import{s as Dm,o as Um,n as B}from"../chunks/scheduler.9bc65507.js";import{S as Wm,i as Im,g as c,s as a,r as f,A as Zm,h as p,f as i,c as r,j as C,u as g,x as u,k as j,y as s,a as m,v as _,d as b,t as T,w as M}from"../chunks/index.707bf1b6.js";import{T as I}from"../chunks/Tip.c2ecdbf4.js";import{D as J}from"../chunks/Docstring.17db21ae.js";import{C as W}from"../chunks/CodeBlock.54a9f38d.js";import{E as U}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{P as ri}from"../chunks/PipelineTag.44585822.js";import{H as Z}from"../chunks/Heading.342b1fa6.js";function Gm($){let t,h="Examples:",o,l,k;return l=new W({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DistilBertConfig, DistilBertModel

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss`,wrap:!1}}),{c(){t=c("p"),t.textContent=h,o=a(),f(l.$$.fragment)},l(n){t=p(n,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),o=r(n),g(l.$$.fragment,n)},m(n,y){m(n,t,y),m(n,o,y),_(l,n,y),k=!0},p:B,i(n){k||(b(l.$$.fragment,n),k=!0)},o(n){T(l.$$.fragment,n),k=!1},d(n){n&&(i(t),i(o)),M(l,n)}}}function eu($){let t,h="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",o,l,k="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",n,y,N=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,D,x,G=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,z,F,q=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){t=c("p"),t.innerHTML=h,o=a(),l=c("ul"),l.innerHTML=k,n=a(),y=c("p"),y.innerHTML=N,D=a(),x=c("ul"),x.innerHTML=G,z=a(),F=c("p"),F.innerHTML=q},l(w){t=p(w,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=h),o=r(w),l=p(w,"UL",{"data-svelte-h":!0}),u(l)!=="svelte-qm1t26"&&(l.innerHTML=k),n=r(w),y=p(w,"P",{"data-svelte-h":!0}),u(y)!=="svelte-1v9qsc5"&&(y.innerHTML=N),D=r(w),x=p(w,"UL",{"data-svelte-h":!0}),u(x)!=="svelte-15scerc"&&(x.innerHTML=G),z=r(w),F=p(w,"P",{"data-svelte-h":!0}),u(F)!=="svelte-1an3odd"&&(F.innerHTML=q)},m(w,v){m(w,t,v),m(w,o,v),m(w,l,v),m(w,n,v),m(w,y,v),m(w,D,v),m(w,x,v),m(w,z,v),m(w,F,v)},p:B,d(w){w&&(i(t),i(o),i(l),i(n),i(y),i(D),i(x),i(z),i(F))}}}function tu($){let t,h=`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(){t=c("p"),t.innerHTML=h},l(o){t=p(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(o,l){m(o,t,l)},p:B,d(o){o&&i(t)}}}function nu($){let t,h="Example:",o,l,k;return l=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBURkRpc3RpbEJlcnRNb2RlbCUwQWltcG9ydCUyMHRlbnNvcmZsb3clMjBhcyUyMHRmJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIpJTBBJTBBaW5wdXRzJTIwJTNEJTIwdG9rZW5pemVyKCUyMkhlbGxvJTJDJTIwbXklMjBkb2clMjBpcyUyMGN1dGUlMjIlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnRmJTIyKSUwQW91dHB1dHMlMjAlM0QlMjBtb2RlbChpbnB1dHMpJTBBJTBBbGFzdF9oaWRkZW5fc3RhdGVzJTIwJTNEJTIwb3V0cHV0cy5sYXN0X2hpZGRlbl9zdGF0ZQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFDistilBertModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

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

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

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

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

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=labels)`,wrap:!1}}),{c(){f(t.$$.fragment)},l(o){g(t.$$.fragment,o)},m(o,l){_(t,o,l),h=!0},p:B,i(o){h||(b(t.$$.fragment,o),h=!0)},o(o){T(t.$$.fragment,o),h=!1},d(o){M(t,o)}}}function iu($){let t,h="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",o,l,k="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",n,y,N=`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
and layers. Because of this support, when using methods like <code>model.fit()</code> things should “just work” for you - just
pass your inputs and labels in any format that <code>model.fit()</code> supports! If, however, you want to use the second
format outside of Keras methods like <code>fit()</code> and <code>predict()</code>, such as when creating your own layers or models with
the Keras <code>Functional</code> API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`,D,x,G=`<li>a single Tensor with <code>input_ids</code> only and nothing else: <code>model(input_ids)</code></li> <li>a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
<code>model([input_ids, attention_mask])</code> or <code>model([input_ids, attention_mask, token_type_ids])</code></li> <li>a dictionary with one or several input Tensors associated to the input names given in the docstring:
<code>model({&quot;input_ids&quot;: input_ids, &quot;token_type_ids&quot;: token_type_ids})</code></li>`,z,F,q=`Note that when creating models and layers with
<a href="https://keras.io/guides/making_new_layers_and_models_via_subclassing/" rel="nofollow">subclassing</a> then you don’t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`;return{c(){t=c("p"),t.innerHTML=h,o=a(),l=c("ul"),l.innerHTML=k,n=a(),y=c("p"),y.innerHTML=N,D=a(),x=c("ul"),x.innerHTML=G,z=a(),F=c("p"),F.innerHTML=q},l(w){t=p(w,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=h),o=r(w),l=p(w,"UL",{"data-svelte-h":!0}),u(l)!=="svelte-qm1t26"&&(l.innerHTML=k),n=r(w),y=p(w,"P",{"data-svelte-h":!0}),u(y)!=="svelte-1v9qsc5"&&(y.innerHTML=N),D=r(w),x=p(w,"UL",{"data-svelte-h":!0}),u(x)!=="svelte-15scerc"&&(x.innerHTML=G),z=r(w),F=p(w,"P",{"data-svelte-h":!0}),u(F)!=="svelte-1an3odd"&&(F.innerHTML=q)},m(w,v){m(w,t,v),m(w,o,v),m(w,l,v),m(w,n,v),m(w,y,v),m(w,D,v),m(w,x,v),m(w,z,v),m(w,F,v)},p:B,d(w){w&&(i(t),i(o),i(l),i(n),i(y),i(D),i(x),i(z),i(F))}}}function lu($){let t,h=`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(){t=c("p"),t.innerHTML=h},l(o){t=p(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(o,l){m(o,t,l)},p:B,d(o){o&&i(t)}}}function du($){let t,h="Example:",o,l,k;return l=new W({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, TFDistilBertForSequenceClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tensorflow <span class="hljs-keyword">as</span> tf

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, start_positions=target_start_index, end_positions=target_end_index)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = tf.math.reduce_mean(outputs.loss)`,wrap:!1}}),{c(){f(t.$$.fragment)},l(o){g(t.$$.fragment,o)},m(o,l){_(t,o,l),h=!0},p:B,i(o){h||(b(t.$$.fragment,o),h=!0)},o(o){T(t.$$.fragment,o),h=!1},d(o){M(t,o)}}}function wu($){let t,h=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=c("p"),t.innerHTML=h},l(o){t=p(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(o,l){m(o,t,l)},p:B,d(o){o&&i(t)}}}function ku($){let t,h="Example:",o,l,k;return l=new W({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxDistilBertModel

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){t=c("p"),t.textContent=h,o=a(),f(l.$$.fragment)},l(n){t=p(n,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),o=r(n),g(l.$$.fragment,n)},m(n,y){m(n,t,y),m(n,o,y),_(l,n,y),k=!0},p:B,i(n){k||(b(l.$$.fragment,n),k=!0)},o(n){T(l.$$.fragment,n),k=!1},d(n){n&&(i(t),i(o)),M(l,n)}}}function $u($){let t,h=`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(){t=c("p"),t.innerHTML=h},l(o){t=p(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(o,l){m(o,t,l)},p:B,d(o){o&&i(t)}}}function vu($){let t,h="Example:",o,l,k;return l=new W({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxDistilBertForMaskedLM

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

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

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){t=c("p"),t.textContent=h,o=a(),f(l.$$.fragment)},l(n){t=p(n,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),o=r(n),g(l.$$.fragment,n)},m(n,y){m(n,t,y),m(n,o,y),_(l,n,y),k=!0},p:B,i(n){k||(b(l.$$.fragment,n),k=!0)},o(n){T(l.$$.fragment,n),k=!1},d(n){n&&(i(t),i(o)),M(l,n)}}}function Bu($){let t,h=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=c("p"),t.innerHTML=h},l(o){t=p(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(o,l){m(o,t,l)},p:B,d(o){o&&i(t)}}}function ju($){let t,h="Example:",o,l,k;return l=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBGbGF4RGlzdGlsQmVydEZvck11bHRpcGxlQ2hvaWNlJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBGbGF4RGlzdGlsQmVydEZvck11bHRpcGxlQ2hvaWNlLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMiklMEElMEFwcm9tcHQlMjAlM0QlMjAlMjJJbiUyMEl0YWx5JTJDJTIwcGl6emElMjBzZXJ2ZWQlMjBpbiUyMGZvcm1hbCUyMHNldHRpbmdzJTJDJTIwc3VjaCUyMGFzJTIwYXQlMjBhJTIwcmVzdGF1cmFudCUyQyUyMGlzJTIwcHJlc2VudGVkJTIwdW5zbGljZWQuJTIyJTBBY2hvaWNlMCUyMCUzRCUyMCUyMkl0JTIwaXMlMjBlYXRlbiUyMHdpdGglMjBhJTIwZm9yayUyMGFuZCUyMGElMjBrbmlmZS4lMjIlMEFjaG9pY2UxJTIwJTNEJTIwJTIySXQlMjBpcyUyMGVhdGVuJTIwd2hpbGUlMjBoZWxkJTIwaW4lMjB0aGUlMjBoYW5kLiUyMiUwQSUwQWVuY29kaW5nJTIwJTNEJTIwdG9rZW5pemVyKCU1QnByb21wdCUyQyUyMHByb21wdCU1RCUyQyUyMCU1QmNob2ljZTAlMkMlMjBjaG9pY2UxJTVEJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJqYXglMjIlMkMlMjBwYWRkaW5nJTNEVHJ1ZSklMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKiolN0JrJTNBJTIwdiU1Qk5vbmUlMkMlMjAlM0ElNUQlMjBmb3IlMjBrJTJDJTIwdiUyMGluJTIwZW5jb2RpbmcuaXRlbXMoKSU3RCklMEElMEFsb2dpdHMlMjAlM0QlMjBvdXRwdXRzLmxvZ2l0cw==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxDistilBertForMultipleChoice

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

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

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

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

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

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

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

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)
<span class="hljs-meta">&gt;&gt;&gt; </span>start_scores = outputs.start_logits
<span class="hljs-meta">&gt;&gt;&gt; </span>end_scores = outputs.end_logits`,wrap:!1}}),{c(){t=c("p"),t.textContent=h,o=a(),f(l.$$.fragment)},l(n){t=p(n,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),o=r(n),g(l.$$.fragment,n)},m(n,y){m(n,t,y),m(n,o,y),_(l,n,y),k=!0},p:B,i(n){k||(b(l.$$.fragment,n),k=!0)},o(n){T(l.$$.fragment,n),k=!1},d(n){n&&(i(t),i(o)),M(l,n)}}}function Uu($){let t,h,o,l,k,n,y,N='<a href="https://huggingface.co/models?filter=distilbert"><img alt="Models" src="https://img.shields.io/badge/All_model_pages-distilbert-blueviolet"/></a> <a href="https://huggingface.co/spaces/docs-demos/distilbert-base-uncased"><img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue"/></a> <a href="https://huggingface.co/papers/1910.01108"><img alt="Paper page" src="https://img.shields.io/badge/Paper%20page-1910.01108-green"/></a>',D,x,G,z,F=`The DistilBERT model was proposed in the blog post <a href="https://medium.com/huggingface/distilbert-8cf3380435b5" rel="nofollow">Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
distilled version of BERT</a>, and the paper <a href="https://arxiv.org/papers/1910.01108" rel="nofollow">DistilBERT, a
distilled version of BERT: smaller, faster, cheaper and lighter</a>. DistilBERT is a
small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
<em>bert-base-uncased</em>, runs 60% faster while preserving over 95% of BERT’s performances as measured on the GLUE language
understanding benchmark.`,q,w,v="The abstract from the paper is the following:",Sa,bn,Tc=`<em>As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP),
operating these large models in on-the-edge and/or under constrained computational training or inference budgets
remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
study.</em>`,Aa,Tn,Mc="Tips:",Pa,Mn,yc=`<li><p>DistilBERT doesn’t have <code>token_type_ids</code>, you don’t need to indicate which token belongs to which segment. Just
separate your segments with the separation token <code>tokenizer.sep_token</code> (or <code>[SEP]</code>).</p></li> <li><p>DistilBERT doesn’t have options to select the input positions (<code>position_ids</code> input). This could be added if
necessary though, just let us know if you need this option.</p></li> <li><p>Same as BERT but smaller. Trained by distillation of the pretrained BERT model, meaning it’s been trained to predict the same probabilities as the larger model. The actual objective is a combination of:</p> <ul><li>finding the same probabilities as the teacher model</li> <li>predicting the masked tokens correctly (but no next-sentence objective)</li> <li>a cosine similarity between the hidden states of the student and the teacher model</li></ul></li>`,Ya,yn,wc=`This model was contributed by <a href="https://huggingface.co/victorsanh" rel="nofollow">victorsanh</a>. This model jax version was
contributed by <a href="https://huggingface.co/kamalkraj" rel="nofollow">kamalkraj</a>. The original code can be found <a href="https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation" rel="nofollow">here</a>.`,Oa,wn,Ka,kn,kc="A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DistilBERT. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.",er,$n,tr,vn,$c='<li>A blog post on <a href="https://huggingface.co/blog/sentiment-analysis-python" rel="nofollow">Getting Started with Sentiment Analysis using Python</a> with DistilBERT.</li> <li>A blog post on how to <a href="https://huggingface.co/blog/fastai" rel="nofollow">train DistilBERT with Blurr for sequence classification</a>.</li> <li>A blog post on how to use <a href="https://huggingface.co/blog/ray-tune" rel="nofollow">Ray to tune DistilBERT hyperparameters</a>.</li> <li>A blog post on how to <a href="https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face" rel="nofollow">train DistilBERT with Hugging Face and Amazon SageMaker</a>.</li> <li>A notebook on how to <a href="https://colab.research.google.com/github/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb" rel="nofollow">finetune DistilBERT for multi-label classification</a>. 🌎</li> <li>A notebook on how to <a href="https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb" rel="nofollow">finetune DistilBERT for multiclass classification with PyTorch</a>. 🌎</li> <li>A notebook on how to <a href="https://colab.research.google.com/github/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb" rel="nofollow">finetune DistilBERT for text classification in TensorFlow</a>. 🌎</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/text-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/text-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForSequenceClassification">FlaxDistilBertForSequenceClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/flax/text-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/text_classification_flax.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="../tasks/sequence_classification">Text classification task guide</a></li>',nr,xn,sr,Fn,vc='<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/token-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/token-classification" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/token_classification-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForTokenClassification">FlaxDistilBertForTokenClassification</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/flax/token-classification" rel="nofollow">example script</a>.</li> <li><a href="https://huggingface.co/course/chapter7/2?fw=pt" rel="nofollow">Token classification</a> chapter of the 🤗 Hugging Face Course.</li> <li><a href="../tasks/token_classification">Token classification task guide</a></li>',or,Bn,ar,jn,xc='<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/language-modeling#robertabertdistilbert-and-masked-language-modeling" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/language-modeling#run_mlmpy" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForMaskedLM">FlaxDistilBertForMaskedLM</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/flax/language-modeling#masked-language-modeling" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/masked_language_modeling_flax.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="https://huggingface.co/course/chapter7/3?fw=pt" rel="nofollow">Masked language modeling</a> chapter of the 🤗 Hugging Face Course.</li> <li><a href="../tasks/masked_language_modeling">Masked language modeling task guide</a></li>',rr,Cn,ir,Jn,Fc='<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/question-answering" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/question-answering" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/question_answering-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.FlaxDistilBertForQuestionAnswering">FlaxDistilBertForQuestionAnswering</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/flax/question-answering" rel="nofollow">example script</a>.</li> <li><a href="https://huggingface.co/course/chapter7/7?fw=pt" rel="nofollow">Question answering</a> chapter of the 🤗 Hugging Face Course.</li> <li><a href="../tasks/question_answering">Question answering task guide</a></li>',lr,zn,Bc="<strong>Multiple choice</strong>",dr,Dn,jc='<li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/multiple-choice" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> is supported by this <a href="https://github.com/huggingface/transformers/tree/main/examples/tensorflow/multiple-choice" rel="nofollow">example script</a> and <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multiple_choice-tf.ipynb" rel="nofollow">notebook</a>.</li> <li><a href="../tasks/multiple_choice">Multiple choice task guide</a></li>',cr,Un,Cc="⚗️ Optimization",pr,Wn,Jc='<li>A blog post on how to <a href="https://huggingface.co/blog/intel" rel="nofollow">quantize DistilBERT with 🤗 Optimum and Intel</a>.</li> <li>A blog post on how <a href="https://www.philschmid.de/optimizing-transformers-with-optimum-gpu" rel="nofollow">Optimizing Transformers for GPUs with 🤗 Optimum</a>.</li> <li>A blog post on <a href="https://www.philschmid.de/optimizing-transformers-with-optimum" rel="nofollow">Optimizing Transformers with Hugging Face Optimum</a>.</li>',mr,In,zc="⚡️ Inference",ur,Zn,Dc='<li>A blog post on how to <a href="https://huggingface.co/blog/bert-inferentia-sagemaker" rel="nofollow">Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia</a> with DistilBERT.</li> <li>A blog post on <a href="https://www.philschmid.de/sagemaker-serverless-huggingface-distilbert" rel="nofollow">Serverless Inference with Hugging Face’s Transformers, DistilBERT and Amazon SageMaker</a>.</li>',hr,Gn,Uc="🚀 Deploy",fr,qn,Wc='<li>A blog post on how to <a href="https://huggingface.co/blog/how-to-deploy-a-pipeline-to-google-clouds" rel="nofollow">deploy DistilBERT on Google Cloud</a>.</li> <li>A blog post on how to <a href="https://huggingface.co/blog/deploy-hugging-face-models-easily-with-amazon-sagemaker" rel="nofollow">deploy DistilBERT with Amazon SageMaker</a>.</li> <li>A blog post on how to <a href="https://www.philschmid.de/terraform-huggingface-amazon-sagemaker" rel="nofollow">Deploy BERT with Hugging Face Transformers, Amazon SageMaker and Terraform module</a>.</li>',gr,Nn,_r,ve,Ln,ii,eo,Ic=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> or a <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a>. It
is used to instantiate a DistilBERT model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the DistilBERT
<a href="https://huggingface.co/distilbert-base-uncased" rel="nofollow">distilbert-base-uncased</a> architecture.`,li,to,Zc=`Configuration objects inherit from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,di,Tt,br,Rn,Tr,L,Hn,ci,no,Gc="Construct a DistilBERT tokenizer. Based on WordPiece.",pi,so,qc=`This tokenizer inherits from <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`,mi,ft,Xn,ui,oo,Nc=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:`,hi,ao,Lc="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",fi,Mt,Vn,gi,ro,Rc="Converts a sequence of tokens (string) in a single string.",_i,Ge,En,bi,io,Hc="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",Ti,yt,Mi,lo,Xc="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",yi,wt,Qn,wi,co,Vc=`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.`,Mr,Sn,yr,ie,An,ki,po,Ec="Construct a “fast” DistilBERT tokenizer (backed by HuggingFace’s <em>tokenizers</em> library). Based on WordPiece.",$i,mo,Qc=`This tokenizer inherits from <a href="/docs/transformers/main/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`,vi,gt,Pn,xi,uo,Sc=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERT sequence has the following format:`,Fi,ho,Ac="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",Bi,qe,Yn,ji,fo,Pc="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",Ci,kt,Ji,go,Yc="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",wr,On,kr,le,Kn,zi,_o,Oc="The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",Di,bo,Kc=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Ui,To,ep=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Wi,Ne,es,Ii,Mo,tp='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> forward method, overrides the <code>__call__</code> special method.',Zi,$t,Gi,vt,$r,ts,vr,de,ns,qi,yo,np="DistilBert Model with a <code>masked language modeling</code> head on top.",Ni,wo,sp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Li,ko,op=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Ri,Le,ss,Hi,$o,ap='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> forward method, overrides the <code>__call__</code> special method.',Xi,xt,Vi,Ft,xr,os,Fr,ce,as,Ei,vo,rp=`DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`,Qi,xo,ip=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Si,Fo,lp=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Ai,Me,rs,Pi,Bo,dp='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',Yi,Bt,Oi,jt,Ki,Ct,Br,is,jr,pe,ls,el,jo,cp=`DistilBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.`,tl,Co,pp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,nl,Jo,mp=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,sl,Re,ds,ol,zo,up='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> forward method, overrides the <code>__call__</code> special method.',al,Jt,rl,zt,Cr,cs,Jr,me,ps,il,Do,hp=`DistilBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.`,ll,Uo,fp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,dl,Wo,gp=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,cl,He,ms,pl,Io,_p='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',ml,Dt,ul,Ut,zr,us,Dr,ue,hs,hl,Zo,bp=`DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers on top of the hidden-states output to compute <code>span start logits</code> and <code>span end logits</code>).`,fl,Go,Tp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,gl,qo,Mp=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,_l,Xe,fs,bl,No,yp='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',Tl,Wt,Ml,It,Ur,gs,Wr,S,_s,yl,Lo,wp="The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",wl,Ro,kp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,kl,Ho,$p=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">tf.keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,$l,Zt,vl,Ve,bs,xl,Xo,vp='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> forward method, overrides the <code>__call__</code> special method.',Fl,Gt,Bl,qt,Ir,Ts,Zr,A,Ms,jl,Vo,xp="DistilBert Model with a <code>masked language modeling</code> head on top.",Cl,Eo,Fp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Jl,Qo,Bp=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">tf.keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,zl,Nt,Dl,ye,ys,Ul,So,jp='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> forward method, overrides the <code>__call__</code> special method.',Wl,Lt,Il,Rt,Zl,Ht,Gr,ws,qr,P,ks,Gl,Ao,Cp=`DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`,ql,Po,Jp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Nl,Yo,zp=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">tf.keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,Ll,Xt,Rl,we,$s,Hl,Oo,Dp='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',Xl,Vt,Vl,Et,El,Qt,Nr,vs,Lr,Y,xs,Ql,Ko,Up=`DistilBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.`,Sl,ea,Wp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Al,ta,Ip=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">tf.keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,Pl,St,Yl,Ee,Fs,Ol,na,Zp='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> forward method, overrides the <code>__call__</code> special method.',Kl,At,ed,Pt,Rr,Bs,Hr,O,js,td,sa,Gp=`DistilBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.`,nd,oa,qp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,sd,aa,Np=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">tf.keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,od,Yt,ad,ke,Cs,rd,ra,Lp='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',id,Ot,ld,Kt,dd,en,Xr,Js,Vr,K,zs,cd,ia,Rp=`DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layer on top of the hidden-states output to compute <code>span start logits</code> and <code>span end logits</code>).`,pd,la,Hp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel">TFPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,md,da,Xp=`This model is also a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/Model" rel="nofollow">tf.keras.Model</a> subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`,ud,tn,hd,$e,Ds,fd,ca,Vp='The <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',gd,nn,_d,sn,bd,on,Er,Us,Qr,R,Ws,Td,pa,Ep="The bare DistilBert Model transformer outputting raw hidden-states without any specific head on top.",Md,ma,Qp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,yd,ua,Sp=`This model is also a Flax Linen <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" rel="nofollow">flax.linen.Module</a>
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`,wd,ha,Ap="Finally, this model supports inherent JAX features such as:",kd,fa,Pp='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',$d,Qe,Is,vd,ga,Yp="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",xd,an,Fd,rn,Sr,Zs,Ar,H,Gs,Bd,_a,Op="DistilBert Model with a <code>language modeling</code> head on top.",jd,ba,Kp=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,Cd,Ta,em=`This model is also a Flax Linen <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" 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.`,Jd,Ma,tm="Finally, this model supports inherent JAX features such as:",zd,ya,nm='<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>',Dd,Se,qs,Ud,wa,sm="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Wd,ln,Id,dn,Pr,Ns,Yr,X,Ls,Zd,ka,om=`DistilBert Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`,Gd,$a,am=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,qd,va,rm=`This model is also a Flax Linen <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" 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.`,Nd,xa,im="Finally, this model supports inherent JAX features such as:",Ld,Fa,lm='<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>',Rd,Ae,Rs,Hd,Ba,dm="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Xd,cn,Vd,pn,Or,Hs,Kr,V,Xs,Ed,ja,cm=`DistilBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.`,Qd,Ca,pm=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,Sd,Ja,mm=`This model is also a Flax Linen <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" 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.`,Ad,za,um="Finally, this model supports inherent JAX features such as:",Pd,Da,hm='<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>',Yd,Pe,Vs,Od,Ua,fm="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Kd,mn,ec,un,ei,Es,ti,E,Qs,tc,Wa,gm=`DistilBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.`,nc,Ia,_m=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,sc,Za,bm=`This model is also a Flax Linen <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" 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.`,oc,Ga,Tm="Finally, this model supports inherent JAX features such as:",ac,qa,Mm='<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>',rc,Ye,Ss,ic,Na,ym="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",lc,hn,dc,fn,ni,As,si,Q,Ps,cc,La,wm=`DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers on top of the hidden-states output to compute <code>span start logits</code> and <code>span end logits</code>).`,pc,Ra,km=`This model inherits from <a href="/docs/transformers/main/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading, saving and converting weights from PyTorch models)`,mc,Ha,$m=`This model is also a Flax Linen <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" 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.`,uc,Xa,vm="Finally, this model supports inherent JAX features such as:",hc,Va,xm='<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>',fc,Oe,Ys,gc,Ea,Fm="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",_c,gn,bc,_n,oi,Qa,ai;return k=new Z({props:{title:"DistilBERT",local:"distilbert",headingTag:"h1"}}),x=new Z({props:{title:"Overview",local:"overview",headingTag:"h2"}}),wn=new Z({props:{title:"Resources",local:"resources",headingTag:"h2"}}),$n=new ri({props:{pipeline:"text-classification"}}),xn=new ri({props:{pipeline:"token-classification"}}),Bn=new ri({props:{pipeline:"fill-mask"}}),Cn=new ri({props:{pipeline:"question-answering"}}),Nn=new Z({props:{title:"DistilBertConfig",local:"transformers.DistilBertConfig",headingTag:"h2"}}),Ln=new J({props:{name:"class transformers.DistilBertConfig",anchor:"transformers.DistilBertConfig",parameters:[{name:"vocab_size",val:" = 30522"},{name:"max_position_embeddings",val:" = 512"},{name:"sinusoidal_pos_embds",val:" = False"},{name:"n_layers",val:" = 6"},{name:"n_heads",val:" = 12"},{name:"dim",val:" = 768"},{name:"hidden_dim",val:" = 3072"},{name:"dropout",val:" = 0.1"},{name:"attention_dropout",val:" = 0.1"},{name:"activation",val:" = 'gelu'"},{name:"initializer_range",val:" = 0.02"},{name:"qa_dropout",val:" = 0.1"},{name:"seq_classif_dropout",val:" = 0.2"},{name:"pad_token_id",val:" = 0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DistilBertConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30522) &#x2014;
Vocabulary size of the DistilBERT model. Defines the number of different tokens that can be represented by
the <code>inputs_ids</code> passed when calling <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> or <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a>.`,name:"vocab_size"},{anchor:"transformers.DistilBertConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.DistilBertConfig.sinusoidal_pos_embds",description:`<strong>sinusoidal_pos_embds</strong> (<code>boolean</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use sinusoidal positional embeddings.`,name:"sinusoidal_pos_embds"},{anchor:"transformers.DistilBertConfig.n_layers",description:`<strong>n_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"n_layers"},{anchor:"transformers.DistilBertConfig.n_heads",description:`<strong>n_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 12) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"n_heads"},{anchor:"transformers.DistilBertConfig.dim",description:`<strong>dim</strong> (<code>int</code>, <em>optional</em>, defaults to 768) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"dim"},{anchor:"transformers.DistilBertConfig.hidden_dim",description:`<strong>hidden_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 3072) &#x2014;
The size of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in the Transformer encoder.`,name:"hidden_dim"},{anchor:"transformers.DistilBertConfig.dropout",description:`<strong>dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.DistilBertConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.DistilBertConfig.activation",description:`<strong>activation</strong> (<code>str</code> or <code>Callable</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
<code>&quot;relu&quot;</code>, <code>&quot;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"activation"},{anchor:"transformers.DistilBertConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.DistilBertConfig.qa_dropout",description:`<strong>qa_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probabilities used in the question answering model <a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a>.`,name:"qa_dropout"},{anchor:"transformers.DistilBertConfig.seq_classif_dropout",description:`<strong>seq_classif_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.2) &#x2014;
The dropout probabilities used in the sequence classification and the multiple choice model
<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a>.`,name:"seq_classif_dropout"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/configuration_distilbert.py#L45"}}),Tt=new U({props:{anchor:"transformers.DistilBertConfig.example",$$slots:{default:[Gm]},$$scope:{ctx:$}}}),Rn=new Z({props:{title:"DistilBertTokenizer",local:"transformers.DistilBertTokenizer",headingTag:"h2"}}),Hn=new J({props:{name:"class transformers.DistilBertTokenizer",anchor:"transformers.DistilBertTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = True"},{name:"do_basic_tokenize",val:" = True"},{name:"never_split",val:" = None"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DistilBertTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.DistilBertTokenizer.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.DistilBertTokenizer.do_basic_tokenize",description:`<strong>do_basic_tokenize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to do basic tokenization before WordPiece.`,name:"do_basic_tokenize"},{anchor:"transformers.DistilBertTokenizer.never_split",description:`<strong>never_split</strong> (<code>Iterable</code>, <em>optional</em>) &#x2014;
Collection of tokens which will never be split during tokenization. Only has an effect when
<code>do_basic_tokenize=True</code>`,name:"never_split"},{anchor:"transformers.DistilBertTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.DistilBertTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.DistilBertTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.DistilBertTokenizer.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.`,name:"cls_token"},{anchor:"transformers.DistilBertTokenizer.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"},{anchor:"transformers.DistilBertTokenizer.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters.</p>
<p>This should likely be deactivated for Japanese (see this
<a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.DistilBertTokenizer.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original BERT).`,name:"strip_accents"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/tokenization_distilbert.py#L89"}}),Xn=new J({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.DistilBertTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizer.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/tokenization_distilbert.py#L230",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>List[int]</code></p>
`}}),Vn=new J({props:{name:"convert_tokens_to_string",anchor:"transformers.DistilBertTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/tokenization_distilbert.py#L224"}}),En=new J({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.DistilBertTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/tokenization_distilbert.py#L285",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>
`}}),yt=new U({props:{anchor:"transformers.DistilBertTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[qm]},$$scope:{ctx:$}}}),Qn=new J({props:{name:"get_special_tokens_mask",anchor:"transformers.DistilBertTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.DistilBertTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/tokenization_distilbert.py#L256",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>
`}}),Sn=new Z({props:{title:"DistilBertTokenizerFast",local:"transformers.DistilBertTokenizerFast",headingTag:"h2"}}),An=new J({props:{name:"class transformers.DistilBertTokenizerFast",anchor:"transformers.DistilBertTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{name:"unk_token",val:" = '[UNK]'"},{name:"sep_token",val:" = '[SEP]'"},{name:"pad_token",val:" = '[PAD]'"},{name:"cls_token",val:" = '[CLS]'"},{name:"mask_token",val:" = '[MASK]'"},{name:"tokenize_chinese_chars",val:" = True"},{name:"strip_accents",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DistilBertTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
File containing the vocabulary.`,name:"vocab_file"},{anchor:"transformers.DistilBertTokenizerFast.do_lower_case",description:`<strong>do_lower_case</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to lowercase the input when tokenizing.`,name:"do_lower_case"},{anchor:"transformers.DistilBertTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[UNK]&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.DistilBertTokenizerFast.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
sequence classification or for a text and a question for question answering. It is also used as the last
token of a sequence built with special tokens.`,name:"sep_token"},{anchor:"transformers.DistilBertTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[PAD]&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.DistilBertTokenizerFast.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The classifier token which is used when doing sequence classification (classification of the whole sequence
instead of per-token classification). It is the first token of the sequence when built with special tokens.`,name:"cls_token"},{anchor:"transformers.DistilBertTokenizerFast.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"},{anchor:"transformers.DistilBertTokenizerFast.clean_text",description:`<strong>clean_text</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to clean the text before tokenization by removing any control characters and replacing all
whitespaces by the classic one.`,name:"clean_text"},{anchor:"transformers.DistilBertTokenizerFast.tokenize_chinese_chars",description:`<strong>tokenize_chinese_chars</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to tokenize Chinese characters. This should likely be deactivated for Japanese (see <a href="https://github.com/huggingface/transformers/issues/328" rel="nofollow">this
issue</a>).`,name:"tokenize_chinese_chars"},{anchor:"transformers.DistilBertTokenizerFast.strip_accents",description:`<strong>strip_accents</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to strip all accents. If this option is not specified, then it will be determined by the
value for <code>lowercase</code> (as in the original BERT).`,name:"strip_accents"},{anchor:"transformers.DistilBertTokenizerFast.wordpieces_prefix",description:`<strong>wordpieces_prefix</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;##&quot;</code>) &#x2014;
The prefix for subwords.`,name:"wordpieces_prefix"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/tokenization_distilbert_fast.py#L84"}}),Pn=new J({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.DistilBertTokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:""},{name:"token_ids_1",val:" = None"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizerFast.build_inputs_with_special_tokens.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs to which the special tokens will be added.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizerFast.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/tokenization_distilbert_fast.py#L174",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>
`}}),Yn=new J({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.DistilBertTokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"}],parametersDescription:[{anchor:"transformers.DistilBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.DistilBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/tokenization_distilbert_fast.py#L199",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>
`}}),kt=new U({props:{anchor:"transformers.DistilBertTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[Nm]},$$scope:{ctx:$}}}),On=new Z({props:{title:"DistilBertModel",local:"transformers.DistilBertModel",headingTag:"h2"}}),Kn=new J({props:{name:"class transformers.DistilBertModel",anchor:"transformers.DistilBertModel",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L495"}}),es=new J({props:{name:"forward",anchor:"transformers.DistilBertModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DistilBertModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertModel.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L567",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),$t=new I({props:{$$slots:{default:[Lm]},$$scope:{ctx:$}}}),vt=new U({props:{anchor:"transformers.DistilBertModel.forward.example",$$slots:{default:[Rm]},$$scope:{ctx:$}}}),ts=new Z({props:{title:"DistilBertForMaskedLM",local:"transformers.DistilBertForMaskedLM",headingTag:"h2"}}),ns=new J({props:{name:"class transformers.DistilBertForMaskedLM",anchor:"transformers.DistilBertForMaskedLM",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L623"}}),ss=new J({props:{name:"forward",anchor:"transformers.DistilBertForMaskedLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DistilBertForMaskedLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForMaskedLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForMaskedLM.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForMaskedLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForMaskedLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForMaskedLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForMaskedLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForMaskedLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L667",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),xt=new I({props:{$$slots:{default:[Hm]},$$scope:{ctx:$}}}),Ft=new U({props:{anchor:"transformers.DistilBertForMaskedLM.forward.example",$$slots:{default:[Xm]},$$scope:{ctx:$}}}),os=new Z({props:{title:"DistilBertForSequenceClassification",local:"transformers.DistilBertForSequenceClassification",headingTag:"h2"}}),as=new J({props:{name:"class transformers.DistilBertForSequenceClassification",anchor:"transformers.DistilBertForSequenceClassification",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L730"}}),rs=new J({props:{name:"forward",anchor:"transformers.DistilBertForSequenceClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DistilBertForSequenceClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForSequenceClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForSequenceClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForSequenceClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForSequenceClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForSequenceClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForSequenceClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L764",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Bt=new I({props:{$$slots:{default:[Vm]},$$scope:{ctx:$}}}),jt=new U({props:{anchor:"transformers.DistilBertForSequenceClassification.forward.example",$$slots:{default:[Em]},$$scope:{ctx:$}}}),Ct=new U({props:{anchor:"transformers.DistilBertForSequenceClassification.forward.example-2",$$slots:{default:[Qm]},$$scope:{ctx:$}}}),is=new Z({props:{title:"DistilBertForMultipleChoice",local:"transformers.DistilBertForMultipleChoice",headingTag:"h2"}}),ls=new J({props:{name:"class transformers.DistilBertForMultipleChoice",anchor:"transformers.DistilBertForMultipleChoice",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L1059"}}),ds=new J({props:{name:"forward",anchor:"transformers.DistilBertForMultipleChoice.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DistilBertForMultipleChoice.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForMultipleChoice.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForMultipleChoice.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForMultipleChoice.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForMultipleChoice.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForMultipleChoice.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForMultipleChoice.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForMultipleChoice.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices-1]</code> where <code>num_choices</code> is the size of the second dimension of the input tensors. (See
<code>input_ids</code> above)`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L1091",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Jt=new I({props:{$$slots:{default:[Sm]},$$scope:{ctx:$}}}),zt=new U({props:{anchor:"transformers.DistilBertForMultipleChoice.forward.example",$$slots:{default:[Am]},$$scope:{ctx:$}}}),cs=new Z({props:{title:"DistilBertForTokenClassification",local:"transformers.DistilBertForTokenClassification",headingTag:"h2"}}),ps=new J({props:{name:"class transformers.DistilBertForTokenClassification",anchor:"transformers.DistilBertForTokenClassification",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L965"}}),ms=new J({props:{name:"forward",anchor:"transformers.DistilBertForTokenClassification.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DistilBertForTokenClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>({0})</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForTokenClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>({0})</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForTokenClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForTokenClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>({0}, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForTokenClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForTokenClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForTokenClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForTokenClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L997",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Dt=new I({props:{$$slots:{default:[Pm]},$$scope:{ctx:$}}}),Ut=new U({props:{anchor:"transformers.DistilBertForTokenClassification.forward.example",$$slots:{default:[Ym]},$$scope:{ctx:$}}}),us=new Z({props:{title:"DistilBertForQuestionAnswering",local:"transformers.DistilBertForQuestionAnswering",headingTag:"h2"}}),hs=new J({props:{name:"class transformers.DistilBertForQuestionAnswering",anchor:"transformers.DistilBertForQuestionAnswering",parameters:[{name:"config",val:": PretrainedConfig"}],parametersDescription:[{anchor:"transformers.DistilBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L847"}}),fs=new J({props:{name:"forward",anchor:"transformers.DistilBertForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"head_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.Tensor] = None"},{name:"start_positions",val:": typing.Optional[torch.Tensor] = None"},{name:"end_positions",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.DistilBertForQuestionAnswering.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.start_positions",description:`<strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.DistilBertForQuestionAnswering.forward.end_positions",description:`<strong>end_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_distilbert.py#L881",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Wt=new I({props:{$$slots:{default:[Om]},$$scope:{ctx:$}}}),It=new U({props:{anchor:"transformers.DistilBertForQuestionAnswering.forward.example",$$slots:{default:[Km]},$$scope:{ctx:$}}}),gs=new Z({props:{title:"TFDistilBertModel",local:"transformers.TFDistilBertModel",headingTag:"h2"}}),_s=new J({props:{name:"class transformers.TFDistilBertModel",anchor:"transformers.TFDistilBertModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L516"}}),Zt=new I({props:{$$slots:{default:[eu]},$$scope:{ctx:$}}}),bs=new J({props:{name:"call",anchor:"transformers.TFDistilBertModel.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertModel.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertModel.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertModel.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertModel.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertModel.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertModel.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L521",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput"
>transformers.modeling_tf_outputs.TFBaseModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Gt=new I({props:{$$slots:{default:[tu]},$$scope:{ctx:$}}}),qt=new U({props:{anchor:"transformers.TFDistilBertModel.call.example",$$slots:{default:[nu]},$$scope:{ctx:$}}}),Ts=new Z({props:{title:"TFDistilBertForMaskedLM",local:"transformers.TFDistilBertForMaskedLM",headingTag:"h2"}}),Ms=new J({props:{name:"class transformers.TFDistilBertForMaskedLM",anchor:"transformers.TFDistilBertForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L596"}}),Nt=new I({props:{$$slots:{default:[su]},$$scope:{ctx:$}}}),ys=new J({props:{name:"call",anchor:"transformers.TFDistilBertForMaskedLM.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForMaskedLM.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForMaskedLM.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForMaskedLM.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForMaskedLM.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForMaskedLM.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForMaskedLM.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForMaskedLM.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForMaskedLM.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForMaskedLM.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the masked language modeling loss. Indices should be in <code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are ignored (masked), the
loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L616",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Lt=new I({props:{$$slots:{default:[ou]},$$scope:{ctx:$}}}),Rt=new U({props:{anchor:"transformers.TFDistilBertForMaskedLM.call.example",$$slots:{default:[au]},$$scope:{ctx:$}}}),Ht=new U({props:{anchor:"transformers.TFDistilBertForMaskedLM.call.example-2",$$slots:{default:[ru]},$$scope:{ctx:$}}}),ws=new Z({props:{title:"TFDistilBertForSequenceClassification",local:"transformers.TFDistilBertForSequenceClassification",headingTag:"h2"}}),ks=new J({props:{name:"class transformers.TFDistilBertForSequenceClassification",anchor:"transformers.TFDistilBertForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L678"}}),Xt=new I({props:{$$slots:{default:[iu]},$$scope:{ctx:$}}}),$s=new J({props:{name:"call",anchor:"transformers.TFDistilBertForSequenceClassification.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForSequenceClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForSequenceClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L695",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Vt=new I({props:{$$slots:{default:[lu]},$$scope:{ctx:$}}}),Et=new U({props:{anchor:"transformers.TFDistilBertForSequenceClassification.call.example",$$slots:{default:[du]},$$scope:{ctx:$}}}),Qt=new U({props:{anchor:"transformers.TFDistilBertForSequenceClassification.call.example-2",$$slots:{default:[cu]},$$scope:{ctx:$}}}),vs=new Z({props:{title:"TFDistilBertForMultipleChoice",local:"transformers.TFDistilBertForMultipleChoice",headingTag:"h2"}}),xs=new J({props:{name:"class transformers.TFDistilBertForMultipleChoice",anchor:"transformers.TFDistilBertForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L825"}}),St=new I({props:{$$slots:{default:[pu]},$$scope:{ctx:$}}}),Fs=new J({props:{name:"call",anchor:"transformers.TFDistilBertForMultipleChoice.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForMultipleChoice.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForMultipleChoice.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the multiple choice classification loss. Indices should be in <code>[0, ..., num_choices]</code>
where <code>num_choices</code> is the size of the second dimension of the input tensors. (See <code>input_ids</code> above)`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L841",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),At=new I({props:{$$slots:{default:[mu]},$$scope:{ctx:$}}}),Pt=new U({props:{anchor:"transformers.TFDistilBertForMultipleChoice.call.example",$$slots:{default:[uu]},$$scope:{ctx:$}}}),Bs=new Z({props:{title:"TFDistilBertForTokenClassification",local:"transformers.TFDistilBertForTokenClassification",headingTag:"h2"}}),js=new J({props:{name:"class transformers.TFDistilBertForTokenClassification",anchor:"transformers.TFDistilBertForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L757"}}),Yt=new I({props:{$$slots:{default:[hu]},$$scope:{ctx:$}}}),Cs=new J({props:{name:"call",anchor:"transformers.TFDistilBertForTokenClassification.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"labels",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForTokenClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForTokenClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForTokenClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForTokenClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForTokenClassification.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForTokenClassification.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForTokenClassification.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForTokenClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForTokenClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the token classification loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L768",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Ot=new I({props:{$$slots:{default:[fu]},$$scope:{ctx:$}}}),Kt=new U({props:{anchor:"transformers.TFDistilBertForTokenClassification.call.example",$$slots:{default:[gu]},$$scope:{ctx:$}}}),en=new U({props:{anchor:"transformers.TFDistilBertForTokenClassification.call.example-2",$$slots:{default:[_u]},$$scope:{ctx:$}}}),Js=new Z({props:{title:"TFDistilBertForQuestionAnswering",local:"transformers.TFDistilBertForQuestionAnswering",headingTag:"h2"}}),zs=new J({props:{name:"class transformers.TFDistilBertForQuestionAnswering",anchor:"transformers.TFDistilBertForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFDistilBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L919"}}),tn=new I({props:{$$slots:{default:[bu]},$$scope:{ctx:$}}}),Ds=new J({props:{name:"call",anchor:"transformers.TFDistilBertForQuestionAnswering.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"head_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"inputs_embeds",val:": np.ndarray | tf.Tensor | None = None"},{name:"output_attentions",val:": Optional[bool] = None"},{name:"output_hidden_states",val:": Optional[bool] = None"},{name:"return_dict",val:": Optional[bool] = None"},{name:"start_positions",val:": np.ndarray | tf.Tensor | None = None"},{name:"end_positions",val:": np.ndarray | tf.Tensor | None = None"},{name:"training",val:": Optional[bool] = False"}],parametersDescription:[{anchor:"transformers.TFDistilBertForQuestionAnswering.call.input_ids",description:`<strong>input_ids</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.attention_mask",description:`<strong>attention_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.head_mask",description:`<strong>head_mask</strong> (<code>Numpy array</code> or <code>tf.Tensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the
config will be used instead.`,name:"output_attentions"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
used instead.`,name:"output_hidden_states"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple. This argument can be used in
eager mode, in graph mode the value will always be set to True.`,name:"return_dict"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).`,name:"training"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.start_positions",description:`<strong>start_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.TFDistilBertForQuestionAnswering.call.end_positions",description:`<strong>end_positions</strong> (<code>tf.Tensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_tf_distilbert.py#L930",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),nn=new I({props:{$$slots:{default:[Tu]},$$scope:{ctx:$}}}),sn=new U({props:{anchor:"transformers.TFDistilBertForQuestionAnswering.call.example",$$slots:{default:[Mu]},$$scope:{ctx:$}}}),on=new U({props:{anchor:"transformers.TFDistilBertForQuestionAnswering.call.example-2",$$slots:{default:[yu]},$$scope:{ctx:$}}}),Us=new Z({props:{title:"FlaxDistilBertModel",local:"transformers.FlaxDistilBertModel",headingTag:"h2"}}),Ws=new J({props:{name:"class transformers.FlaxDistilBertModel",anchor:"transformers.FlaxDistilBertModel",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L532"}}),Is=new J({props:{name:"__call__",anchor:"transformers.FlaxDistilBertModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertModel.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertModel.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455"}}),an=new I({props:{$$slots:{default:[wu]},$$scope:{ctx:$}}}),rn=new U({props:{anchor:"transformers.FlaxDistilBertModel.__call__.example",$$slots:{default:[ku]},$$scope:{ctx:$}}}),Zs=new Z({props:{title:"FlaxDistilBertForMaskedLM",local:"transformers.FlaxDistilBertForMaskedLM",headingTag:"h2"}}),Gs=new J({props:{name:"class transformers.FlaxDistilBertForMaskedLM",anchor:"transformers.FlaxDistilBertForMaskedLM",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L605"}}),qs=new J({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForMaskedLM.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ln=new I({props:{$$slots:{default:[$u]},$$scope:{ctx:$}}}),dn=new U({props:{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.example",$$slots:{default:[vu]},$$scope:{ctx:$}}}),Ns=new Z({props:{title:"FlaxDistilBertForSequenceClassification",local:"transformers.FlaxDistilBertForSequenceClassification",headingTag:"h2"}}),Ls=new J({props:{name:"class transformers.FlaxDistilBertForSequenceClassification",anchor:"transformers.FlaxDistilBertForSequenceClassification",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L672"}}),Rs=new J({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),cn=new I({props:{$$slots:{default:[xu]},$$scope:{ctx:$}}}),pn=new U({props:{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.example",$$slots:{default:[Fu]},$$scope:{ctx:$}}}),Hs=new Z({props:{title:"FlaxDistilBertForMultipleChoice",local:"transformers.FlaxDistilBertForMultipleChoice",headingTag:"h2"}}),Xs=new J({props:{name:"class transformers.FlaxDistilBertForMultipleChoice",anchor:"transformers.FlaxDistilBertForMultipleChoice",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L751"}}),Vs=new J({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),mn=new I({props:{$$slots:{default:[Bu]},$$scope:{ctx:$}}}),un=new U({props:{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.example",$$slots:{default:[ju]},$$scope:{ctx:$}}}),Es=new Z({props:{title:"FlaxDistilBertForTokenClassification",local:"transformers.FlaxDistilBertForTokenClassification",headingTag:"h2"}}),Qs=new J({props:{name:"class transformers.FlaxDistilBertForTokenClassification",anchor:"transformers.FlaxDistilBertForTokenClassification",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L816"}}),Ss=new J({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForTokenClassification.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),hn=new I({props:{$$slots:{default:[Cu]},$$scope:{ctx:$}}}),fn=new U({props:{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.example",$$slots:{default:[Ju]},$$scope:{ctx:$}}}),As=new Z({props:{title:"FlaxDistilBertForQuestionAnswering",local:"transformers.FlaxDistilBertForQuestionAnswering",headingTag:"h2"}}),Ps=new J({props:{name:"class transformers.FlaxDistilBertForQuestionAnswering",anchor:"transformers.FlaxDistilBertForQuestionAnswering",parameters:[{name:"config",val:": DistilBertConfig"},{name:"input_shape",val:": typing.Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/en/model_doc/distilbert#transformers.DistilBertConfig">DistilBertConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L885"}}),Ys=new J({props:{name:"__call__",anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"head_mask",val:" = None"},{name:"params",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/main/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"
>transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),gn=new I({props:{$$slots:{default:[zu]},$$scope:{ctx:$}}}),_n=new U({props:{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.example",$$slots:{default:[Du]},$$scope:{ctx:$}}}),{c(){t=c("meta"),h=a(),o=c("p"),l=a(),f(k.$$.fragment),n=a(),y=c("div"),y.innerHTML=N,D=a(),f(x.$$.fragment),G=a(),z=c("p"),z.innerHTML=F,q=a(),w=c("p"),w.textContent=v,Sa=a(),bn=c("p"),bn.innerHTML=Tc,Aa=a(),Tn=c("p"),Tn.textContent=Mc,Pa=a(),Mn=c("ul"),Mn.innerHTML=yc,Ya=a(),yn=c("p"),yn.innerHTML=wc,Oa=a(),f(wn.$$.fragment),Ka=a(),kn=c("p"),kn.textContent=kc,er=a(),f($n.$$.fragment),tr=a(),vn=c("ul"),vn.innerHTML=$c,nr=a(),f(xn.$$.fragment),sr=a(),Fn=c("ul"),Fn.innerHTML=vc,or=a(),f(Bn.$$.fragment),ar=a(),jn=c("ul"),jn.innerHTML=xc,rr=a(),f(Cn.$$.fragment),ir=a(),Jn=c("ul"),Jn.innerHTML=Fc,lr=a(),zn=c("p"),zn.innerHTML=Bc,dr=a(),Dn=c("ul"),Dn.innerHTML=jc,cr=a(),Un=c("p"),Un.textContent=Cc,pr=a(),Wn=c("ul"),Wn.innerHTML=Jc,mr=a(),In=c("p"),In.textContent=zc,ur=a(),Zn=c("ul"),Zn.innerHTML=Dc,hr=a(),Gn=c("p"),Gn.textContent=Uc,fr=a(),qn=c("ul"),qn.innerHTML=Wc,gr=a(),f(Nn.$$.fragment),_r=a(),ve=c("div"),f(Ln.$$.fragment),ii=a(),eo=c("p"),eo.innerHTML=Ic,li=a(),to=c("p"),to.innerHTML=Zc,di=a(),f(Tt.$$.fragment),br=a(),f(Rn.$$.fragment),Tr=a(),L=c("div"),f(Hn.$$.fragment),ci=a(),no=c("p"),no.textContent=Gc,pi=a(),so=c("p"),so.innerHTML=qc,mi=a(),ft=c("div"),f(Xn.$$.fragment),ui=a(),oo=c("p"),oo.textContent=Nc,hi=a(),ao=c("ul"),ao.innerHTML=Lc,fi=a(),Mt=c("div"),f(Vn.$$.fragment),gi=a(),ro=c("p"),ro.textContent=Rc,_i=a(),Ge=c("div"),f(En.$$.fragment),bi=a(),io=c("p"),io.textContent=Hc,Ti=a(),f(yt.$$.fragment),Mi=a(),lo=c("p"),lo.innerHTML=Xc,yi=a(),wt=c("div"),f(Qn.$$.fragment),wi=a(),co=c("p"),co.innerHTML=Vc,Mr=a(),f(Sn.$$.fragment),yr=a(),ie=c("div"),f(An.$$.fragment),ki=a(),po=c("p"),po.innerHTML=Ec,$i=a(),mo=c("p"),mo.innerHTML=Qc,vi=a(),gt=c("div"),f(Pn.$$.fragment),xi=a(),uo=c("p"),uo.textContent=Sc,Fi=a(),ho=c("ul"),ho.innerHTML=Ac,Bi=a(),qe=c("div"),f(Yn.$$.fragment),ji=a(),fo=c("p"),fo.textContent=Pc,Ci=a(),f(kt.$$.fragment),Ji=a(),go=c("p"),go.innerHTML=Yc,wr=a(),f(On.$$.fragment),kr=a(),le=c("div"),f(Kn.$$.fragment),zi=a(),_o=c("p"),_o.textContent=Oc,Di=a(),bo=c("p"),bo.innerHTML=Kc,Ui=a(),To=c("p"),To.innerHTML=ep,Wi=a(),Ne=c("div"),f(es.$$.fragment),Ii=a(),Mo=c("p"),Mo.innerHTML=tp,Zi=a(),f($t.$$.fragment),Gi=a(),f(vt.$$.fragment),$r=a(),f(ts.$$.fragment),vr=a(),de=c("div"),f(ns.$$.fragment),qi=a(),yo=c("p"),yo.innerHTML=np,Ni=a(),wo=c("p"),wo.innerHTML=sp,Li=a(),ko=c("p"),ko.innerHTML=op,Ri=a(),Le=c("div"),f(ss.$$.fragment),Hi=a(),$o=c("p"),$o.innerHTML=ap,Xi=a(),f(xt.$$.fragment),Vi=a(),f(Ft.$$.fragment),xr=a(),f(os.$$.fragment),Fr=a(),ce=c("div"),f(as.$$.fragment),Ei=a(),vo=c("p"),vo.textContent=rp,Qi=a(),xo=c("p"),xo.innerHTML=ip,Si=a(),Fo=c("p"),Fo.innerHTML=lp,Ai=a(),Me=c("div"),f(rs.$$.fragment),Pi=a(),Bo=c("p"),Bo.innerHTML=dp,Yi=a(),f(Bt.$$.fragment),Oi=a(),f(jt.$$.fragment),Ki=a(),f(Ct.$$.fragment),Br=a(),f(is.$$.fragment),jr=a(),pe=c("div"),f(ls.$$.fragment),el=a(),jo=c("p"),jo.textContent=cp,tl=a(),Co=c("p"),Co.innerHTML=pp,nl=a(),Jo=c("p"),Jo.innerHTML=mp,sl=a(),Re=c("div"),f(ds.$$.fragment),ol=a(),zo=c("p"),zo.innerHTML=up,al=a(),f(Jt.$$.fragment),rl=a(),f(zt.$$.fragment),Cr=a(),f(cs.$$.fragment),Jr=a(),me=c("div"),f(ps.$$.fragment),il=a(),Do=c("p"),Do.textContent=hp,ll=a(),Uo=c("p"),Uo.innerHTML=fp,dl=a(),Wo=c("p"),Wo.innerHTML=gp,cl=a(),He=c("div"),f(ms.$$.fragment),pl=a(),Io=c("p"),Io.innerHTML=_p,ml=a(),f(Dt.$$.fragment),ul=a(),f(Ut.$$.fragment),zr=a(),f(us.$$.fragment),Dr=a(),ue=c("div"),f(hs.$$.fragment),hl=a(),Zo=c("p"),Zo.innerHTML=bp,fl=a(),Go=c("p"),Go.innerHTML=Tp,gl=a(),qo=c("p"),qo.innerHTML=Mp,_l=a(),Xe=c("div"),f(fs.$$.fragment),bl=a(),No=c("p"),No.innerHTML=yp,Tl=a(),f(Wt.$$.fragment),Ml=a(),f(It.$$.fragment),Ur=a(),f(gs.$$.fragment),Wr=a(),S=c("div"),f(_s.$$.fragment),yl=a(),Lo=c("p"),Lo.textContent=wp,wl=a(),Ro=c("p"),Ro.innerHTML=kp,kl=a(),Ho=c("p"),Ho.innerHTML=$p,$l=a(),f(Zt.$$.fragment),vl=a(),Ve=c("div"),f(bs.$$.fragment),xl=a(),Xo=c("p"),Xo.innerHTML=vp,Fl=a(),f(Gt.$$.fragment),Bl=a(),f(qt.$$.fragment),Ir=a(),f(Ts.$$.fragment),Zr=a(),A=c("div"),f(Ms.$$.fragment),jl=a(),Vo=c("p"),Vo.innerHTML=xp,Cl=a(),Eo=c("p"),Eo.innerHTML=Fp,Jl=a(),Qo=c("p"),Qo.innerHTML=Bp,zl=a(),f(Nt.$$.fragment),Dl=a(),ye=c("div"),f(ys.$$.fragment),Ul=a(),So=c("p"),So.innerHTML=jp,Wl=a(),f(Lt.$$.fragment),Il=a(),f(Rt.$$.fragment),Zl=a(),f(Ht.$$.fragment),Gr=a(),f(ws.$$.fragment),qr=a(),P=c("div"),f(ks.$$.fragment),Gl=a(),Ao=c("p"),Ao.textContent=Cp,ql=a(),Po=c("p"),Po.innerHTML=Jp,Nl=a(),Yo=c("p"),Yo.innerHTML=zp,Ll=a(),f(Xt.$$.fragment),Rl=a(),we=c("div"),f($s.$$.fragment),Hl=a(),Oo=c("p"),Oo.innerHTML=Dp,Xl=a(),f(Vt.$$.fragment),Vl=a(),f(Et.$$.fragment),El=a(),f(Qt.$$.fragment),Nr=a(),f(vs.$$.fragment),Lr=a(),Y=c("div"),f(xs.$$.fragment),Ql=a(),Ko=c("p"),Ko.textContent=Up,Sl=a(),ea=c("p"),ea.innerHTML=Wp,Al=a(),ta=c("p"),ta.innerHTML=Ip,Pl=a(),f(St.$$.fragment),Yl=a(),Ee=c("div"),f(Fs.$$.fragment),Ol=a(),na=c("p"),na.innerHTML=Zp,Kl=a(),f(At.$$.fragment),ed=a(),f(Pt.$$.fragment),Rr=a(),f(Bs.$$.fragment),Hr=a(),O=c("div"),f(js.$$.fragment),td=a(),sa=c("p"),sa.textContent=Gp,nd=a(),oa=c("p"),oa.innerHTML=qp,sd=a(),aa=c("p"),aa.innerHTML=Np,od=a(),f(Yt.$$.fragment),ad=a(),ke=c("div"),f(Cs.$$.fragment),rd=a(),ra=c("p"),ra.innerHTML=Lp,id=a(),f(Ot.$$.fragment),ld=a(),f(Kt.$$.fragment),dd=a(),f(en.$$.fragment),Xr=a(),f(Js.$$.fragment),Vr=a(),K=c("div"),f(zs.$$.fragment),cd=a(),ia=c("p"),ia.innerHTML=Rp,pd=a(),la=c("p"),la.innerHTML=Hp,md=a(),da=c("p"),da.innerHTML=Xp,ud=a(),f(tn.$$.fragment),hd=a(),$e=c("div"),f(Ds.$$.fragment),fd=a(),ca=c("p"),ca.innerHTML=Vp,gd=a(),f(nn.$$.fragment),_d=a(),f(sn.$$.fragment),bd=a(),f(on.$$.fragment),Er=a(),f(Us.$$.fragment),Qr=a(),R=c("div"),f(Ws.$$.fragment),Td=a(),pa=c("p"),pa.textContent=Ep,Md=a(),ma=c("p"),ma.innerHTML=Qp,yd=a(),ua=c("p"),ua.innerHTML=Sp,wd=a(),ha=c("p"),ha.textContent=Ap,kd=a(),fa=c("ul"),fa.innerHTML=Pp,$d=a(),Qe=c("div"),f(Is.$$.fragment),vd=a(),ga=c("p"),ga.innerHTML=Yp,xd=a(),f(an.$$.fragment),Fd=a(),f(rn.$$.fragment),Sr=a(),f(Zs.$$.fragment),Ar=a(),H=c("div"),f(Gs.$$.fragment),Bd=a(),_a=c("p"),_a.innerHTML=Op,jd=a(),ba=c("p"),ba.innerHTML=Kp,Cd=a(),Ta=c("p"),Ta.innerHTML=em,Jd=a(),Ma=c("p"),Ma.textContent=tm,zd=a(),ya=c("ul"),ya.innerHTML=nm,Dd=a(),Se=c("div"),f(qs.$$.fragment),Ud=a(),wa=c("p"),wa.innerHTML=sm,Wd=a(),f(ln.$$.fragment),Id=a(),f(dn.$$.fragment),Pr=a(),f(Ns.$$.fragment),Yr=a(),X=c("div"),f(Ls.$$.fragment),Zd=a(),ka=c("p"),ka.textContent=om,Gd=a(),$a=c("p"),$a.innerHTML=am,qd=a(),va=c("p"),va.innerHTML=rm,Nd=a(),xa=c("p"),xa.textContent=im,Ld=a(),Fa=c("ul"),Fa.innerHTML=lm,Rd=a(),Ae=c("div"),f(Rs.$$.fragment),Hd=a(),Ba=c("p"),Ba.innerHTML=dm,Xd=a(),f(cn.$$.fragment),Vd=a(),f(pn.$$.fragment),Or=a(),f(Hs.$$.fragment),Kr=a(),V=c("div"),f(Xs.$$.fragment),Ed=a(),ja=c("p"),ja.textContent=cm,Qd=a(),Ca=c("p"),Ca.innerHTML=pm,Sd=a(),Ja=c("p"),Ja.innerHTML=mm,Ad=a(),za=c("p"),za.textContent=um,Pd=a(),Da=c("ul"),Da.innerHTML=hm,Yd=a(),Pe=c("div"),f(Vs.$$.fragment),Od=a(),Ua=c("p"),Ua.innerHTML=fm,Kd=a(),f(mn.$$.fragment),ec=a(),f(un.$$.fragment),ei=a(),f(Es.$$.fragment),ti=a(),E=c("div"),f(Qs.$$.fragment),tc=a(),Wa=c("p"),Wa.textContent=gm,nc=a(),Ia=c("p"),Ia.innerHTML=_m,sc=a(),Za=c("p"),Za.innerHTML=bm,oc=a(),Ga=c("p"),Ga.textContent=Tm,ac=a(),qa=c("ul"),qa.innerHTML=Mm,rc=a(),Ye=c("div"),f(Ss.$$.fragment),ic=a(),Na=c("p"),Na.innerHTML=ym,lc=a(),f(hn.$$.fragment),dc=a(),f(fn.$$.fragment),ni=a(),f(As.$$.fragment),si=a(),Q=c("div"),f(Ps.$$.fragment),cc=a(),La=c("p"),La.innerHTML=wm,pc=a(),Ra=c("p"),Ra.innerHTML=km,mc=a(),Ha=c("p"),Ha.innerHTML=$m,uc=a(),Xa=c("p"),Xa.textContent=vm,hc=a(),Va=c("ul"),Va.innerHTML=xm,fc=a(),Oe=c("div"),f(Ys.$$.fragment),gc=a(),Ea=c("p"),Ea.innerHTML=Fm,_c=a(),f(gn.$$.fragment),bc=a(),f(_n.$$.fragment),oi=a(),Qa=c("p"),this.h()},l(e){const d=Zm("svelte-u9bgzb",document.head);t=p(d,"META",{name:!0,content:!0}),d.forEach(i),h=r(e),o=p(e,"P",{}),C(o).forEach(i),l=r(e),g(k.$$.fragment,e),n=r(e),y=p(e,"DIV",{class:!0,"data-svelte-h":!0}),u(y)!=="svelte-19va6f1"&&(y.innerHTML=N),D=r(e),g(x.$$.fragment,e),G=r(e),z=p(e,"P",{"data-svelte-h":!0}),u(z)!=="svelte-b0u3fm"&&(z.innerHTML=F),q=r(e),w=p(e,"P",{"data-svelte-h":!0}),u(w)!=="svelte-vfdo9a"&&(w.textContent=v),Sa=r(e),bn=p(e,"P",{"data-svelte-h":!0}),u(bn)!=="svelte-yrk2ws"&&(bn.innerHTML=Tc),Aa=r(e),Tn=p(e,"P",{"data-svelte-h":!0}),u(Tn)!=="svelte-axv494"&&(Tn.textContent=Mc),Pa=r(e),Mn=p(e,"UL",{"data-svelte-h":!0}),u(Mn)!=="svelte-1pw0ah6"&&(Mn.innerHTML=yc),Ya=r(e),yn=p(e,"P",{"data-svelte-h":!0}),u(yn)!=="svelte-d89fny"&&(yn.innerHTML=wc),Oa=r(e),g(wn.$$.fragment,e),Ka=r(e),kn=p(e,"P",{"data-svelte-h":!0}),u(kn)!=="svelte-1204ph7"&&(kn.textContent=kc),er=r(e),g($n.$$.fragment,e),tr=r(e),vn=p(e,"UL",{"data-svelte-h":!0}),u(vn)!=="svelte-1q3hqbk"&&(vn.innerHTML=$c),nr=r(e),g(xn.$$.fragment,e),sr=r(e),Fn=p(e,"UL",{"data-svelte-h":!0}),u(Fn)!=="svelte-awfhsd"&&(Fn.innerHTML=vc),or=r(e),g(Bn.$$.fragment,e),ar=r(e),jn=p(e,"UL",{"data-svelte-h":!0}),u(jn)!=="svelte-by25ns"&&(jn.innerHTML=xc),rr=r(e),g(Cn.$$.fragment,e),ir=r(e),Jn=p(e,"UL",{"data-svelte-h":!0}),u(Jn)!=="svelte-1boj976"&&(Jn.innerHTML=Fc),lr=r(e),zn=p(e,"P",{"data-svelte-h":!0}),u(zn)!=="svelte-cplu6u"&&(zn.innerHTML=Bc),dr=r(e),Dn=p(e,"UL",{"data-svelte-h":!0}),u(Dn)!=="svelte-prm6y5"&&(Dn.innerHTML=jc),cr=r(e),Un=p(e,"P",{"data-svelte-h":!0}),u(Un)!=="svelte-liq2q7"&&(Un.textContent=Cc),pr=r(e),Wn=p(e,"UL",{"data-svelte-h":!0}),u(Wn)!=="svelte-1i3rzd0"&&(Wn.innerHTML=Jc),mr=r(e),In=p(e,"P",{"data-svelte-h":!0}),u(In)!=="svelte-1wntqpp"&&(In.textContent=zc),ur=r(e),Zn=p(e,"UL",{"data-svelte-h":!0}),u(Zn)!=="svelte-1hcjkg2"&&(Zn.innerHTML=Dc),hr=r(e),Gn=p(e,"P",{"data-svelte-h":!0}),u(Gn)!=="svelte-lk14e4"&&(Gn.textContent=Uc),fr=r(e),qn=p(e,"UL",{"data-svelte-h":!0}),u(qn)!=="svelte-ii18nw"&&(qn.innerHTML=Wc),gr=r(e),g(Nn.$$.fragment,e),_r=r(e),ve=p(e,"DIV",{class:!0});var Ke=C(ve);g(Ln.$$.fragment,Ke),ii=r(Ke),eo=p(Ke,"P",{"data-svelte-h":!0}),u(eo)!=="svelte-6etqha"&&(eo.innerHTML=Ic),li=r(Ke),to=p(Ke,"P",{"data-svelte-h":!0}),u(to)!=="svelte-o55m63"&&(to.innerHTML=Zc),di=r(Ke),g(Tt.$$.fragment,Ke),Ke.forEach(i),br=r(e),g(Rn.$$.fragment,e),Tr=r(e),L=p(e,"DIV",{class:!0});var ee=C(L);g(Hn.$$.fragment,ee),ci=r(ee),no=p(ee,"P",{"data-svelte-h":!0}),u(no)!=="svelte-10o6nt5"&&(no.textContent=Gc),pi=r(ee),so=p(ee,"P",{"data-svelte-h":!0}),u(so)!=="svelte-1c3t5ty"&&(so.innerHTML=qc),mi=r(ee),ft=p(ee,"DIV",{class:!0});var _t=C(ft);g(Xn.$$.fragment,_t),ui=r(_t),oo=p(_t,"P",{"data-svelte-h":!0}),u(oo)!=="svelte-t7qurq"&&(oo.textContent=Nc),hi=r(_t),ao=p(_t,"UL",{"data-svelte-h":!0}),u(ao)!=="svelte-xi6653"&&(ao.innerHTML=Lc),_t.forEach(i),fi=r(ee),Mt=p(ee,"DIV",{class:!0});var Os=C(Mt);g(Vn.$$.fragment,Os),gi=r(Os),ro=p(Os,"P",{"data-svelte-h":!0}),u(ro)!=="svelte-b3k2yi"&&(ro.textContent=Rc),Os.forEach(i),_i=r(ee),Ge=p(ee,"DIV",{class:!0});var et=C(Ge);g(En.$$.fragment,et),bi=r(et),io=p(et,"P",{"data-svelte-h":!0}),u(io)!=="svelte-gn6wi7"&&(io.textContent=Hc),Ti=r(et),g(yt.$$.fragment,et),Mi=r(et),lo=p(et,"P",{"data-svelte-h":!0}),u(lo)!=="svelte-owoxgn"&&(lo.innerHTML=Xc),et.forEach(i),yi=r(ee),wt=p(ee,"DIV",{class:!0});var Ks=C(wt);g(Qn.$$.fragment,Ks),wi=r(Ks),co=p(Ks,"P",{"data-svelte-h":!0}),u(co)!=="svelte-1f4f5kp"&&(co.innerHTML=Vc),Ks.forEach(i),ee.forEach(i),Mr=r(e),g(Sn.$$.fragment,e),yr=r(e),ie=p(e,"DIV",{class:!0});var xe=C(ie);g(An.$$.fragment,xe),ki=r(xe),po=p(xe,"P",{"data-svelte-h":!0}),u(po)!=="svelte-88fhae"&&(po.innerHTML=Ec),$i=r(xe),mo=p(xe,"P",{"data-svelte-h":!0}),u(mo)!=="svelte-fh0aq"&&(mo.innerHTML=Qc),vi=r(xe),gt=p(xe,"DIV",{class:!0});var bt=C(gt);g(Pn.$$.fragment,bt),xi=r(bt),uo=p(bt,"P",{"data-svelte-h":!0}),u(uo)!=="svelte-t7qurq"&&(uo.textContent=Sc),Fi=r(bt),ho=p(bt,"UL",{"data-svelte-h":!0}),u(ho)!=="svelte-xi6653"&&(ho.innerHTML=Ac),bt.forEach(i),Bi=r(xe),qe=p(xe,"DIV",{class:!0});var tt=C(qe);g(Yn.$$.fragment,tt),ji=r(tt),fo=p(tt,"P",{"data-svelte-h":!0}),u(fo)!=="svelte-gn6wi7"&&(fo.textContent=Pc),Ci=r(tt),g(kt.$$.fragment,tt),Ji=r(tt),go=p(tt,"P",{"data-svelte-h":!0}),u(go)!=="svelte-owoxgn"&&(go.innerHTML=Yc),tt.forEach(i),xe.forEach(i),wr=r(e),g(On.$$.fragment,e),kr=r(e),le=p(e,"DIV",{class:!0});var Fe=C(le);g(Kn.$$.fragment,Fe),zi=r(Fe),_o=p(Fe,"P",{"data-svelte-h":!0}),u(_o)!=="svelte-1ololo3"&&(_o.textContent=Oc),Di=r(Fe),bo=p(Fe,"P",{"data-svelte-h":!0}),u(bo)!=="svelte-6pahdo"&&(bo.innerHTML=Kc),Ui=r(Fe),To=p(Fe,"P",{"data-svelte-h":!0}),u(To)!=="svelte-hswkmf"&&(To.innerHTML=ep),Wi=r(Fe),Ne=p(Fe,"DIV",{class:!0});var nt=C(Ne);g(es.$$.fragment,nt),Ii=r(nt),Mo=p(nt,"P",{"data-svelte-h":!0}),u(Mo)!=="svelte-19uvjml"&&(Mo.innerHTML=tp),Zi=r(nt),g($t.$$.fragment,nt),Gi=r(nt),g(vt.$$.fragment,nt),nt.forEach(i),Fe.forEach(i),$r=r(e),g(ts.$$.fragment,e),vr=r(e),de=p(e,"DIV",{class:!0});var Be=C(de);g(ns.$$.fragment,Be),qi=r(Be),yo=p(Be,"P",{"data-svelte-h":!0}),u(yo)!=="svelte-23pii1"&&(yo.innerHTML=np),Ni=r(Be),wo=p(Be,"P",{"data-svelte-h":!0}),u(wo)!=="svelte-6pahdo"&&(wo.innerHTML=sp),Li=r(Be),ko=p(Be,"P",{"data-svelte-h":!0}),u(ko)!=="svelte-hswkmf"&&(ko.innerHTML=op),Ri=r(Be),Le=p(Be,"DIV",{class:!0});var st=C(Le);g(ss.$$.fragment,st),Hi=r(st),$o=p(st,"P",{"data-svelte-h":!0}),u($o)!=="svelte-p3k5e1"&&($o.innerHTML=ap),Xi=r(st),g(xt.$$.fragment,st),Vi=r(st),g(Ft.$$.fragment,st),st.forEach(i),Be.forEach(i),xr=r(e),g(os.$$.fragment,e),Fr=r(e),ce=p(e,"DIV",{class:!0});var je=C(ce);g(as.$$.fragment,je),Ei=r(je),vo=p(je,"P",{"data-svelte-h":!0}),u(vo)!=="svelte-1kr5cyc"&&(vo.textContent=rp),Qi=r(je),xo=p(je,"P",{"data-svelte-h":!0}),u(xo)!=="svelte-6pahdo"&&(xo.innerHTML=ip),Si=r(je),Fo=p(je,"P",{"data-svelte-h":!0}),u(Fo)!=="svelte-hswkmf"&&(Fo.innerHTML=lp),Ai=r(je),Me=p(je,"DIV",{class:!0});var Ce=C(Me);g(rs.$$.fragment,Ce),Pi=r(Ce),Bo=p(Ce,"P",{"data-svelte-h":!0}),u(Bo)!=="svelte-4izf8x"&&(Bo.innerHTML=dp),Yi=r(Ce),g(Bt.$$.fragment,Ce),Oi=r(Ce),g(jt.$$.fragment,Ce),Ki=r(Ce),g(Ct.$$.fragment,Ce),Ce.forEach(i),je.forEach(i),Br=r(e),g(is.$$.fragment,e),jr=r(e),pe=p(e,"DIV",{class:!0});var Je=C(pe);g(ls.$$.fragment,Je),el=r(Je),jo=p(Je,"P",{"data-svelte-h":!0}),u(jo)!=="svelte-i84iso"&&(jo.textContent=cp),tl=r(Je),Co=p(Je,"P",{"data-svelte-h":!0}),u(Co)!=="svelte-6pahdo"&&(Co.innerHTML=pp),nl=r(Je),Jo=p(Je,"P",{"data-svelte-h":!0}),u(Jo)!=="svelte-hswkmf"&&(Jo.innerHTML=mp),sl=r(Je),Re=p(Je,"DIV",{class:!0});var ot=C(Re);g(ds.$$.fragment,ot),ol=r(ot),zo=p(ot,"P",{"data-svelte-h":!0}),u(zo)!=="svelte-vt0gx"&&(zo.innerHTML=up),al=r(ot),g(Jt.$$.fragment,ot),rl=r(ot),g(zt.$$.fragment,ot),ot.forEach(i),Je.forEach(i),Cr=r(e),g(cs.$$.fragment,e),Jr=r(e),me=p(e,"DIV",{class:!0});var ze=C(me);g(ps.$$.fragment,ze),il=r(ze),Do=p(ze,"P",{"data-svelte-h":!0}),u(Do)!=="svelte-14y5xfr"&&(Do.textContent=hp),ll=r(ze),Uo=p(ze,"P",{"data-svelte-h":!0}),u(Uo)!=="svelte-6pahdo"&&(Uo.innerHTML=fp),dl=r(ze),Wo=p(ze,"P",{"data-svelte-h":!0}),u(Wo)!=="svelte-hswkmf"&&(Wo.innerHTML=gp),cl=r(ze),He=p(ze,"DIV",{class:!0});var at=C(He);g(ms.$$.fragment,at),pl=r(at),Io=p(at,"P",{"data-svelte-h":!0}),u(Io)!=="svelte-xpdp1"&&(Io.innerHTML=_p),ml=r(at),g(Dt.$$.fragment,at),ul=r(at),g(Ut.$$.fragment,at),at.forEach(i),ze.forEach(i),zr=r(e),g(us.$$.fragment,e),Dr=r(e),ue=p(e,"DIV",{class:!0});var De=C(ue);g(hs.$$.fragment,De),hl=r(De),Zo=p(De,"P",{"data-svelte-h":!0}),u(Zo)!=="svelte-18jj0o9"&&(Zo.innerHTML=bp),fl=r(De),Go=p(De,"P",{"data-svelte-h":!0}),u(Go)!=="svelte-6pahdo"&&(Go.innerHTML=Tp),gl=r(De),qo=p(De,"P",{"data-svelte-h":!0}),u(qo)!=="svelte-hswkmf"&&(qo.innerHTML=Mp),_l=r(De),Xe=p(De,"DIV",{class:!0});var rt=C(Xe);g(fs.$$.fragment,rt),bl=r(rt),No=p(rt,"P",{"data-svelte-h":!0}),u(No)!=="svelte-pqrjo3"&&(No.innerHTML=yp),Tl=r(rt),g(Wt.$$.fragment,rt),Ml=r(rt),g(It.$$.fragment,rt),rt.forEach(i),De.forEach(i),Ur=r(e),g(gs.$$.fragment,e),Wr=r(e),S=p(e,"DIV",{class:!0});var he=C(S);g(_s.$$.fragment,he),yl=r(he),Lo=p(he,"P",{"data-svelte-h":!0}),u(Lo)!=="svelte-1ololo3"&&(Lo.textContent=wp),wl=r(he),Ro=p(he,"P",{"data-svelte-h":!0}),u(Ro)!=="svelte-1qaxm70"&&(Ro.innerHTML=kp),kl=r(he),Ho=p(he,"P",{"data-svelte-h":!0}),u(Ho)!=="svelte-1ivrf8m"&&(Ho.innerHTML=$p),$l=r(he),g(Zt.$$.fragment,he),vl=r(he),Ve=p(he,"DIV",{class:!0});var it=C(Ve);g(bs.$$.fragment,it),xl=r(it),Xo=p(it,"P",{"data-svelte-h":!0}),u(Xo)!=="svelte-nuexjd"&&(Xo.innerHTML=vp),Fl=r(it),g(Gt.$$.fragment,it),Bl=r(it),g(qt.$$.fragment,it),it.forEach(i),he.forEach(i),Ir=r(e),g(Ts.$$.fragment,e),Zr=r(e),A=p(e,"DIV",{class:!0});var fe=C(A);g(Ms.$$.fragment,fe),jl=r(fe),Vo=p(fe,"P",{"data-svelte-h":!0}),u(Vo)!=="svelte-23pii1"&&(Vo.innerHTML=xp),Cl=r(fe),Eo=p(fe,"P",{"data-svelte-h":!0}),u(Eo)!=="svelte-1qaxm70"&&(Eo.innerHTML=Fp),Jl=r(fe),Qo=p(fe,"P",{"data-svelte-h":!0}),u(Qo)!=="svelte-1ivrf8m"&&(Qo.innerHTML=Bp),zl=r(fe),g(Nt.$$.fragment,fe),Dl=r(fe),ye=p(fe,"DIV",{class:!0});var Ue=C(ye);g(ys.$$.fragment,Ue),Ul=r(Ue),So=p(Ue,"P",{"data-svelte-h":!0}),u(So)!=="svelte-1f4v30t"&&(So.innerHTML=jp),Wl=r(Ue),g(Lt.$$.fragment,Ue),Il=r(Ue),g(Rt.$$.fragment,Ue),Zl=r(Ue),g(Ht.$$.fragment,Ue),Ue.forEach(i),fe.forEach(i),Gr=r(e),g(ws.$$.fragment,e),qr=r(e),P=p(e,"DIV",{class:!0});var ge=C(P);g(ks.$$.fragment,ge),Gl=r(ge),Ao=p(ge,"P",{"data-svelte-h":!0}),u(Ao)!=="svelte-1kr5cyc"&&(Ao.textContent=Cp),ql=r(ge),Po=p(ge,"P",{"data-svelte-h":!0}),u(Po)!=="svelte-1qaxm70"&&(Po.innerHTML=Jp),Nl=r(ge),Yo=p(ge,"P",{"data-svelte-h":!0}),u(Yo)!=="svelte-1ivrf8m"&&(Yo.innerHTML=zp),Ll=r(ge),g(Xt.$$.fragment,ge),Rl=r(ge),we=p(ge,"DIV",{class:!0});var We=C(we);g($s.$$.fragment,We),Hl=r(We),Oo=p(We,"P",{"data-svelte-h":!0}),u(Oo)!=="svelte-1ilc5e1"&&(Oo.innerHTML=Dp),Xl=r(We),g(Vt.$$.fragment,We),Vl=r(We),g(Et.$$.fragment,We),El=r(We),g(Qt.$$.fragment,We),We.forEach(i),ge.forEach(i),Nr=r(e),g(vs.$$.fragment,e),Lr=r(e),Y=p(e,"DIV",{class:!0});var _e=C(Y);g(xs.$$.fragment,_e),Ql=r(_e),Ko=p(_e,"P",{"data-svelte-h":!0}),u(Ko)!=="svelte-i84iso"&&(Ko.textContent=Up),Sl=r(_e),ea=p(_e,"P",{"data-svelte-h":!0}),u(ea)!=="svelte-1qaxm70"&&(ea.innerHTML=Wp),Al=r(_e),ta=p(_e,"P",{"data-svelte-h":!0}),u(ta)!=="svelte-1ivrf8m"&&(ta.innerHTML=Ip),Pl=r(_e),g(St.$$.fragment,_e),Yl=r(_e),Ee=p(_e,"DIV",{class:!0});var lt=C(Ee);g(Fs.$$.fragment,lt),Ol=r(lt),na=p(lt,"P",{"data-svelte-h":!0}),u(na)!=="svelte-uw4xlt"&&(na.innerHTML=Zp),Kl=r(lt),g(At.$$.fragment,lt),ed=r(lt),g(Pt.$$.fragment,lt),lt.forEach(i),_e.forEach(i),Rr=r(e),g(Bs.$$.fragment,e),Hr=r(e),O=p(e,"DIV",{class:!0});var be=C(O);g(js.$$.fragment,be),td=r(be),sa=p(be,"P",{"data-svelte-h":!0}),u(sa)!=="svelte-14y5xfr"&&(sa.textContent=Gp),nd=r(be),oa=p(be,"P",{"data-svelte-h":!0}),u(oa)!=="svelte-1qaxm70"&&(oa.innerHTML=qp),sd=r(be),aa=p(be,"P",{"data-svelte-h":!0}),u(aa)!=="svelte-1ivrf8m"&&(aa.innerHTML=Np),od=r(be),g(Yt.$$.fragment,be),ad=r(be),ke=p(be,"DIV",{class:!0});var Ie=C(ke);g(Cs.$$.fragment,Ie),rd=r(Ie),ra=p(Ie,"P",{"data-svelte-h":!0}),u(ra)!=="svelte-1m3yzhd"&&(ra.innerHTML=Lp),id=r(Ie),g(Ot.$$.fragment,Ie),ld=r(Ie),g(Kt.$$.fragment,Ie),dd=r(Ie),g(en.$$.fragment,Ie),Ie.forEach(i),be.forEach(i),Xr=r(e),g(Js.$$.fragment,e),Vr=r(e),K=p(e,"DIV",{class:!0});var Te=C(K);g(zs.$$.fragment,Te),cd=r(Te),ia=p(Te,"P",{"data-svelte-h":!0}),u(ia)!=="svelte-zfb4ky"&&(ia.innerHTML=Rp),pd=r(Te),la=p(Te,"P",{"data-svelte-h":!0}),u(la)!=="svelte-1qaxm70"&&(la.innerHTML=Hp),md=r(Te),da=p(Te,"P",{"data-svelte-h":!0}),u(da)!=="svelte-1ivrf8m"&&(da.innerHTML=Xp),ud=r(Te),g(tn.$$.fragment,Te),hd=r(Te),$e=p(Te,"DIV",{class:!0});var Ze=C($e);g(Ds.$$.fragment,Ze),fd=r(Ze),ca=p(Ze,"P",{"data-svelte-h":!0}),u(ca)!=="svelte-1xdc4if"&&(ca.innerHTML=Vp),gd=r(Ze),g(nn.$$.fragment,Ze),_d=r(Ze),g(sn.$$.fragment,Ze),bd=r(Ze),g(on.$$.fragment,Ze),Ze.forEach(i),Te.forEach(i),Er=r(e),g(Us.$$.fragment,e),Qr=r(e),R=p(e,"DIV",{class:!0});var te=C(R);g(Ws.$$.fragment,te),Td=r(te),pa=p(te,"P",{"data-svelte-h":!0}),u(pa)!=="svelte-3lz6al"&&(pa.textContent=Ep),Md=r(te),ma=p(te,"P",{"data-svelte-h":!0}),u(ma)!=="svelte-3daxed"&&(ma.innerHTML=Qp),yd=r(te),ua=p(te,"P",{"data-svelte-h":!0}),u(ua)!=="svelte-9ybkh"&&(ua.innerHTML=Sp),wd=r(te),ha=p(te,"P",{"data-svelte-h":!0}),u(ha)!=="svelte-1pplc4a"&&(ha.textContent=Ap),kd=r(te),fa=p(te,"UL",{"data-svelte-h":!0}),u(fa)!=="svelte-1w7z84m"&&(fa.innerHTML=Pp),$d=r(te),Qe=p(te,"DIV",{class:!0});var dt=C(Qe);g(Is.$$.fragment,dt),vd=r(dt),ga=p(dt,"P",{"data-svelte-h":!0}),u(ga)!=="svelte-16oef0h"&&(ga.innerHTML=Yp),xd=r(dt),g(an.$$.fragment,dt),Fd=r(dt),g(rn.$$.fragment,dt),dt.forEach(i),te.forEach(i),Sr=r(e),g(Zs.$$.fragment,e),Ar=r(e),H=p(e,"DIV",{class:!0});var ne=C(H);g(Gs.$$.fragment,ne),Bd=r(ne),_a=p(ne,"P",{"data-svelte-h":!0}),u(_a)!=="svelte-5bwii0"&&(_a.innerHTML=Op),jd=r(ne),ba=p(ne,"P",{"data-svelte-h":!0}),u(ba)!=="svelte-3daxed"&&(ba.innerHTML=Kp),Cd=r(ne),Ta=p(ne,"P",{"data-svelte-h":!0}),u(Ta)!=="svelte-9ybkh"&&(Ta.innerHTML=em),Jd=r(ne),Ma=p(ne,"P",{"data-svelte-h":!0}),u(Ma)!=="svelte-1pplc4a"&&(Ma.textContent=tm),zd=r(ne),ya=p(ne,"UL",{"data-svelte-h":!0}),u(ya)!=="svelte-1w7z84m"&&(ya.innerHTML=nm),Dd=r(ne),Se=p(ne,"DIV",{class:!0});var ct=C(Se);g(qs.$$.fragment,ct),Ud=r(ct),wa=p(ct,"P",{"data-svelte-h":!0}),u(wa)!=="svelte-16oef0h"&&(wa.innerHTML=sm),Wd=r(ct),g(ln.$$.fragment,ct),Id=r(ct),g(dn.$$.fragment,ct),ct.forEach(i),ne.forEach(i),Pr=r(e),g(Ns.$$.fragment,e),Yr=r(e),X=p(e,"DIV",{class:!0});var se=C(X);g(Ls.$$.fragment,se),Zd=r(se),ka=p(se,"P",{"data-svelte-h":!0}),u(ka)!=="svelte-1kr5cyc"&&(ka.textContent=om),Gd=r(se),$a=p(se,"P",{"data-svelte-h":!0}),u($a)!=="svelte-3daxed"&&($a.innerHTML=am),qd=r(se),va=p(se,"P",{"data-svelte-h":!0}),u(va)!=="svelte-9ybkh"&&(va.innerHTML=rm),Nd=r(se),xa=p(se,"P",{"data-svelte-h":!0}),u(xa)!=="svelte-1pplc4a"&&(xa.textContent=im),Ld=r(se),Fa=p(se,"UL",{"data-svelte-h":!0}),u(Fa)!=="svelte-1w7z84m"&&(Fa.innerHTML=lm),Rd=r(se),Ae=p(se,"DIV",{class:!0});var pt=C(Ae);g(Rs.$$.fragment,pt),Hd=r(pt),Ba=p(pt,"P",{"data-svelte-h":!0}),u(Ba)!=="svelte-16oef0h"&&(Ba.innerHTML=dm),Xd=r(pt),g(cn.$$.fragment,pt),Vd=r(pt),g(pn.$$.fragment,pt),pt.forEach(i),se.forEach(i),Or=r(e),g(Hs.$$.fragment,e),Kr=r(e),V=p(e,"DIV",{class:!0});var oe=C(V);g(Xs.$$.fragment,oe),Ed=r(oe),ja=p(oe,"P",{"data-svelte-h":!0}),u(ja)!=="svelte-i84iso"&&(ja.textContent=cm),Qd=r(oe),Ca=p(oe,"P",{"data-svelte-h":!0}),u(Ca)!=="svelte-3daxed"&&(Ca.innerHTML=pm),Sd=r(oe),Ja=p(oe,"P",{"data-svelte-h":!0}),u(Ja)!=="svelte-9ybkh"&&(Ja.innerHTML=mm),Ad=r(oe),za=p(oe,"P",{"data-svelte-h":!0}),u(za)!=="svelte-1pplc4a"&&(za.textContent=um),Pd=r(oe),Da=p(oe,"UL",{"data-svelte-h":!0}),u(Da)!=="svelte-1w7z84m"&&(Da.innerHTML=hm),Yd=r(oe),Pe=p(oe,"DIV",{class:!0});var mt=C(Pe);g(Vs.$$.fragment,mt),Od=r(mt),Ua=p(mt,"P",{"data-svelte-h":!0}),u(Ua)!=="svelte-16oef0h"&&(Ua.innerHTML=fm),Kd=r(mt),g(mn.$$.fragment,mt),ec=r(mt),g(un.$$.fragment,mt),mt.forEach(i),oe.forEach(i),ei=r(e),g(Es.$$.fragment,e),ti=r(e),E=p(e,"DIV",{class:!0});var ae=C(E);g(Qs.$$.fragment,ae),tc=r(ae),Wa=p(ae,"P",{"data-svelte-h":!0}),u(Wa)!=="svelte-14y5xfr"&&(Wa.textContent=gm),nc=r(ae),Ia=p(ae,"P",{"data-svelte-h":!0}),u(Ia)!=="svelte-3daxed"&&(Ia.innerHTML=_m),sc=r(ae),Za=p(ae,"P",{"data-svelte-h":!0}),u(Za)!=="svelte-9ybkh"&&(Za.innerHTML=bm),oc=r(ae),Ga=p(ae,"P",{"data-svelte-h":!0}),u(Ga)!=="svelte-1pplc4a"&&(Ga.textContent=Tm),ac=r(ae),qa=p(ae,"UL",{"data-svelte-h":!0}),u(qa)!=="svelte-1w7z84m"&&(qa.innerHTML=Mm),rc=r(ae),Ye=p(ae,"DIV",{class:!0});var ut=C(Ye);g(Ss.$$.fragment,ut),ic=r(ut),Na=p(ut,"P",{"data-svelte-h":!0}),u(Na)!=="svelte-16oef0h"&&(Na.innerHTML=ym),lc=r(ut),g(hn.$$.fragment,ut),dc=r(ut),g(fn.$$.fragment,ut),ut.forEach(i),ae.forEach(i),ni=r(e),g(As.$$.fragment,e),si=r(e),Q=p(e,"DIV",{class:!0});var re=C(Q);g(Ps.$$.fragment,re),cc=r(re),La=p(re,"P",{"data-svelte-h":!0}),u(La)!=="svelte-18jj0o9"&&(La.innerHTML=wm),pc=r(re),Ra=p(re,"P",{"data-svelte-h":!0}),u(Ra)!=="svelte-3daxed"&&(Ra.innerHTML=km),mc=r(re),Ha=p(re,"P",{"data-svelte-h":!0}),u(Ha)!=="svelte-9ybkh"&&(Ha.innerHTML=$m),uc=r(re),Xa=p(re,"P",{"data-svelte-h":!0}),u(Xa)!=="svelte-1pplc4a"&&(Xa.textContent=vm),hc=r(re),Va=p(re,"UL",{"data-svelte-h":!0}),u(Va)!=="svelte-1w7z84m"&&(Va.innerHTML=xm),fc=r(re),Oe=p(re,"DIV",{class:!0});var ht=C(Oe);g(Ys.$$.fragment,ht),gc=r(ht),Ea=p(ht,"P",{"data-svelte-h":!0}),u(Ea)!=="svelte-16oef0h"&&(Ea.innerHTML=Fm),_c=r(ht),g(gn.$$.fragment,ht),bc=r(ht),g(_n.$$.fragment,ht),ht.forEach(i),re.forEach(i),oi=r(e),Qa=p(e,"P",{}),C(Qa).forEach(i),this.h()},h(){j(t,"name","hf:doc:metadata"),j(t,"content",Wu),j(y,"class","flex flex-wrap space-x-1"),j(ve,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(ft,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Mt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Ge,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(wt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(gt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(qe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Ne,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(pe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(He,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Xe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Ve,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(ye,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(A,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(we,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Ee,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(ke,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j($e,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Qe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Se,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Ae,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Pe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Ye,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Oe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),j(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,d){s(document.head,t),m(e,h,d),m(e,o,d),m(e,l,d),_(k,e,d),m(e,n,d),m(e,y,d),m(e,D,d),_(x,e,d),m(e,G,d),m(e,z,d),m(e,q,d),m(e,w,d),m(e,Sa,d),m(e,bn,d),m(e,Aa,d),m(e,Tn,d),m(e,Pa,d),m(e,Mn,d),m(e,Ya,d),m(e,yn,d),m(e,Oa,d),_(wn,e,d),m(e,Ka,d),m(e,kn,d),m(e,er,d),_($n,e,d),m(e,tr,d),m(e,vn,d),m(e,nr,d),_(xn,e,d),m(e,sr,d),m(e,Fn,d),m(e,or,d),_(Bn,e,d),m(e,ar,d),m(e,jn,d),m(e,rr,d),_(Cn,e,d),m(e,ir,d),m(e,Jn,d),m(e,lr,d),m(e,zn,d),m(e,dr,d),m(e,Dn,d),m(e,cr,d),m(e,Un,d),m(e,pr,d),m(e,Wn,d),m(e,mr,d),m(e,In,d),m(e,ur,d),m(e,Zn,d),m(e,hr,d),m(e,Gn,d),m(e,fr,d),m(e,qn,d),m(e,gr,d),_(Nn,e,d),m(e,_r,d),m(e,ve,d),_(Ln,ve,null),s(ve,ii),s(ve,eo),s(ve,li),s(ve,to),s(ve,di),_(Tt,ve,null),m(e,br,d),_(Rn,e,d),m(e,Tr,d),m(e,L,d),_(Hn,L,null),s(L,ci),s(L,no),s(L,pi),s(L,so),s(L,mi),s(L,ft),_(Xn,ft,null),s(ft,ui),s(ft,oo),s(ft,hi),s(ft,ao),s(L,fi),s(L,Mt),_(Vn,Mt,null),s(Mt,gi),s(Mt,ro),s(L,_i),s(L,Ge),_(En,Ge,null),s(Ge,bi),s(Ge,io),s(Ge,Ti),_(yt,Ge,null),s(Ge,Mi),s(Ge,lo),s(L,yi),s(L,wt),_(Qn,wt,null),s(wt,wi),s(wt,co),m(e,Mr,d),_(Sn,e,d),m(e,yr,d),m(e,ie,d),_(An,ie,null),s(ie,ki),s(ie,po),s(ie,$i),s(ie,mo),s(ie,vi),s(ie,gt),_(Pn,gt,null),s(gt,xi),s(gt,uo),s(gt,Fi),s(gt,ho),s(ie,Bi),s(ie,qe),_(Yn,qe,null),s(qe,ji),s(qe,fo),s(qe,Ci),_(kt,qe,null),s(qe,Ji),s(qe,go),m(e,wr,d),_(On,e,d),m(e,kr,d),m(e,le,d),_(Kn,le,null),s(le,zi),s(le,_o),s(le,Di),s(le,bo),s(le,Ui),s(le,To),s(le,Wi),s(le,Ne),_(es,Ne,null),s(Ne,Ii),s(Ne,Mo),s(Ne,Zi),_($t,Ne,null),s(Ne,Gi),_(vt,Ne,null),m(e,$r,d),_(ts,e,d),m(e,vr,d),m(e,de,d),_(ns,de,null),s(de,qi),s(de,yo),s(de,Ni),s(de,wo),s(de,Li),s(de,ko),s(de,Ri),s(de,Le),_(ss,Le,null),s(Le,Hi),s(Le,$o),s(Le,Xi),_(xt,Le,null),s(Le,Vi),_(Ft,Le,null),m(e,xr,d),_(os,e,d),m(e,Fr,d),m(e,ce,d),_(as,ce,null),s(ce,Ei),s(ce,vo),s(ce,Qi),s(ce,xo),s(ce,Si),s(ce,Fo),s(ce,Ai),s(ce,Me),_(rs,Me,null),s(Me,Pi),s(Me,Bo),s(Me,Yi),_(Bt,Me,null),s(Me,Oi),_(jt,Me,null),s(Me,Ki),_(Ct,Me,null),m(e,Br,d),_(is,e,d),m(e,jr,d),m(e,pe,d),_(ls,pe,null),s(pe,el),s(pe,jo),s(pe,tl),s(pe,Co),s(pe,nl),s(pe,Jo),s(pe,sl),s(pe,Re),_(ds,Re,null),s(Re,ol),s(Re,zo),s(Re,al),_(Jt,Re,null),s(Re,rl),_(zt,Re,null),m(e,Cr,d),_(cs,e,d),m(e,Jr,d),m(e,me,d),_(ps,me,null),s(me,il),s(me,Do),s(me,ll),s(me,Uo),s(me,dl),s(me,Wo),s(me,cl),s(me,He),_(ms,He,null),s(He,pl),s(He,Io),s(He,ml),_(Dt,He,null),s(He,ul),_(Ut,He,null),m(e,zr,d),_(us,e,d),m(e,Dr,d),m(e,ue,d),_(hs,ue,null),s(ue,hl),s(ue,Zo),s(ue,fl),s(ue,Go),s(ue,gl),s(ue,qo),s(ue,_l),s(ue,Xe),_(fs,Xe,null),s(Xe,bl),s(Xe,No),s(Xe,Tl),_(Wt,Xe,null),s(Xe,Ml),_(It,Xe,null),m(e,Ur,d),_(gs,e,d),m(e,Wr,d),m(e,S,d),_(_s,S,null),s(S,yl),s(S,Lo),s(S,wl),s(S,Ro),s(S,kl),s(S,Ho),s(S,$l),_(Zt,S,null),s(S,vl),s(S,Ve),_(bs,Ve,null),s(Ve,xl),s(Ve,Xo),s(Ve,Fl),_(Gt,Ve,null),s(Ve,Bl),_(qt,Ve,null),m(e,Ir,d),_(Ts,e,d),m(e,Zr,d),m(e,A,d),_(Ms,A,null),s(A,jl),s(A,Vo),s(A,Cl),s(A,Eo),s(A,Jl),s(A,Qo),s(A,zl),_(Nt,A,null),s(A,Dl),s(A,ye),_(ys,ye,null),s(ye,Ul),s(ye,So),s(ye,Wl),_(Lt,ye,null),s(ye,Il),_(Rt,ye,null),s(ye,Zl),_(Ht,ye,null),m(e,Gr,d),_(ws,e,d),m(e,qr,d),m(e,P,d),_(ks,P,null),s(P,Gl),s(P,Ao),s(P,ql),s(P,Po),s(P,Nl),s(P,Yo),s(P,Ll),_(Xt,P,null),s(P,Rl),s(P,we),_($s,we,null),s(we,Hl),s(we,Oo),s(we,Xl),_(Vt,we,null),s(we,Vl),_(Et,we,null),s(we,El),_(Qt,we,null),m(e,Nr,d),_(vs,e,d),m(e,Lr,d),m(e,Y,d),_(xs,Y,null),s(Y,Ql),s(Y,Ko),s(Y,Sl),s(Y,ea),s(Y,Al),s(Y,ta),s(Y,Pl),_(St,Y,null),s(Y,Yl),s(Y,Ee),_(Fs,Ee,null),s(Ee,Ol),s(Ee,na),s(Ee,Kl),_(At,Ee,null),s(Ee,ed),_(Pt,Ee,null),m(e,Rr,d),_(Bs,e,d),m(e,Hr,d),m(e,O,d),_(js,O,null),s(O,td),s(O,sa),s(O,nd),s(O,oa),s(O,sd),s(O,aa),s(O,od),_(Yt,O,null),s(O,ad),s(O,ke),_(Cs,ke,null),s(ke,rd),s(ke,ra),s(ke,id),_(Ot,ke,null),s(ke,ld),_(Kt,ke,null),s(ke,dd),_(en,ke,null),m(e,Xr,d),_(Js,e,d),m(e,Vr,d),m(e,K,d),_(zs,K,null),s(K,cd),s(K,ia),s(K,pd),s(K,la),s(K,md),s(K,da),s(K,ud),_(tn,K,null),s(K,hd),s(K,$e),_(Ds,$e,null),s($e,fd),s($e,ca),s($e,gd),_(nn,$e,null),s($e,_d),_(sn,$e,null),s($e,bd),_(on,$e,null),m(e,Er,d),_(Us,e,d),m(e,Qr,d),m(e,R,d),_(Ws,R,null),s(R,Td),s(R,pa),s(R,Md),s(R,ma),s(R,yd),s(R,ua),s(R,wd),s(R,ha),s(R,kd),s(R,fa),s(R,$d),s(R,Qe),_(Is,Qe,null),s(Qe,vd),s(Qe,ga),s(Qe,xd),_(an,Qe,null),s(Qe,Fd),_(rn,Qe,null),m(e,Sr,d),_(Zs,e,d),m(e,Ar,d),m(e,H,d),_(Gs,H,null),s(H,Bd),s(H,_a),s(H,jd),s(H,ba),s(H,Cd),s(H,Ta),s(H,Jd),s(H,Ma),s(H,zd),s(H,ya),s(H,Dd),s(H,Se),_(qs,Se,null),s(Se,Ud),s(Se,wa),s(Se,Wd),_(ln,Se,null),s(Se,Id),_(dn,Se,null),m(e,Pr,d),_(Ns,e,d),m(e,Yr,d),m(e,X,d),_(Ls,X,null),s(X,Zd),s(X,ka),s(X,Gd),s(X,$a),s(X,qd),s(X,va),s(X,Nd),s(X,xa),s(X,Ld),s(X,Fa),s(X,Rd),s(X,Ae),_(Rs,Ae,null),s(Ae,Hd),s(Ae,Ba),s(Ae,Xd),_(cn,Ae,null),s(Ae,Vd),_(pn,Ae,null),m(e,Or,d),_(Hs,e,d),m(e,Kr,d),m(e,V,d),_(Xs,V,null),s(V,Ed),s(V,ja),s(V,Qd),s(V,Ca),s(V,Sd),s(V,Ja),s(V,Ad),s(V,za),s(V,Pd),s(V,Da),s(V,Yd),s(V,Pe),_(Vs,Pe,null),s(Pe,Od),s(Pe,Ua),s(Pe,Kd),_(mn,Pe,null),s(Pe,ec),_(un,Pe,null),m(e,ei,d),_(Es,e,d),m(e,ti,d),m(e,E,d),_(Qs,E,null),s(E,tc),s(E,Wa),s(E,nc),s(E,Ia),s(E,sc),s(E,Za),s(E,oc),s(E,Ga),s(E,ac),s(E,qa),s(E,rc),s(E,Ye),_(Ss,Ye,null),s(Ye,ic),s(Ye,Na),s(Ye,lc),_(hn,Ye,null),s(Ye,dc),_(fn,Ye,null),m(e,ni,d),_(As,e,d),m(e,si,d),m(e,Q,d),_(Ps,Q,null),s(Q,cc),s(Q,La),s(Q,pc),s(Q,Ra),s(Q,mc),s(Q,Ha),s(Q,uc),s(Q,Xa),s(Q,hc),s(Q,Va),s(Q,fc),s(Q,Oe),_(Ys,Oe,null),s(Oe,gc),s(Oe,Ea),s(Oe,_c),_(gn,Oe,null),s(Oe,bc),_(_n,Oe,null),m(e,oi,d),m(e,Qa,d),ai=!0},p(e,[d]){const Ke={};d&2&&(Ke.$$scope={dirty:d,ctx:e}),Tt.$set(Ke);const ee={};d&2&&(ee.$$scope={dirty:d,ctx:e}),yt.$set(ee);const _t={};d&2&&(_t.$$scope={dirty:d,ctx:e}),kt.$set(_t);const Os={};d&2&&(Os.$$scope={dirty:d,ctx:e}),$t.$set(Os);const et={};d&2&&(et.$$scope={dirty:d,ctx:e}),vt.$set(et);const Ks={};d&2&&(Ks.$$scope={dirty:d,ctx:e}),xt.$set(Ks);const xe={};d&2&&(xe.$$scope={dirty:d,ctx:e}),Ft.$set(xe);const bt={};d&2&&(bt.$$scope={dirty:d,ctx:e}),Bt.$set(bt);const tt={};d&2&&(tt.$$scope={dirty:d,ctx:e}),jt.$set(tt);const Fe={};d&2&&(Fe.$$scope={dirty:d,ctx:e}),Ct.$set(Fe);const nt={};d&2&&(nt.$$scope={dirty:d,ctx:e}),Jt.$set(nt);const Be={};d&2&&(Be.$$scope={dirty:d,ctx:e}),zt.$set(Be);const st={};d&2&&(st.$$scope={dirty:d,ctx:e}),Dt.$set(st);const je={};d&2&&(je.$$scope={dirty:d,ctx:e}),Ut.$set(je);const Ce={};d&2&&(Ce.$$scope={dirty:d,ctx:e}),Wt.$set(Ce);const Je={};d&2&&(Je.$$scope={dirty:d,ctx:e}),It.$set(Je);const ot={};d&2&&(ot.$$scope={dirty:d,ctx:e}),Zt.$set(ot);const ze={};d&2&&(ze.$$scope={dirty:d,ctx:e}),Gt.$set(ze);const at={};d&2&&(at.$$scope={dirty:d,ctx:e}),qt.$set(at);const De={};d&2&&(De.$$scope={dirty:d,ctx:e}),Nt.$set(De);const rt={};d&2&&(rt.$$scope={dirty:d,ctx:e}),Lt.$set(rt);const he={};d&2&&(he.$$scope={dirty:d,ctx:e}),Rt.$set(he);const it={};d&2&&(it.$$scope={dirty:d,ctx:e}),Ht.$set(it);const fe={};d&2&&(fe.$$scope={dirty:d,ctx:e}),Xt.$set(fe);const Ue={};d&2&&(Ue.$$scope={dirty:d,ctx:e}),Vt.$set(Ue);const ge={};d&2&&(ge.$$scope={dirty:d,ctx:e}),Et.$set(ge);const We={};d&2&&(We.$$scope={dirty:d,ctx:e}),Qt.$set(We);const _e={};d&2&&(_e.$$scope={dirty:d,ctx:e}),St.$set(_e);const lt={};d&2&&(lt.$$scope={dirty:d,ctx:e}),At.$set(lt);const be={};d&2&&(be.$$scope={dirty:d,ctx:e}),Pt.$set(be);const Ie={};d&2&&(Ie.$$scope={dirty:d,ctx:e}),Yt.$set(Ie);const Te={};d&2&&(Te.$$scope={dirty:d,ctx:e}),Ot.$set(Te);const Ze={};d&2&&(Ze.$$scope={dirty:d,ctx:e}),Kt.$set(Ze);const te={};d&2&&(te.$$scope={dirty:d,ctx:e}),en.$set(te);const dt={};d&2&&(dt.$$scope={dirty:d,ctx:e}),tn.$set(dt);const ne={};d&2&&(ne.$$scope={dirty:d,ctx:e}),nn.$set(ne);const ct={};d&2&&(ct.$$scope={dirty:d,ctx:e}),sn.$set(ct);const se={};d&2&&(se.$$scope={dirty:d,ctx:e}),on.$set(se);const pt={};d&2&&(pt.$$scope={dirty:d,ctx:e}),an.$set(pt);const oe={};d&2&&(oe.$$scope={dirty:d,ctx:e}),rn.$set(oe);const mt={};d&2&&(mt.$$scope={dirty:d,ctx:e}),ln.$set(mt);const ae={};d&2&&(ae.$$scope={dirty:d,ctx:e}),dn.$set(ae);const ut={};d&2&&(ut.$$scope={dirty:d,ctx:e}),cn.$set(ut);const re={};d&2&&(re.$$scope={dirty:d,ctx:e}),pn.$set(re);const ht={};d&2&&(ht.$$scope={dirty:d,ctx:e}),mn.$set(ht);const Bm={};d&2&&(Bm.$$scope={dirty:d,ctx:e}),un.$set(Bm);const jm={};d&2&&(jm.$$scope={dirty:d,ctx:e}),hn.$set(jm);const Cm={};d&2&&(Cm.$$scope={dirty:d,ctx:e}),fn.$set(Cm);const Jm={};d&2&&(Jm.$$scope={dirty:d,ctx:e}),gn.$set(Jm);const zm={};d&2&&(zm.$$scope={dirty:d,ctx:e}),_n.$set(zm)},i(e){ai||(b(k.$$.fragment,e),b(x.$$.fragment,e),b(wn.$$.fragment,e),b($n.$$.fragment,e),b(xn.$$.fragment,e),b(Bn.$$.fragment,e),b(Cn.$$.fragment,e),b(Nn.$$.fragment,e),b(Ln.$$.fragment,e),b(Tt.$$.fragment,e),b(Rn.$$.fragment,e),b(Hn.$$.fragment,e),b(Xn.$$.fragment,e),b(Vn.$$.fragment,e),b(En.$$.fragment,e),b(yt.$$.fragment,e),b(Qn.$$.fragment,e),b(Sn.$$.fragment,e),b(An.$$.fragment,e),b(Pn.$$.fragment,e),b(Yn.$$.fragment,e),b(kt.$$.fragment,e),b(On.$$.fragment,e),b(Kn.$$.fragment,e),b(es.$$.fragment,e),b($t.$$.fragment,e),b(vt.$$.fragment,e),b(ts.$$.fragment,e),b(ns.$$.fragment,e),b(ss.$$.fragment,e),b(xt.$$.fragment,e),b(Ft.$$.fragment,e),b(os.$$.fragment,e),b(as.$$.fragment,e),b(rs.$$.fragment,e),b(Bt.$$.fragment,e),b(jt.$$.fragment,e),b(Ct.$$.fragment,e),b(is.$$.fragment,e),b(ls.$$.fragment,e),b(ds.$$.fragment,e),b(Jt.$$.fragment,e),b(zt.$$.fragment,e),b(cs.$$.fragment,e),b(ps.$$.fragment,e),b(ms.$$.fragment,e),b(Dt.$$.fragment,e),b(Ut.$$.fragment,e),b(us.$$.fragment,e),b(hs.$$.fragment,e),b(fs.$$.fragment,e),b(Wt.$$.fragment,e),b(It.$$.fragment,e),b(gs.$$.fragment,e),b(_s.$$.fragment,e),b(Zt.$$.fragment,e),b(bs.$$.fragment,e),b(Gt.$$.fragment,e),b(qt.$$.fragment,e),b(Ts.$$.fragment,e),b(Ms.$$.fragment,e),b(Nt.$$.fragment,e),b(ys.$$.fragment,e),b(Lt.$$.fragment,e),b(Rt.$$.fragment,e),b(Ht.$$.fragment,e),b(ws.$$.fragment,e),b(ks.$$.fragment,e),b(Xt.$$.fragment,e),b($s.$$.fragment,e),b(Vt.$$.fragment,e),b(Et.$$.fragment,e),b(Qt.$$.fragment,e),b(vs.$$.fragment,e),b(xs.$$.fragment,e),b(St.$$.fragment,e),b(Fs.$$.fragment,e),b(At.$$.fragment,e),b(Pt.$$.fragment,e),b(Bs.$$.fragment,e),b(js.$$.fragment,e),b(Yt.$$.fragment,e),b(Cs.$$.fragment,e),b(Ot.$$.fragment,e),b(Kt.$$.fragment,e),b(en.$$.fragment,e),b(Js.$$.fragment,e),b(zs.$$.fragment,e),b(tn.$$.fragment,e),b(Ds.$$.fragment,e),b(nn.$$.fragment,e),b(sn.$$.fragment,e),b(on.$$.fragment,e),b(Us.$$.fragment,e),b(Ws.$$.fragment,e),b(Is.$$.fragment,e),b(an.$$.fragment,e),b(rn.$$.fragment,e),b(Zs.$$.fragment,e),b(Gs.$$.fragment,e),b(qs.$$.fragment,e),b(ln.$$.fragment,e),b(dn.$$.fragment,e),b(Ns.$$.fragment,e),b(Ls.$$.fragment,e),b(Rs.$$.fragment,e),b(cn.$$.fragment,e),b(pn.$$.fragment,e),b(Hs.$$.fragment,e),b(Xs.$$.fragment,e),b(Vs.$$.fragment,e),b(mn.$$.fragment,e),b(un.$$.fragment,e),b(Es.$$.fragment,e),b(Qs.$$.fragment,e),b(Ss.$$.fragment,e),b(hn.$$.fragment,e),b(fn.$$.fragment,e),b(As.$$.fragment,e),b(Ps.$$.fragment,e),b(Ys.$$.fragment,e),b(gn.$$.fragment,e),b(_n.$$.fragment,e),ai=!0)},o(e){T(k.$$.fragment,e),T(x.$$.fragment,e),T(wn.$$.fragment,e),T($n.$$.fragment,e),T(xn.$$.fragment,e),T(Bn.$$.fragment,e),T(Cn.$$.fragment,e),T(Nn.$$.fragment,e),T(Ln.$$.fragment,e),T(Tt.$$.fragment,e),T(Rn.$$.fragment,e),T(Hn.$$.fragment,e),T(Xn.$$.fragment,e),T(Vn.$$.fragment,e),T(En.$$.fragment,e),T(yt.$$.fragment,e),T(Qn.$$.fragment,e),T(Sn.$$.fragment,e),T(An.$$.fragment,e),T(Pn.$$.fragment,e),T(Yn.$$.fragment,e),T(kt.$$.fragment,e),T(On.$$.fragment,e),T(Kn.$$.fragment,e),T(es.$$.fragment,e),T($t.$$.fragment,e),T(vt.$$.fragment,e),T(ts.$$.fragment,e),T(ns.$$.fragment,e),T(ss.$$.fragment,e),T(xt.$$.fragment,e),T(Ft.$$.fragment,e),T(os.$$.fragment,e),T(as.$$.fragment,e),T(rs.$$.fragment,e),T(Bt.$$.fragment,e),T(jt.$$.fragment,e),T(Ct.$$.fragment,e),T(is.$$.fragment,e),T(ls.$$.fragment,e),T(ds.$$.fragment,e),T(Jt.$$.fragment,e),T(zt.$$.fragment,e),T(cs.$$.fragment,e),T(ps.$$.fragment,e),T(ms.$$.fragment,e),T(Dt.$$.fragment,e),T(Ut.$$.fragment,e),T(us.$$.fragment,e),T(hs.$$.fragment,e),T(fs.$$.fragment,e),T(Wt.$$.fragment,e),T(It.$$.fragment,e),T(gs.$$.fragment,e),T(_s.$$.fragment,e),T(Zt.$$.fragment,e),T(bs.$$.fragment,e),T(Gt.$$.fragment,e),T(qt.$$.fragment,e),T(Ts.$$.fragment,e),T(Ms.$$.fragment,e),T(Nt.$$.fragment,e),T(ys.$$.fragment,e),T(Lt.$$.fragment,e),T(Rt.$$.fragment,e),T(Ht.$$.fragment,e),T(ws.$$.fragment,e),T(ks.$$.fragment,e),T(Xt.$$.fragment,e),T($s.$$.fragment,e),T(Vt.$$.fragment,e),T(Et.$$.fragment,e),T(Qt.$$.fragment,e),T(vs.$$.fragment,e),T(xs.$$.fragment,e),T(St.$$.fragment,e),T(Fs.$$.fragment,e),T(At.$$.fragment,e),T(Pt.$$.fragment,e),T(Bs.$$.fragment,e),T(js.$$.fragment,e),T(Yt.$$.fragment,e),T(Cs.$$.fragment,e),T(Ot.$$.fragment,e),T(Kt.$$.fragment,e),T(en.$$.fragment,e),T(Js.$$.fragment,e),T(zs.$$.fragment,e),T(tn.$$.fragment,e),T(Ds.$$.fragment,e),T(nn.$$.fragment,e),T(sn.$$.fragment,e),T(on.$$.fragment,e),T(Us.$$.fragment,e),T(Ws.$$.fragment,e),T(Is.$$.fragment,e),T(an.$$.fragment,e),T(rn.$$.fragment,e),T(Zs.$$.fragment,e),T(Gs.$$.fragment,e),T(qs.$$.fragment,e),T(ln.$$.fragment,e),T(dn.$$.fragment,e),T(Ns.$$.fragment,e),T(Ls.$$.fragment,e),T(Rs.$$.fragment,e),T(cn.$$.fragment,e),T(pn.$$.fragment,e),T(Hs.$$.fragment,e),T(Xs.$$.fragment,e),T(Vs.$$.fragment,e),T(mn.$$.fragment,e),T(un.$$.fragment,e),T(Es.$$.fragment,e),T(Qs.$$.fragment,e),T(Ss.$$.fragment,e),T(hn.$$.fragment,e),T(fn.$$.fragment,e),T(As.$$.fragment,e),T(Ps.$$.fragment,e),T(Ys.$$.fragment,e),T(gn.$$.fragment,e),T(_n.$$.fragment,e),ai=!1},d(e){e&&(i(h),i(o),i(l),i(n),i(y),i(D),i(G),i(z),i(q),i(w),i(Sa),i(bn),i(Aa),i(Tn),i(Pa),i(Mn),i(Ya),i(yn),i(Oa),i(Ka),i(kn),i(er),i(tr),i(vn),i(nr),i(sr),i(Fn),i(or),i(ar),i(jn),i(rr),i(ir),i(Jn),i(lr),i(zn),i(dr),i(Dn),i(cr),i(Un),i(pr),i(Wn),i(mr),i(In),i(ur),i(Zn),i(hr),i(Gn),i(fr),i(qn),i(gr),i(_r),i(ve),i(br),i(Tr),i(L),i(Mr),i(yr),i(ie),i(wr),i(kr),i(le),i($r),i(vr),i(de),i(xr),i(Fr),i(ce),i(Br),i(jr),i(pe),i(Cr),i(Jr),i(me),i(zr),i(Dr),i(ue),i(Ur),i(Wr),i(S),i(Ir),i(Zr),i(A),i(Gr),i(qr),i(P),i(Nr),i(Lr),i(Y),i(Rr),i(Hr),i(O),i(Xr),i(Vr),i(K),i(Er),i(Qr),i(R),i(Sr),i(Ar),i(H),i(Pr),i(Yr),i(X),i(Or),i(Kr),i(V),i(ei),i(ti),i(E),i(ni),i(si),i(Q),i(oi),i(Qa)),i(t),M(k,e),M(x,e),M(wn,e),M($n,e),M(xn,e),M(Bn,e),M(Cn,e),M(Nn,e),M(Ln),M(Tt),M(Rn,e),M(Hn),M(Xn),M(Vn),M(En),M(yt),M(Qn),M(Sn,e),M(An),M(Pn),M(Yn),M(kt),M(On,e),M(Kn),M(es),M($t),M(vt),M(ts,e),M(ns),M(ss),M(xt),M(Ft),M(os,e),M(as),M(rs),M(Bt),M(jt),M(Ct),M(is,e),M(ls),M(ds),M(Jt),M(zt),M(cs,e),M(ps),M(ms),M(Dt),M(Ut),M(us,e),M(hs),M(fs),M(Wt),M(It),M(gs,e),M(_s),M(Zt),M(bs),M(Gt),M(qt),M(Ts,e),M(Ms),M(Nt),M(ys),M(Lt),M(Rt),M(Ht),M(ws,e),M(ks),M(Xt),M($s),M(Vt),M(Et),M(Qt),M(vs,e),M(xs),M(St),M(Fs),M(At),M(Pt),M(Bs,e),M(js),M(Yt),M(Cs),M(Ot),M(Kt),M(en),M(Js,e),M(zs),M(tn),M(Ds),M(nn),M(sn),M(on),M(Us,e),M(Ws),M(Is),M(an),M(rn),M(Zs,e),M(Gs),M(qs),M(ln),M(dn),M(Ns,e),M(Ls),M(Rs),M(cn),M(pn),M(Hs,e),M(Xs),M(Vs),M(mn),M(un),M(Es,e),M(Qs),M(Ss),M(hn),M(fn),M(As,e),M(Ps),M(Ys),M(gn),M(_n)}}}const Wu='{"title":"DistilBERT","local":"distilbert","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Resources","local":"resources","sections":[],"depth":2},{"title":"DistilBertConfig","local":"transformers.DistilBertConfig","sections":[],"depth":2},{"title":"DistilBertTokenizer","local":"transformers.DistilBertTokenizer","sections":[],"depth":2},{"title":"DistilBertTokenizerFast","local":"transformers.DistilBertTokenizerFast","sections":[],"depth":2},{"title":"DistilBertModel","local":"transformers.DistilBertModel","sections":[],"depth":2},{"title":"DistilBertForMaskedLM","local":"transformers.DistilBertForMaskedLM","sections":[],"depth":2},{"title":"DistilBertForSequenceClassification","local":"transformers.DistilBertForSequenceClassification","sections":[],"depth":2},{"title":"DistilBertForMultipleChoice","local":"transformers.DistilBertForMultipleChoice","sections":[],"depth":2},{"title":"DistilBertForTokenClassification","local":"transformers.DistilBertForTokenClassification","sections":[],"depth":2},{"title":"DistilBertForQuestionAnswering","local":"transformers.DistilBertForQuestionAnswering","sections":[],"depth":2},{"title":"TFDistilBertModel","local":"transformers.TFDistilBertModel","sections":[],"depth":2},{"title":"TFDistilBertForMaskedLM","local":"transformers.TFDistilBertForMaskedLM","sections":[],"depth":2},{"title":"TFDistilBertForSequenceClassification","local":"transformers.TFDistilBertForSequenceClassification","sections":[],"depth":2},{"title":"TFDistilBertForMultipleChoice","local":"transformers.TFDistilBertForMultipleChoice","sections":[],"depth":2},{"title":"TFDistilBertForTokenClassification","local":"transformers.TFDistilBertForTokenClassification","sections":[],"depth":2},{"title":"TFDistilBertForQuestionAnswering","local":"transformers.TFDistilBertForQuestionAnswering","sections":[],"depth":2},{"title":"FlaxDistilBertModel","local":"transformers.FlaxDistilBertModel","sections":[],"depth":2},{"title":"FlaxDistilBertForMaskedLM","local":"transformers.FlaxDistilBertForMaskedLM","sections":[],"depth":2},{"title":"FlaxDistilBertForSequenceClassification","local":"transformers.FlaxDistilBertForSequenceClassification","sections":[],"depth":2},{"title":"FlaxDistilBertForMultipleChoice","local":"transformers.FlaxDistilBertForMultipleChoice","sections":[],"depth":2},{"title":"FlaxDistilBertForTokenClassification","local":"transformers.FlaxDistilBertForTokenClassification","sections":[],"depth":2},{"title":"FlaxDistilBertForQuestionAnswering","local":"transformers.FlaxDistilBertForQuestionAnswering","sections":[],"depth":2}],"depth":1}';function Iu($){return Um(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Vu extends Wm{constructor(t){super(),Im(this,t,Iu,Uu,Dm,{})}}export{Vu as component};
