ut.$$.fragment),ke=s(),ze=c("div"),g(Je.$$.fragment),ot=s(),J=c("p"),J.innerHTML=Rt,St=s(),R=c("div"),g(Qt.$$.fragment),Tt=s(),me=c("p"),me.innerHTML=to,At=s(),g(Ve.$$.fragment),Xn=s(),g(Ue.$$.fragment),Ee=s(),g(Mt.$$.fragment),Bt=s(),g(qe.$$.fragment),D=s(),E=c("div"),g(S.$$.fragment),Xe=s(),rn=c("p"),rn.textContent=ln,st=s(),Re=c("p"),Re.innerHTML=qt,Ot=s(),G=c("div"),g(vt.$$.fragment),Se=s(),Yt=c("p"),Yt.innerHTML=P,Q=s(),g(Te.$$.fragment),Dt=s(),g(Qe.$$.fragment),Rn=s(),g(Ie.$$.fragment),wt=s(),g(It.$$.fragment),Ae=s(),Ze=c("div"),g(Zt.$$.fragment),We=s(),at=c("p"),at.textContent=Sn,rt=s(),bn=c("p"),bn.innerHTML=vn,A=s(),X=c("div"),g(ue.$$.fragment),Oe=s(),dn=c("p"),dn.innerHTML=cn,Wt=s(),g(it.$$.fragment),he=s(),g(Ye.$$.fragment),Me=s(),g(lt.$$.fragment),q=s(),H=c("div"),g(m.$$.fragment),x=s(),ge=c("p"),ge.textContent=O,Be=s(),Kt=c("p"),Kt.innerHTML=wn,pn=s(),V=c("div"),g(De.$$.fragment),Le=s(),en=c("p"),en.innerHTML=$n,Qn=s(),g(xn.$$.fragment),uo=s(),g(fe.$$.fragment),yn=s(),g(jn.$$.fragment),Fn=s(),$t=c("div"),g(Cn.$$.fragment),kn=s(),An=c("p"),An.innerHTML=zn,io=s(),tn=c("p"),tn.innerHTML=ho,K=s(),se=c("div"),g(Jn.$$.fragment),On=s(),no=c("p"),no.innerHTML=ko,Yn=s(),g(Un.$$.fragment),go=s(),g(Lt.$$.fragment),To=s(),g(Tn.$$.fragment),this.h()},l(i){f(e.$$.fragment,i),u=a(i),n=p(i,"DIV",{class:!0});var v=j(n);f(r.$$.fragment,v),M=a(v),t=p(v,"P",{"data-svelte-h":!0}),T(t)!=="svelte-y21dxp"&&(t.innerHTML=B),un=a(v),ie=p(v,"P",{"data-svelte-h":!0}),T(ie)!=="svelte-1du13oj"&&(ie.innerHTML=so),hn=a(v),le=p(v,"P",{"data-svelte-h":!0}),T(le)!=="svelte-174erte"&&(le.innerHTML=ao),gn=a(v),W=p(v,"DIV",{class:!0});var Ke=j(W);f(on.$$.fragment,Ke),fn=a(Ke),de=p(Ke,"P",{"data-svelte-h":!0}),T(de)!=="svelte-27j6mn"&&(de.innerHTML=ro),_n=a(Ke),f(I.$$.fragment,Ke),Pt=a(Ke),f(tt.$$.fragment,Ke),Ke.forEach(l),v.forEach(l),xe=a(i),f(Ft.$$.fragment,i),_t=a(i),ce=p(i,"DIV",{class:!0});var Dn=j(ce);f(bt.$$.fragment,Dn),ye=a(Dn),te=p(Dn,"DIV",{class:!0});var Nt=j(te);f(Z.$$.fragment,Nt),yt=a(Nt),U=p(Nt,"P",{"data-svelte-h":!0}),T(U)!=="svelte-rlgkxv"&&(U.innerHTML=nt),Vn=a(Nt),f(je.$$.fragment,Nt),sn=a(Nt),f(Ct.$$.fragment,Nt),Nt.forEach(l),Dn.forEach(l),zt=a(i),f(Fe.$$.fragment,i),an=a(i),N=p(i,"DIV",{class:!0});var ae=j(N);f(Jt.$$.fragment,ae),L=a(ae),pe=p(ae,"P",{"data-svelte-h":!0}),T(pe)!=="svelte-1bxx5tz"&&(pe.innerHTML=eo),Ht=a(ae),ne=p(ae,"DIV",{class:!0});var Ne=j(ne);f(Vt.$$.fragment,Ne),Et=a(Ne),oe=p(Ne,"P",{"data-svelte-h":!0}),T(oe)!=="svelte-1yeeldr"&&(oe.innerHTML=Xt),He=a(Ne),f(Y.$$.fragment,Ne),En=a(Ne),f(Ce.$$.fragment,Ne),Ne.forEach(l),ae.forEach(l),kt=a(i),f(Ut.$$.fragment,i),ke=a(i),ze=p(i,"DIV",{class:!0});var mn=j(ze);f(Je.$$.fragment,mn),ot=a(mn),J=p(mn,"P",{"data-svelte-h":!0}),T(J)!=="svelte-xdrvf0"&&(J.innerHTML=Rt),St=a(mn),R=p(mn,"DIV",{class:!0});var ve=j(R);f(Qt.$$.fragment,ve),Tt=a(ve),me=p(ve,"P",{"data-svelte-h":!0}),T(me)!=="svelte-cxb3fj"&&(me.innerHTML=to),At=a(ve),f(Ve.$$.fragment,ve),Xn=a(ve),f(Ue.$$.fragment,ve),Ee=a(ve),f(Mt.$$.fragment,ve),ve.forEach(l),mn.forEach(l),Bt=a(i),f(qe.$$.fragment,i),D=a(i),E=p(i,"DIV",{class:!0});var nn=j(E);f(S.$$.fragment,nn),Xe=a(nn),rn=p(nn,"P",{"data-svelte-h":!0}),T(rn)!=="svelte-ip8mlz"&&(rn.textContent=ln),st=a(nn),Re=p(nn,"P",{"data-svelte-h":!0}),T(Re)!=="svelte-68lg8f"&&(Re.innerHTML=qt),Ot=a(nn),G=p(nn,"DIV",{class:!0});var dt=j(G);f(vt.$$.fragment,dt),Se=a(dt),Yt=p(dt,"P",{"data-svelte-h":!0}),T(Yt)!=="svelte-ozz5dt"&&(Yt.innerHTML=P),Q=a(dt),f(Te.$$.fragment,dt),Dt=a(dt),f(Qe.$$.fragment,dt),Rn=a(dt),f(Ie.$$.fragment,dt),dt.forEach(l),nn.forEach(l),wt=a(i),f(It.$$.fragment,i),Ae=a(i),Ze=p(i,"DIV",{class:!0});var we=j(Ze);f(Zt.$$.fragment,we),We=a(we),at=p(we,"P",{"data-svelte-h":!0}),T(at)!=="svelte-1u97t9z"&&(at.textContent=Sn),rt=a(we),bn=p(we,"P",{"data-svelte-h":!0}),T(bn)!=="svelte-68lg8f"&&(bn.innerHTML=vn),A=a(we),X=p(we,"DIV",{class:!0});var Gt=j(X);f(ue.$$.fragment,Gt),Oe=a(Gt),dn=p(Gt,"P",{"data-svelte-h":!0}),T(dn)!=="svelte-1rrrbil"&&(dn.innerHTML=cn),Wt=a(Gt),f(it.$$.fragment,Gt),he=a(Gt),f(Ye.$$.fragment,Gt),Gt.forEach(l),we.forEach(l),Me=a(i),f(lt.$$.fragment,i),q=a(i),H=p(i,"DIV",{class:!0});var $e=j(H);f(m.$$.fragment,$e),x=a($e),ge=p($e,"P",{"data-svelte-h":!0}),T(ge)!=="svelte-1stsyjq"&&(ge.textContent=O),Be=a($e),Kt=p($e,"P",{"data-svelte-h":!0}),T(Kt)!=="svelte-68lg8f"&&(Kt.innerHTML=wn),pn=a($e),V=p($e,"DIV",{class:!0});var xt=j(V);f(De.$$.fragment,xt),Le=a(xt),en=p(xt,"P",{"data-svelte-h":!0}),T(en)!=="svelte-1uh02pz"&&(en.innerHTML=$n),Qn=a(xt),f(xn.$$.fragment,xt),uo=a(xt),f(fe.$$.fragment,xt),xt.forEach(l),$e.forEach(l),yn=a(i),f(jn.$$.fragment,i),Fn=a(i),$t=p(i,"DIV",{class:!0});var Ge=j($t);f(Cn.$$.fragment,Ge),kn=a(Ge),An=p(Ge,"P",{"data-svelte-h":!0}),T(An)!=="svelte-1s4517o"&&(An.innerHTML=zn),io=a(Ge),tn=p(Ge,"P",{"data-svelte-h":!0}),T(tn)!=="svelte-68lg8f"&&(tn.innerHTML=ho),K=a(Ge),se=p(Ge,"DIV",{class:!0});var et=j(se);f(Jn.$$.fragment,et),On=a(et),no=p(et,"P",{"data-svelte-h":!0}),T(no)!=="svelte-260uw7"&&(no.innerHTML=ko),Yn=a(et),f(Un.$$.fragment,et),go=a(et),f(Lt.$$.fragment,et),To=a(et),f(Tn.$$.fragment,et),et.forEach(l),Ge.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(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(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(ne,"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(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(ze,"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(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(Ze,"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(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(se,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F($t,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(i,v){_(e,i,v),h(i,u,v),h(i,n,v),_(r,n,null),o(n,M),o(n,t),o(n,un),o(n,ie),o(n,hn),o(n,le),o(n,gn),o(n,W),_(on,W,null),o(W,fn),o(W,de),o(W,_n),_(I,W,null),o(W,Pt),_(tt,W,null),h(i,xe,v),_(Ft,i,v),h(i,_t,v),h(i,ce,v),_(bt,ce,null),o(ce,ye),o(ce,te),_(Z,te,null),o(te,yt),o(te,U),o(te,Vn),_(je,te,null),o(te,sn),_(Ct,te,null),h(i,zt,v),_(Fe,i,v),h(i,an,v),h(i,N,v),_(Jt,N,null),o(N,L),o(N,pe),o(N,Ht),o(N,ne),_(Vt,ne,null),o(ne,Et),o(ne,oe),o(ne,He),_(Y,ne,null),o(ne,En),_(Ce,ne,null),h(i,kt,v),_(Ut,i,v),h(i,ke,v),h(i,ze,v),_(Je,ze,null),o(ze,ot),o(ze,J),o(ze,St),o(ze,R),_(Qt,R,null),o(R,Tt),o(R,me),o(R,At),_(Ve,R,null),o(R,Xn),_(Ue,R,null),o(R,Ee),_(Mt,R,null),h(i,Bt,v),_(qe,i,v),h(i,D,v),h(i,E,v),_(S,E,null),o(E,Xe),o(E,rn),o(E,st),o(E,Re),o(E,Ot),o(E,G),_(vt,G,null),o(G,Se),o(G,Yt),o(G,Q),_(Te,G,null),o(G,Dt),_(Qe,G,null),o(G,Rn),_(Ie,G,null),h(i,wt,v),_(It,i,v),h(i,Ae,v),h(i,Ze,v),_(Zt,Ze,null),o(Ze,We),o(Ze,at),o(Ze,rt),o(Ze,bn),o(Ze,A),o(Ze,X),_(ue,X,null),o(X,Oe),o(X,dn),o(X,Wt),_(it,X,null),o(X,he),_(Ye,X,null),h(i,Me,v),_(lt,i,v),h(i,q,v),h(i,H,v),_(m,H,null),o(H,x),o(H,ge),o(H,Be),o(H,Kt),o(H,pn),o(H,V),_(De,V,null),o(V,Le),o(V,en),o(V,Qn),_(xn,V,null),o(V,uo),_(fe,V,null),h(i,yn,v),_(jn,i,v),h(i,Fn,v),h(i,$t,v),_(Cn,$t,null),o($t,kn),o($t,An),o($t,io),o($t,tn),o($t,K),o($t,se),_(Jn,se,null),o(se,On),o(se,no),o(se,Yn),_(Un,se,null),o(se,go),_(Lt,se,null),o(se,To),_(Tn,se,null),qn=!0},p(i,v){const Ke={};v&2&&(Ke.$$scope={dirty:v,ctx:i}),I.$set(Ke);const Dn={};v&2&&(Dn.$$scope={dirty:v,ctx:i}),tt.$set(Dn);const Nt={};v&2&&(Nt.$$scope={dirty:v,ctx:i}),je.$set(Nt);const ae={};v&2&&(ae.$$scope={dirty:v,ctx:i}),Ct.$set(ae);const Ne={};v&2&&(Ne.$$scope={dirty:v,ctx:i}),Y.$set(Ne);const mn={};v&2&&(mn.$$scope={dirty:v,ctx:i}),Ce.$set(mn);const ve={};v&2&&(ve.$$scope={dirty:v,ctx:i}),Ve.$set(ve);const nn={};v&2&&(nn.$$scope={dirty:v,ctx:i}),Ue.$set(nn);const dt={};v&2&&(dt.$$scope={dirty:v,ctx:i}),Mt.$set(dt);const we={};v&2&&(we.$$scope={dirty:v,ctx:i}),Te.$set(we);const Gt={};v&2&&(Gt.$$scope={dirty:v,ctx:i}),Qe.$set(Gt);const $e={};v&2&&($e.$$scope={dirty:v,ctx:i}),Ie.$set($e);const xt={};v&2&&(xt.$$scope={dirty:v,ctx:i}),it.$set(xt);const Ge={};v&2&&(Ge.$$scope={dirty:v,ctx:i}),Ye.$set(Ge);const et={};v&2&&(et.$$scope={dirty:v,ctx:i}),xn.$set(et);const re={};v&2&&(re.$$scope={dirty:v,ctx:i}),fe.$set(re);const Kn={};v&2&&(Kn.$$scope={dirty:v,ctx:i}),Un.$set(Kn);const Mo={};v&2&&(Mo.$$scope={dirty:v,ctx:i}),Lt.$set(Mo);const oo={};v&2&&(oo.$$scope={dirty:v,ctx:i}),Tn.$set(oo)},i(i){qn||(b(e.$$.fragment,i),b(r.$$.fragment,i),b(on.$$.fragment,i),b(I.$$.fragment,i),b(tt.$$.fragment,i),b(Ft.$$.fragment,i),b(bt.$$.fragment,i),b(Z.$$.fragment,i),b(je.$$.fragment,i),b(Ct.$$.fragment,i),b(Fe.$$.fragment,i),b(Jt.$$.fragment,i),b(Vt.$$.fragment,i),b(Y.$$.fragment,i),b(Ce.$$.fragment,i),b(Ut.$$.fragment,i),b(Je.$$.fragment,i),b(Qt.$$.fragment,i),b(Ve.$$.fragment,i),b(Ue.$$.fragment,i),b(Mt.$$.fragment,i),b(qe.$$.fragment,i),b(S.$$.fragment,i),b(vt.$$.fragment,i),b(Te.$$.fragment,i),b(Qe.$$.fragment,i),b(Ie.$$.fragment,i),b(It.$$.fragment,i),b(Zt.$$.fragment,i),b(ue.$$.fragment,i),b(it.$$.fragment,i),b(Ye.$$.fragment,i),b(lt.$$.fragment,i),b(m.$$.fragment,i),b(De.$$.fragment,i),b(xn.$$.fragment,i),b(fe.$$.fragment,i),b(jn.$$.fragment,i),b(Cn.$$.fragment,i),b(Jn.$$.fragment,i),b(Un.$$.fragment,i),b(Lt.$$.fragment,i),b(Tn.$$.fragment,i),qn=!0)},o(i){y(e.$$.fragment,i),y(r.$$.fragment,i),y(on.$$.fragment,i),y(I.$$.fragment,i),y(tt.$$.fragment,i),y(Ft.$$.fragment,i),y(bt.$$.fragment,i),y(Z.$$.fragment,i),y(je.$$.fragment,i),y(Ct.$$.fragment,i),y(Fe.$$.fragment,i),y(Jt.$$.fragment,i),y(Vt.$$.fragment,i),y(Y.$$.fragment,i),y(Ce.$$.fragment,i),y(Ut.$$.fragment,i),y(Je.$$.fragment,i),y(Qt.$$.fragment,i),y(Ve.$$.fragment,i),y(Ue.$$.fragment,i),y(Mt.$$.fragment,i),y(qe.$$.fragment,i),y(S.$$.fragment,i),y(vt.$$.fragment,i),y(Te.$$.fragment,i),y(Qe.$$.fragment,i),y(Ie.$$.fragment,i),y(It.$$.fragment,i),y(Zt.$$.fragment,i),y(ue.$$.fragment,i),y(it.$$.fragment,i),y(Ye.$$.fragment,i),y(lt.$$.fragment,i),y(m.$$.fragment,i),y(De.$$.fragment,i),y(xn.$$.fragment,i),y(fe.$$.fragment,i),y(jn.$$.fragment,i),y(Cn.$$.fragment,i),y(Jn.$$.fragment,i),y(Un.$$.fragment,i),y(Lt.$$.fragment,i),y(Tn.$$.fragment,i),qn=!1},d(i){i&&(l(u),l(n),l(xe),l(_t),l(ce),l(zt),l(an),l(N),l(kt),l(ke),l(ze),l(Bt),l(D),l(E),l(wt),l(Ae),l(Ze),l(Me),l(q),l(H),l(yn),l(Fn),l($t)),k(e,i),k(r),k(on),k(I),k(tt),k(Ft,i),k(bt),k(Z),k(je),k(Ct),k(Fe,i),k(Jt),k(Vt),k(Y),k(Ce),k(Ut,i),k(Je),k(Qt),k(Ve),k(Ue),k(Mt),k(qe,i),k(S),k(vt),k(Te),k(Qe),k(Ie),k(It,i),k(Zt),k(ue),k(it),k(Ye),k(lt,i),k(m),k(De),k(xn),k(fe),k(jn,i),k(Cn),k(Jn),k(Un),k(Lt),k(Tn)}}}function Vs(w){let e,u;return e=new us({props:{$$slots:{default:[Hs]},$$scope:{ctx:w}}}),{c(){g(e.$$.fragment)},l(n){f(e.$$.fragment,n)},m(n,r){_(e,n,r),u=!0},p(n,r){const M={};r&2&&(M.$$scope={dirty:r,ctx:n}),e.$set(M)},i(n){u||(b(e.$$.fragment,n),u=!0)},o(n){y(e.$$.fragment,n),u=!1},d(n){k(e,n)}}}function Es(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}),T(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Xs(w){let e,u="Example:",n,r,M;return r=new be({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, 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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function Rs(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}),T(e)!=="svelte-fincs2"&&(e.innerHTML=u)},m(n,r){h(n,e,r)},p:z,d(n){n&&l(e)}}}function Ss(w){let e,u="Example:",n,r,M;return r=new be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}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}),T(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 be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(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}),T(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 be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(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}),T(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 be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(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}),T(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 be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(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}),T(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 be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(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}),T(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 be({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(),g(r.$$.fragment)},l(t){e=p(t,"P",{"data-svelte-h":!0}),T(e)!=="svelte-11lpom8"&&(e.textContent=u),n=a(t),f(r.$$.fragment,t)},m(t,B){h(t,e,B),h(t,n,B),_(r,t,B),M=!0},p:z,i(t){M||(b(r.$$.fragment,t),M=!0)},o(t){y(r.$$.fragment,t),M=!1},d(t){t&&(l(e),l(n)),k(r,t)}}}function ra(w){let e,u,n,r,M,t,B="The bare BigBird Model transformer outputting raw hidden-states without any specific head on top.",un,ie,so=`This model inherits from <a href="/docs/transformers/pr_31161/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)`,hn,le,ao=`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.`,gn,W,on="Finally, this model supports inherent JAX features such as:",fn,de,ro='<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,I,Pt,tt,xe,Ft="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",_t,ce,bt,ye,te,Z,yt,U,nt,Vn,je,sn="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.",Ct,zt,Fe=`This model inherits from <a href="/docs/transformers/pr_31161/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)`,an,N,Jt=`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.`,L,pe,eo="Finally, this model supports inherent JAX features such as:",Ht,ne,Vt='<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>',Et,oe,Xt,He,Y,En="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Ce,kt,Ut,ke,ze,Je,ot,J,Rt,St,R,Qt=`BigBird Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
autoregressive tasks.`,Tt,me,to=`This model inherits from <a href="/docs/transformers/pr_31161/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)`,At,Ve,Xn=`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.`,Ue,Ee,Mt="Finally, this model supports inherent JAX features such as:",Bt,qe,D='<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>',E,S,Xe,rn,ln,st="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Re,qt,Ot,G,vt,Se,Yt,P,Q,Te,Dt,Qe="BigBird Model with a <code>language modeling</code> head on top.",Rn,Ie,wt=`This model inherits from <a href="/docs/transformers/pr_31161/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)`,It,Ae,Ze=`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.`,Zt,We,at="Finally, this model supports inherent JAX features such as:",Sn,rt,bn='<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>',vn,A,X,ue,Oe,dn="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",cn,Wt,it,he,Ye,Me,lt,q,H,m,x,ge=`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.`,O,Be,Kt=`This model inherits from <a href="/docs/transformers/pr_31161/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)`,wn,pn,V=`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.`,De,Le,en="Finally, this model supports inherent JAX features such as:",$n,Qn,xn='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',uo,fe,yn,jn,Fn,$t="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Cn,kn,An,zn,io,tn,ho,K,se,Jn,On,no=`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.`,ko,Yn,Un=`This model inherits from <a href="/docs/transformers/pr_31161/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)`,go,Lt,To=`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.`,Tn,qn,i="Finally, this model supports inherent JAX features such as:",v,Ke,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>',Nt,ae,Ne,mn,ve,nn="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",dt,we,Gt,$e,xt,Ge,et,re,Kn,Mo,oo,es=`BigBird Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.`,Wo,Bo,ts=`This model inherits from <a href="/docs/transformers/pr_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,Lo,vo,ns=`This model is also a
<a href="https://flax.readthedocs.io/en/latest/api_reference/flax.linen/module.html" rel="nofollow">flax.linen.Module</a> subclass. Use it as
a regular Flax linen Module and refer to the Flax documentation for all matter related to general usage and
behavior.`,No,wo,os="Finally, this model supports inherent JAX features such as:",Go,$o,ss='<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>',Po,Mn,fo,Ho,xo,as="The <code>FlaxBigBirdPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Vo,lo,Eo,co,qo,_o,Io,Pe,bo,Xo,jo,rs=`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>).`,Ro,Fo,is=`This model inherits from <a href="/docs/transformers/pr_31161/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)`,So,Co,ls=`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.`,Qo,zo,ds="Finally, this model supports inherent JAX features such as:",Ao,Jo,cs='<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>',Oo,Bn,yo,Yo,Uo,ps='The <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.FlaxBigBirdForQuestionAnswering">FlaxBigBirdForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',Do,po,Ko,mo,Zo;return e=new ee({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_31161/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_31161/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_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1886"}}),Pt=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling"
>transformers.modeling_flax_outputs.FlaxBaseModelOutputWithPooling</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ce=new jt({props:{$$slots:{default:[Es]},$$scope:{ctx:w}}}),ye=new _e({props:{anchor:"transformers.FlaxBigBirdModel.__call__.example",$$slots:{default:[Xs]},$$scope:{ctx:w}}}),Z=new ee({props:{title:"FlaxBigBirdForPreTraining",local:"transformers.FlaxBigBirdForPreTraining",headingTag:"h2"}}),nt=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_31161/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_31161/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_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/big_bird/modeling_flax_big_bird.py#L1960"}}),Xt=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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>
`}}),kt=new jt({props:{$$slots:{default:[Rs]},$$scope:{ctx:w}}}),ke=new _e({props:{anchor:"transformers.FlaxBigBirdForPreTraining.__call__.example",$$slots:{default:[Ss]},$$scope:{ctx:w}}}),Je=new ee({props:{title:"FlaxBigBirdForCausalLM",local:"transformers.FlaxBigBirdForCausalLM",headingTag:"h2"}}),Rt=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_31161/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_31161/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_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2592"}}),Xe=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions"
>transformers.modeling_flax_outputs.FlaxCausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),qt=new jt({props:{$$slots:{default:[Qs]},$$scope:{ctx:w}}}),G=new _e({props:{anchor:"transformers.FlaxBigBirdForCausalLM.__call__.example",$$slots:{default:[As]},$$scope:{ctx:w}}}),Se=new ee({props:{title:"FlaxBigBirdForMaskedLM",local:"transformers.FlaxBigBirdForMaskedLM",headingTag:"h2"}}),Q=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_31161/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_31161/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_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2059"}}),X=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Wt=new jt({props:{$$slots:{default:[Os]},$$scope:{ctx:w}}}),he=new _e({props:{anchor:"transformers.FlaxBigBirdForMaskedLM.__call__.example",$$slots:{default:[Ys]},$$scope:{ctx:w}}}),Me=new ee({props:{title:"FlaxBigBirdForSequenceClassification",local:"transformers.FlaxBigBirdForSequenceClassification",headingTag:"h2"}}),H=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_31161/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_31161/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_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2143"}}),yn=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),kn=new jt({props:{$$slots:{default:[Ds]},$$scope:{ctx:w}}}),zn=new _e({props:{anchor:"transformers.FlaxBigBirdForSequenceClassification.__call__.example",$$slots:{default:[Ks]},$$scope:{ctx:w}}}),tn=new ee({props:{title:"FlaxBigBirdForMultipleChoice",local:"transformers.FlaxBigBirdForMultipleChoice",headingTag:"h2"}}),se=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_31161/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_31161/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_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/src/transformers/models/big_bird/modeling_flax_big_bird.py#L2225"}}),Ne=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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),we=new jt({props:{$$slots:{default:[ea]},$$scope:{ctx:w}}}),$e=new _e({props:{anchor:"transformers.FlaxBigBirdForMultipleChoice.__call__.example",$$slots:{default:[ta]},$$scope:{ctx:w}}}),Ge=new ee({props:{title:"FlaxBigBirdForTokenClassification",local:"transformers.FlaxBigBirdForTokenClassification",headingTag:"h2"}}),Kn=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_31161/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_31161/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_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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_31161/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_31161/ja/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),lo=new jt({props:{$$slots:{default:[na]},$$scope:{ctx:w}}}),co=new _e({props:{anchor:"transformers.FlaxBigBirdForTokenClassification.__call__.example",$$slots:{default:[oa]},$$scope:{ctx:w}}}),_o=new ee({props:{title:"FlaxBigBirdForQuestionAnswering",local:"transformers.FlaxBigBirdForQuestionAnswering",headingTag:"h2"}}),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_31161/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_31161/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_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31161/ja/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31161/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_31161/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31161/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31161/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_31161/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_31161/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_31161/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>
`}}),po=new jt({props:{$$slots:{default:[sa]},$$scope:{ctx:w}}}),mo=new _e({props:{anchor:"transformers.FlaxBigBirdForQuestionAnswering.__call__.example",$$slots:{default:[aa]},$$scope:{ctx:w}}}),{c(){g(e.$$.fragment),u=s(),n=c("div"),g(r.$$.fragment),M=s(),t=c("p"),t.textContent=B,un=s(),ie=c("p"),ie.innerHTML=so,hn=s(),le=c("p"),le.innerHTML=ao,gn=s(),W=c("p"),W.textContent=on,fn=s(),de=c("ul"),de.innerHTML=ro,_n=s(),I=c("div"),g(Pt.$$.fragment),tt=s(),xe=c("p"),xe.innerHTML=Ft,_t=s(),g(ce.$$.fragment),bt=s(),g(ye.$$.fragment),te=s(),g(Z.$$.fragment),yt=s(),U=c("div"),g(nt.$$.fragment),Vn=s(),je=c("p"),je.innerHTML=sn,Ct=s(),zt=c("p"),zt.innerHTML=Fe,an=s(),N=c("p"),N.innerHTML=Jt,L=s(),pe=c("p"),pe.textContent=eo,Ht=s(),ne=c("ul"),ne.innerHTML=Vt,Et=s(),oe=c("div"),g(Xt.$$.fragment),He=s(),Y=c("p"),Y.innerHTML=En,Ce=s(),g(kt.$$.fragment),Ut=s(),g(ke.$$.fragment),ze=s(),g(Je.$$.fragment),ot=s(),J=c("div"),g(Rt.$$.fragment),St=s(),R=c("p"),R.textContent=Qt,Tt=s(),me=c("p"),me.innerHTML=to,At=s(),Ve=c("p"),Ve.innerHTML=Xn,Ue=s(),Ee=c("p"),Ee.textContent=Mt,Bt=s(),qe=c("ul"),qe.innerHTML=D,E=s(),S=c("div"),g(Xe.$$.fragment),rn=s(),ln=c("p"),ln.innerHTML=st,Re=s(),g(qt.$$.fragment),Ot=s(),g(G.$$.fragment),vt=s(),g(Se.$$.fragment),Yt=s(),P=c("div"),g(Q.$$.fragment),Te=s(),Dt=c("p"),Dt.innerHTML=Qe,Rn=s(),Ie=c("p"),Ie.innerHTML=wt,It=s(),Ae=c("p"),Ae.innerHTML=Ze,Zt=s(),We=c("p"),We.textContent=at,Sn=s(),rt=c("ul"),rt.innerHTML=bn,vn=s(),A=c("div"),g(X.$$.fragment),ue=s(),Oe=c("p"),Oe.innerHTML=dn,cn=s(),g(Wt.$$.fragment),it=s(),g(he.$$.fragment),Ye=s(),g(Me.$$.fragment),lt=s(),q=c("div"),g(H.$$.fragment),m=s(),x=c("p"),x.textContent=ge,O=s(),Be=c("p"),Be.innerHTML=Kt,wn=s(),pn=c("p"),pn.innerHTML=V,De=s(),Le=c("p"),Le.textContent=en,$n=s(),Qn=c("ul"),Qn.innerHTML=xn,uo=s(),fe=c("div"),g(yn.$$.fragment),jn=s(),Fn=c("p"),Fn.innerHTML=$t,Cn=s(),g(kn.$$.fragment),An=s(),g(zn.$$.fragment),io=s(),g(tn.$$.fragment),ho=s(),K=c("div"),g(se.$$.fragment),Jn=s(),On=c("p"),On.textContent=no,ko=s(),Yn=c("p"),Yn.innerHTML=Un,go=s(),Lt=c("p"),Lt.innerHTML=To,Tn=s(),qn=c("p"),qn.textContent=i,v=s(),Ke=c("ul"),Ke.innerHTML=Dn,Nt=s(),ae=c("div"),g(Ne.$$.fragment),mn=s(),ve=c("p"),ve.innerHTML=nn,dt=s(),g(we.$$.fragment),Gt=s(),g($e.$$.fragment),xt=s(),g(Ge.$$.fragment),et=s(),re=c("div"),g(Kn.$$.fragment),Mo=s(),oo=c("p"),oo.textContent=es,Wo=s(),Bo=c("p"),Bo.innerHTML=ts,Lo=s(),vo=c("p"),vo.innerHTML=ns,No=s(),wo=c("p"),wo.textContent=os,Go=s(),$o=c("ul"),$o.innerHTML=ss,Po=s(),Mn=c("div"),g(fo.$$.fragment),Ho=s(),xo=c("p"),xo.innerHTML=as,Vo=s(),g(lo.$$.fragment),Eo=s(),g(co.$$.fragment),qo=s(),g(_o.$$.fragment),Io=s(),Pe=c("div"),g(bo.$$.fragment),Xo=s(),jo=c("p"),jo.innerHTML=rs,Ro=s(),Fo=c("p"),Fo.innerHTML=is,So=s(),Co=c("p"),Co.innerHTML=ls,Qo=s(),zo=c("p"),zo.textContent=ds,Ao=s(),Jo=c("ul"),Jo.innerHTML=cs,Oo=s(),Bn=c("div"),g(yo.$$.fragment),Yo=s(),Uo=c("p"),Uo.innerHTML=ps,Do=s(),g(po.$$.fragment),Ko=s(),g(mo.$$.fragment),this.h()},l(d){f(e.$$.fragment,d),u=a(d),n=p(d,"DIV",{class:!0});var $=j(n);f(r.$$.fragment,$),M=a($),t=p($,"P",{"data-svelte-h":!0}),T(t)!=="svelte-1lmko6o"&&(t.textContent=B),un=a($),ie=p($,"P",{"data-svelte-h":!0}),T(ie)!=="svelte-1p70jqt"&&(ie.innerHTML=so),hn=a($),le=p($,"P",{"data-svelte-h":!0}),T(le)!=="svelte-10nfsf3"&&(le.innerHTML=ao),gn=a($),W=p($,"P",{"data-svelte-h":!0}),T(W)!=="svelte-1pplc4a"&&(W.textContent=on),fn=a($),de=p($,"UL",{"data-svelte-h":!0}),T(de)!=="svelte-1w7z84m"&&(de.innerHTML=ro),_n=a($),I=p($,"DIV",{class:!0});var In=j(I);f(Pt.$$.fragment,In),tt=a(In),xe=p(In,"P",{"data-svelte-h":!0}),T(xe)!=="svelte-1qm0bay"&&(xe.innerHTML=Ft),_t=a(In),f(ce.$$.fragment,In),bt=a(In),f(ye.$$.fragment,In),In.forEach(l),$.forEach(l),te=a(d),f(Z.$$.fragment,d),yt=a(d),U=p(d,"DIV",{class:!0});var ct=j(U);f(nt.$$.fragment,ct),Vn=a(ct),je=p(ct,"P",{"data-svelte-h":!0}),T(je)!=="svelte-1hro5a3"&&(je.innerHTML=sn),Ct=a(ct),zt=p(ct,"P",{"data-svelte-h":!0}),T(zt)!=="svelte-1p70jqt"&&(zt.innerHTML=Fe),an=a(ct),N=p(ct,"P",{"data-svelte-h":!0}),T(N)!=="svelte-10nfsf3"&&(N.innerHTML=Jt),L=a(ct),pe=p(ct,"P",{"data-svelte-h":!0}),T(pe)!=="svelte-1pplc4a"&&(pe.textContent=eo),Ht=a(ct),ne=p(ct,"UL",{"data-svelte-h":!0}),T(ne)!=="svelte-1w7z84m"&&(ne.innerHTML=Vt),Et=a(ct),oe=p(ct,"DIV",{class:!0});var Zn=j(oe);f(Xt.$$.fragment,Zn),He=a(Zn),Y=p(Zn,"P",{"data-svelte-h":!0}),T(Y)!=="svelte-1qm0bay"&&(Y.innerHTML=En),Ce=a(Zn),f(kt.$$.fragment,Zn),Ut=a(Zn),f(ke.$$.fragment,Zn),Zn.forEach(l),ct.forEach(l),ze=a(d),f(Je.$$.fragment,d),ot=a(d),J=p(d,"DIV",{class:!0});var pt=j(J);f(Rt.$$.fragment,pt),St=a(pt),R=p(pt,"P",{"data-svelte-h":!0}),T(R)!=="svelte-15formz"&&(R.textContent=Qt),Tt=a(pt),me=p(pt,"P",{"data-svelte-h":!0}),T(me)!=="svelte-1p70jqt"&&(me.innerHTML=to),At=a(pt),Ve=p(pt,"P",{"data-svelte-h":!0}),T(Ve)!=="svelte-10nfsf3"&&(Ve.innerHTML=Xn),Ue=a(pt),Ee=p(pt,"P",{"data-svelte-h":!0}),T(Ee)!=="svelte-1pplc4a"&&(Ee.textContent=Mt),Bt=a(pt),qe=p(pt,"UL",{"data-svelte-h":!0}),T(qe)!=="svelte-1w7z84m"&&(qe.innerHTML=D),E=a(pt),S=p(pt,"DIV",{class:!0});var Wn=j(S);f(Xe.$$.fragment,Wn),rn=a(Wn),ln=p(Wn,"P",{"data-svelte-h":!0}),T(ln)!=="svelte-1qm0bay"&&(ln.innerHTML=st),Re=a(Wn),f(qt.$$.fragment,Wn),Ot=a(Wn),f(G.$$.fragment,Wn),Wn.forEach(l),pt.forEach(l),vt=a(d),f(Se.$$.fragment,d),Yt=a(d),P=p(d,"DIV",{class:!0});var mt=j(P);f(Q.$$.fragment,mt),Te=a(mt),Dt=p(mt,"P",{"data-svelte-h":!0}),T(Dt)!=="svelte-1pm8ai3"&&(Dt.innerHTML=Qe),Rn=a(mt),Ie=p(mt,"P",{"data-svelte-h":!0}),T(Ie)!=="svelte-1p70jqt"&&(Ie.innerHTML=wt),It=a(mt),Ae=p(mt,"P",{"data-svelte-h":!0}),T(Ae)!=="svelte-10nfsf3"&&(Ae.innerHTML=Ze),Zt=a(mt),We=p(mt,"P",{"data-svelte-h":!0}),T(We)!=="svelte-1pplc4a"&&(We.textContent=at),Sn=a(mt),rt=p(mt,"UL",{"data-svelte-h":!0}),T(rt)!=="svelte-1w7z84m"&&(rt.innerHTML=bn),vn=a(mt),A=p(mt,"DIV",{class:!0});var Ln=j(A);f(X.$$.fragment,Ln),ue=a(Ln),Oe=p(Ln,"P",{"data-svelte-h":!0}),T(Oe)!=="svelte-1qm0bay"&&(Oe.innerHTML=dn),cn=a(Ln),f(Wt.$$.fragment,Ln),it=a(Ln),f(he.$$.fragment,Ln),Ln.forEach(l),mt.forEach(l),Ye=a(d),f(Me.$$.fragment,d),lt=a(d),q=p(d,"DIV",{class:!0});var ut=j(q);f(H.$$.fragment,ut),m=a(ut),x=p(ut,"P",{"data-svelte-h":!0}),T(x)!=="svelte-ip8mlz"&&(x.textContent=ge),O=a(ut),Be=p(ut,"P",{"data-svelte-h":!0}),T(Be)!=="svelte-1p70jqt"&&(Be.innerHTML=Kt),wn=a(ut),pn=p(ut,"P",{"data-svelte-h":!0}),T(pn)!=="svelte-10nfsf3"&&(pn.innerHTML=V),De=a(ut),Le=p(ut,"P",{"data-svelte-h":!0}),T(Le)!=="svelte-1pplc4a"&&(Le.textContent=en),$n=a(ut),Qn=p(ut,"UL",{"data-svelte-h":!0}),T(Qn)!=="svelte-1w7z84m"&&(Qn.innerHTML=xn),uo=a(ut),fe=p(ut,"DIV",{class:!0});var Nn=j(fe);f(yn.$$.fragment,Nn),jn=a(Nn),Fn=p(Nn,"P",{"data-svelte-h":!0}),T(Fn)!=="svelte-1qm0bay"&&(Fn.innerHTML=$t),Cn=a(Nn),f(kn.$$.fragment,Nn),An=a(Nn),f(zn.$$.fragment,Nn),Nn.forEach(l),ut.forEach(l),io=a(d),f(tn.$$.fragment,d),ho=a(d),K=p(d,"DIV",{class:!0});var ht=j(K);f(se.$$.fragment,ht),Jn=a(ht),On=p(ht,"P",{"data-svelte-h":!0}),T(On)!=="svelte-1u97t9z"&&(On.textContent=no),ko=a(ht),Yn=p(ht,"P",{"data-svelte-h":!0}),T(Yn)!=="svelte-1p70jqt"&&(Yn.innerHTML=Un),go=a(ht),Lt=p(ht,"P",{"data-svelte-h":!0}),T(Lt)!=="svelte-10nfsf3"&&(Lt.innerHTML=To),Tn=a(ht),qn=p(ht,"P",{"data-svelte-h":!0}),T(qn)!=="svelte-1pplc4a"&&(qn.textContent=i),v=a(ht),Ke=p(ht,"UL",{"data-svelte-h":!0}),T(Ke)!=="svelte-1w7z84m"&&(Ke.innerHTML=Dn),Nt=a(ht),ae=p(ht,"DIV",{class:!0});var Gn=j(ae);f(Ne.$$.fragment,Gn),mn=a(Gn),ve=p(Gn,"P",{"data-svelte-h":!0}),T(ve)!=="svelte-1qm0bay"&&(ve.innerHTML=nn),dt=a(Gn),f(we.$$.fragment,Gn),Gt=a(Gn),f($e.$$.fragment,Gn),Gn.forEach(l),ht.forEach(l),xt=a(d),f(Ge.$$.fragment,d),et=a(d),re=p(d,"DIV",{class:!0});var gt=j(re);f(Kn.$$.fragment,gt),Mo=a(gt),oo=p(gt,"P",{"data-svelte-h":!0}),T(oo)!=="svelte-1stsyjq"&&(oo.textContent=es),Wo=a(gt),Bo=p(gt,"P",{"data-svelte-h":!0}),T(Bo)!=="svelte-1p70jqt"&&(Bo.innerHTML=ts),Lo=a(gt),vo=p(gt,"P",{"data-svelte-h":!0}),T(vo)!=="svelte-10nfsf3"&&(vo.innerHTML=ns),No=a(gt),wo=p(gt,"P",{"data-svelte-h":!0}),T(wo)!=="svelte-1pplc4a"&&(wo.textContent=os),Go=a(gt),$o=p(gt,"UL",{"data-svelte-h":!0}),T($o)!=="svelte-1w7z84m"&&($o.innerHTML=ss),Po=a(gt),Mn=p(gt,"DIV",{class:!0});var Pn=j(Mn);f(fo.$$.fragment,Pn),Ho=a(Pn),xo=p(Pn,"P",{"data-svelte-h":!0}),T(xo)!=="svelte-1qm0bay"&&(xo.innerHTML=as),Vo=a(Pn),f(lo.$$.fragment,Pn),Eo=a(Pn),f(co.$$.fragment,Pn),Pn.forEach(l),gt.forEach(l),qo=a(d),f(_o.$$.fragment,d),Io=a(d),Pe=p(d,"DIV",{class:!0});var ft=j(Pe);f(bo.$$.fragment,ft),Xo=a(ft),jo=p(ft,"P",{"data-svelte-h":!0}),T(jo)!=="svelte-1s4517o"&&(jo.innerHTML=rs),Ro=a(ft),Fo=p(ft,"P",{"data-svelte-h":!0}),T(Fo)!=="svelte-1p70jqt"&&(Fo.innerHTML=is),So=a(ft),Co=p(ft,"P",{"data-svelte-h":!0}),T(Co)!=="svelte-10nfsf3"&&(Co.innerHTML=ls),Qo=a(ft),zo=p(ft,"P",{"data-svelte-h":!0}),T(zo)!=="svelte-1pplc4a"&&(zo.textContent=ds),Ao=a(ft),Jo=p(ft,"UL",{"data-svelte-h":!0}),T(Jo)!=="svelte-1w7z84m"&&(Jo.innerHTML=cs),Oo=a(ft),Bn=p(ft,"DIV",{class:!0});var Hn=j(Bn);f(yo.$$.fragment,Hn),Yo=a(Hn),Uo=p(Hn,"P",{"data-svelte-h":!0}),T(Uo)!=="svelte-13gvm5z"&&(Uo.innerHTML=ps),Do=a(Hn),f(po.$$.fragment,Hn),Ko=a(Hn),f(mo.$$.fragment,Hn),Hn.forEach(l),ft.forEach(l),this.h()},h(){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(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(oe,"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(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(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(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(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(fe,"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(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(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Mn,"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(Bn,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Pe,"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,$){_(e,d,$),h(d,u,$),h(d,n,$),_(r,n,null),o(n,M),o(n,t),o(n,un),o(n,ie),o(n,hn),o(n,le),o(n,gn),o(n,W),o(n,fn),o(n,de),o(n,_n),o(n,I),_(Pt,I,null),o(I,tt),o(I,xe),o(I,_t),_(ce,I,null),o(I,bt),_(ye,I,null),h(d,te,$),_(Z,d,$),h(d,yt,$),h(d,U,$),_(nt,U,null),o(U,Vn),o(U,je),o(U,Ct),o(U,zt),o(U,an),o(U,N),o(U,L),o(U,pe),o(U,Ht),o(U,ne),o(U,Et),o(U,oe),_(Xt,oe,null),o(oe,He),o(oe,Y),o(oe,Ce),_(kt,oe,null),o(oe,Ut),_(ke,oe,null),h(d,ze,$),_(Je,d,$),h(d,ot,$),h(d,J,$),_(Rt,J,null),o(J,St),o(J,R),o(J,Tt),o(J,me),o(J,At),o(J,Ve),o(J,Ue),o(J,Ee),o(J,Bt),o(J,qe),o(J,E),o(J,S),_(Xe,S,null),o(S,rn),o(S,ln),o(S,Re),_(qt,S,null),o(S,Ot),_(G,S,null),h(d,vt,$),_(Se,d,$),h(d,Yt,$),h(d,P,$),_(Q,P,null),o(P,Te),o(P,Dt),o(P,Rn),o(P,Ie),o(P,It),o(P,Ae),o(P,Zt),o(P,We),o(P,Sn),o(P,rt),o(P,vn),o(P,A),_(X,A,null),o(A,ue),o(A,Oe),o(A,cn),_(Wt,A,null),o(A,it),_(he,A,null),h(d,Ye,$),_(Me,d,$),h(d,lt,$),h(d,q,$),_(H,q,null),o(q,m),o(q,x),o(q,O),o(q,Be),o(q,wn),o(q,pn),o(q,De),o(q,Le),o(q,$n),o(q,Qn),o(q,uo),o(q,fe),_(yn,fe,null),o(fe,jn),o(fe,Fn),o(fe,Cn),_(kn,fe,null),o(fe,An),_(zn,fe,null),h(d,io,$),_(tn,d,$),h(d,ho,$),h(d,K,$),_(se,K,null),o(K,Jn),o(K,On),o(K,ko),o(K,Yn),o(K,go),o(K,Lt),o(K,Tn),o(K,qn),o(K,v),o(K,Ke),o(K,Nt),o(K,ae),_(Ne,ae,null),o(ae,mn),o(ae,ve),o(ae,dt),_(we,ae,null),o(ae,Gt),_($e,ae,null),h(d,xt,$),_(Ge,d,$),h(d,et,$),h(d,re,$),_(Kn,re,null),o(re,Mo),o(re,oo),o(re,Wo),o(re,Bo),o(re,Lo),o(re,vo),o(re,No),o(re,wo),o(re,Go),o(re,$o),o(re,Po),o(re,Mn),_(fo,Mn,null),o(Mn,Ho),o(Mn,xo),o(Mn,Vo),_(lo,Mn,null),o(Mn,Eo),_(co,Mn,null),h(d,qo,$),_(_o,d,$),h(d,Io,$),h(d,Pe,$),_(bo,Pe,null),o(Pe,Xo),o(Pe,jo),o(Pe,Ro),o(Pe,Fo),o(Pe,So),o(Pe,Co),o(Pe,Qo),o(Pe,zo),o(Pe,Ao),o(Pe,Jo),o(Pe,Oo),o(Pe,Bn),_(yo,Bn,null),o(Bn,Yo),o(Bn,Uo),o(Bn,Do),_(po,Bn,null),o(Bn,Ko),_(mo,Bn,null),Zo=!0},p(d,$){const In={};$&2&&(In.$$scope={dirty:$,ctx:d}),ce.$set(In);const ct={};$&2&&(ct.$$scope={dirty:$,ctx:d}),ye.$set(ct);const Zn={};$&2&&(Zn.$$scope={dirty:$,ctx:d}),kt.$set(Zn);const pt={};$&2&&(pt.$$scope={dirty:$,ctx:d}),ke.$set(pt);const Wn={};$&2&&(Wn.$$scope={dirty:$,ctx:d}),qt.$set(Wn);const mt={};$&2&&(mt.$$scope={dirty:$,ctx:d}),G.$set(mt);const Ln={};$&2&&(Ln.$$scope={dirty:$,ctx:d}),Wt.$set(Ln);const ut={};$&2&&(ut.$$scope={dirty:$,ctx:d}),he.$set(ut);const Nn={};$&2&&(Nn.$$scope={dirty:$,ctx:d}),kn.$set(Nn);const ht={};$&2&&(ht.$$scope={dirty:$,ctx:d}),zn.$set(ht);const Gn={};$&2&&(Gn.$$scope={dirty:$,ctx:d}),we.$set(Gn);const gt={};$&2&&(gt.$$scope={dirty:$,ctx:d}),$e.$set(gt);const Pn={};$&2&&(Pn.$$scope={dirty:$,ctx:d}),lo.$set(Pn);const ft={};$&2&&(ft.$$scope={dirty:$,ctx:d}),co.$set(ft);const Hn={};$&2&&(Hn.$$scope={dirty:$,ctx:d}),po.$set(Hn);const ms={};$&2&&(ms.$$scope={dirty:$,ctx:d}),mo.$set(ms)},i(d){Zo||(b(e.$$.fragment,d),b(r.$$.fragment,d),b(Pt.$$.fragment,d),b(ce.$$.fragment,d),b(ye.$$.fragment,d),b(Z.$$.fragment,d),b(nt.$$.fragment,d),b(Xt.$$.fragment,d),b(kt.$$.fragment,d),b(ke.$$.fragment,d),b(Je.$$.fragment,d),b(Rt.$$.fragment,d),b(Xe.$$.fragment,d),b(qt.$$.fragment,d),b(G.$$.fragment,d),b(Se.$$.fragment,d),b(Q.$$.fragment,d),b(X.$$.fragment,d),b(Wt.$$.fragment,d),b(he.$$.fragment,d),b(Me.$$.fragment,d),b(H.$$.fragment,d),b(yn.$$.fragment,d),b(kn.$$.fragment,d),b(zn.$$.fragment,d),b(tn.$$.fragment,d),b(se.$$.fragment,d),b(Ne.$$.fragment,d),b(we.$$.fragment,d),b($e.$$.fragment,d),b(Ge.$$.fragment,d),b(Kn.$$.fragment,d),b(fo.$$.fragment,d),b(lo.$$.fragment,d),b(co.$$.fragment,d),b(_o.$$.fragment,d),b(bo.$$.fragment,d),b(yo.$$.fragment,d),b(po.$$.fragment,d),b(mo.$$.fragment,d),Zo=!0)},o(d){y(e.$$.fragment,d),y(r.$$.fragment,d),y(Pt.$$.fragment,d),y(ce.$$.fragment,d),y(ye.$$.fragment,d),y(Z.$$.fragment,d),y(nt.$$.fragment,d),y(Xt.$$.fragment,d),y(kt.$$.fragment,d),y(ke.$$.fragment,d),y(Je.$$.fragment,d),y(Rt.$$.fragment,d),y(Xe.$$.fragment,d),y(qt.$$.fragment,d),y(G.$$.fragment,d),y(Se.$$.fragment,d),y(Q.$$.fragment,d),y(X.$$.fragment,d),y(Wt.$$.fragment,d),y(he.$$.fragment,d),y(Me.$$.fragment,d),y(H.$$.fragment,d),y(yn.$$.fragment,d),y(kn.$$.fragment,d),y(zn.$$.fragment,d),y(tn.$$.fragment,d),y(se.$$.fragment,d),y(Ne.$$.fragment,d),y(we.$$.fragment,d),y($e.$$.fragment,d),y(Ge.$$.fragment,d),y(Kn.$$.fragment,d),y(fo.$$.fragment,d),y(lo.$$.fragment,d),y(co.$$.fragment,d),y(_o.$$.fragment,d),y(bo.$$.fragment,d),y(yo.$$.fragment,d),y(po.$$.fragment,d),y(mo.$$.fragment,d),Zo=!1},d(d){d&&(l(u),l(n),l(te),l(yt),l(U),l(ze),l(ot),l(J),l(vt),l(Yt),l(P),l(Ye),l(lt),l(q),l(io),l(ho),l(K),l(xt),l(et),l(re),l(qo),l(Io),l(Pe)),k(e,d),k(r),k(Pt),k(ce),k(ye),k(Z,d),k(nt),k(Xt),k(kt),k(ke),k(Je,d),k(Rt),k(Xe),k(qt),k(G),k(Se,d),k(Q),k(X),k(Wt),k(he),k(Me,d),k(H),k(yn),k(kn),k(zn),k(tn,d),k(se),k(Ne),k(we),k($e),k(Ge,d),k(Kn),k(fo),k(lo),k(co),k(_o,d),k(bo),k(yo),k(po),k(mo)}}}function ia(w){let e,u;return e=new us({props:{$$slots:{default:[ra]},$$scope:{ctx:w}}}),{c(){g(e.$$.fragment)},l(n){f(e.$$.fragment,n)},m(n,r){_(e,n,r),u=!0},p(n,r){const M={};r&2&&(M.$$scope={dirty:r,ctx:n}),e.$set(M)},i(n){u||(b(e.$$.fragment,n),u=!0)},o(n){y(e.$$.fragment,n),u=!1},d(n){k(e,n)}}}function la(w){let e,u,n,r,M,t,B,un,ie,so=`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。`,hn,le,ao="論文の要約は次のとおりです。",gn,W,on=`<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>`,fn,de,ro="チップ：",_n,I,Pt=`<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>`,tt,xe,Ft,_t,ce='<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>',bt,ye,te,Z,yt,U,nt,Vn=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_31161/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.`,je,sn,Ct=`Configuration objects inherit from <a href="/docs/transformers/pr_31161/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_31161/ja/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,zt,Fe,an,N,Jt,L,pe,eo,Ht,ne='Construct a BigBird tokenizer. Based on <a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a>.',Vt,Et,oe=`This tokenizer inherits from <a href="/docs/transformers/pr_31161/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.`,Xt,He,Y,En,Ce,kt=`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:`,Ut,ke,ze="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",Je,ot,J,Rt,St,R=`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.`,Qt,Tt,me,to,At,Ve=`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).`,Xn,Ue,Ee,Mt,Bt,qe,D,E,S,Xe,rn=`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_31161/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`,ln,st,Re,qt,Ot,G=`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:`,vt,Se,Yt="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",P,Q,Te,Dt,Qe,Rn="Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT",Ie,wt,It,Ae,Ze="if token_ids_1 is None, only returns the first portion of the mask (0s).",Zt,We,at,Sn,rt,bn=`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.`,vn,A,X,ue,Oe,dn,cn,Wt='Output type of <a href="/docs/transformers/pr_31161/ja/model_doc/big_bird#transformers.BigBirdForPreTraining">BigBirdForPreTraining</a>.',it,he,Ye,Me,lt,q,H;return M=new ee({props:{title:"BigBird",local:"bigbird",headingTag:"h1"}}),B=new ee({props:{title:"Overview",local:"overview",headingTag:"h2"}}),xe=new ee({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"}}),ye=new ee({props:{title:"BigBirdConfig",local:"transformers.BigBirdConfig",headingTag:"h2"}}),yt=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_31161/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_31161/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_31161/src/transformers/models/big_bird/configuration_big_bird.py#L28"}}),Fe=new _e({props:{anchor:"transformers.BigBirdConfig.example",$$slots:{default:[Ts]},$$scope:{ctx:w}}}),N=new ee({props:{title:"BigBirdTokenizer",local:"transformers.BigBirdTokenizer",headingTag:"h2"}}),pe=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_31161/src/transformers/models/big_bird/tokenization_big_bird.py#L33"}}),Y=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_31161/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>
`}}),J=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_31161/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>
`}}),me=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_31161/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>
`}}),Ee=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_31161/src/transformers/models/big_bird/tokenization_big_bird.py#L231"}}),Bt=new ee({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_31161/src/transformers/models/big_bird/tokenization_big_bird_fast.py#L38"}}),Re=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_31161/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>
`}}),Te=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_31161/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>
`}}),wt=new _e({props:{anchor:"transformers.BigBirdTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[Ms]},$$scope:{ctx:w}}}),at=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_31161/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>
`}}),A=new ee({props:{title:"BigBird specific outputs",local:"transformers.models.big_bird.modeling_big_bird.BigBirdForPreTrainingOutput",headingTag:"h2"}}),Oe=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_31161/src/transformers/models/big_bird/modeling_big_bird.py#L1834"}}),he=new ys({props:{pytorch:!0,tensorflow:!1,jax:!0,$$slots:{jax:[ia],pytorch:[Vs]},$$scope:{ctx:w}}}),Me=new ks({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(),g(M.$$.fragment),t=s(),g(B.$$.fragment),un=s(),ie=c("p"),ie.innerHTML=so,hn=s(),le=c("p"),le.textContent=ao,gn=s(),W=c("p"),W.innerHTML=on,fn=s(),de=c("p"),de.textContent=ro,_n=s(),I=c("ul"),I.innerHTML=Pt,tt=s(),g(xe.$$.fragment),Ft=s(),_t=c("ul"),_t.innerHTML=ce,bt=s(),g(ye.$$.fragment),te=s(),Z=c("div"),g(yt.$$.fragment),U=s(),nt=c("p"),nt.innerHTML=Vn,je=s(),sn=c("p"),sn.innerHTML=Ct,zt=s(),g(Fe.$$.fragment),an=s(),g(N.$$.fragment),Jt=s(),L=c("div"),g(pe.$$.fragment),eo=s(),Ht=c("p"),Ht.innerHTML=ne,Vt=s(),Et=c("p"),Et.innerHTML=oe,Xt=s(),He=c("div"),g(Y.$$.fragment),En=s(),Ce=c("p"),Ce.textContent=kt,Ut=s(),ke=c("ul"),ke.innerHTML=ze,Je=s(),ot=c("div"),g(J.$$.fragment),Rt=s(),St=c("p"),St.innerHTML=R,Qt=s(),Tt=c("div"),g(me.$$.fragment),to=s(),At=c("p"),At.innerHTML=Ve,Xn=s(),Ue=c("div"),g(Ee.$$.fragment),Mt=s(),g(Bt.$$.fragment),qe=s(),D=c("div"),g(E.$$.fragment),S=s(),Xe=c("p"),Xe.innerHTML=rn,ln=s(),st=c("div"),g(Re.$$.fragment),qt=s(),Ot=c("p"),Ot.textContent=G,vt=s(),Se=c("ul"),Se.innerHTML=Yt,P=s(),Q=c("div"),g(Te.$$.fragment),Dt=s(),Qe=c("p"),Qe.textContent=Rn,Ie=s(),g(wt.$$.fragment),It=s(),Ae=c("p"),Ae.textContent=Ze,Zt=s(),We=c("div"),g(at.$$.fragment),Sn=s(),rt=c("p"),rt.innerHTML=bn,vn=s(),g(A.$$.fragment),X=s(),ue=c("div"),g(Oe.$$.fragment),dn=s(),cn=c("p"),cn.innerHTML=Wt,it=s(),g(he.$$.fragment),Ye=s(),g(Me.$$.fragment),lt=s(),q=c("p"),this.h()},l(m){const x=bs("svelte-u9bgzb",document.head);e=p(x,"META",{name:!0,content:!0}),x.forEach(l),u=a(m),n=p(m,"P",{}),j(n).forEach(l),r=a(m),f(M.$$.fragment,m),t=a(m),f(B.$$.fragment,m),un=a(m),ie=p(m,"P",{"data-svelte-h":!0}),T(ie)!=="svelte-17xuljh"&&(ie.innerHTML=so),hn=a(m),le=p(m,"P",{"data-svelte-h":!0}),T(le)!=="svelte-1cv3nri"&&(le.textContent=ao),gn=a(m),W=p(m,"P",{"data-svelte-h":!0}),T(W)!=="svelte-1n7i5w4"&&(W.innerHTML=on),fn=a(m),de=p(m,"P",{"data-svelte-h":!0}),T(de)!=="svelte-fhhf9b"&&(de.textContent=ro),_n=a(m),I=p(m,"UL",{"data-svelte-h":!0}),T(I)!=="svelte-2igudh"&&(I.innerHTML=Pt),tt=a(m),f(xe.$$.fragment,m),Ft=a(m),_t=p(m,"UL",{"data-svelte-h":!0}),T(_t)!=="svelte-hsgc6s"&&(_t.innerHTML=ce),bt=a(m),f(ye.$$.fragment,m),te=a(m),Z=p(m,"DIV",{class:!0});var ge=j(Z);f(yt.$$.fragment,ge),U=a(ge),nt=p(ge,"P",{"data-svelte-h":!0}),T(nt)!=="svelte-1c5gegv"&&(nt.innerHTML=Vn),je=a(ge),sn=p(ge,"P",{"data-svelte-h":!0}),T(sn)!=="svelte-1l1uvv3"&&(sn.innerHTML=Ct),zt=a(ge),f(Fe.$$.fragment,ge),ge.forEach(l),an=a(m),f(N.$$.fragment,m),Jt=a(m),L=p(m,"DIV",{class:!0});var O=j(L);f(pe.$$.fragment,O),eo=a(O),Ht=p(O,"P",{"data-svelte-h":!0}),T(Ht)!=="svelte-icdx2b"&&(Ht.innerHTML=ne),Vt=a(O),Et=p(O,"P",{"data-svelte-h":!0}),T(Et)!=="svelte-sg0uha"&&(Et.innerHTML=oe),Xt=a(O),He=p(O,"DIV",{class:!0});var Be=j(He);f(Y.$$.fragment,Be),En=a(Be),Ce=p(Be,"P",{"data-svelte-h":!0}),T(Ce)!=="svelte-1wtdd6g"&&(Ce.textContent=kt),Ut=a(Be),ke=p(Be,"UL",{"data-svelte-h":!0}),T(ke)!=="svelte-xi6653"&&(ke.innerHTML=ze),Be.forEach(l),Je=a(O),ot=p(O,"DIV",{class:!0});var Kt=j(ot);f(J.$$.fragment,Kt),Rt=a(Kt),St=p(Kt,"P",{"data-svelte-h":!0}),T(St)!=="svelte-1f4f5kp"&&(St.innerHTML=R),Kt.forEach(l),Qt=a(O),Tt=p(O,"DIV",{class:!0});var wn=j(Tt);f(me.$$.fragment,wn),to=a(wn),At=p(wn,"P",{"data-svelte-h":!0}),T(At)!=="svelte-nvbrp2"&&(At.innerHTML=Ve),wn.forEach(l),Xn=a(O),Ue=p(O,"DIV",{class:!0});var pn=j(Ue);f(Ee.$$.fragment,pn),pn.forEach(l),O.forEach(l),Mt=a(m),f(Bt.$$.fragment,m),qe=a(m),D=p(m,"DIV",{class:!0});var V=j(D);f(E.$$.fragment,V),S=a(V),Xe=p(V,"P",{"data-svelte-h":!0}),T(Xe)!=="svelte-1gmaecq"&&(Xe.innerHTML=rn),ln=a(V),st=p(V,"DIV",{class:!0});var De=j(st);f(Re.$$.fragment,De),qt=a(De),Ot=p(De,"P",{"data-svelte-h":!0}),T(Ot)!=="svelte-1of3j8a"&&(Ot.textContent=G),vt=a(De),Se=p(De,"UL",{"data-svelte-h":!0}),T(Se)!=="svelte-xi6653"&&(Se.innerHTML=Yt),De.forEach(l),P=a(V),Q=p(V,"DIV",{class:!0});var Le=j(Q);f(Te.$$.fragment,Le),Dt=a(Le),Qe=p(Le,"P",{"data-svelte-h":!0}),T(Qe)!=="svelte-13bfd60"&&(Qe.textContent=Rn),Ie=a(Le),f(wt.$$.fragment,Le),It=a(Le),Ae=p(Le,"P",{"data-svelte-h":!0}),T(Ae)!=="svelte-wtrslu"&&(Ae.textContent=Ze),Le.forEach(l),Zt=a(V),We=p(V,"DIV",{class:!0});var en=j(We);f(at.$$.fragment,en),Sn=a(en),rt=p(en,"P",{"data-svelte-h":!0}),T(rt)!=="svelte-1l6i2y2"&&(rt.innerHTML=bn),en.forEach(l),V.forEach(l),vn=a(m),f(A.$$.fragment,m),X=a(m),ue=p(m,"DIV",{class:!0});var $n=j(ue);f(Oe.$$.fragment,$n),dn=a($n),cn=p($n,"P",{"data-svelte-h":!0}),T(cn)!=="svelte-1co82lf"&&(cn.innerHTML=Wt),$n.forEach(l),it=a(m),f(he.$$.fragment,m),Ye=a(m),f(Me.$$.fragment,m),lt=a(m),q=p(m,"P",{}),j(q).forEach(l),this.h()},h(){F(e,"name","hf:doc:metadata"),F(e,"content",da),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(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(ot,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Tt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),F(Ue,"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(st,"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(We,"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(ue,"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){o(document.head,e),h(m,u,x),h(m,n,x),h(m,r,x),_(M,m,x),h(m,t,x),_(B,m,x),h(m,un,x),h(m,ie,x),h(m,hn,x),h(m,le,x),h(m,gn,x),h(m,W,x),h(m,fn,x),h(m,de,x),h(m,_n,x),h(m,I,x),h(m,tt,x),_(xe,m,x),h(m,Ft,x),h(m,_t,x),h(m,bt,x),_(ye,m,x),h(m,te,x),h(m,Z,x),_(yt,Z,null),o(Z,U),o(Z,nt),o(Z,je),o(Z,sn),o(Z,zt),_(Fe,Z,null),h(m,an,x),_(N,m,x),h(m,Jt,x),h(m,L,x),_(pe,L,null),o(L,eo),o(L,Ht),o(L,Vt),o(L,Et),o(L,Xt),o(L,He),_(Y,He,null),o(He,En),o(He,Ce),o(He,Ut),o(He,ke),o(L,Je),o(L,ot),_(J,ot,null),o(ot,Rt),o(ot,St),o(L,Qt),o(L,Tt),_(me,Tt,null),o(Tt,to),o(Tt,At),o(L,Xn),o(L,Ue),_(Ee,Ue,null),h(m,Mt,x),_(Bt,m,x),h(m,qe,x),h(m,D,x),_(E,D,null),o(D,S),o(D,Xe),o(D,ln),o(D,st),_(Re,st,null),o(st,qt),o(st,Ot),o(st,vt),o(st,Se),o(D,P),o(D,Q),_(Te,Q,null),o(Q,Dt),o(Q,Qe),o(Q,Ie),_(wt,Q,null),o(Q,It),o(Q,Ae),o(D,Zt),o(D,We),_(at,We,null),o(We,Sn),o(We,rt),h(m,vn,x),_(A,m,x),h(m,X,x),h(m,ue,x),_(Oe,ue,null),o(ue,dn),o(ue,cn),h(m,it,x),_(he,m,x),h(m,Ye,x),_(Me,m,x),h(m,lt,x),h(m,q,x),H=!0},p(m,[x]){const ge={};x&2&&(ge.$$scope={dirty:x,ctx:m}),Fe.$set(ge);const O={};x&2&&(O.$$scope={dirty:x,ctx:m}),wt.$set(O);const Be={};x&2&&(Be.$$scope={dirty:x,ctx:m}),he.$set(Be)},i(m){H||(b(M.$$.fragment,m),b(B.$$.fragment,m),b(xe.$$.fragment,m),b(ye.$$.fragment,m),b(yt.$$.fragment,m),b(Fe.$$.fragment,m),b(N.$$.fragment,m),b(pe.$$.fragment,m),b(Y.$$.fragment,m),b(J.$$.fragment,m),b(me.$$.fragment,m),b(Ee.$$.fragment,m),b(Bt.$$.fragment,m),b(E.$$.fragment,m),b(Re.$$.fragment,m),b(Te.$$.fragment,m),b(wt.$$.fragment,m),b(at.$$.fragment,m),b(A.$$.fragment,m),b(Oe.$$.fragment,m),b(he.$$.fragment,m),b(Me.$$.fragment,m),H=!0)},o(m){y(M.$$.fragment,m),y(B.$$.fragment,m),y(xe.$$.fragment,m),y(ye.$$.fragment,m),y(yt.$$.fragment,m),y(Fe.$$.fragment,m),y(N.$$.fragment,m),y(pe.$$.fragment,m),y(Y.$$.fragment,m),y(J.$$.fragment,m),y(me.$$.fragment,m),y(Ee.$$.fragment,m),y(Bt.$$.fragment,m),y(E.$$.fragment,m),y(Re.$$.fragment,m),y(Te.$$.fragment,m),y(wt.$$.fragment,m),y(at.$$.fragment,m),y(A.$$.fragment,m),y(Oe.$$.fragment,m),y(he.$$.fragment,m),y(Me.$$.fragment,m),H=!1},d(m){m&&(l(u),l(n),l(r),l(t),l(un),l(ie),l(hn),l(le),l(gn),l(W),l(fn),l(de),l(_n),l(I),l(tt),l(Ft),l(_t),l(bt),l(te),l(Z),l(an),l(Jt),l(L),l(Mt),l(qe),l(D),l(vn),l(X),l(ue),l(it),l(Ye),l(lt),l(q)),l(e),k(M,m),k(B,m),k(xe,m),k(ye,m),k(yt),k(Fe),k(N,m),k(pe),k(Y),k(J),k(me),k(Ee),k(Bt,m),k(E),k(Re),k(Te),k(wt),k(at),k(A,m),k(Oe),k(he,m),k(Me,m)}}}const da='{"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":"FlaxBigBirdForPreTraining","local":"transformers.FlaxBigBirdForPreTraining","sections":[],"depth":2},{"title":"FlaxBigBirdForCausalLM","local":"transformers.FlaxBigBirdForCausalLM","sections":[],"depth":2},{"title":"FlaxBigBirdForMaskedLM","local":"transformers.FlaxBigBirdForMaskedLM","sections":[],"depth":2},{"title":"FlaxBigBirdForSequenceClassification","local":"transformers.FlaxBigBirdForSequenceClassification","sections":[],"depth":2},{"title":"FlaxBigBirdForMultipleChoice","local":"transformers.FlaxBigBirdForMultipleChoice","sections":[],"depth":2},{"title":"FlaxBigBirdForTokenClassification","local":"transformers.FlaxBigBirdForTokenClassification","sections":[],"depth":2},{"title":"FlaxBigBirdForQuestionAnswering","local":"transformers.FlaxBigBirdForQuestionAnswering","sections":[],"depth":2}],"depth":1}';function ca(w){return gs(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ya extends fs{constructor(e){super(),_s(this,e,ca,la,hs,{})}}export{ya as component};
