import{s as Bg,o as jg,n as j}from"../chunks/scheduler.9bc65507.js";import{S as Jg,i as zg,g as a,s as i,u as f,A as Dg,h as r,f as s,c as l,j as $,v as g,y as u,k as y,o as n,a as p,w as _,d as b,t as T,x as M}from"../chunks/index.78c82d43.js";import{T as Z}from"../chunks/Tip.87d55b76.js";import{D as J}from"../chunks/Docstring.f3d04bd2.js";import{C as W}from"../chunks/CodeBlock.48618ec2.js";import{I as N}from"../chunks/IconCopyLink.bedaa44d.js";import{E as U}from"../chunks/ExampleCodeBlock.872b014d.js";import{P as Gd}from"../chunks/PipelineTag.75b0f707.js";function Ug(x){let t,h="Examples:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-kvfsh7"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Wg(x){let t,h="pair mask has the following format:",d,c,w;return c=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 |`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-qjgeij"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Ig(x){let t,h="pair mask has the following format:",d,c,w;return c=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 |`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-qjgeij"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Zg(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function Ng(x){let t,h="Example:",d,c,w;return c=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBEaXN0aWxCZXJ0TW9kZWwlMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQtYmFzZS11bmNhc2VkJTIyKSUwQW1vZGVsJTIwJTNEJTIwRGlzdGlsQmVydE1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJkaXN0aWxiZXJ0LWJhc2UtdW5jYXNlZCUyMiklMEElMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoJTIySGVsbG8lMkMlMjBteSUyMGRvZyUyMGlzJTIwY3V0ZSUyMiUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpJTBBb3V0cHV0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKSUwQSUwQWxhc3RfaGlkZGVuX3N0YXRlcyUyMCUzRCUyMG91dHB1dHMubGFzdF9oaWRkZW5fc3RhdGU=",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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Hg(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function Gg(x){let t,h="Example:",d,c,w;return c=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)`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function qg(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function Lg(x){let t,h="Example of single-label classification:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-ykxpe4"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Rg(x){let t,h="Example of multi-label classification:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1l8e32d"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Eg(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function Xg(x){let t,h="Examples:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-kvfsh7"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Vg(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function Ag(x){let t,h="Example:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Sg(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function Pg(x){let t,h="Example:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Qg(x){let t,h="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",d,c,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",o,v,I=`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,B,H=`<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,C,G=`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=a("p"),t.innerHTML=h,d=i(),c=a("ul"),c.innerHTML=w,o=i(),v=a("p"),v.innerHTML=I,D=i(),B=a("ul"),B.innerHTML=H,z=i(),C=a("p"),C.innerHTML=G},l(k){t=r(k,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=h),d=l(k),c=r(k,"UL",{"data-svelte-h":!0}),u(c)!=="svelte-qm1t26"&&(c.innerHTML=w),o=l(k),v=r(k,"P",{"data-svelte-h":!0}),u(v)!=="svelte-1v9qsc5"&&(v.innerHTML=I),D=l(k),B=r(k,"UL",{"data-svelte-h":!0}),u(B)!=="svelte-15scerc"&&(B.innerHTML=H),z=l(k),C=r(k,"P",{"data-svelte-h":!0}),u(C)!=="svelte-1an3odd"&&(C.innerHTML=G)},m(k,F){p(k,t,F),p(k,d,F),p(k,c,F),p(k,o,F),p(k,v,F),p(k,D,F),p(k,B,F),p(k,z,F),p(k,C,F)},p:j,d(k){k&&(s(t),s(d),s(c),s(o),s(v),s(D),s(B),s(z),s(C))}}}function Yg(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function Og(x){let t,h="Example:",d,c,w;return c=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, 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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function Kg(x){let t,h="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",d,c,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",o,v,I=`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,B,H=`<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,C,G=`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=a("p"),t.innerHTML=h,d=i(),c=a("ul"),c.innerHTML=w,o=i(),v=a("p"),v.innerHTML=I,D=i(),B=a("ul"),B.innerHTML=H,z=i(),C=a("p"),C.innerHTML=G},l(k){t=r(k,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=h),d=l(k),c=r(k,"UL",{"data-svelte-h":!0}),u(c)!=="svelte-qm1t26"&&(c.innerHTML=w),o=l(k),v=r(k,"P",{"data-svelte-h":!0}),u(v)!=="svelte-1v9qsc5"&&(v.innerHTML=I),D=l(k),B=r(k,"UL",{"data-svelte-h":!0}),u(B)!=="svelte-15scerc"&&(B.innerHTML=H),z=l(k),C=r(k,"P",{"data-svelte-h":!0}),u(C)!=="svelte-1an3odd"&&(C.innerHTML=G)},m(k,F){p(k,t,F),p(k,d,F),p(k,c,F),p(k,o,F),p(k,v,F),p(k,D,F),p(k,B,F),p(k,z,F),p(k,C,F)},p:j,d(k){k&&(s(t),s(d),s(c),s(o),s(v),s(D),s(B),s(z),s(C))}}}function e_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function t_(x){let t,h="Example:",d,c,w;return c=new W({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBURkRpc3RpbEJlcnRGb3JNYXNrZWRMTSUwQWltcG9ydCUyMHRlbnNvcmZsb3clMjBhcyUyMHRmJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIpJTBBbW9kZWwlMjAlM0QlMjBURkRpc3RpbEJlcnRGb3JNYXNrZWRMTS5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsYmVydC1iYXNlLXVuY2FzZWQlMjIpJTBBJTBBaW5wdXRzJTIwJTNEJTIwdG9rZW5pemVyKCUyMlRoZSUyMGNhcGl0YWwlMjBvZiUyMEZyYW5jZSUyMGlzJTIwJTVCTUFTSyU1RC4lMjIlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnRmJTIyKSUwQWxvZ2l0cyUyMCUzRCUyMG1vZGVsKCoqaW5wdXRzKS5sb2dpdHMlMEElMEElMjMlMjByZXRyaWV2ZSUyMGluZGV4JTIwb2YlMjAlNUJNQVNLJTVEJTBBbWFza190b2tlbl9pbmRleCUyMCUzRCUyMHRmLndoZXJlKChpbnB1dHMuaW5wdXRfaWRzJTIwJTNEJTNEJTIwdG9rZW5pemVyLm1hc2tfdG9rZW5faWQpJTVCMCU1RCklMEFzZWxlY3RlZF9sb2dpdHMlMjAlM0QlMjB0Zi5nYXRoZXJfbmQobG9naXRzJTVCMCU1RCUyQyUyMGluZGljZXMlM0RtYXNrX3Rva2VuX2luZGV4KSUwQSUwQXByZWRpY3RlZF90b2tlbl9pZCUyMCUzRCUyMHRmLm1hdGguYXJnbWF4KHNlbGVjdGVkX2xvZ2l0cyUyQyUyMGF4aXMlM0QtMSk=",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>)`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function n_(x){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)`}}),{c(){f(t.$$.fragment)},l(d){g(t.$$.fragment,d)},m(d,c){_(t,d,c),h=!0},p:j,i(d){h||(b(t.$$.fragment,d),h=!0)},o(d){T(t.$$.fragment,d),h=!1},d(d){M(t,d)}}}function s_(x){let t,h="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",d,c,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",o,v,I=`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,B,H=`<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,C,G=`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=a("p"),t.innerHTML=h,d=i(),c=a("ul"),c.innerHTML=w,o=i(),v=a("p"),v.innerHTML=I,D=i(),B=a("ul"),B.innerHTML=H,z=i(),C=a("p"),C.innerHTML=G},l(k){t=r(k,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=h),d=l(k),c=r(k,"UL",{"data-svelte-h":!0}),u(c)!=="svelte-qm1t26"&&(c.innerHTML=w),o=l(k),v=r(k,"P",{"data-svelte-h":!0}),u(v)!=="svelte-1v9qsc5"&&(v.innerHTML=I),D=l(k),B=r(k,"UL",{"data-svelte-h":!0}),u(B)!=="svelte-15scerc"&&(B.innerHTML=H),z=l(k),C=r(k,"P",{"data-svelte-h":!0}),u(C)!=="svelte-1an3odd"&&(C.innerHTML=G)},m(k,F){p(k,t,F),p(k,d,F),p(k,c,F),p(k,o,F),p(k,v,F),p(k,D,F),p(k,B,F),p(k,z,F),p(k,C,F)},p:j,d(k){k&&(s(t),s(d),s(c),s(o),s(v),s(D),s(B),s(z),s(C))}}}function o_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function a_(x){let t,h="Example:",d,c,w;return c=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>])`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function r_(x){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'}}),{c(){f(t.$$.fragment)},l(d){g(t.$$.fragment,d)},m(d,c){_(t,d,c),h=!0},p:j,i(d){h||(b(t.$$.fragment,d),h=!0)},o(d){T(t.$$.fragment,d),h=!1},d(d){M(t,d)}}}function i_(x){let t,h="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",d,c,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",o,v,I=`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,B,H=`<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,C,G=`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=a("p"),t.innerHTML=h,d=i(),c=a("ul"),c.innerHTML=w,o=i(),v=a("p"),v.innerHTML=I,D=i(),B=a("ul"),B.innerHTML=H,z=i(),C=a("p"),C.innerHTML=G},l(k){t=r(k,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=h),d=l(k),c=r(k,"UL",{"data-svelte-h":!0}),u(c)!=="svelte-qm1t26"&&(c.innerHTML=w),o=l(k),v=r(k,"P",{"data-svelte-h":!0}),u(v)!=="svelte-1v9qsc5"&&(v.innerHTML=I),D=l(k),B=r(k,"UL",{"data-svelte-h":!0}),u(B)!=="svelte-15scerc"&&(B.innerHTML=H),z=l(k),C=r(k,"P",{"data-svelte-h":!0}),u(C)!=="svelte-1an3odd"&&(C.innerHTML=G)},m(k,F){p(k,t,F),p(k,d,F),p(k,c,F),p(k,o,F),p(k,v,F),p(k,D,F),p(k,B,F),p(k,z,F),p(k,C,F)},p:j,d(k){k&&(s(t),s(d),s(c),s(o),s(v),s(D),s(B),s(z),s(C))}}}function l_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function d_(x){let t,h="Example:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function c_(x){let t,h="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",d,c,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",o,v,I=`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,B,H=`<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,C,G=`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=a("p"),t.innerHTML=h,d=i(),c=a("ul"),c.innerHTML=w,o=i(),v=a("p"),v.innerHTML=I,D=i(),B=a("ul"),B.innerHTML=H,z=i(),C=a("p"),C.innerHTML=G},l(k){t=r(k,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=h),d=l(k),c=r(k,"UL",{"data-svelte-h":!0}),u(c)!=="svelte-qm1t26"&&(c.innerHTML=w),o=l(k),v=r(k,"P",{"data-svelte-h":!0}),u(v)!=="svelte-1v9qsc5"&&(v.innerHTML=I),D=l(k),B=r(k,"UL",{"data-svelte-h":!0}),u(B)!=="svelte-15scerc"&&(B.innerHTML=H),z=l(k),C=r(k,"P",{"data-svelte-h":!0}),u(C)!=="svelte-1an3odd"&&(C.innerHTML=G)},m(k,F){p(k,t,F),p(k,d,F),p(k,c,F),p(k,o,F),p(k,v,F),p(k,D,F),p(k,B,F),p(k,z,F),p(k,C,F)},p:j,d(k){k&&(s(t),s(d),s(c),s(o),s(v),s(D),s(B),s(z),s(C))}}}function p_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function m_(x){let t,h="Example:",d,c,w;return c=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()]`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function u_(x){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)`}}),{c(){f(t.$$.fragment)},l(d){g(t.$$.fragment,d)},m(d,c){_(t,d,c),h=!0},p:j,i(d){h||(b(t.$$.fragment,d),h=!0)},o(d){T(t.$$.fragment,d),h=!1},d(d){M(t,d)}}}function h_(x){let t,h="TensorFlow models and layers in <code>transformers</code> accept two formats as input:",d,c,w="<li>having all inputs as keyword arguments (like PyTorch models), or</li> <li>having all inputs as a list, tuple or dict in the first positional argument.</li>",o,v,I=`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,B,H=`<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,C,G=`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=a("p"),t.innerHTML=h,d=i(),c=a("ul"),c.innerHTML=w,o=i(),v=a("p"),v.innerHTML=I,D=i(),B=a("ul"),B.innerHTML=H,z=i(),C=a("p"),C.innerHTML=G},l(k){t=r(k,"P",{"data-svelte-h":!0}),u(t)!=="svelte-1ajbfxg"&&(t.innerHTML=h),d=l(k),c=r(k,"UL",{"data-svelte-h":!0}),u(c)!=="svelte-qm1t26"&&(c.innerHTML=w),o=l(k),v=r(k,"P",{"data-svelte-h":!0}),u(v)!=="svelte-1v9qsc5"&&(v.innerHTML=I),D=l(k),B=r(k,"UL",{"data-svelte-h":!0}),u(B)!=="svelte-15scerc"&&(B.innerHTML=H),z=l(k),C=r(k,"P",{"data-svelte-h":!0}),u(C)!=="svelte-1an3odd"&&(C.innerHTML=G)},m(k,F){p(k,t,F),p(k,d,F),p(k,c,F),p(k,o,F),p(k,v,F),p(k,D,F),p(k,B,F),p(k,z,F),p(k,C,F)},p:j,d(k){k&&(s(t),s(d),s(c),s(o),s(v),s(D),s(B),s(z),s(C))}}}function f_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function g_(x){let t,h="Example:",d,c,w;return c=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>]`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function __(x){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)`}}),{c(){f(t.$$.fragment)},l(d){g(t.$$.fragment,d)},m(d,c){_(t,d,c),h=!0},p:j,i(d){h||(b(t.$$.fragment,d),h=!0)},o(d){T(t.$$.fragment,d),h=!1},d(d){M(t,d)}}}function b_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function T_(x){let t,h="Example:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function M_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function y_(x){let t,h="Example:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function v_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function w_(x){let t,h="Example:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function k_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function $_(x){let t,h="Example:",d,c,w;return c=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, 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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function x_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function F_(x){let t,h="Example:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function C_(x){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=a("p"),t.innerHTML=h},l(d){t=r(d,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(d,c){p(d,t,c)},p:j,d(d){d&&s(t)}}}function B_(x){let t,h="Example:",d,c,w;return c=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`}}),{c(){t=a("p"),t.textContent=h,d=i(),f(c.$$.fragment)},l(o){t=r(o,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=h),d=l(o),g(c.$$.fragment,o)},m(o,v){p(o,t,v),p(o,d,v),_(c,o,v),w=!0},p:j,i(o){w||(b(c.$$.fragment,o),w=!0)},o(o){T(c.$$.fragment,o),w=!1},d(o){o&&(s(t),s(d)),M(c,o)}}}function j_(x){let t,h,d,c,w,o,v,I,D,B,H="DistilBERT",z,C,G='<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>',k,F,Jt,yi,ds,qd,ra,vu="Overview",ul,cs,wu=`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.`,hl,ps,ku="The abstract from the paper is the following:",fl,ms,$u=`<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>`,gl,us,xu="Tips:",_l,hs,Fu=`<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>`,bl,fs,Cu=`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>.`,Tl,it,zt,vi,gs,Ld,ia,Bu="Resources",Ml,_s,ju="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.",yl,bs,vl,Ts,Ju='<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/v4.33.3/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/v4.33.3/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/v4.33.3/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>',wl,Ms,kl,ys,zu='<li><a href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/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>',$l,vs,xl,ws,Du='<li><a href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/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>',Fl,ks,Cl,$s,Uu='<li><a href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/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>',Bl,xs,Wu="<strong>Multiple choice</strong>",jl,Fs,Iu='<li><a href="/docs/transformers/v4.33.3/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/v4.33.3/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>',Jl,Cs,Zu="⚗️ Optimization",zl,Bs,Nu='<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>',Dl,js,Hu="⚡️ Inference",Ul,Js,Gu='<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>',Wl,zs,qu="🚀 Deploy",Il,Ds,Lu='<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>',Zl,lt,Dt,wi,Us,Rd,la,Ru="DistilBertConfig",Nl,Te,Ws,Ed,da,Eu=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> or a <a href="/docs/transformers/v4.33.3/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.`,Xd,ca,Xu=`Configuration objects inherit from <a href="/docs/transformers/v4.33.3/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/v4.33.3/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Vd,Ut,Hl,dt,Wt,ki,Is,Ad,pa,Vu="DistilBertTokenizer",Gl,q,Zs,Sd,ma,Au="Construct a DistilBERT tokenizer. Based on WordPiece.",Pd,ua,Su=`This tokenizer inherits from <a href="/docs/transformers/v4.33.3/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.`,Qd,Ke,Ns,Yd,ha,Pu=`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:`,Od,fa,Qu="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",Kd,It,Hs,ec,ga,Yu="Converts a sequence of tokens (string) in a single string.",tc,xe,Gs,nc,_a,Ou="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",sc,Zt,oc,ba,Ku="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",ac,Nt,qs,rc,Ta,eh=`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.`,ql,ct,Ht,$i,Ls,ic,Ma,th="DistilBertTokenizerFast",Ll,te,Rs,lc,ya,nh="Construct a “fast” DistilBERT tokenizer (backed by HuggingFace’s <em>tokenizers</em> library). Based on WordPiece.",dc,va,sh=`This tokenizer inherits from <a href="/docs/transformers/v4.33.3/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.`,cc,et,Es,pc,wa,oh=`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:`,mc,ka,ah="<li>single sequence: <code>[CLS] X [SEP]</code></li> <li>pair of sequences: <code>[CLS] A [SEP] B [SEP]</code></li>",uc,Fe,Xs,hc,$a,rh="Create a mask from the two sequences passed to be used in a sequence-pair classification task. A BERT sequence",fc,Gt,gc,xa,ih="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",Rl,pt,qt,xi,Vs,_c,Fa,lh="DistilBertModel",El,ne,As,bc,Ca,dh="The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",Tc,Ba,ch=`This model inherits from <a href="/docs/transformers/v4.33.3/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Mc,ja,ph=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,yc,Ce,Ss,vc,Ja,mh='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> forward method, overrides the <code>__call__</code> special method.',wc,Lt,kc,Rt,Xl,mt,Et,Fi,Ps,$c,za,uh="DistilBertForMaskedLM",Vl,se,Qs,xc,Da,hh="DistilBert Model with a <code>masked language modeling</code> head on top.",Fc,Ua,fh=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,Cc,Wa,gh=`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.`,Bc,Be,Ys,jc,Ia,_h='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertForMaskedLM">DistilBertForMaskedLM</a> forward method, overrides the <code>__call__</code> special method.',Jc,Xt,zc,Vt,Al,ut,At,Ci,Os,Dc,Za,bh="DistilBertForSequenceClassification",Sl,oe,Ks,Uc,Na,Th=`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.`,Wc,Ha,Mh=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,Ic,Ga,yh=`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.`,Zc,le,eo,Nc,qa,vh='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',Hc,St,Gc,Pt,qc,Qt,Pl,ht,Yt,Bi,to,Lc,La,wh="DistilBertForMultipleChoice",Ql,ae,no,Rc,Ra,kh=`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.`,Ec,Ea,$h=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,Xc,Xa,xh=`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.`,Vc,je,so,Ac,Va,Fh='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertForMultipleChoice">DistilBertForMultipleChoice</a> forward method, overrides the <code>__call__</code> special method.',Sc,Ot,Pc,Kt,Yl,ft,en,ji,oo,Qc,Aa,Ch="DistilBertForTokenClassification",Ol,re,ao,Yc,Sa,Bh=`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.`,Oc,Pa,jh=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,Kc,Qa,Jh=`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.`,ep,Je,ro,tp,Ya,zh='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertForTokenClassification">DistilBertForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',np,tn,sp,nn,Kl,gt,sn,Ji,io,op,Oa,Dh="DistilBertForQuestionAnswering",ed,ie,lo,ap,Ka,Uh=`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>).`,rp,er,Wh=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,ip,tr,Ih=`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.`,lp,ze,co,dp,nr,Zh='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.DistilBertForQuestionAnswering">DistilBertForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',cp,on,pp,an,td,_t,rn,zi,po,mp,sr,Nh="TFDistilBertModel",nd,S,mo,up,or,Hh="The bare DistilBERT encoder/transformer outputting raw hidden-states without any specific head on top.",hp,ar,Gh=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,fp,rr,qh=`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.`,gp,ln,_p,De,uo,bp,ir,Lh='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.TFDistilBertModel">TFDistilBertModel</a> forward method, overrides the <code>__call__</code> special method.',Tp,dn,Mp,cn,sd,bt,pn,Di,ho,yp,lr,Rh="TFDistilBertForMaskedLM",od,P,fo,vp,dr,Eh="DistilBert Model with a <code>masked language modeling</code> head on top.",wp,cr,Xh=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,kp,pr,Vh=`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.`,$p,mn,xp,de,go,Fp,mr,Ah='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.TFDistilBertForMaskedLM">TFDistilBertForMaskedLM</a> forward method, overrides the <code>__call__</code> special method.',Cp,un,Bp,hn,jp,fn,ad,Tt,gn,Ui,_o,Jp,ur,Sh="TFDistilBertForSequenceClassification",rd,Q,bo,zp,hr,Ph=`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.`,Dp,fr,Qh=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,Up,gr,Yh=`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.`,Wp,_n,Ip,ce,To,Zp,_r,Oh='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.TFDistilBertForSequenceClassification">TFDistilBertForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',Np,bn,Hp,Tn,Gp,Mn,id,Mt,yn,Wi,Mo,qp,br,Kh="TFDistilBertForMultipleChoice",ld,Y,yo,Lp,Tr,ef=`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.`,Rp,Mr,tf=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,Ep,yr,nf=`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.`,Xp,vn,Vp,Ue,vo,Ap,vr,sf='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.TFDistilBertForMultipleChoice">TFDistilBertForMultipleChoice</a> forward method, overrides the <code>__call__</code> special method.',Sp,wn,Pp,kn,dd,yt,$n,Ii,wo,Qp,wr,of="TFDistilBertForTokenClassification",cd,O,ko,Yp,kr,af=`DistilBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.`,Op,$r,rf=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,Kp,xr,lf=`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.`,em,xn,tm,pe,$o,nm,Fr,df='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.TFDistilBertForTokenClassification">TFDistilBertForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',sm,Fn,om,Cn,am,Bn,pd,vt,jn,Zi,xo,rm,Cr,cf="TFDistilBertForQuestionAnswering",md,K,Fo,im,Br,pf=`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>).`,lm,jr,mf=`This model inherits from <a href="/docs/transformers/v4.33.3/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.)`,dm,Jr,uf=`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.`,cm,Jn,pm,me,Co,mm,zr,hf='The <a href="/docs/transformers/v4.33.3/en/model_doc/distilbert#transformers.TFDistilBertForQuestionAnswering">TFDistilBertForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',um,zn,hm,Dn,fm,Un,ud,wt,Wn,Ni,Bo,gm,Dr,ff="FlaxDistilBertModel",hd,L,jo,_m,Ur,gf="The bare DistilBert Model transformer outputting raw hidden-states without any specific head on top.",bm,Wr,_f=`This model inherits from <a href="/docs/transformers/v4.33.3/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)`,Tm,Ir,bf=`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.`,Mm,Zr,Tf="Finally, this model supports inherent JAX features such as:",ym,Nr,Mf='<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>',vm,We,Jo,wm,Hr,yf="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",km,In,$m,Zn,fd,kt,Nn,Hi,zo,xm,Gr,vf="FlaxDistilBertForMaskedLM",gd,R,Do,Fm,qr,wf="DistilBert Model with a <code>language modeling</code> head on top.",Cm,Lr,kf=`This model inherits from <a href="/docs/transformers/v4.33.3/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)`,Bm,Rr,$f=`This model is also a Flax Linen <a href="https://flax.readthedocs.io/en/latest/flax.linen.html#module" rel="nofollow">flax.linen.Module</a>
subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
general usage and behavior.`,jm,Er,xf="Finally, this model supports inherent JAX features such as:",Jm,Xr,Ff='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',zm,Ie,Uo,Dm,Vr,Cf="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Um,Hn,Wm,Gn,_d,$t,qn,Gi,Wo,Im,Ar,Bf="FlaxDistilBertForSequenceClassification",bd,E,Io,Zm,Sr,jf=`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.`,Nm,Pr,Jf=`This model inherits from <a href="/docs/transformers/v4.33.3/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)`,Hm,Qr,zf=`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.`,Gm,Yr,Df="Finally, this model supports inherent JAX features such as:",qm,Or,Uf='<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>',Lm,Ze,Zo,Rm,Kr,Wf="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Em,Ln,Xm,Rn,Td,xt,En,qi,No,Vm,ei,If="FlaxDistilBertForMultipleChoice",Md,X,Ho,Am,ti,Zf=`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.`,Sm,ni,Nf=`This model inherits from <a href="/docs/transformers/v4.33.3/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)`,Pm,si,Hf=`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.`,Qm,oi,Gf="Finally, this model supports inherent JAX features such as:",Ym,ai,qf='<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>',Om,Ne,Go,Km,ri,Lf="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",eu,Xn,tu,Vn,yd,Ft,An,Li,qo,nu,ii,Rf="FlaxDistilBertForTokenClassification",vd,V,Lo,su,li,Ef=`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.`,ou,di,Xf=`This model inherits from <a href="/docs/transformers/v4.33.3/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)`,au,ci,Vf=`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.`,ru,pi,Af="Finally, this model supports inherent JAX features such as:",iu,mi,Sf='<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>',lu,He,Ro,du,ui,Pf="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",cu,Sn,pu,Pn,wd,Ct,Qn,Ri,Eo,mu,hi,Qf="FlaxDistilBertForQuestionAnswering",kd,A,Xo,uu,fi,Yf=`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>).`,hu,gi,Of=`This model inherits from <a href="/docs/transformers/v4.33.3/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)`,fu,_i,Kf=`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.`,gu,bi,eg="Finally, this model supports inherent JAX features such as:",_u,Ti,tg='<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>',bu,Ge,Vo,Tu,Mi,ng="The <code>FlaxDistilBertPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",Mu,Yn,yu,On,$d,Ei,xd;return I=new N({}),ds=new N({}),gs=new N({}),bs=new Gd({props:{pipeline:"text-classification"}}),Ms=new Gd({props:{pipeline:"token-classification"}}),vs=new Gd({props:{pipeline:"fill-mask"}}),ks=new Gd({props:{pipeline:"question-answering"}}),Us=new N({}),Ws=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/v4.33.3/en/model_doc/distilbert#transformers.DistilBertModel">DistilBertModel</a> or <a href="/docs/transformers/v4.33.3/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/v4.33.3/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/v4.33.3/en/model_doc/distilbert#transformers.DistilBertForSequenceClassification">DistilBertForSequenceClassification</a>.`,name:"seq_classif_dropout"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/distilbert/configuration_distilbert.py#L45"}}),Ut=new U({props:{anchor:"transformers.DistilBertConfig.example",$$slots:{default:[Ug]},$$scope:{ctx:x}}}),Is=new N({}),Zs=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/v4.33.3/src/transformers/models/distilbert/tokenization_distilbert.py#L89"}}),Ns=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/v4.33.3/src/transformers/models/distilbert/tokenization_distilbert.py#L230",returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Hs=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/v4.33.3/src/transformers/models/distilbert/tokenization_distilbert.py#L224"}}),Gs=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/v4.33.3/src/transformers/models/distilbert/tokenization_distilbert.py#L285",returnDescription:`
<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Zt=new U({props:{anchor:"transformers.DistilBertTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[Wg]},$$scope:{ctx:x}}}),qs=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/v4.33.3/src/transformers/models/distilbert/tokenization_distilbert.py#L256",returnDescription:`
<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Ls=new N({}),Rs=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/v4.33.3/src/transformers/models/distilbert/tokenization_distilbert_fast.py#L84"}}),Es=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/v4.33.3/src/transformers/models/distilbert/tokenization_distilbert_fast.py#L174",returnDescription:`
<p>List of <a href="../glossary#input-ids">input IDs</a> with the appropriate special tokens.</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Xs=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/v4.33.3/src/transformers/models/distilbert/tokenization_distilbert_fast.py#L199",returnDescription:`
<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`
<p><code>List[int]</code></p>
`}}),Gt=new U({props:{anchor:"transformers.DistilBertTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[Ig]},$$scope:{ctx:x}}}),Vs=new N({}),As=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L495"}}),Ss=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L567",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_outputs.BaseModelOutput"
>transformers.modeling_outputs.BaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Lt=new Z({props:{$$slots:{default:[Zg]},$$scope:{ctx:x}}}),Rt=new U({props:{anchor:"transformers.DistilBertModel.forward.example",$$slots:{default:[Ng]},$$scope:{ctx:x}}}),Ps=new N({}),Qs=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L623"}}),Ys=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L667",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Xt=new Z({props:{$$slots:{default:[Hg]},$$scope:{ctx:x}}}),Vt=new U({props:{anchor:"transformers.DistilBertForMaskedLM.forward.example",$$slots:{default:[Gg]},$$scope:{ctx:x}}}),Os=new N({}),Ks=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L730"}}),eo=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L764",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),St=new Z({props:{$$slots:{default:[qg]},$$scope:{ctx:x}}}),Pt=new U({props:{anchor:"transformers.DistilBertForSequenceClassification.forward.example",$$slots:{default:[Lg]},$$scope:{ctx:x}}}),Qt=new U({props:{anchor:"transformers.DistilBertForSequenceClassification.forward.example-2",$$slots:{default:[Rg]},$$scope:{ctx:x}}}),to=new N({}),no=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L1059"}}),so=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L1091",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ot=new Z({props:{$$slots:{default:[Eg]},$$scope:{ctx:x}}}),Kt=new U({props:{anchor:"transformers.DistilBertForMultipleChoice.forward.example",$$slots:{default:[Xg]},$$scope:{ctx:x}}}),oo=new N({}),ao=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L965"}}),ro=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L997",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),tn=new Z({props:{$$slots:{default:[Vg]},$$scope:{ctx:x}}}),nn=new U({props:{anchor:"transformers.DistilBertForTokenClassification.forward.example",$$slots:{default:[Ag]},$$scope:{ctx:x}}}),io=new N({}),lo=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L847"}}),co=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_distilbert.py#L881",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),on=new Z({props:{$$slots:{default:[Sg]},$$scope:{ctx:x}}}),an=new U({props:{anchor:"transformers.DistilBertForQuestionAnswering.forward.example",$$slots:{default:[Pg]},$$scope:{ctx:x}}}),po=new N({}),mo=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L516"}}),ln=new Z({props:{$$slots:{default:[Qg]},$$scope:{ctx:x}}}),uo=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L521",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutput"
>transformers.modeling_tf_outputs.TFBaseModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),dn=new Z({props:{$$slots:{default:[Yg]},$$scope:{ctx:x}}}),cn=new U({props:{anchor:"transformers.TFDistilBertModel.call.example",$$slots:{default:[Og]},$$scope:{ctx:x}}}),ho=new N({}),fo=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L596"}}),mn=new Z({props:{$$slots:{default:[Kg]},$$scope:{ctx:x}}}),go=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L616",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),un=new Z({props:{$$slots:{default:[e_]},$$scope:{ctx:x}}}),hn=new U({props:{anchor:"transformers.TFDistilBertForMaskedLM.call.example",$$slots:{default:[t_]},$$scope:{ctx:x}}}),fn=new U({props:{anchor:"transformers.TFDistilBertForMaskedLM.call.example-2",$$slots:{default:[n_]},$$scope:{ctx:x}}}),_o=new N({}),bo=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L678"}}),_n=new Z({props:{$$slots:{default:[s_]},$$scope:{ctx:x}}}),To=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L695",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),bn=new Z({props:{$$slots:{default:[o_]},$$scope:{ctx:x}}}),Tn=new U({props:{anchor:"transformers.TFDistilBertForSequenceClassification.call.example",$$slots:{default:[a_]},$$scope:{ctx:x}}}),Mn=new U({props:{anchor:"transformers.TFDistilBertForSequenceClassification.call.example-2",$$slots:{default:[r_]},$$scope:{ctx:x}}}),Mo=new N({}),yo=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L825"}}),vn=new Z({props:{$$slots:{default:[i_]},$$scope:{ctx:x}}}),vo=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L841",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),wn=new Z({props:{$$slots:{default:[l_]},$$scope:{ctx:x}}}),kn=new U({props:{anchor:"transformers.TFDistilBertForMultipleChoice.call.example",$$slots:{default:[d_]},$$scope:{ctx:x}}}),wo=new N({}),ko=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L757"}}),xn=new Z({props:{$$slots:{default:[c_]},$$scope:{ctx:x}}}),$o=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L768",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Fn=new Z({props:{$$slots:{default:[p_]},$$scope:{ctx:x}}}),Cn=new U({props:{anchor:"transformers.TFDistilBertForTokenClassification.call.example",$$slots:{default:[m_]},$$scope:{ctx:x}}}),Bn=new U({props:{anchor:"transformers.TFDistilBertForTokenClassification.call.example-2",$$slots:{default:[u_]},$$scope:{ctx:x}}}),xo=new N({}),Fo=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L919"}}),Jn=new Z({props:{$$slots:{default:[h_]},$$scope:{ctx:x}}}),Co=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_tf_distilbert.py#L930",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),zn=new Z({props:{$$slots:{default:[f_]},$$scope:{ctx:x}}}),Dn=new U({props:{anchor:"transformers.TFDistilBertForQuestionAnswering.call.example",$$slots:{default:[g_]},$$scope:{ctx:x}}}),Un=new U({props:{anchor:"transformers.TFDistilBertForQuestionAnswering.call.example-2",$$slots:{default:[__]},$$scope:{ctx:x}}}),Bo=new N({}),jo=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L532"}}),Jo=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455"}}),In=new Z({props:{$$slots:{default:[b_]},$$scope:{ctx:x}}}),Zn=new U({props:{anchor:"transformers.FlaxDistilBertModel.__call__.example",$$slots:{default:[T_]},$$scope:{ctx:x}}}),zo=new N({}),Do=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L605"}}),Uo=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Hn=new Z({props:{$$slots:{default:[M_]},$$scope:{ctx:x}}}),Gn=new U({props:{anchor:"transformers.FlaxDistilBertForMaskedLM.__call__.example",$$slots:{default:[y_]},$$scope:{ctx:x}}}),Wo=new N({}),Io=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L672"}}),Zo=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput"
>transformers.modeling_flax_outputs.FlaxSequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ln=new Z({props:{$$slots:{default:[v_]},$$scope:{ctx:x}}}),Rn=new U({props:{anchor:"transformers.FlaxDistilBertForSequenceClassification.__call__.example",$$slots:{default:[w_]},$$scope:{ctx:x}}}),No=new N({}),Ho=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L751"}}),Go=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput"
>transformers.modeling_flax_outputs.FlaxMultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Xn=new Z({props:{$$slots:{default:[k_]},$$scope:{ctx:x}}}),Vn=new U({props:{anchor:"transformers.FlaxDistilBertForMultipleChoice.__call__.example",$$slots:{default:[$_]},$$scope:{ctx:x}}}),qo=new N({}),Lo=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L816"}}),Ro=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_flax_outputs.FlaxTokenClassifierOutput"
>transformers.modeling_flax_outputs.FlaxTokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Sn=new Z({props:{$$slots:{default:[x_]},$$scope:{ctx:x}}}),Pn=new U({props:{anchor:"transformers.FlaxDistilBertForTokenClassification.__call__.example",$$slots:{default:[F_]},$$scope:{ctx:x}}}),Eo=new N({}),Xo=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/v4.33.3/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/v4.33.3/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/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L885"}}),Vo=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/v4.33.3/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.33.3/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast.__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/v4.33.3/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.33.3/src/transformers/models/distilbert/modeling_flax_distilbert.py#L455",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.33.3/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/v4.33.3/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:`
<p><a
  href="/docs/transformers/v4.33.3/en/main_classes/output#transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput"
>transformers.modeling_flax_outputs.FlaxQuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Yn=new Z({props:{$$slots:{default:[C_]},$$scope:{ctx:x}}}),On=new U({props:{anchor:"transformers.FlaxDistilBertForQuestionAnswering.__call__.example",$$slots:{default:[B_]},$$scope:{ctx:x}}}),{c(){t=a("meta"),h=i(),d=a("p"),c=i(),w=a("h1"),o=a("a"),v=a("span"),f(I.$$.fragment),D=i(),B=a("span"),B.textContent=H,z=i(),C=a("div"),C.innerHTML=G,k=i(),F=a("h2"),Jt=a("a"),yi=a("span"),f(ds.$$.fragment),qd=i(),ra=a("span"),ra.textContent=vu,ul=i(),cs=a("p"),cs.innerHTML=wu,hl=i(),ps=a("p"),ps.textContent=ku,fl=i(),ms=a("p"),ms.innerHTML=$u,gl=i(),us=a("p"),us.textContent=xu,_l=i(),hs=a("ul"),hs.innerHTML=Fu,bl=i(),fs=a("p"),fs.innerHTML=Cu,Tl=i(),it=a("h2"),zt=a("a"),vi=a("span"),f(gs.$$.fragment),Ld=i(),ia=a("span"),ia.textContent=Bu,Ml=i(),_s=a("p"),_s.textContent=ju,yl=i(),f(bs.$$.fragment),vl=i(),Ts=a("ul"),Ts.innerHTML=Ju,wl=i(),f(Ms.$$.fragment),kl=i(),ys=a("ul"),ys.innerHTML=zu,$l=i(),f(vs.$$.fragment),xl=i(),ws=a("ul"),ws.innerHTML=Du,Fl=i(),f(ks.$$.fragment),Cl=i(),$s=a("ul"),$s.innerHTML=Uu,Bl=i(),xs=a("p"),xs.innerHTML=Wu,jl=i(),Fs=a("ul"),Fs.innerHTML=Iu,Jl=i(),Cs=a("p"),Cs.textContent=Zu,zl=i(),Bs=a("ul"),Bs.innerHTML=Nu,Dl=i(),js=a("p"),js.textContent=Hu,Ul=i(),Js=a("ul"),Js.innerHTML=Gu,Wl=i(),zs=a("p"),zs.textContent=qu,Il=i(),Ds=a("ul"),Ds.innerHTML=Lu,Zl=i(),lt=a("h2"),Dt=a("a"),wi=a("span"),f(Us.$$.fragment),Rd=i(),la=a("span"),la.textContent=Ru,Nl=i(),Te=a("div"),f(Ws.$$.fragment),Ed=i(),da=a("p"),da.innerHTML=Eu,Xd=i(),ca=a("p"),ca.innerHTML=Xu,Vd=i(),f(Ut.$$.fragment),Hl=i(),dt=a("h2"),Wt=a("a"),ki=a("span"),f(Is.$$.fragment),Ad=i(),pa=a("span"),pa.textContent=Vu,Gl=i(),q=a("div"),f(Zs.$$.fragment),Sd=i(),ma=a("p"),ma.textContent=Au,Pd=i(),ua=a("p"),ua.innerHTML=Su,Qd=i(),Ke=a("div"),f(Ns.$$.fragment),Yd=i(),ha=a("p"),ha.textContent=Pu,Od=i(),fa=a("ul"),fa.innerHTML=Qu,Kd=i(),It=a("div"),f(Hs.$$.fragment),ec=i(),ga=a("p"),ga.textContent=Yu,tc=i(),xe=a("div"),f(Gs.$$.fragment),nc=i(),_a=a("p"),_a.textContent=Ou,sc=i(),f(Zt.$$.fragment),oc=i(),ba=a("p"),ba.innerHTML=Ku,ac=i(),Nt=a("div"),f(qs.$$.fragment),rc=i(),Ta=a("p"),Ta.innerHTML=eh,ql=i(),ct=a("h2"),Ht=a("a"),$i=a("span"),f(Ls.$$.fragment),ic=i(),Ma=a("span"),Ma.textContent=th,Ll=i(),te=a("div"),f(Rs.$$.fragment),lc=i(),ya=a("p"),ya.innerHTML=nh,dc=i(),va=a("p"),va.innerHTML=sh,cc=i(),et=a("div"),f(Es.$$.fragment),pc=i(),wa=a("p"),wa.textContent=oh,mc=i(),ka=a("ul"),ka.innerHTML=ah,uc=i(),Fe=a("div"),f(Xs.$$.fragment),hc=i(),$a=a("p"),$a.textContent=rh,fc=i(),f(Gt.$$.fragment),gc=i(),xa=a("p"),xa.innerHTML=ih,Rl=i(),pt=a("h2"),qt=a("a"),xi=a("span"),f(Vs.$$.fragment),_c=i(),Fa=a("span"),Fa.textContent=lh,El=i(),ne=a("div"),f(As.$$.fragment),bc=i(),Ca=a("p"),Ca.textContent=dh,Tc=i(),Ba=a("p"),Ba.innerHTML=ch,Mc=i(),ja=a("p"),ja.innerHTML=ph,yc=i(),Ce=a("div"),f(Ss.$$.fragment),vc=i(),Ja=a("p"),Ja.innerHTML=mh,wc=i(),f(Lt.$$.fragment),kc=i(),f(Rt.$$.fragment),Xl=i(),mt=a("h2"),Et=a("a"),Fi=a("span"),f(Ps.$$.fragment),$c=i(),za=a("span"),za.textContent=uh,Vl=i(),se=a("div"),f(Qs.$$.fragment),xc=i(),Da=a("p"),Da.innerHTML=hh,Fc=i(),Ua=a("p"),Ua.innerHTML=fh,Cc=i(),Wa=a("p"),Wa.innerHTML=gh,Bc=i(),Be=a("div"),f(Ys.$$.fragment),jc=i(),Ia=a("p"),Ia.innerHTML=_h,Jc=i(),f(Xt.$$.fragment),zc=i(),f(Vt.$$.fragment),Al=i(),ut=a("h2"),At=a("a"),Ci=a("span"),f(Os.$$.fragment),Dc=i(),Za=a("span"),Za.textContent=bh,Sl=i(),oe=a("div"),f(Ks.$$.fragment),Uc=i(),Na=a("p"),Na.textContent=Th,Wc=i(),Ha=a("p"),Ha.innerHTML=Mh,Ic=i(),Ga=a("p"),Ga.innerHTML=yh,Zc=i(),le=a("div"),f(eo.$$.fragment),Nc=i(),qa=a("p"),qa.innerHTML=vh,Hc=i(),f(St.$$.fragment),Gc=i(),f(Pt.$$.fragment),qc=i(),f(Qt.$$.fragment),Pl=i(),ht=a("h2"),Yt=a("a"),Bi=a("span"),f(to.$$.fragment),Lc=i(),La=a("span"),La.textContent=wh,Ql=i(),ae=a("div"),f(no.$$.fragment),Rc=i(),Ra=a("p"),Ra.textContent=kh,Ec=i(),Ea=a("p"),Ea.innerHTML=$h,Xc=i(),Xa=a("p"),Xa.innerHTML=xh,Vc=i(),je=a("div"),f(so.$$.fragment),Ac=i(),Va=a("p"),Va.innerHTML=Fh,Sc=i(),f(Ot.$$.fragment),Pc=i(),f(Kt.$$.fragment),Yl=i(),ft=a("h2"),en=a("a"),ji=a("span"),f(oo.$$.fragment),Qc=i(),Aa=a("span"),Aa.textContent=Ch,Ol=i(),re=a("div"),f(ao.$$.fragment),Yc=i(),Sa=a("p"),Sa.textContent=Bh,Oc=i(),Pa=a("p"),Pa.innerHTML=jh,Kc=i(),Qa=a("p"),Qa.innerHTML=Jh,ep=i(),Je=a("div"),f(ro.$$.fragment),tp=i(),Ya=a("p"),Ya.innerHTML=zh,np=i(),f(tn.$$.fragment),sp=i(),f(nn.$$.fragment),Kl=i(),gt=a("h2"),sn=a("a"),Ji=a("span"),f(io.$$.fragment),op=i(),Oa=a("span"),Oa.textContent=Dh,ed=i(),ie=a("div"),f(lo.$$.fragment),ap=i(),Ka=a("p"),Ka.innerHTML=Uh,rp=i(),er=a("p"),er.innerHTML=Wh,ip=i(),tr=a("p"),tr.innerHTML=Ih,lp=i(),ze=a("div"),f(co.$$.fragment),dp=i(),nr=a("p"),nr.innerHTML=Zh,cp=i(),f(on.$$.fragment),pp=i(),f(an.$$.fragment),td=i(),_t=a("h2"),rn=a("a"),zi=a("span"),f(po.$$.fragment),mp=i(),sr=a("span"),sr.textContent=Nh,nd=i(),S=a("div"),f(mo.$$.fragment),up=i(),or=a("p"),or.textContent=Hh,hp=i(),ar=a("p"),ar.innerHTML=Gh,fp=i(),rr=a("p"),rr.innerHTML=qh,gp=i(),f(ln.$$.fragment),_p=i(),De=a("div"),f(uo.$$.fragment),bp=i(),ir=a("p"),ir.innerHTML=Lh,Tp=i(),f(dn.$$.fragment),Mp=i(),f(cn.$$.fragment),sd=i(),bt=a("h2"),pn=a("a"),Di=a("span"),f(ho.$$.fragment),yp=i(),lr=a("span"),lr.textContent=Rh,od=i(),P=a("div"),f(fo.$$.fragment),vp=i(),dr=a("p"),dr.innerHTML=Eh,wp=i(),cr=a("p"),cr.innerHTML=Xh,kp=i(),pr=a("p"),pr.innerHTML=Vh,$p=i(),f(mn.$$.fragment),xp=i(),de=a("div"),f(go.$$.fragment),Fp=i(),mr=a("p"),mr.innerHTML=Ah,Cp=i(),f(un.$$.fragment),Bp=i(),f(hn.$$.fragment),jp=i(),f(fn.$$.fragment),ad=i(),Tt=a("h2"),gn=a("a"),Ui=a("span"),f(_o.$$.fragment),Jp=i(),ur=a("span"),ur.textContent=Sh,rd=i(),Q=a("div"),f(bo.$$.fragment),zp=i(),hr=a("p"),hr.textContent=Ph,Dp=i(),fr=a("p"),fr.innerHTML=Qh,Up=i(),gr=a("p"),gr.innerHTML=Yh,Wp=i(),f(_n.$$.fragment),Ip=i(),ce=a("div"),f(To.$$.fragment),Zp=i(),_r=a("p"),_r.innerHTML=Oh,Np=i(),f(bn.$$.fragment),Hp=i(),f(Tn.$$.fragment),Gp=i(),f(Mn.$$.fragment),id=i(),Mt=a("h2"),yn=a("a"),Wi=a("span"),f(Mo.$$.fragment),qp=i(),br=a("span"),br.textContent=Kh,ld=i(),Y=a("div"),f(yo.$$.fragment),Lp=i(),Tr=a("p"),Tr.textContent=ef,Rp=i(),Mr=a("p"),Mr.innerHTML=tf,Ep=i(),yr=a("p"),yr.innerHTML=nf,Xp=i(),f(vn.$$.fragment),Vp=i(),Ue=a("div"),f(vo.$$.fragment),Ap=i(),vr=a("p"),vr.innerHTML=sf,Sp=i(),f(wn.$$.fragment),Pp=i(),f(kn.$$.fragment),dd=i(),yt=a("h2"),$n=a("a"),Ii=a("span"),f(wo.$$.fragment),Qp=i(),wr=a("span"),wr.textContent=of,cd=i(),O=a("div"),f(ko.$$.fragment),Yp=i(),kr=a("p"),kr.textContent=af,Op=i(),$r=a("p"),$r.innerHTML=rf,Kp=i(),xr=a("p"),xr.innerHTML=lf,em=i(),f(xn.$$.fragment),tm=i(),pe=a("div"),f($o.$$.fragment),nm=i(),Fr=a("p"),Fr.innerHTML=df,sm=i(),f(Fn.$$.fragment),om=i(),f(Cn.$$.fragment),am=i(),f(Bn.$$.fragment),pd=i(),vt=a("h2"),jn=a("a"),Zi=a("span"),f(xo.$$.fragment),rm=i(),Cr=a("span"),Cr.textContent=cf,md=i(),K=a("div"),f(Fo.$$.fragment),im=i(),Br=a("p"),Br.innerHTML=pf,lm=i(),jr=a("p"),jr.innerHTML=mf,dm=i(),Jr=a("p"),Jr.innerHTML=uf,cm=i(),f(Jn.$$.fragment),pm=i(),me=a("div"),f(Co.$$.fragment),mm=i(),zr=a("p"),zr.innerHTML=hf,um=i(),f(zn.$$.fragment),hm=i(),f(Dn.$$.fragment),fm=i(),f(Un.$$.fragment),ud=i(),wt=a("h2"),Wn=a("a"),Ni=a("span"),f(Bo.$$.fragment),gm=i(),Dr=a("span"),Dr.textContent=ff,hd=i(),L=a("div"),f(jo.$$.fragment),_m=i(),Ur=a("p"),Ur.textContent=gf,bm=i(),Wr=a("p"),Wr.innerHTML=_f,Tm=i(),Ir=a("p"),Ir.innerHTML=bf,Mm=i(),Zr=a("p"),Zr.textContent=Tf,ym=i(),Nr=a("ul"),Nr.innerHTML=Mf,vm=i(),We=a("div"),f(Jo.$$.fragment),wm=i(),Hr=a("p"),Hr.innerHTML=yf,km=i(),f(In.$$.fragment),$m=i(),f(Zn.$$.fragment),fd=i(),kt=a("h2"),Nn=a("a"),Hi=a("span"),f(zo.$$.fragment),xm=i(),Gr=a("span"),Gr.textContent=vf,gd=i(),R=a("div"),f(Do.$$.fragment),Fm=i(),qr=a("p"),qr.innerHTML=wf,Cm=i(),Lr=a("p"),Lr.innerHTML=kf,Bm=i(),Rr=a("p"),Rr.innerHTML=$f,jm=i(),Er=a("p"),Er.textContent=xf,Jm=i(),Xr=a("ul"),Xr.innerHTML=Ff,zm=i(),Ie=a("div"),f(Uo.$$.fragment),Dm=i(),Vr=a("p"),Vr.innerHTML=Cf,Um=i(),f(Hn.$$.fragment),Wm=i(),f(Gn.$$.fragment),_d=i(),$t=a("h2"),qn=a("a"),Gi=a("span"),f(Wo.$$.fragment),Im=i(),Ar=a("span"),Ar.textContent=Bf,bd=i(),E=a("div"),f(Io.$$.fragment),Zm=i(),Sr=a("p"),Sr.textContent=jf,Nm=i(),Pr=a("p"),Pr.innerHTML=Jf,Hm=i(),Qr=a("p"),Qr.innerHTML=zf,Gm=i(),Yr=a("p"),Yr.textContent=Df,qm=i(),Or=a("ul"),Or.innerHTML=Uf,Lm=i(),Ze=a("div"),f(Zo.$$.fragment),Rm=i(),Kr=a("p"),Kr.innerHTML=Wf,Em=i(),f(Ln.$$.fragment),Xm=i(),f(Rn.$$.fragment),Td=i(),xt=a("h2"),En=a("a"),qi=a("span"),f(No.$$.fragment),Vm=i(),ei=a("span"),ei.textContent=If,Md=i(),X=a("div"),f(Ho.$$.fragment),Am=i(),ti=a("p"),ti.textContent=Zf,Sm=i(),ni=a("p"),ni.innerHTML=Nf,Pm=i(),si=a("p"),si.innerHTML=Hf,Qm=i(),oi=a("p"),oi.textContent=Gf,Ym=i(),ai=a("ul"),ai.innerHTML=qf,Om=i(),Ne=a("div"),f(Go.$$.fragment),Km=i(),ri=a("p"),ri.innerHTML=Lf,eu=i(),f(Xn.$$.fragment),tu=i(),f(Vn.$$.fragment),yd=i(),Ft=a("h2"),An=a("a"),Li=a("span"),f(qo.$$.fragment),nu=i(),ii=a("span"),ii.textContent=Rf,vd=i(),V=a("div"),f(Lo.$$.fragment),su=i(),li=a("p"),li.textContent=Ef,ou=i(),di=a("p"),di.innerHTML=Xf,au=i(),ci=a("p"),ci.innerHTML=Vf,ru=i(),pi=a("p"),pi.textContent=Af,iu=i(),mi=a("ul"),mi.innerHTML=Sf,lu=i(),He=a("div"),f(Ro.$$.fragment),du=i(),ui=a("p"),ui.innerHTML=Pf,cu=i(),f(Sn.$$.fragment),pu=i(),f(Pn.$$.fragment),wd=i(),Ct=a("h2"),Qn=a("a"),Ri=a("span"),f(Eo.$$.fragment),mu=i(),hi=a("span"),hi.textContent=Qf,kd=i(),A=a("div"),f(Xo.$$.fragment),uu=i(),fi=a("p"),fi.innerHTML=Yf,hu=i(),gi=a("p"),gi.innerHTML=Of,fu=i(),_i=a("p"),_i.innerHTML=Kf,gu=i(),bi=a("p"),bi.textContent=eg,_u=i(),Ti=a("ul"),Ti.innerHTML=tg,bu=i(),Ge=a("div"),f(Vo.$$.fragment),Tu=i(),Mi=a("p"),Mi.innerHTML=ng,Mu=i(),f(Yn.$$.fragment),yu=i(),f(On.$$.fragment),$d=i(),Ei=a("p"),this.h()},l(e){const m=Dg("svelte-1phssyn",document.head);t=r(m,"META",{name:!0,content:!0}),m.forEach(s),h=l(e),d=r(e,"P",{}),$(d).forEach(s),c=l(e),w=r(e,"H1",{class:!0});var Ao=$(w);o=r(Ao,"A",{id:!0,class:!0,href:!0});var Xi=$(o);v=r(Xi,"SPAN",{});var Vi=$(v);g(I.$$.fragment,Vi),Vi.forEach(s),Xi.forEach(s),D=l(Ao),B=r(Ao,"SPAN",{"data-svelte-h":!0}),u(B)!=="svelte-1gejm97"&&(B.textContent=H),Ao.forEach(s),z=l(e),C=r(e,"DIV",{class:!0,"data-svelte-h":!0}),u(C)!=="svelte-19va6f1"&&(C.innerHTML=G),k=l(e),F=r(e,"H2",{class:!0});var So=$(F);Jt=r(So,"A",{id:!0,class:!0,href:!0});var Ai=$(Jt);yi=r(Ai,"SPAN",{});var Si=$(yi);g(ds.$$.fragment,Si),Si.forEach(s),Ai.forEach(s),qd=l(So),ra=r(So,"SPAN",{"data-svelte-h":!0}),u(ra)!=="svelte-1jsw1pg"&&(ra.textContent=vu),So.forEach(s),ul=l(e),cs=r(e,"P",{"data-svelte-h":!0}),u(cs)!=="svelte-b0u3fm"&&(cs.innerHTML=wu),hl=l(e),ps=r(e,"P",{"data-svelte-h":!0}),u(ps)!=="svelte-vfdo9a"&&(ps.textContent=ku),fl=l(e),ms=r(e,"P",{"data-svelte-h":!0}),u(ms)!=="svelte-yrk2ws"&&(ms.innerHTML=$u),gl=l(e),us=r(e,"P",{"data-svelte-h":!0}),u(us)!=="svelte-axv494"&&(us.textContent=xu),_l=l(e),hs=r(e,"UL",{"data-svelte-h":!0}),u(hs)!=="svelte-1pw0ah6"&&(hs.innerHTML=Fu),bl=l(e),fs=r(e,"P",{"data-svelte-h":!0}),u(fs)!=="svelte-d89fny"&&(fs.innerHTML=Cu),Tl=l(e),it=r(e,"H2",{class:!0});var Po=$(it);zt=r(Po,"A",{id:!0,class:!0,href:!0});var Pi=$(zt);vi=r(Pi,"SPAN",{});var Qi=$(vi);g(gs.$$.fragment,Qi),Qi.forEach(s),Pi.forEach(s),Ld=l(Po),ia=r(Po,"SPAN",{"data-svelte-h":!0}),u(ia)!=="svelte-w4zzv6"&&(ia.textContent=Bu),Po.forEach(s),Ml=l(e),_s=r(e,"P",{"data-svelte-h":!0}),u(_s)!=="svelte-1204ph7"&&(_s.textContent=ju),yl=l(e),g(bs.$$.fragment,e),vl=l(e),Ts=r(e,"UL",{"data-svelte-h":!0}),u(Ts)!=="svelte-3gzndu"&&(Ts.innerHTML=Ju),wl=l(e),g(Ms.$$.fragment,e),kl=l(e),ys=r(e,"UL",{"data-svelte-h":!0}),u(ys)!=="svelte-mn4kjp"&&(ys.innerHTML=zu),$l=l(e),g(vs.$$.fragment,e),xl=l(e),ws=r(e,"UL",{"data-svelte-h":!0}),u(ws)!=="svelte-19qvq3m"&&(ws.innerHTML=Du),Fl=l(e),g(ks.$$.fragment,e),Cl=l(e),$s=r(e,"UL",{"data-svelte-h":!0}),u($s)!=="svelte-1nf60do"&&($s.innerHTML=Uu),Bl=l(e),xs=r(e,"P",{"data-svelte-h":!0}),u(xs)!=="svelte-cplu6u"&&(xs.innerHTML=Wu),jl=l(e),Fs=r(e,"UL",{"data-svelte-h":!0}),u(Fs)!=="svelte-1la1pmn"&&(Fs.innerHTML=Iu),Jl=l(e),Cs=r(e,"P",{"data-svelte-h":!0}),u(Cs)!=="svelte-liq2q7"&&(Cs.textContent=Zu),zl=l(e),Bs=r(e,"UL",{"data-svelte-h":!0}),u(Bs)!=="svelte-1i3rzd0"&&(Bs.innerHTML=Nu),Dl=l(e),js=r(e,"P",{"data-svelte-h":!0}),u(js)!=="svelte-1wntqpp"&&(js.textContent=Hu),Ul=l(e),Js=r(e,"UL",{"data-svelte-h":!0}),u(Js)!=="svelte-1hcjkg2"&&(Js.innerHTML=Gu),Wl=l(e),zs=r(e,"P",{"data-svelte-h":!0}),u(zs)!=="svelte-lk14e4"&&(zs.textContent=qu),Il=l(e),Ds=r(e,"UL",{"data-svelte-h":!0}),u(Ds)!=="svelte-ii18nw"&&(Ds.innerHTML=Lu),Zl=l(e),lt=r(e,"H2",{class:!0});var Qo=$(lt);Dt=r(Qo,"A",{id:!0,class:!0,href:!0});var Yi=$(Dt);wi=r(Yi,"SPAN",{});var Oi=$(wi);g(Us.$$.fragment,Oi),Oi.forEach(s),Yi.forEach(s),Rd=l(Qo),la=r(Qo,"SPAN",{"data-svelte-h":!0}),u(la)!=="svelte-1yr48it"&&(la.textContent=Ru),Qo.forEach(s),Nl=l(e),Te=r(e,"DIV",{class:!0});var Ae=$(Te);g(Ws.$$.fragment,Ae),Ed=l(Ae),da=r(Ae,"P",{"data-svelte-h":!0}),u(da)!=="svelte-5o33k8"&&(da.innerHTML=Eu),Xd=l(Ae),ca=r(Ae,"P",{"data-svelte-h":!0}),u(ca)!=="svelte-cfh5wl"&&(ca.innerHTML=Xu),Vd=l(Ae),g(Ut.$$.fragment,Ae),Ae.forEach(s),Hl=l(e),dt=r(e,"H2",{class:!0});var Yo=$(dt);Wt=r(Yo,"A",{id:!0,class:!0,href:!0});var Ki=$(Wt);ki=r(Ki,"SPAN",{});var el=$(ki);g(Is.$$.fragment,el),el.forEach(s),Ki.forEach(s),Ad=l(Yo),pa=r(Yo,"SPAN",{"data-svelte-h":!0}),u(pa)!=="svelte-17kgsw"&&(pa.textContent=Vu),Yo.forEach(s),Gl=l(e),q=r(e,"DIV",{class:!0});var ee=$(q);g(Zs.$$.fragment,ee),Sd=l(ee),ma=r(ee,"P",{"data-svelte-h":!0}),u(ma)!=="svelte-10o6nt5"&&(ma.textContent=Au),Pd=l(ee),ua=r(ee,"P",{"data-svelte-h":!0}),u(ua)!=="svelte-14xlfoy"&&(ua.innerHTML=Su),Qd=l(ee),Ke=r(ee,"DIV",{class:!0});var Bt=$(Ke);g(Ns.$$.fragment,Bt),Yd=l(Bt),ha=r(Bt,"P",{"data-svelte-h":!0}),u(ha)!=="svelte-t7qurq"&&(ha.textContent=Pu),Od=l(Bt),fa=r(Bt,"UL",{"data-svelte-h":!0}),u(fa)!=="svelte-xi6653"&&(fa.innerHTML=Qu),Bt.forEach(s),Kd=l(ee),It=r(ee,"DIV",{class:!0});var Oo=$(It);g(Hs.$$.fragment,Oo),ec=l(Oo),ga=r(Oo,"P",{"data-svelte-h":!0}),u(ga)!=="svelte-b3k2yi"&&(ga.textContent=Yu),Oo.forEach(s),tc=l(ee),xe=r(ee,"DIV",{class:!0});var Se=$(xe);g(Gs.$$.fragment,Se),nc=l(Se),_a=r(Se,"P",{"data-svelte-h":!0}),u(_a)!=="svelte-gn6wi7"&&(_a.textContent=Ou),sc=l(Se),g(Zt.$$.fragment,Se),oc=l(Se),ba=r(Se,"P",{"data-svelte-h":!0}),u(ba)!=="svelte-owoxgn"&&(ba.innerHTML=Ku),Se.forEach(s),ac=l(ee),Nt=r(ee,"DIV",{class:!0});var Ko=$(Nt);g(qs.$$.fragment,Ko),rc=l(Ko),Ta=r(Ko,"P",{"data-svelte-h":!0}),u(Ta)!=="svelte-1f4f5kp"&&(Ta.innerHTML=eh),Ko.forEach(s),ee.forEach(s),ql=l(e),ct=r(e,"H2",{class:!0});var ea=$(ct);Ht=r(ea,"A",{id:!0,class:!0,href:!0});var tl=$(Ht);$i=r(tl,"SPAN",{});var nl=$($i);g(Ls.$$.fragment,nl),nl.forEach(s),tl.forEach(s),ic=l(ea),Ma=r(ea,"SPAN",{"data-svelte-h":!0}),u(Ma)!=="svelte-xiqaxu"&&(Ma.textContent=th),ea.forEach(s),Ll=l(e),te=r(e,"DIV",{class:!0});var Me=$(te);g(Rs.$$.fragment,Me),lc=l(Me),ya=r(Me,"P",{"data-svelte-h":!0}),u(ya)!=="svelte-88fhae"&&(ya.innerHTML=nh),dc=l(Me),va=r(Me,"P",{"data-svelte-h":!0}),u(va)!=="svelte-10uwgx2"&&(va.innerHTML=sh),cc=l(Me),et=r(Me,"DIV",{class:!0});var jt=$(et);g(Es.$$.fragment,jt),pc=l(jt),wa=r(jt,"P",{"data-svelte-h":!0}),u(wa)!=="svelte-t7qurq"&&(wa.textContent=oh),mc=l(jt),ka=r(jt,"UL",{"data-svelte-h":!0}),u(ka)!=="svelte-xi6653"&&(ka.innerHTML=ah),jt.forEach(s),uc=l(Me),Fe=r(Me,"DIV",{class:!0});var Pe=$(Fe);g(Xs.$$.fragment,Pe),hc=l(Pe),$a=r(Pe,"P",{"data-svelte-h":!0}),u($a)!=="svelte-gn6wi7"&&($a.textContent=rh),fc=l(Pe),g(Gt.$$.fragment,Pe),gc=l(Pe),xa=r(Pe,"P",{"data-svelte-h":!0}),u(xa)!=="svelte-owoxgn"&&(xa.innerHTML=ih),Pe.forEach(s),Me.forEach(s),Rl=l(e),pt=r(e,"H2",{class:!0});var ta=$(pt);qt=r(ta,"A",{id:!0,class:!0,href:!0});var sl=$(qt);xi=r(sl,"SPAN",{});var ol=$(xi);g(Vs.$$.fragment,ol),ol.forEach(s),sl.forEach(s),_c=l(ta),Fa=r(ta,"SPAN",{"data-svelte-h":!0}),u(Fa)!=="svelte-1wzwmls"&&(Fa.textContent=lh),ta.forEach(s),El=l(e),ne=r(e,"DIV",{class:!0});var ye=$(ne);g(As.$$.fragment,ye),bc=l(ye),Ca=r(ye,"P",{"data-svelte-h":!0}),u(Ca)!=="svelte-1ololo3"&&(Ca.textContent=dh),Tc=l(ye),Ba=r(ye,"P",{"data-svelte-h":!0}),u(Ba)!=="svelte-ev63c"&&(Ba.innerHTML=ch),Mc=l(ye),ja=r(ye,"P",{"data-svelte-h":!0}),u(ja)!=="svelte-hswkmf"&&(ja.innerHTML=ph),yc=l(ye),Ce=r(ye,"DIV",{class:!0});var Qe=$(Ce);g(Ss.$$.fragment,Qe),vc=l(Qe),Ja=r(Qe,"P",{"data-svelte-h":!0}),u(Ja)!=="svelte-1pd7kwv"&&(Ja.innerHTML=mh),wc=l(Qe),g(Lt.$$.fragment,Qe),kc=l(Qe),g(Rt.$$.fragment,Qe),Qe.forEach(s),ye.forEach(s),Xl=l(e),mt=r(e,"H2",{class:!0});var na=$(mt);Et=r(na,"A",{id:!0,class:!0,href:!0});var al=$(Et);Fi=r(al,"SPAN",{});var rl=$(Fi);g(Ps.$$.fragment,rl),rl.forEach(s),al.forEach(s),$c=l(na),za=r(na,"SPAN",{"data-svelte-h":!0}),u(za)!=="svelte-6m8b6w"&&(za.textContent=uh),na.forEach(s),Vl=l(e),se=r(e,"DIV",{class:!0});var ve=$(se);g(Qs.$$.fragment,ve),xc=l(ve),Da=r(ve,"P",{"data-svelte-h":!0}),u(Da)!=="svelte-23pii1"&&(Da.innerHTML=hh),Fc=l(ve),Ua=r(ve,"P",{"data-svelte-h":!0}),u(Ua)!=="svelte-ev63c"&&(Ua.innerHTML=fh),Cc=l(ve),Wa=r(ve,"P",{"data-svelte-h":!0}),u(Wa)!=="svelte-hswkmf"&&(Wa.innerHTML=gh),Bc=l(ve),Be=r(ve,"DIV",{class:!0});var Ye=$(Be);g(Ys.$$.fragment,Ye),jc=l(Ye),Ia=r(Ye,"P",{"data-svelte-h":!0}),u(Ia)!=="svelte-11juqlv"&&(Ia.innerHTML=_h),Jc=l(Ye),g(Xt.$$.fragment,Ye),zc=l(Ye),g(Vt.$$.fragment,Ye),Ye.forEach(s),ve.forEach(s),Al=l(e),ut=r(e,"H2",{class:!0});var sa=$(ut);At=r(sa,"A",{id:!0,class:!0,href:!0});var il=$(At);Ci=r(il,"SPAN",{});var ll=$(Ci);g(Os.$$.fragment,ll),ll.forEach(s),il.forEach(s),Dc=l(sa),Za=r(sa,"SPAN",{"data-svelte-h":!0}),u(Za)!=="svelte-2cl57n"&&(Za.textContent=bh),sa.forEach(s),Sl=l(e),oe=r(e,"DIV",{class:!0});var we=$(oe);g(Ks.$$.fragment,we),Uc=l(we),Na=r(we,"P",{"data-svelte-h":!0}),u(Na)!=="svelte-1kr5cyc"&&(Na.textContent=Th),Wc=l(we),Ha=r(we,"P",{"data-svelte-h":!0}),u(Ha)!=="svelte-ev63c"&&(Ha.innerHTML=Mh),Ic=l(we),Ga=r(we,"P",{"data-svelte-h":!0}),u(Ga)!=="svelte-hswkmf"&&(Ga.innerHTML=yh),Zc=l(we),le=r(we,"DIV",{class:!0});var ke=$(le);g(eo.$$.fragment,ke),Nc=l(ke),qa=r(ke,"P",{"data-svelte-h":!0}),u(qa)!=="svelte-1txc4l7"&&(qa.innerHTML=vh),Hc=l(ke),g(St.$$.fragment,ke),Gc=l(ke),g(Pt.$$.fragment,ke),qc=l(ke),g(Qt.$$.fragment,ke),ke.forEach(s),we.forEach(s),Pl=l(e),ht=r(e,"H2",{class:!0});var oa=$(ht);Yt=r(oa,"A",{id:!0,class:!0,href:!0});var dl=$(Yt);Bi=r(dl,"SPAN",{});var cl=$(Bi);g(to.$$.fragment,cl),cl.forEach(s),dl.forEach(s),Lc=l(oa),La=r(oa,"SPAN",{"data-svelte-h":!0}),u(La)!=="svelte-725cyh"&&(La.textContent=wh),oa.forEach(s),Ql=l(e),ae=r(e,"DIV",{class:!0});var $e=$(ae);g(no.$$.fragment,$e),Rc=l($e),Ra=r($e,"P",{"data-svelte-h":!0}),u(Ra)!=="svelte-i84iso"&&(Ra.textContent=kh),Ec=l($e),Ea=r($e,"P",{"data-svelte-h":!0}),u(Ea)!=="svelte-ev63c"&&(Ea.innerHTML=$h),Xc=l($e),Xa=r($e,"P",{"data-svelte-h":!0}),u(Xa)!=="svelte-hswkmf"&&(Xa.innerHTML=xh),Vc=l($e),je=r($e,"DIV",{class:!0});var Oe=$(je);g(so.$$.fragment,Oe),Ac=l(Oe),Va=r(Oe,"P",{"data-svelte-h":!0}),u(Va)!=="svelte-1sz6y3"&&(Va.innerHTML=Fh),Sc=l(Oe),g(Ot.$$.fragment,Oe),Pc=l(Oe),g(Kt.$$.fragment,Oe),Oe.forEach(s),$e.forEach(s),Yl=l(e),ft=r(e,"H2",{class:!0});var aa=$(ft);en=r(aa,"A",{id:!0,class:!0,href:!0});var pl=$(en);ji=r(pl,"SPAN",{});var ml=$(ji);g(oo.$$.fragment,ml),ml.forEach(s),pl.forEach(s),Qc=l(aa),Aa=r(aa,"SPAN",{"data-svelte-h":!0}),u(Aa)!=="svelte-s74w8j"&&(Aa.textContent=Ch),aa.forEach(s),Ol=l(e),re=r(e,"DIV",{class:!0});var tt=$(re);g(ao.$$.fragment,tt),Yc=l(tt),Sa=r(tt,"P",{"data-svelte-h":!0}),u(Sa)!=="svelte-14y5xfr"&&(Sa.textContent=Bh),Oc=l(tt),Pa=r(tt,"P",{"data-svelte-h":!0}),u(Pa)!=="svelte-ev63c"&&(Pa.innerHTML=jh),Kc=l(tt),Qa=r(tt,"P",{"data-svelte-h":!0}),u(Qa)!=="svelte-hswkmf"&&(Qa.innerHTML=Jh),ep=l(tt),Je=r(tt,"DIV",{class:!0});var Kn=$(Je);g(ro.$$.fragment,Kn),tp=l(Kn),Ya=r(Kn,"P",{"data-svelte-h":!0}),u(Ya)!=="svelte-idegmv"&&(Ya.innerHTML=zh),np=l(Kn),g(tn.$$.fragment,Kn),sp=l(Kn),g(nn.$$.fragment,Kn),Kn.forEach(s),tt.forEach(s),Kl=l(e),gt=r(e,"H2",{class:!0});var Fd=$(gt);sn=r(Fd,"A",{id:!0,class:!0,href:!0});var sg=$(sn);Ji=r(sg,"SPAN",{});var og=$(Ji);g(io.$$.fragment,og),og.forEach(s),sg.forEach(s),op=l(Fd),Oa=r(Fd,"SPAN",{"data-svelte-h":!0}),u(Oa)!=="svelte-ojo4gy"&&(Oa.textContent=Dh),Fd.forEach(s),ed=l(e),ie=r(e,"DIV",{class:!0});var nt=$(ie);g(lo.$$.fragment,nt),ap=l(nt),Ka=r(nt,"P",{"data-svelte-h":!0}),u(Ka)!=="svelte-18jj0o9"&&(Ka.innerHTML=Uh),rp=l(nt),er=r(nt,"P",{"data-svelte-h":!0}),u(er)!=="svelte-ev63c"&&(er.innerHTML=Wh),ip=l(nt),tr=r(nt,"P",{"data-svelte-h":!0}),u(tr)!=="svelte-hswkmf"&&(tr.innerHTML=Ih),lp=l(nt),ze=r(nt,"DIV",{class:!0});var es=$(ze);g(co.$$.fragment,es),dp=l(es),nr=r(es,"P",{"data-svelte-h":!0}),u(nr)!=="svelte-evn5xx"&&(nr.innerHTML=Zh),cp=l(es),g(on.$$.fragment,es),pp=l(es),g(an.$$.fragment,es),es.forEach(s),nt.forEach(s),td=l(e),_t=r(e,"H2",{class:!0});var Cd=$(_t);rn=r(Cd,"A",{id:!0,class:!0,href:!0});var ag=$(rn);zi=r(ag,"SPAN",{});var rg=$(zi);g(po.$$.fragment,rg),rg.forEach(s),ag.forEach(s),mp=l(Cd),sr=r(Cd,"SPAN",{"data-svelte-h":!0}),u(sr)!=="svelte-1sjkafq"&&(sr.textContent=Nh),Cd.forEach(s),nd=l(e),S=r(e,"DIV",{class:!0});var qe=$(S);g(mo.$$.fragment,qe),up=l(qe),or=r(qe,"P",{"data-svelte-h":!0}),u(or)!=="svelte-1ololo3"&&(or.textContent=Hh),hp=l(qe),ar=r(qe,"P",{"data-svelte-h":!0}),u(ar)!=="svelte-1e9g3w8"&&(ar.innerHTML=Gh),fp=l(qe),rr=r(qe,"P",{"data-svelte-h":!0}),u(rr)!=="svelte-1ivrf8m"&&(rr.innerHTML=qh),gp=l(qe),g(ln.$$.fragment,qe),_p=l(qe),De=r(qe,"DIV",{class:!0});var ts=$(De);g(uo.$$.fragment,ts),bp=l(ts),ir=r(ts,"P",{"data-svelte-h":!0}),u(ir)!=="svelte-153yz3n"&&(ir.innerHTML=Lh),Tp=l(ts),g(dn.$$.fragment,ts),Mp=l(ts),g(cn.$$.fragment,ts),ts.forEach(s),qe.forEach(s),sd=l(e),bt=r(e,"H2",{class:!0});var Bd=$(bt);pn=r(Bd,"A",{id:!0,class:!0,href:!0});var ig=$(pn);Di=r(ig,"SPAN",{});var lg=$(Di);g(ho.$$.fragment,lg),lg.forEach(s),ig.forEach(s),yp=l(Bd),lr=r(Bd,"SPAN",{"data-svelte-h":!0}),u(lr)!=="svelte-4mwy6"&&(lr.textContent=Rh),Bd.forEach(s),od=l(e),P=r(e,"DIV",{class:!0});var Le=$(P);g(fo.$$.fragment,Le),vp=l(Le),dr=r(Le,"P",{"data-svelte-h":!0}),u(dr)!=="svelte-23pii1"&&(dr.innerHTML=Eh),wp=l(Le),cr=r(Le,"P",{"data-svelte-h":!0}),u(cr)!=="svelte-1e9g3w8"&&(cr.innerHTML=Xh),kp=l(Le),pr=r(Le,"P",{"data-svelte-h":!0}),u(pr)!=="svelte-1ivrf8m"&&(pr.innerHTML=Vh),$p=l(Le),g(mn.$$.fragment,Le),xp=l(Le),de=r(Le,"DIV",{class:!0});var st=$(de);g(go.$$.fragment,st),Fp=l(st),mr=r(st,"P",{"data-svelte-h":!0}),u(mr)!=="svelte-11g5snj"&&(mr.innerHTML=Ah),Cp=l(st),g(un.$$.fragment,st),Bp=l(st),g(hn.$$.fragment,st),jp=l(st),g(fn.$$.fragment,st),st.forEach(s),Le.forEach(s),ad=l(e),Tt=r(e,"H2",{class:!0});var jd=$(Tt);gn=r(jd,"A",{id:!0,class:!0,href:!0});var dg=$(gn);Ui=r(dg,"SPAN",{});var cg=$(Ui);g(_o.$$.fragment,cg),cg.forEach(s),dg.forEach(s),Jp=l(jd),ur=r(jd,"SPAN",{"data-svelte-h":!0}),u(ur)!=="svelte-1j9mhkd"&&(ur.textContent=Sh),jd.forEach(s),rd=l(e),Q=r(e,"DIV",{class:!0});var Re=$(Q);g(bo.$$.fragment,Re),zp=l(Re),hr=r(Re,"P",{"data-svelte-h":!0}),u(hr)!=="svelte-1kr5cyc"&&(hr.textContent=Ph),Dp=l(Re),fr=r(Re,"P",{"data-svelte-h":!0}),u(fr)!=="svelte-1e9g3w8"&&(fr.innerHTML=Qh),Up=l(Re),gr=r(Re,"P",{"data-svelte-h":!0}),u(gr)!=="svelte-1ivrf8m"&&(gr.innerHTML=Yh),Wp=l(Re),g(_n.$$.fragment,Re),Ip=l(Re),ce=r(Re,"DIV",{class:!0});var ot=$(ce);g(To.$$.fragment,ot),Zp=l(ot),_r=r(ot,"P",{"data-svelte-h":!0}),u(_r)!=="svelte-zuc01f"&&(_r.innerHTML=Oh),Np=l(ot),g(bn.$$.fragment,ot),Hp=l(ot),g(Tn.$$.fragment,ot),Gp=l(ot),g(Mn.$$.fragment,ot),ot.forEach(s),Re.forEach(s),id=l(e),Mt=r(e,"H2",{class:!0});var Jd=$(Mt);yn=r(Jd,"A",{id:!0,class:!0,href:!0});var pg=$(yn);Wi=r(pg,"SPAN",{});var mg=$(Wi);g(Mo.$$.fragment,mg),mg.forEach(s),pg.forEach(s),qp=l(Jd),br=r(Jd,"SPAN",{"data-svelte-h":!0}),u(br)!=="svelte-zyk7bj"&&(br.textContent=Kh),Jd.forEach(s),ld=l(e),Y=r(e,"DIV",{class:!0});var Ee=$(Y);g(yo.$$.fragment,Ee),Lp=l(Ee),Tr=r(Ee,"P",{"data-svelte-h":!0}),u(Tr)!=="svelte-i84iso"&&(Tr.textContent=ef),Rp=l(Ee),Mr=r(Ee,"P",{"data-svelte-h":!0}),u(Mr)!=="svelte-1e9g3w8"&&(Mr.innerHTML=tf),Ep=l(Ee),yr=r(Ee,"P",{"data-svelte-h":!0}),u(yr)!=="svelte-1ivrf8m"&&(yr.innerHTML=nf),Xp=l(Ee),g(vn.$$.fragment,Ee),Vp=l(Ee),Ue=r(Ee,"DIV",{class:!0});var ns=$(Ue);g(vo.$$.fragment,ns),Ap=l(ns),vr=r(ns,"P",{"data-svelte-h":!0}),u(vr)!=="svelte-atueej"&&(vr.innerHTML=sf),Sp=l(ns),g(wn.$$.fragment,ns),Pp=l(ns),g(kn.$$.fragment,ns),ns.forEach(s),Ee.forEach(s),dd=l(e),yt=r(e,"H2",{class:!0});var zd=$(yt);$n=r(zd,"A",{id:!0,class:!0,href:!0});var ug=$($n);Ii=r(ug,"SPAN",{});var hg=$(Ii);g(wo.$$.fragment,hg),hg.forEach(s),ug.forEach(s),Qp=l(zd),wr=r(zd,"SPAN",{"data-svelte-h":!0}),u(wr)!=="svelte-tsn1nh"&&(wr.textContent=of),zd.forEach(s),cd=l(e),O=r(e,"DIV",{class:!0});var Xe=$(O);g(ko.$$.fragment,Xe),Yp=l(Xe),kr=r(Xe,"P",{"data-svelte-h":!0}),u(kr)!=="svelte-14y5xfr"&&(kr.textContent=af),Op=l(Xe),$r=r(Xe,"P",{"data-svelte-h":!0}),u($r)!=="svelte-1e9g3w8"&&($r.innerHTML=rf),Kp=l(Xe),xr=r(Xe,"P",{"data-svelte-h":!0}),u(xr)!=="svelte-1ivrf8m"&&(xr.innerHTML=lf),em=l(Xe),g(xn.$$.fragment,Xe),tm=l(Xe),pe=r(Xe,"DIV",{class:!0});var at=$(pe);g($o.$$.fragment,at),nm=l(at),Fr=r(at,"P",{"data-svelte-h":!0}),u(Fr)!=="svelte-xj2ycr"&&(Fr.innerHTML=df),sm=l(at),g(Fn.$$.fragment,at),om=l(at),g(Cn.$$.fragment,at),am=l(at),g(Bn.$$.fragment,at),at.forEach(s),Xe.forEach(s),pd=l(e),vt=r(e,"H2",{class:!0});var Dd=$(vt);jn=r(Dd,"A",{id:!0,class:!0,href:!0});var fg=$(jn);Zi=r(fg,"SPAN",{});var gg=$(Zi);g(xo.$$.fragment,gg),gg.forEach(s),fg.forEach(s),rm=l(Dd),Cr=r(Dd,"SPAN",{"data-svelte-h":!0}),u(Cr)!=="svelte-mszj6c"&&(Cr.textContent=cf),Dd.forEach(s),md=l(e),K=r(e,"DIV",{class:!0});var Ve=$(K);g(Fo.$$.fragment,Ve),im=l(Ve),Br=r(Ve,"P",{"data-svelte-h":!0}),u(Br)!=="svelte-zfb4ky"&&(Br.innerHTML=pf),lm=l(Ve),jr=r(Ve,"P",{"data-svelte-h":!0}),u(jr)!=="svelte-1e9g3w8"&&(jr.innerHTML=mf),dm=l(Ve),Jr=r(Ve,"P",{"data-svelte-h":!0}),u(Jr)!=="svelte-1ivrf8m"&&(Jr.innerHTML=uf),cm=l(Ve),g(Jn.$$.fragment,Ve),pm=l(Ve),me=r(Ve,"DIV",{class:!0});var rt=$(me);g(Co.$$.fragment,rt),mm=l(rt),zr=r(rt,"P",{"data-svelte-h":!0}),u(zr)!=="svelte-12hi4ap"&&(zr.innerHTML=hf),um=l(rt),g(zn.$$.fragment,rt),hm=l(rt),g(Dn.$$.fragment,rt),fm=l(rt),g(Un.$$.fragment,rt),rt.forEach(s),Ve.forEach(s),ud=l(e),wt=r(e,"H2",{class:!0});var Ud=$(wt);Wn=r(Ud,"A",{id:!0,class:!0,href:!0});var _g=$(Wn);Ni=r(_g,"SPAN",{});var bg=$(Ni);g(Bo.$$.fragment,bg),bg.forEach(s),_g.forEach(s),gm=l(Ud),Dr=r(Ud,"SPAN",{"data-svelte-h":!0}),u(Dr)!=="svelte-1s753s5"&&(Dr.textContent=ff),Ud.forEach(s),hd=l(e),L=r(e,"DIV",{class:!0});var ue=$(L);g(jo.$$.fragment,ue),_m=l(ue),Ur=r(ue,"P",{"data-svelte-h":!0}),u(Ur)!=="svelte-3lz6al"&&(Ur.textContent=gf),bm=l(ue),Wr=r(ue,"P",{"data-svelte-h":!0}),u(Wr)!=="svelte-1gfofcr"&&(Wr.innerHTML=_f),Tm=l(ue),Ir=r(ue,"P",{"data-svelte-h":!0}),u(Ir)!=="svelte-9ybkh"&&(Ir.innerHTML=bf),Mm=l(ue),Zr=r(ue,"P",{"data-svelte-h":!0}),u(Zr)!=="svelte-1pplc4a"&&(Zr.textContent=Tf),ym=l(ue),Nr=r(ue,"UL",{"data-svelte-h":!0}),u(Nr)!=="svelte-1w7z84m"&&(Nr.innerHTML=Mf),vm=l(ue),We=r(ue,"DIV",{class:!0});var ss=$(We);g(Jo.$$.fragment,ss),wm=l(ss),Hr=r(ss,"P",{"data-svelte-h":!0}),u(Hr)!=="svelte-16oef0h"&&(Hr.innerHTML=yf),km=l(ss),g(In.$$.fragment,ss),$m=l(ss),g(Zn.$$.fragment,ss),ss.forEach(s),ue.forEach(s),fd=l(e),kt=r(e,"H2",{class:!0});var Wd=$(kt);Nn=r(Wd,"A",{id:!0,class:!0,href:!0});var Tg=$(Nn);Hi=r(Tg,"SPAN",{});var Mg=$(Hi);g(zo.$$.fragment,Mg),Mg.forEach(s),Tg.forEach(s),xm=l(Wd),Gr=r(Wd,"SPAN",{"data-svelte-h":!0}),u(Gr)!=="svelte-bk6kzh"&&(Gr.textContent=vf),Wd.forEach(s),gd=l(e),R=r(e,"DIV",{class:!0});var he=$(R);g(Do.$$.fragment,he),Fm=l(he),qr=r(he,"P",{"data-svelte-h":!0}),u(qr)!=="svelte-5bwii0"&&(qr.innerHTML=wf),Cm=l(he),Lr=r(he,"P",{"data-svelte-h":!0}),u(Lr)!=="svelte-1gfofcr"&&(Lr.innerHTML=kf),Bm=l(he),Rr=r(he,"P",{"data-svelte-h":!0}),u(Rr)!=="svelte-9ybkh"&&(Rr.innerHTML=$f),jm=l(he),Er=r(he,"P",{"data-svelte-h":!0}),u(Er)!=="svelte-1pplc4a"&&(Er.textContent=xf),Jm=l(he),Xr=r(he,"UL",{"data-svelte-h":!0}),u(Xr)!=="svelte-1w7z84m"&&(Xr.innerHTML=Ff),zm=l(he),Ie=r(he,"DIV",{class:!0});var os=$(Ie);g(Uo.$$.fragment,os),Dm=l(os),Vr=r(os,"P",{"data-svelte-h":!0}),u(Vr)!=="svelte-16oef0h"&&(Vr.innerHTML=Cf),Um=l(os),g(Hn.$$.fragment,os),Wm=l(os),g(Gn.$$.fragment,os),os.forEach(s),he.forEach(s),_d=l(e),$t=r(e,"H2",{class:!0});var Id=$($t);qn=r(Id,"A",{id:!0,class:!0,href:!0});var yg=$(qn);Gi=r(yg,"SPAN",{});var vg=$(Gi);g(Wo.$$.fragment,vg),vg.forEach(s),yg.forEach(s),Im=l(Id),Ar=r(Id,"SPAN",{"data-svelte-h":!0}),u(Ar)!=="svelte-eprs4y"&&(Ar.textContent=Bf),Id.forEach(s),bd=l(e),E=r(e,"DIV",{class:!0});var fe=$(E);g(Io.$$.fragment,fe),Zm=l(fe),Sr=r(fe,"P",{"data-svelte-h":!0}),u(Sr)!=="svelte-1kr5cyc"&&(Sr.textContent=jf),Nm=l(fe),Pr=r(fe,"P",{"data-svelte-h":!0}),u(Pr)!=="svelte-1gfofcr"&&(Pr.innerHTML=Jf),Hm=l(fe),Qr=r(fe,"P",{"data-svelte-h":!0}),u(Qr)!=="svelte-9ybkh"&&(Qr.innerHTML=zf),Gm=l(fe),Yr=r(fe,"P",{"data-svelte-h":!0}),u(Yr)!=="svelte-1pplc4a"&&(Yr.textContent=Df),qm=l(fe),Or=r(fe,"UL",{"data-svelte-h":!0}),u(Or)!=="svelte-1w7z84m"&&(Or.innerHTML=Uf),Lm=l(fe),Ze=r(fe,"DIV",{class:!0});var as=$(Ze);g(Zo.$$.fragment,as),Rm=l(as),Kr=r(as,"P",{"data-svelte-h":!0}),u(Kr)!=="svelte-16oef0h"&&(Kr.innerHTML=Wf),Em=l(as),g(Ln.$$.fragment,as),Xm=l(as),g(Rn.$$.fragment,as),as.forEach(s),fe.forEach(s),Td=l(e),xt=r(e,"H2",{class:!0});var Zd=$(xt);En=r(Zd,"A",{id:!0,class:!0,href:!0});var wg=$(En);qi=r(wg,"SPAN",{});var kg=$(qi);g(No.$$.fragment,kg),kg.forEach(s),wg.forEach(s),Vm=l(Zd),ei=r(Zd,"SPAN",{"data-svelte-h":!0}),u(ei)!=="svelte-16gke7g"&&(ei.textContent=If),Zd.forEach(s),Md=l(e),X=r(e,"DIV",{class:!0});var ge=$(X);g(Ho.$$.fragment,ge),Am=l(ge),ti=r(ge,"P",{"data-svelte-h":!0}),u(ti)!=="svelte-i84iso"&&(ti.textContent=Zf),Sm=l(ge),ni=r(ge,"P",{"data-svelte-h":!0}),u(ni)!=="svelte-1gfofcr"&&(ni.innerHTML=Nf),Pm=l(ge),si=r(ge,"P",{"data-svelte-h":!0}),u(si)!=="svelte-9ybkh"&&(si.innerHTML=Hf),Qm=l(ge),oi=r(ge,"P",{"data-svelte-h":!0}),u(oi)!=="svelte-1pplc4a"&&(oi.textContent=Gf),Ym=l(ge),ai=r(ge,"UL",{"data-svelte-h":!0}),u(ai)!=="svelte-1w7z84m"&&(ai.innerHTML=qf),Om=l(ge),Ne=r(ge,"DIV",{class:!0});var rs=$(Ne);g(Go.$$.fragment,rs),Km=l(rs),ri=r(rs,"P",{"data-svelte-h":!0}),u(ri)!=="svelte-16oef0h"&&(ri.innerHTML=Lf),eu=l(rs),g(Xn.$$.fragment,rs),tu=l(rs),g(Vn.$$.fragment,rs),rs.forEach(s),ge.forEach(s),yd=l(e),Ft=r(e,"H2",{class:!0});var Nd=$(Ft);An=r(Nd,"A",{id:!0,class:!0,href:!0});var $g=$(An);Li=r($g,"SPAN",{});var xg=$(Li);g(qo.$$.fragment,xg),xg.forEach(s),$g.forEach(s),nu=l(Nd),ii=r(Nd,"SPAN",{"data-svelte-h":!0}),u(ii)!=="svelte-gnp4ki"&&(ii.textContent=Rf),Nd.forEach(s),vd=l(e),V=r(e,"DIV",{class:!0});var _e=$(V);g(Lo.$$.fragment,_e),su=l(_e),li=r(_e,"P",{"data-svelte-h":!0}),u(li)!=="svelte-14y5xfr"&&(li.textContent=Ef),ou=l(_e),di=r(_e,"P",{"data-svelte-h":!0}),u(di)!=="svelte-1gfofcr"&&(di.innerHTML=Xf),au=l(_e),ci=r(_e,"P",{"data-svelte-h":!0}),u(ci)!=="svelte-9ybkh"&&(ci.innerHTML=Vf),ru=l(_e),pi=r(_e,"P",{"data-svelte-h":!0}),u(pi)!=="svelte-1pplc4a"&&(pi.textContent=Af),iu=l(_e),mi=r(_e,"UL",{"data-svelte-h":!0}),u(mi)!=="svelte-1w7z84m"&&(mi.innerHTML=Sf),lu=l(_e),He=r(_e,"DIV",{class:!0});var is=$(He);g(Ro.$$.fragment,is),du=l(is),ui=r(is,"P",{"data-svelte-h":!0}),u(ui)!=="svelte-16oef0h"&&(ui.innerHTML=Pf),cu=l(is),g(Sn.$$.fragment,is),pu=l(is),g(Pn.$$.fragment,is),is.forEach(s),_e.forEach(s),wd=l(e),Ct=r(e,"H2",{class:!0});var Hd=$(Ct);Qn=r(Hd,"A",{id:!0,class:!0,href:!0});var Fg=$(Qn);Ri=r(Fg,"SPAN",{});var Cg=$(Ri);g(Eo.$$.fragment,Cg),Cg.forEach(s),Fg.forEach(s),mu=l(Hd),hi=r(Hd,"SPAN",{"data-svelte-h":!0}),u(hi)!=="svelte-16d644z"&&(hi.textContent=Qf),Hd.forEach(s),kd=l(e),A=r(e,"DIV",{class:!0});var be=$(A);g(Xo.$$.fragment,be),uu=l(be),fi=r(be,"P",{"data-svelte-h":!0}),u(fi)!=="svelte-18jj0o9"&&(fi.innerHTML=Yf),hu=l(be),gi=r(be,"P",{"data-svelte-h":!0}),u(gi)!=="svelte-1gfofcr"&&(gi.innerHTML=Of),fu=l(be),_i=r(be,"P",{"data-svelte-h":!0}),u(_i)!=="svelte-9ybkh"&&(_i.innerHTML=Kf),gu=l(be),bi=r(be,"P",{"data-svelte-h":!0}),u(bi)!=="svelte-1pplc4a"&&(bi.textContent=eg),_u=l(be),Ti=r(be,"UL",{"data-svelte-h":!0}),u(Ti)!=="svelte-1w7z84m"&&(Ti.innerHTML=tg),bu=l(be),Ge=r(be,"DIV",{class:!0});var ls=$(Ge);g(Vo.$$.fragment,ls),Tu=l(ls),Mi=r(ls,"P",{"data-svelte-h":!0}),u(Mi)!=="svelte-16oef0h"&&(Mi.innerHTML=ng),Mu=l(ls),g(Yn.$$.fragment,ls),yu=l(ls),g(On.$$.fragment,ls),ls.forEach(s),be.forEach(s),$d=l(e),Ei=r(e,"P",{}),$(Ei).forEach(s),this.h()},h(){y(t,"name","hf:doc:metadata"),y(t,"content",JSON.stringify(J_)),y(o,"id","distilbert"),y(o,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(o,"href","#distilbert"),y(w,"class","relative group"),y(C,"class","flex flex-wrap space-x-1"),y(Jt,"id","overview"),y(Jt,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(Jt,"href","#overview"),y(F,"class","relative group"),y(zt,"id","resources"),y(zt,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(zt,"href","#resources"),y(it,"class","relative group"),y(Dt,"id","transformers.DistilBertConfig"),y(Dt,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(Dt,"href","#transformers.DistilBertConfig"),y(lt,"class","relative group"),y(Te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Wt,"id","transformers.DistilBertTokenizer"),y(Wt,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(Wt,"href","#transformers.DistilBertTokenizer"),y(dt,"class","relative group"),y(Ke,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(It,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(xe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Nt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Ht,"id","transformers.DistilBertTokenizerFast"),y(Ht,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(Ht,"href","#transformers.DistilBertTokenizerFast"),y(ct,"class","relative group"),y(et,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Fe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(te,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(qt,"id","transformers.DistilBertModel"),y(qt,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(qt,"href","#transformers.DistilBertModel"),y(pt,"class","relative group"),y(Ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(ne,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Et,"id","transformers.DistilBertForMaskedLM"),y(Et,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(Et,"href","#transformers.DistilBertForMaskedLM"),y(mt,"class","relative group"),y(Be,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(se,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(At,"id","transformers.DistilBertForSequenceClassification"),y(At,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(At,"href","#transformers.DistilBertForSequenceClassification"),y(ut,"class","relative group"),y(le,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(oe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Yt,"id","transformers.DistilBertForMultipleChoice"),y(Yt,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(Yt,"href","#transformers.DistilBertForMultipleChoice"),y(ht,"class","relative group"),y(je,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(ae,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(en,"id","transformers.DistilBertForTokenClassification"),y(en,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(en,"href","#transformers.DistilBertForTokenClassification"),y(ft,"class","relative group"),y(Je,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(re,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(sn,"id","transformers.DistilBertForQuestionAnswering"),y(sn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(sn,"href","#transformers.DistilBertForQuestionAnswering"),y(gt,"class","relative group"),y(ze,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(rn,"id","transformers.TFDistilBertModel"),y(rn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(rn,"href","#transformers.TFDistilBertModel"),y(_t,"class","relative group"),y(De,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(S,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(pn,"id","transformers.TFDistilBertForMaskedLM"),y(pn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(pn,"href","#transformers.TFDistilBertForMaskedLM"),y(bt,"class","relative group"),y(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(gn,"id","transformers.TFDistilBertForSequenceClassification"),y(gn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(gn,"href","#transformers.TFDistilBertForSequenceClassification"),y(Tt,"class","relative group"),y(ce,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(yn,"id","transformers.TFDistilBertForMultipleChoice"),y(yn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(yn,"href","#transformers.TFDistilBertForMultipleChoice"),y(Mt,"class","relative group"),y(Ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y($n,"id","transformers.TFDistilBertForTokenClassification"),y($n,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y($n,"href","#transformers.TFDistilBertForTokenClassification"),y(yt,"class","relative group"),y(pe,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(jn,"id","transformers.TFDistilBertForQuestionAnswering"),y(jn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(jn,"href","#transformers.TFDistilBertForQuestionAnswering"),y(vt,"class","relative group"),y(me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Wn,"id","transformers.FlaxDistilBertModel"),y(Wn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(Wn,"href","#transformers.FlaxDistilBertModel"),y(wt,"class","relative group"),y(We,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Nn,"id","transformers.FlaxDistilBertForMaskedLM"),y(Nn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(Nn,"href","#transformers.FlaxDistilBertForMaskedLM"),y(kt,"class","relative group"),y(Ie,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(qn,"id","transformers.FlaxDistilBertForSequenceClassification"),y(qn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(qn,"href","#transformers.FlaxDistilBertForSequenceClassification"),y($t,"class","relative group"),y(Ze,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(E,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(En,"id","transformers.FlaxDistilBertForMultipleChoice"),y(En,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(En,"href","#transformers.FlaxDistilBertForMultipleChoice"),y(xt,"class","relative group"),y(Ne,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(An,"id","transformers.FlaxDistilBertForTokenClassification"),y(An,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(An,"href","#transformers.FlaxDistilBertForTokenClassification"),y(Ft,"class","relative group"),y(He,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(Qn,"id","transformers.FlaxDistilBertForQuestionAnswering"),y(Qn,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),y(Qn,"href","#transformers.FlaxDistilBertForQuestionAnswering"),y(Ct,"class","relative group"),y(Ge,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),y(A,"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,m){n(document.head,t),p(e,h,m),p(e,d,m),p(e,c,m),p(e,w,m),n(w,o),n(o,v),_(I,v,null),n(w,D),n(w,B),p(e,z,m),p(e,C,m),p(e,k,m),p(e,F,m),n(F,Jt),n(Jt,yi),_(ds,yi,null),n(F,qd),n(F,ra),p(e,ul,m),p(e,cs,m),p(e,hl,m),p(e,ps,m),p(e,fl,m),p(e,ms,m),p(e,gl,m),p(e,us,m),p(e,_l,m),p(e,hs,m),p(e,bl,m),p(e,fs,m),p(e,Tl,m),p(e,it,m),n(it,zt),n(zt,vi),_(gs,vi,null),n(it,Ld),n(it,ia),p(e,Ml,m),p(e,_s,m),p(e,yl,m),_(bs,e,m),p(e,vl,m),p(e,Ts,m),p(e,wl,m),_(Ms,e,m),p(e,kl,m),p(e,ys,m),p(e,$l,m),_(vs,e,m),p(e,xl,m),p(e,ws,m),p(e,Fl,m),_(ks,e,m),p(e,Cl,m),p(e,$s,m),p(e,Bl,m),p(e,xs,m),p(e,jl,m),p(e,Fs,m),p(e,Jl,m),p(e,Cs,m),p(e,zl,m),p(e,Bs,m),p(e,Dl,m),p(e,js,m),p(e,Ul,m),p(e,Js,m),p(e,Wl,m),p(e,zs,m),p(e,Il,m),p(e,Ds,m),p(e,Zl,m),p(e,lt,m),n(lt,Dt),n(Dt,wi),_(Us,wi,null),n(lt,Rd),n(lt,la),p(e,Nl,m),p(e,Te,m),_(Ws,Te,null),n(Te,Ed),n(Te,da),n(Te,Xd),n(Te,ca),n(Te,Vd),_(Ut,Te,null),p(e,Hl,m),p(e,dt,m),n(dt,Wt),n(Wt,ki),_(Is,ki,null),n(dt,Ad),n(dt,pa),p(e,Gl,m),p(e,q,m),_(Zs,q,null),n(q,Sd),n(q,ma),n(q,Pd),n(q,ua),n(q,Qd),n(q,Ke),_(Ns,Ke,null),n(Ke,Yd),n(Ke,ha),n(Ke,Od),n(Ke,fa),n(q,Kd),n(q,It),_(Hs,It,null),n(It,ec),n(It,ga),n(q,tc),n(q,xe),_(Gs,xe,null),n(xe,nc),n(xe,_a),n(xe,sc),_(Zt,xe,null),n(xe,oc),n(xe,ba),n(q,ac),n(q,Nt),_(qs,Nt,null),n(Nt,rc),n(Nt,Ta),p(e,ql,m),p(e,ct,m),n(ct,Ht),n(Ht,$i),_(Ls,$i,null),n(ct,ic),n(ct,Ma),p(e,Ll,m),p(e,te,m),_(Rs,te,null),n(te,lc),n(te,ya),n(te,dc),n(te,va),n(te,cc),n(te,et),_(Es,et,null),n(et,pc),n(et,wa),n(et,mc),n(et,ka),n(te,uc),n(te,Fe),_(Xs,Fe,null),n(Fe,hc),n(Fe,$a),n(Fe,fc),_(Gt,Fe,null),n(Fe,gc),n(Fe,xa),p(e,Rl,m),p(e,pt,m),n(pt,qt),n(qt,xi),_(Vs,xi,null),n(pt,_c),n(pt,Fa),p(e,El,m),p(e,ne,m),_(As,ne,null),n(ne,bc),n(ne,Ca),n(ne,Tc),n(ne,Ba),n(ne,Mc),n(ne,ja),n(ne,yc),n(ne,Ce),_(Ss,Ce,null),n(Ce,vc),n(Ce,Ja),n(Ce,wc),_(Lt,Ce,null),n(Ce,kc),_(Rt,Ce,null),p(e,Xl,m),p(e,mt,m),n(mt,Et),n(Et,Fi),_(Ps,Fi,null),n(mt,$c),n(mt,za),p(e,Vl,m),p(e,se,m),_(Qs,se,null),n(se,xc),n(se,Da),n(se,Fc),n(se,Ua),n(se,Cc),n(se,Wa),n(se,Bc),n(se,Be),_(Ys,Be,null),n(Be,jc),n(Be,Ia),n(Be,Jc),_(Xt,Be,null),n(Be,zc),_(Vt,Be,null),p(e,Al,m),p(e,ut,m),n(ut,At),n(At,Ci),_(Os,Ci,null),n(ut,Dc),n(ut,Za),p(e,Sl,m),p(e,oe,m),_(Ks,oe,null),n(oe,Uc),n(oe,Na),n(oe,Wc),n(oe,Ha),n(oe,Ic),n(oe,Ga),n(oe,Zc),n(oe,le),_(eo,le,null),n(le,Nc),n(le,qa),n(le,Hc),_(St,le,null),n(le,Gc),_(Pt,le,null),n(le,qc),_(Qt,le,null),p(e,Pl,m),p(e,ht,m),n(ht,Yt),n(Yt,Bi),_(to,Bi,null),n(ht,Lc),n(ht,La),p(e,Ql,m),p(e,ae,m),_(no,ae,null),n(ae,Rc),n(ae,Ra),n(ae,Ec),n(ae,Ea),n(ae,Xc),n(ae,Xa),n(ae,Vc),n(ae,je),_(so,je,null),n(je,Ac),n(je,Va),n(je,Sc),_(Ot,je,null),n(je,Pc),_(Kt,je,null),p(e,Yl,m),p(e,ft,m),n(ft,en),n(en,ji),_(oo,ji,null),n(ft,Qc),n(ft,Aa),p(e,Ol,m),p(e,re,m),_(ao,re,null),n(re,Yc),n(re,Sa),n(re,Oc),n(re,Pa),n(re,Kc),n(re,Qa),n(re,ep),n(re,Je),_(ro,Je,null),n(Je,tp),n(Je,Ya),n(Je,np),_(tn,Je,null),n(Je,sp),_(nn,Je,null),p(e,Kl,m),p(e,gt,m),n(gt,sn),n(sn,Ji),_(io,Ji,null),n(gt,op),n(gt,Oa),p(e,ed,m),p(e,ie,m),_(lo,ie,null),n(ie,ap),n(ie,Ka),n(ie,rp),n(ie,er),n(ie,ip),n(ie,tr),n(ie,lp),n(ie,ze),_(co,ze,null),n(ze,dp),n(ze,nr),n(ze,cp),_(on,ze,null),n(ze,pp),_(an,ze,null),p(e,td,m),p(e,_t,m),n(_t,rn),n(rn,zi),_(po,zi,null),n(_t,mp),n(_t,sr),p(e,nd,m),p(e,S,m),_(mo,S,null),n(S,up),n(S,or),n(S,hp),n(S,ar),n(S,fp),n(S,rr),n(S,gp),_(ln,S,null),n(S,_p),n(S,De),_(uo,De,null),n(De,bp),n(De,ir),n(De,Tp),_(dn,De,null),n(De,Mp),_(cn,De,null),p(e,sd,m),p(e,bt,m),n(bt,pn),n(pn,Di),_(ho,Di,null),n(bt,yp),n(bt,lr),p(e,od,m),p(e,P,m),_(fo,P,null),n(P,vp),n(P,dr),n(P,wp),n(P,cr),n(P,kp),n(P,pr),n(P,$p),_(mn,P,null),n(P,xp),n(P,de),_(go,de,null),n(de,Fp),n(de,mr),n(de,Cp),_(un,de,null),n(de,Bp),_(hn,de,null),n(de,jp),_(fn,de,null),p(e,ad,m),p(e,Tt,m),n(Tt,gn),n(gn,Ui),_(_o,Ui,null),n(Tt,Jp),n(Tt,ur),p(e,rd,m),p(e,Q,m),_(bo,Q,null),n(Q,zp),n(Q,hr),n(Q,Dp),n(Q,fr),n(Q,Up),n(Q,gr),n(Q,Wp),_(_n,Q,null),n(Q,Ip),n(Q,ce),_(To,ce,null),n(ce,Zp),n(ce,_r),n(ce,Np),_(bn,ce,null),n(ce,Hp),_(Tn,ce,null),n(ce,Gp),_(Mn,ce,null),p(e,id,m),p(e,Mt,m),n(Mt,yn),n(yn,Wi),_(Mo,Wi,null),n(Mt,qp),n(Mt,br),p(e,ld,m),p(e,Y,m),_(yo,Y,null),n(Y,Lp),n(Y,Tr),n(Y,Rp),n(Y,Mr),n(Y,Ep),n(Y,yr),n(Y,Xp),_(vn,Y,null),n(Y,Vp),n(Y,Ue),_(vo,Ue,null),n(Ue,Ap),n(Ue,vr),n(Ue,Sp),_(wn,Ue,null),n(Ue,Pp),_(kn,Ue,null),p(e,dd,m),p(e,yt,m),n(yt,$n),n($n,Ii),_(wo,Ii,null),n(yt,Qp),n(yt,wr),p(e,cd,m),p(e,O,m),_(ko,O,null),n(O,Yp),n(O,kr),n(O,Op),n(O,$r),n(O,Kp),n(O,xr),n(O,em),_(xn,O,null),n(O,tm),n(O,pe),_($o,pe,null),n(pe,nm),n(pe,Fr),n(pe,sm),_(Fn,pe,null),n(pe,om),_(Cn,pe,null),n(pe,am),_(Bn,pe,null),p(e,pd,m),p(e,vt,m),n(vt,jn),n(jn,Zi),_(xo,Zi,null),n(vt,rm),n(vt,Cr),p(e,md,m),p(e,K,m),_(Fo,K,null),n(K,im),n(K,Br),n(K,lm),n(K,jr),n(K,dm),n(K,Jr),n(K,cm),_(Jn,K,null),n(K,pm),n(K,me),_(Co,me,null),n(me,mm),n(me,zr),n(me,um),_(zn,me,null),n(me,hm),_(Dn,me,null),n(me,fm),_(Un,me,null),p(e,ud,m),p(e,wt,m),n(wt,Wn),n(Wn,Ni),_(Bo,Ni,null),n(wt,gm),n(wt,Dr),p(e,hd,m),p(e,L,m),_(jo,L,null),n(L,_m),n(L,Ur),n(L,bm),n(L,Wr),n(L,Tm),n(L,Ir),n(L,Mm),n(L,Zr),n(L,ym),n(L,Nr),n(L,vm),n(L,We),_(Jo,We,null),n(We,wm),n(We,Hr),n(We,km),_(In,We,null),n(We,$m),_(Zn,We,null),p(e,fd,m),p(e,kt,m),n(kt,Nn),n(Nn,Hi),_(zo,Hi,null),n(kt,xm),n(kt,Gr),p(e,gd,m),p(e,R,m),_(Do,R,null),n(R,Fm),n(R,qr),n(R,Cm),n(R,Lr),n(R,Bm),n(R,Rr),n(R,jm),n(R,Er),n(R,Jm),n(R,Xr),n(R,zm),n(R,Ie),_(Uo,Ie,null),n(Ie,Dm),n(Ie,Vr),n(Ie,Um),_(Hn,Ie,null),n(Ie,Wm),_(Gn,Ie,null),p(e,_d,m),p(e,$t,m),n($t,qn),n(qn,Gi),_(Wo,Gi,null),n($t,Im),n($t,Ar),p(e,bd,m),p(e,E,m),_(Io,E,null),n(E,Zm),n(E,Sr),n(E,Nm),n(E,Pr),n(E,Hm),n(E,Qr),n(E,Gm),n(E,Yr),n(E,qm),n(E,Or),n(E,Lm),n(E,Ze),_(Zo,Ze,null),n(Ze,Rm),n(Ze,Kr),n(Ze,Em),_(Ln,Ze,null),n(Ze,Xm),_(Rn,Ze,null),p(e,Td,m),p(e,xt,m),n(xt,En),n(En,qi),_(No,qi,null),n(xt,Vm),n(xt,ei),p(e,Md,m),p(e,X,m),_(Ho,X,null),n(X,Am),n(X,ti),n(X,Sm),n(X,ni),n(X,Pm),n(X,si),n(X,Qm),n(X,oi),n(X,Ym),n(X,ai),n(X,Om),n(X,Ne),_(Go,Ne,null),n(Ne,Km),n(Ne,ri),n(Ne,eu),_(Xn,Ne,null),n(Ne,tu),_(Vn,Ne,null),p(e,yd,m),p(e,Ft,m),n(Ft,An),n(An,Li),_(qo,Li,null),n(Ft,nu),n(Ft,ii),p(e,vd,m),p(e,V,m),_(Lo,V,null),n(V,su),n(V,li),n(V,ou),n(V,di),n(V,au),n(V,ci),n(V,ru),n(V,pi),n(V,iu),n(V,mi),n(V,lu),n(V,He),_(Ro,He,null),n(He,du),n(He,ui),n(He,cu),_(Sn,He,null),n(He,pu),_(Pn,He,null),p(e,wd,m),p(e,Ct,m),n(Ct,Qn),n(Qn,Ri),_(Eo,Ri,null),n(Ct,mu),n(Ct,hi),p(e,kd,m),p(e,A,m),_(Xo,A,null),n(A,uu),n(A,fi),n(A,hu),n(A,gi),n(A,fu),n(A,_i),n(A,gu),n(A,bi),n(A,_u),n(A,Ti),n(A,bu),n(A,Ge),_(Vo,Ge,null),n(Ge,Tu),n(Ge,Mi),n(Ge,Mu),_(Yn,Ge,null),n(Ge,yu),_(On,Ge,null),p(e,$d,m),p(e,Ei,m),xd=!0},p(e,[m]){const Ao={};m&2&&(Ao.$$scope={dirty:m,ctx:e}),Ut.$set(Ao);const Xi={};m&2&&(Xi.$$scope={dirty:m,ctx:e}),Zt.$set(Xi);const Vi={};m&2&&(Vi.$$scope={dirty:m,ctx:e}),Gt.$set(Vi);const So={};m&2&&(So.$$scope={dirty:m,ctx:e}),Lt.$set(So);const Ai={};m&2&&(Ai.$$scope={dirty:m,ctx:e}),Rt.$set(Ai);const Si={};m&2&&(Si.$$scope={dirty:m,ctx:e}),Xt.$set(Si);const Po={};m&2&&(Po.$$scope={dirty:m,ctx:e}),Vt.$set(Po);const Pi={};m&2&&(Pi.$$scope={dirty:m,ctx:e}),St.$set(Pi);const Qi={};m&2&&(Qi.$$scope={dirty:m,ctx:e}),Pt.$set(Qi);const Qo={};m&2&&(Qo.$$scope={dirty:m,ctx:e}),Qt.$set(Qo);const Yi={};m&2&&(Yi.$$scope={dirty:m,ctx:e}),Ot.$set(Yi);const Oi={};m&2&&(Oi.$$scope={dirty:m,ctx:e}),Kt.$set(Oi);const Ae={};m&2&&(Ae.$$scope={dirty:m,ctx:e}),tn.$set(Ae);const Yo={};m&2&&(Yo.$$scope={dirty:m,ctx:e}),nn.$set(Yo);const Ki={};m&2&&(Ki.$$scope={dirty:m,ctx:e}),on.$set(Ki);const el={};m&2&&(el.$$scope={dirty:m,ctx:e}),an.$set(el);const ee={};m&2&&(ee.$$scope={dirty:m,ctx:e}),ln.$set(ee);const Bt={};m&2&&(Bt.$$scope={dirty:m,ctx:e}),dn.$set(Bt);const Oo={};m&2&&(Oo.$$scope={dirty:m,ctx:e}),cn.$set(Oo);const Se={};m&2&&(Se.$$scope={dirty:m,ctx:e}),mn.$set(Se);const Ko={};m&2&&(Ko.$$scope={dirty:m,ctx:e}),un.$set(Ko);const ea={};m&2&&(ea.$$scope={dirty:m,ctx:e}),hn.$set(ea);const tl={};m&2&&(tl.$$scope={dirty:m,ctx:e}),fn.$set(tl);const nl={};m&2&&(nl.$$scope={dirty:m,ctx:e}),_n.$set(nl);const Me={};m&2&&(Me.$$scope={dirty:m,ctx:e}),bn.$set(Me);const jt={};m&2&&(jt.$$scope={dirty:m,ctx:e}),Tn.$set(jt);const Pe={};m&2&&(Pe.$$scope={dirty:m,ctx:e}),Mn.$set(Pe);const ta={};m&2&&(ta.$$scope={dirty:m,ctx:e}),vn.$set(ta);const sl={};m&2&&(sl.$$scope={dirty:m,ctx:e}),wn.$set(sl);const ol={};m&2&&(ol.$$scope={dirty:m,ctx:e}),kn.$set(ol);const ye={};m&2&&(ye.$$scope={dirty:m,ctx:e}),xn.$set(ye);const Qe={};m&2&&(Qe.$$scope={dirty:m,ctx:e}),Fn.$set(Qe);const na={};m&2&&(na.$$scope={dirty:m,ctx:e}),Cn.$set(na);const al={};m&2&&(al.$$scope={dirty:m,ctx:e}),Bn.$set(al);const rl={};m&2&&(rl.$$scope={dirty:m,ctx:e}),Jn.$set(rl);const ve={};m&2&&(ve.$$scope={dirty:m,ctx:e}),zn.$set(ve);const Ye={};m&2&&(Ye.$$scope={dirty:m,ctx:e}),Dn.$set(Ye);const sa={};m&2&&(sa.$$scope={dirty:m,ctx:e}),Un.$set(sa);const il={};m&2&&(il.$$scope={dirty:m,ctx:e}),In.$set(il);const ll={};m&2&&(ll.$$scope={dirty:m,ctx:e}),Zn.$set(ll);const we={};m&2&&(we.$$scope={dirty:m,ctx:e}),Hn.$set(we);const ke={};m&2&&(ke.$$scope={dirty:m,ctx:e}),Gn.$set(ke);const oa={};m&2&&(oa.$$scope={dirty:m,ctx:e}),Ln.$set(oa);const dl={};m&2&&(dl.$$scope={dirty:m,ctx:e}),Rn.$set(dl);const cl={};m&2&&(cl.$$scope={dirty:m,ctx:e}),Xn.$set(cl);const $e={};m&2&&($e.$$scope={dirty:m,ctx:e}),Vn.$set($e);const Oe={};m&2&&(Oe.$$scope={dirty:m,ctx:e}),Sn.$set(Oe);const aa={};m&2&&(aa.$$scope={dirty:m,ctx:e}),Pn.$set(aa);const pl={};m&2&&(pl.$$scope={dirty:m,ctx:e}),Yn.$set(pl);const ml={};m&2&&(ml.$$scope={dirty:m,ctx:e}),On.$set(ml)},i(e){xd||(b(I.$$.fragment,e),b(ds.$$.fragment,e),b(gs.$$.fragment,e),b(bs.$$.fragment,e),b(Ms.$$.fragment,e),b(vs.$$.fragment,e),b(ks.$$.fragment,e),b(Us.$$.fragment,e),b(Ws.$$.fragment,e),b(Ut.$$.fragment,e),b(Is.$$.fragment,e),b(Zs.$$.fragment,e),b(Ns.$$.fragment,e),b(Hs.$$.fragment,e),b(Gs.$$.fragment,e),b(Zt.$$.fragment,e),b(qs.$$.fragment,e),b(Ls.$$.fragment,e),b(Rs.$$.fragment,e),b(Es.$$.fragment,e),b(Xs.$$.fragment,e),b(Gt.$$.fragment,e),b(Vs.$$.fragment,e),b(As.$$.fragment,e),b(Ss.$$.fragment,e),b(Lt.$$.fragment,e),b(Rt.$$.fragment,e),b(Ps.$$.fragment,e),b(Qs.$$.fragment,e),b(Ys.$$.fragment,e),b(Xt.$$.fragment,e),b(Vt.$$.fragment,e),b(Os.$$.fragment,e),b(Ks.$$.fragment,e),b(eo.$$.fragment,e),b(St.$$.fragment,e),b(Pt.$$.fragment,e),b(Qt.$$.fragment,e),b(to.$$.fragment,e),b(no.$$.fragment,e),b(so.$$.fragment,e),b(Ot.$$.fragment,e),b(Kt.$$.fragment,e),b(oo.$$.fragment,e),b(ao.$$.fragment,e),b(ro.$$.fragment,e),b(tn.$$.fragment,e),b(nn.$$.fragment,e),b(io.$$.fragment,e),b(lo.$$.fragment,e),b(co.$$.fragment,e),b(on.$$.fragment,e),b(an.$$.fragment,e),b(po.$$.fragment,e),b(mo.$$.fragment,e),b(ln.$$.fragment,e),b(uo.$$.fragment,e),b(dn.$$.fragment,e),b(cn.$$.fragment,e),b(ho.$$.fragment,e),b(fo.$$.fragment,e),b(mn.$$.fragment,e),b(go.$$.fragment,e),b(un.$$.fragment,e),b(hn.$$.fragment,e),b(fn.$$.fragment,e),b(_o.$$.fragment,e),b(bo.$$.fragment,e),b(_n.$$.fragment,e),b(To.$$.fragment,e),b(bn.$$.fragment,e),b(Tn.$$.fragment,e),b(Mn.$$.fragment,e),b(Mo.$$.fragment,e),b(yo.$$.fragment,e),b(vn.$$.fragment,e),b(vo.$$.fragment,e),b(wn.$$.fragment,e),b(kn.$$.fragment,e),b(wo.$$.fragment,e),b(ko.$$.fragment,e),b(xn.$$.fragment,e),b($o.$$.fragment,e),b(Fn.$$.fragment,e),b(Cn.$$.fragment,e),b(Bn.$$.fragment,e),b(xo.$$.fragment,e),b(Fo.$$.fragment,e),b(Jn.$$.fragment,e),b(Co.$$.fragment,e),b(zn.$$.fragment,e),b(Dn.$$.fragment,e),b(Un.$$.fragment,e),b(Bo.$$.fragment,e),b(jo.$$.fragment,e),b(Jo.$$.fragment,e),b(In.$$.fragment,e),b(Zn.$$.fragment,e),b(zo.$$.fragment,e),b(Do.$$.fragment,e),b(Uo.$$.fragment,e),b(Hn.$$.fragment,e),b(Gn.$$.fragment,e),b(Wo.$$.fragment,e),b(Io.$$.fragment,e),b(Zo.$$.fragment,e),b(Ln.$$.fragment,e),b(Rn.$$.fragment,e),b(No.$$.fragment,e),b(Ho.$$.fragment,e),b(Go.$$.fragment,e),b(Xn.$$.fragment,e),b(Vn.$$.fragment,e),b(qo.$$.fragment,e),b(Lo.$$.fragment,e),b(Ro.$$.fragment,e),b(Sn.$$.fragment,e),b(Pn.$$.fragment,e),b(Eo.$$.fragment,e),b(Xo.$$.fragment,e),b(Vo.$$.fragment,e),b(Yn.$$.fragment,e),b(On.$$.fragment,e),xd=!0)},o(e){T(I.$$.fragment,e),T(ds.$$.fragment,e),T(gs.$$.fragment,e),T(bs.$$.fragment,e),T(Ms.$$.fragment,e),T(vs.$$.fragment,e),T(ks.$$.fragment,e),T(Us.$$.fragment,e),T(Ws.$$.fragment,e),T(Ut.$$.fragment,e),T(Is.$$.fragment,e),T(Zs.$$.fragment,e),T(Ns.$$.fragment,e),T(Hs.$$.fragment,e),T(Gs.$$.fragment,e),T(Zt.$$.fragment,e),T(qs.$$.fragment,e),T(Ls.$$.fragment,e),T(Rs.$$.fragment,e),T(Es.$$.fragment,e),T(Xs.$$.fragment,e),T(Gt.$$.fragment,e),T(Vs.$$.fragment,e),T(As.$$.fragment,e),T(Ss.$$.fragment,e),T(Lt.$$.fragment,e),T(Rt.$$.fragment,e),T(Ps.$$.fragment,e),T(Qs.$$.fragment,e),T(Ys.$$.fragment,e),T(Xt.$$.fragment,e),T(Vt.$$.fragment,e),T(Os.$$.fragment,e),T(Ks.$$.fragment,e),T(eo.$$.fragment,e),T(St.$$.fragment,e),T(Pt.$$.fragment,e),T(Qt.$$.fragment,e),T(to.$$.fragment,e),T(no.$$.fragment,e),T(so.$$.fragment,e),T(Ot.$$.fragment,e),T(Kt.$$.fragment,e),T(oo.$$.fragment,e),T(ao.$$.fragment,e),T(ro.$$.fragment,e),T(tn.$$.fragment,e),T(nn.$$.fragment,e),T(io.$$.fragment,e),T(lo.$$.fragment,e),T(co.$$.fragment,e),T(on.$$.fragment,e),T(an.$$.fragment,e),T(po.$$.fragment,e),T(mo.$$.fragment,e),T(ln.$$.fragment,e),T(uo.$$.fragment,e),T(dn.$$.fragment,e),T(cn.$$.fragment,e),T(ho.$$.fragment,e),T(fo.$$.fragment,e),T(mn.$$.fragment,e),T(go.$$.fragment,e),T(un.$$.fragment,e),T(hn.$$.fragment,e),T(fn.$$.fragment,e),T(_o.$$.fragment,e),T(bo.$$.fragment,e),T(_n.$$.fragment,e),T(To.$$.fragment,e),T(bn.$$.fragment,e),T(Tn.$$.fragment,e),T(Mn.$$.fragment,e),T(Mo.$$.fragment,e),T(yo.$$.fragment,e),T(vn.$$.fragment,e),T(vo.$$.fragment,e),T(wn.$$.fragment,e),T(kn.$$.fragment,e),T(wo.$$.fragment,e),T(ko.$$.fragment,e),T(xn.$$.fragment,e),T($o.$$.fragment,e),T(Fn.$$.fragment,e),T(Cn.$$.fragment,e),T(Bn.$$.fragment,e),T(xo.$$.fragment,e),T(Fo.$$.fragment,e),T(Jn.$$.fragment,e),T(Co.$$.fragment,e),T(zn.$$.fragment,e),T(Dn.$$.fragment,e),T(Un.$$.fragment,e),T(Bo.$$.fragment,e),T(jo.$$.fragment,e),T(Jo.$$.fragment,e),T(In.$$.fragment,e),T(Zn.$$.fragment,e),T(zo.$$.fragment,e),T(Do.$$.fragment,e),T(Uo.$$.fragment,e),T(Hn.$$.fragment,e),T(Gn.$$.fragment,e),T(Wo.$$.fragment,e),T(Io.$$.fragment,e),T(Zo.$$.fragment,e),T(Ln.$$.fragment,e),T(Rn.$$.fragment,e),T(No.$$.fragment,e),T(Ho.$$.fragment,e),T(Go.$$.fragment,e),T(Xn.$$.fragment,e),T(Vn.$$.fragment,e),T(qo.$$.fragment,e),T(Lo.$$.fragment,e),T(Ro.$$.fragment,e),T(Sn.$$.fragment,e),T(Pn.$$.fragment,e),T(Eo.$$.fragment,e),T(Xo.$$.fragment,e),T(Vo.$$.fragment,e),T(Yn.$$.fragment,e),T(On.$$.fragment,e),xd=!1},d(e){e&&(s(h),s(d),s(c),s(w),s(z),s(C),s(k),s(F),s(ul),s(cs),s(hl),s(ps),s(fl),s(ms),s(gl),s(us),s(_l),s(hs),s(bl),s(fs),s(Tl),s(it),s(Ml),s(_s),s(yl),s(vl),s(Ts),s(wl),s(kl),s(ys),s($l),s(xl),s(ws),s(Fl),s(Cl),s($s),s(Bl),s(xs),s(jl),s(Fs),s(Jl),s(Cs),s(zl),s(Bs),s(Dl),s(js),s(Ul),s(Js),s(Wl),s(zs),s(Il),s(Ds),s(Zl),s(lt),s(Nl),s(Te),s(Hl),s(dt),s(Gl),s(q),s(ql),s(ct),s(Ll),s(te),s(Rl),s(pt),s(El),s(ne),s(Xl),s(mt),s(Vl),s(se),s(Al),s(ut),s(Sl),s(oe),s(Pl),s(ht),s(Ql),s(ae),s(Yl),s(ft),s(Ol),s(re),s(Kl),s(gt),s(ed),s(ie),s(td),s(_t),s(nd),s(S),s(sd),s(bt),s(od),s(P),s(ad),s(Tt),s(rd),s(Q),s(id),s(Mt),s(ld),s(Y),s(dd),s(yt),s(cd),s(O),s(pd),s(vt),s(md),s(K),s(ud),s(wt),s(hd),s(L),s(fd),s(kt),s(gd),s(R),s(_d),s($t),s(bd),s(E),s(Td),s(xt),s(Md),s(X),s(yd),s(Ft),s(vd),s(V),s(wd),s(Ct),s(kd),s(A),s($d),s(Ei)),s(t),M(I),M(ds),M(gs),M(bs,e),M(Ms,e),M(vs,e),M(ks,e),M(Us),M(Ws),M(Ut),M(Is),M(Zs),M(Ns),M(Hs),M(Gs),M(Zt),M(qs),M(Ls),M(Rs),M(Es),M(Xs),M(Gt),M(Vs),M(As),M(Ss),M(Lt),M(Rt),M(Ps),M(Qs),M(Ys),M(Xt),M(Vt),M(Os),M(Ks),M(eo),M(St),M(Pt),M(Qt),M(to),M(no),M(so),M(Ot),M(Kt),M(oo),M(ao),M(ro),M(tn),M(nn),M(io),M(lo),M(co),M(on),M(an),M(po),M(mo),M(ln),M(uo),M(dn),M(cn),M(ho),M(fo),M(mn),M(go),M(un),M(hn),M(fn),M(_o),M(bo),M(_n),M(To),M(bn),M(Tn),M(Mn),M(Mo),M(yo),M(vn),M(vo),M(wn),M(kn),M(wo),M(ko),M(xn),M($o),M(Fn),M(Cn),M(Bn),M(xo),M(Fo),M(Jn),M(Co),M(zn),M(Dn),M(Un),M(Bo),M(jo),M(Jo),M(In),M(Zn),M(zo),M(Do),M(Uo),M(Hn),M(Gn),M(Wo),M(Io),M(Zo),M(Ln),M(Rn),M(No),M(Ho),M(Go),M(Xn),M(Vn),M(qo),M(Lo),M(Ro),M(Sn),M(Pn),M(Eo),M(Xo),M(Vo),M(Yn),M(On)}}}const J_={local:"distilbert",sections:[{local:"overview",title:"Overview"},{local:"resources",title:"Resources"},{local:"transformers.DistilBertConfig",title:"DistilBertConfig"},{local:"transformers.DistilBertTokenizer",title:"DistilBertTokenizer"},{local:"transformers.DistilBertTokenizerFast",title:"DistilBertTokenizerFast"},{local:"transformers.DistilBertModel",title:"DistilBertModel"},{local:"transformers.DistilBertForMaskedLM",title:"DistilBertForMaskedLM"},{local:"transformers.DistilBertForSequenceClassification",title:"DistilBertForSequenceClassification"},{local:"transformers.DistilBertForMultipleChoice",title:"DistilBertForMultipleChoice"},{local:"transformers.DistilBertForTokenClassification",title:"DistilBertForTokenClassification"},{local:"transformers.DistilBertForQuestionAnswering",title:"DistilBertForQuestionAnswering"},{local:"transformers.TFDistilBertModel",title:"TFDistilBertModel"},{local:"transformers.TFDistilBertForMaskedLM",title:"TFDistilBertForMaskedLM"},{local:"transformers.TFDistilBertForSequenceClassification",title:"TFDistilBertForSequenceClassification"},{local:"transformers.TFDistilBertForMultipleChoice",title:"TFDistilBertForMultipleChoice"},{local:"transformers.TFDistilBertForTokenClassification",title:"TFDistilBertForTokenClassification"},{local:"transformers.TFDistilBertForQuestionAnswering",title:"TFDistilBertForQuestionAnswering"},{local:"transformers.FlaxDistilBertModel",title:"FlaxDistilBertModel"},{local:"transformers.FlaxDistilBertForMaskedLM",title:"FlaxDistilBertForMaskedLM"},{local:"transformers.FlaxDistilBertForSequenceClassification",title:"FlaxDistilBertForSequenceClassification"},{local:"transformers.FlaxDistilBertForMultipleChoice",title:"FlaxDistilBertForMultipleChoice"},{local:"transformers.FlaxDistilBertForTokenClassification",title:"FlaxDistilBertForTokenClassification"},{local:"transformers.FlaxDistilBertForQuestionAnswering",title:"FlaxDistilBertForQuestionAnswering"}],title:"DistilBERT"};function z_(x){return jg(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class q_ extends Jg{constructor(t){super(),zg(this,t,z_,j_,Bg,{})}}export{q_ as component};
