import{S as Wv,i as Nv,s as Av,e as s,k as p,w as v,t as o,M as Sv,c as r,d as t,m,a,x as w,h as n,b as g,G as e,g as _,y as M,q as $,o as R,B,v as Gv,L as He}from"../../chunks/vendor-hf-doc-builder.js";import{T as Ke}from"../../chunks/Tip-hf-doc-builder.js";import{D as ne}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Ye}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as et}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Qe}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Vv(C){let d,b,h,u,y;return u=new Ye({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> RemBertModel, RemBertConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a RemBERT rembert style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = RemBertConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the rembert style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertModel(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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function Lv(C){let d,b,h,u,y;return u=new Ye({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(){d=s("p"),b=o("sequence pair mask has the following format:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"sequence pair mask has the following format:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function Xv(C){let d,b,h,u,y;return u=new Ye({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(){d=s("p"),b=o("sequence pair mask has the following format:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"sequence pair mask has the following format:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function Dv(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function Pv(C){let d,b,h,u,y;return u=new Ye({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, RemBertModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertModel.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function Ov(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function Qv(C){let d,b,h,u,y;return u=new Ye({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, RemBertForCausalLM, RemBertConfig
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config = RemBertConfig.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config.is_decoder = <span class="hljs-literal">True</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForCausalLM.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>, config=config)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>prediction_logits = outputs.logits`}}),{c(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function Hv(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function Yv(C){let d,b,h,u,y;return u=new Ye({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, RemBertForMaskedLM
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function Kv(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function ew(C){let d,b,h,u,y;return u=new Ye({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, RemBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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 = RemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example of single-label classification:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example of single-label classification:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function tw(C){let d,b,h,u,y;return u=new Ye({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, RemBertForSequenceClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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 = RemBertForSequenceClassification.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example of multi-label classification:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example of multi-label classification:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function ow(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function nw(C){let d,b,h,u,y;return u=new Ye({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, RemBertForMultipleChoice
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># the linear classifier still needs to be trained</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function sw(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function rw(C){let d,b,h,u,y;return u=new Ye({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, RemBertForTokenClassification
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function aw(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
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instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function iw(C){let d,b,h,u,y;return u=new Ye({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, RemBertForQuestionAnswering
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = RemBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function lw(C){let d,b,h,u,y,l,c,j,Me,ge,N,ae,te,k,$e,D,Re,_e,S,Be,le,Q,Fe,be,V,Ee,ye,L,me,Ce,oe,J,U,Te,X,je,ke,A,ie,he,z,Je,P,ze,ve,Z,ue,de,W,Ue,O,ce,se,I,fe,x,Ie,xe;return{c(){d=s("p"),b=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),y=o(" accept two formats as input:"),l=p(),c=s("ul"),j=s("li"),Me=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),k=s("p"),$e=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function cw(C){let d,b,h,u,y;return u=new Ye({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, TFRemBertModel
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertModel.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function pw(C){let d,b,h,u,y,l,c,j,Me,ge,N,ae,te,k,$e,D,Re,_e,S,Be,le,Q,Fe,be,V,Ee,ye,L,me,Ce,oe,J,U,Te,X,je,ke,A,ie,he,z,Je,P,ze,ve,Z,ue,de,W,Ue,O,ce,se,I,fe,x,Ie,xe;return{c(){d=s("p"),b=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),y=o(" accept two formats as input:"),l=p(),c=s("ul"),j=s("li"),Me=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),k=s("p"),$e=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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format outside of Keras methods like `),Q=r(E,"CODE",{});var We=a(Q);Fe=n(We,"fit()"),We.forEach(t),be=n(E," and "),V=r(E,"CODE",{});var De=a(V);Ee=n(De,"predict()"),De.forEach(t),ye=n(E,`, such as when creating your own layers or models with
the Keras `),L=r(E,"CODE",{});var Ve=a(L);me=n(Ve,"Functional"),Ve.forEach(t),Ce=n(E,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),E.forEach(t),oe=m(f),J=r(f,"UL",{});var q=a(J);U=r(q,"LI",{});var G=a(U);Te=n(G,"a single Tensor with "),X=r(G,"CODE",{});var Se=a(X);je=n(Se,"input_ids"),Se.forEach(t),ke=n(G," only and nothing else: "),A=r(G,"CODE",{});var Ze=a(A);ie=n(Ze,"model(input_ids)"),Ze.forEach(t),G.forEach(t),he=m(q),z=r(q,"LI",{});var H=a(z);Je=n(H,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),P=r(H,"CODE",{});var Ge=a(P);ze=n(Ge,"model([input_ids, attention_mask])"),Ge.forEach(t),ve=n(H," or "),Z=r(H,"CODE",{});var Pe=a(Z);ue=n(Pe,"model([input_ids, attention_mask, token_type_ids])"),Pe.forEach(t),H.forEach(t),de=m(q),W=r(q,"LI",{});var we=a(W);Ue=n(we,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),O=r(we,"CODE",{});var pe=a(O);ce=n(pe,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),pe.forEach(t),we.forEach(t),q.forEach(t),se=m(f),I=r(f,"P",{});var K=a(I);fe=n(K,`Note that when creating models and layers with
`),x=r(K,"A",{href:!0,rel:!0});var Oe=a(x);Ie=n(Oe,"subclassing"),Oe.forEach(t),xe=n(K,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),K.forEach(t),this.h()},h(){g(x,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(x,"rel","nofollow")},m(f,F){_(f,d,F),e(d,b),e(d,h),e(h,u),e(d,y),_(f,l,F),_(f,c,F),e(c,j),e(j,Me),e(c,ge),e(c,N),e(N,ae),_(f,te,F),_(f,k,F),e(k,$e),e(k,D),e(D,Re),e(k,_e),e(k,S),e(S,Be),e(k,le),e(k,Q),e(Q,Fe),e(k,be),e(k,V),e(V,Ee),e(k,ye),e(k,L),e(L,me),e(k,Ce),_(f,oe,F),_(f,J,F),e(J,U),e(U,Te),e(U,X),e(X,je),e(U,ke),e(U,A),e(A,ie),e(J,he),e(J,z),e(z,Je),e(z,P),e(P,ze),e(z,ve),e(z,Z),e(Z,ue),e(J,de),e(J,W),e(W,Ue),e(W,O),e(O,ce),_(f,se,F),_(f,I,F),e(I,fe),e(I,x),e(x,Ie),e(I,xe)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(te),f&&t(k),f&&t(oe),f&&t(J),f&&t(se),f&&t(I)}}}function mw(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function hw(C){let d,b,h,u,y;return u=new Ye({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, TFRemBertForMaskedLM
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForMaskedLM.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function uw(C){let d,b;return d=new Ye({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(){v(d.$$.fragment)},l(h){w(d.$$.fragment,h)},m(h,u){M(d,h,u),b=!0},p:He,i(h){b||($(d.$$.fragment,h),b=!0)},o(h){R(d.$$.fragment,h),b=!1},d(h){B(d,h)}}}function fw(C){let d,b,h,u,y,l,c,j,Me,ge,N,ae,te,k,$e,D,Re,_e,S,Be,le,Q,Fe,be,V,Ee,ye,L,me,Ce,oe,J,U,Te,X,je,ke,A,ie,he,z,Je,P,ze,ve,Z,ue,de,W,Ue,O,ce,se,I,fe,x,Ie,xe;return{c(){d=s("p"),b=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),y=o(" accept two formats as input:"),l=p(),c=s("ul"),j=s("li"),Me=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),k=s("p"),$e=o(`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 `),D=s("code"),Re=o("model.fit()"),_e=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=s("code"),Be=o("model.fit()"),le=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=s("code"),Fe=o("fit()"),be=o(" and "),V=s("code"),Ee=o("predict()"),ye=o(`, such as when creating your own layers or models with
the Keras `),L=s("code"),me=o("Functional"),Ce=o(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),oe=p(),J=s("ul"),U=s("li"),Te=o("a single Tensor with "),X=s("code"),je=o("input_ids"),ke=o(" only and nothing else: "),A=s("code"),ie=o("model(input_ids)"),he=p(),z=s("li"),Je=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),P=s("code"),ze=o("model([input_ids, attention_mask])"),ve=o(" or "),Z=s("code"),ue=o("model([input_ids, attention_mask, token_type_ids])"),de=p(),W=s("li"),Ue=o(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),O=s("code"),ce=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),se=p(),I=s("p"),fe=o(`Note that when creating models and layers with
`),x=s("a"),Ie=o("subclassing"),xe=o(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(f){d=r(f,"P",{});var F=a(d);b=n(F,"TensorFlow models and layers in "),h=r(F,"CODE",{});var Le=a(h);u=n(Le,"transformers"),Le.forEach(t),y=n(F," accept two formats as input:"),F.forEach(t),l=m(f),c=r(f,"UL",{});var re=a(c);j=r(re,"LI",{});var Ne=a(j);Me=n(Ne,"having all inputs as keyword arguments (like PyTorch models), or"),Ne.forEach(t),ge=m(re),N=r(re,"LI",{});var qe=a(N);ae=n(qe,"having all inputs as a list, tuple or dict in the first positional argument."),qe.forEach(t),re.forEach(t),te=m(f),k=r(f,"P",{});var E=a(k);$e=n(E,`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 `),D=r(E,"CODE",{});var Xe=a(D);Re=n(Xe,"model.fit()"),Xe.forEach(t),_e=n(E,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=r(E,"CODE",{});var Ae=a(S);Be=n(Ae,"model.fit()"),Ae.forEach(t),le=n(E,` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=r(E,"CODE",{});var We=a(Q);Fe=n(We,"fit()"),We.forEach(t),be=n(E," and "),V=r(E,"CODE",{});var De=a(V);Ee=n(De,"predict()"),De.forEach(t),ye=n(E,`, such as when creating your own layers or models with
the Keras `),L=r(E,"CODE",{});var Ve=a(L);me=n(Ve,"Functional"),Ve.forEach(t),Ce=n(E,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),E.forEach(t),oe=m(f),J=r(f,"UL",{});var q=a(J);U=r(q,"LI",{});var G=a(U);Te=n(G,"a single Tensor with "),X=r(G,"CODE",{});var Se=a(X);je=n(Se,"input_ids"),Se.forEach(t),ke=n(G," only and nothing else: "),A=r(G,"CODE",{});var Ze=a(A);ie=n(Ze,"model(input_ids)"),Ze.forEach(t),G.forEach(t),he=m(q),z=r(q,"LI",{});var H=a(z);Je=n(H,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),P=r(H,"CODE",{});var Ge=a(P);ze=n(Ge,"model([input_ids, attention_mask])"),Ge.forEach(t),ve=n(H," or "),Z=r(H,"CODE",{});var Pe=a(Z);ue=n(Pe,"model([input_ids, attention_mask, token_type_ids])"),Pe.forEach(t),H.forEach(t),de=m(q),W=r(q,"LI",{});var we=a(W);Ue=n(we,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),O=r(we,"CODE",{});var pe=a(O);ce=n(pe,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),pe.forEach(t),we.forEach(t),q.forEach(t),se=m(f),I=r(f,"P",{});var K=a(I);fe=n(K,`Note that when creating models and layers with
`),x=r(K,"A",{href:!0,rel:!0});var Oe=a(x);Ie=n(Oe,"subclassing"),Oe.forEach(t),xe=n(K,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),K.forEach(t),this.h()},h(){g(x,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(x,"rel","nofollow")},m(f,F){_(f,d,F),e(d,b),e(d,h),e(h,u),e(d,y),_(f,l,F),_(f,c,F),e(c,j),e(j,Me),e(c,ge),e(c,N),e(N,ae),_(f,te,F),_(f,k,F),e(k,$e),e(k,D),e(D,Re),e(k,_e),e(k,S),e(S,Be),e(k,le),e(k,Q),e(Q,Fe),e(k,be),e(k,V),e(V,Ee),e(k,ye),e(k,L),e(L,me),e(k,Ce),_(f,oe,F),_(f,J,F),e(J,U),e(U,Te),e(U,X),e(X,je),e(U,ke),e(U,A),e(A,ie),e(J,he),e(J,z),e(z,Je),e(z,P),e(P,ze),e(z,ve),e(z,Z),e(Z,ue),e(J,de),e(J,W),e(W,Ue),e(W,O),e(O,ce),_(f,se,F),_(f,I,F),e(I,fe),e(I,x),e(x,Ie),e(I,xe)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(te),f&&t(k),f&&t(oe),f&&t(J),f&&t(se),f&&t(I)}}}function gw(C){let d,b,h,u,y;return u=new Ye({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, TFRemBertForCausalLM
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForCausalLM.from_pretrained(<span class="hljs-string">&quot;google/rembert&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>logits = outputs.logits`}}),{c(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function _w(C){let d,b,h,u,y,l,c,j,Me,ge,N,ae,te,k,$e,D,Re,_e,S,Be,le,Q,Fe,be,V,Ee,ye,L,me,Ce,oe,J,U,Te,X,je,ke,A,ie,he,z,Je,P,ze,ve,Z,ue,de,W,Ue,O,ce,se,I,fe,x,Ie,xe;return{c(){d=s("p"),b=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),y=o(" accept two formats as input:"),l=p(),c=s("ul"),j=s("li"),Me=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),k=s("p"),$e=o(`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 `),D=s("code"),Re=o("model.fit()"),_e=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=s("code"),Be=o("model.fit()"),le=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=s("code"),Fe=o("fit()"),be=o(" and "),V=s("code"),Ee=o("predict()"),ye=o(`, such as when creating your own layers or models with
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`),P=s("code"),ze=o("model([input_ids, attention_mask])"),ve=o(" or "),Z=s("code"),ue=o("model([input_ids, attention_mask, token_type_ids])"),de=p(),W=s("li"),Ue=o(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),O=s("code"),ce=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),se=p(),I=s("p"),fe=o(`Note that when creating models and layers with
`),x=s("a"),Ie=o("subclassing"),xe=o(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(f){d=r(f,"P",{});var F=a(d);b=n(F,"TensorFlow models and layers in "),h=r(F,"CODE",{});var Le=a(h);u=n(Le,"transformers"),Le.forEach(t),y=n(F," accept two formats as input:"),F.forEach(t),l=m(f),c=r(f,"UL",{});var re=a(c);j=r(re,"LI",{});var Ne=a(j);Me=n(Ne,"having all inputs as keyword arguments (like PyTorch models), or"),Ne.forEach(t),ge=m(re),N=r(re,"LI",{});var qe=a(N);ae=n(qe,"having all inputs as a list, tuple or dict in the first positional argument."),qe.forEach(t),re.forEach(t),te=m(f),k=r(f,"P",{});var E=a(k);$e=n(E,`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 `),D=r(E,"CODE",{});var Xe=a(D);Re=n(Xe,"model.fit()"),Xe.forEach(t),_e=n(E,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=r(E,"CODE",{});var Ae=a(S);Be=n(Ae,"model.fit()"),Ae.forEach(t),le=n(E,` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=r(E,"CODE",{});var We=a(Q);Fe=n(We,"fit()"),We.forEach(t),be=n(E," and "),V=r(E,"CODE",{});var De=a(V);Ee=n(De,"predict()"),De.forEach(t),ye=n(E,`, such as when creating your own layers or models with
the Keras `),L=r(E,"CODE",{});var Ve=a(L);me=n(Ve,"Functional"),Ve.forEach(t),Ce=n(E,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),E.forEach(t),oe=m(f),J=r(f,"UL",{});var q=a(J);U=r(q,"LI",{});var G=a(U);Te=n(G,"a single Tensor with "),X=r(G,"CODE",{});var Se=a(X);je=n(Se,"input_ids"),Se.forEach(t),ke=n(G," only and nothing else: "),A=r(G,"CODE",{});var Ze=a(A);ie=n(Ze,"model(input_ids)"),Ze.forEach(t),G.forEach(t),he=m(q),z=r(q,"LI",{});var H=a(z);Je=n(H,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),P=r(H,"CODE",{});var Ge=a(P);ze=n(Ge,"model([input_ids, attention_mask])"),Ge.forEach(t),ve=n(H," or "),Z=r(H,"CODE",{});var Pe=a(Z);ue=n(Pe,"model([input_ids, attention_mask, token_type_ids])"),Pe.forEach(t),H.forEach(t),de=m(q),W=r(q,"LI",{});var we=a(W);Ue=n(we,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),O=r(we,"CODE",{});var pe=a(O);ce=n(pe,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),pe.forEach(t),we.forEach(t),q.forEach(t),se=m(f),I=r(f,"P",{});var K=a(I);fe=n(K,`Note that when creating models and layers with
`),x=r(K,"A",{href:!0,rel:!0});var Oe=a(x);Ie=n(Oe,"subclassing"),Oe.forEach(t),xe=n(K,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),K.forEach(t),this.h()},h(){g(x,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(x,"rel","nofollow")},m(f,F){_(f,d,F),e(d,b),e(d,h),e(h,u),e(d,y),_(f,l,F),_(f,c,F),e(c,j),e(j,Me),e(c,ge),e(c,N),e(N,ae),_(f,te,F),_(f,k,F),e(k,$e),e(k,D),e(D,Re),e(k,_e),e(k,S),e(S,Be),e(k,le),e(k,Q),e(Q,Fe),e(k,be),e(k,V),e(V,Ee),e(k,ye),e(k,L),e(L,me),e(k,Ce),_(f,oe,F),_(f,J,F),e(J,U),e(U,Te),e(U,X),e(X,je),e(U,ke),e(U,A),e(A,ie),e(J,he),e(J,z),e(z,Je),e(z,P),e(P,ze),e(z,ve),e(z,Z),e(Z,ue),e(J,de),e(J,W),e(W,Ue),e(W,O),e(O,ce),_(f,se,F),_(f,I,F),e(I,fe),e(I,x),e(x,Ie),e(I,xe)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(te),f&&t(k),f&&t(oe),f&&t(J),f&&t(se),f&&t(I)}}}function bw(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function yw(C){let d,b,h,u,y;return u=new Ye({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, TFRemBertForSequenceClassification
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function Tw(C){let d,b;return d=new Ye({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 = TFRemBertForSequenceClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){v(d.$$.fragment)},l(h){w(d.$$.fragment,h)},m(h,u){M(d,h,u),b=!0},p:He,i(h){b||($(d.$$.fragment,h),b=!0)},o(h){R(d.$$.fragment,h),b=!1},d(h){B(d,h)}}}function kw(C){let d,b,h,u,y,l,c,j,Me,ge,N,ae,te,k,$e,D,Re,_e,S,Be,le,Q,Fe,be,V,Ee,ye,L,me,Ce,oe,J,U,Te,X,je,ke,A,ie,he,z,Je,P,ze,ve,Z,ue,de,W,Ue,O,ce,se,I,fe,x,Ie,xe;return{c(){d=s("p"),b=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),y=o(" accept two formats as input:"),l=p(),c=s("ul"),j=s("li"),Me=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),k=s("p"),$e=o(`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 `),D=s("code"),Re=o("model.fit()"),_e=o(` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=s("code"),Be=o("model.fit()"),le=o(` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=s("code"),Fe=o("fit()"),be=o(" and "),V=s("code"),Ee=o("predict()"),ye=o(`, such as when creating your own layers or models with
the Keras `),L=s("code"),me=o("Functional"),Ce=o(` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),oe=p(),J=s("ul"),U=s("li"),Te=o("a single Tensor with "),X=s("code"),je=o("input_ids"),ke=o(" only and nothing else: "),A=s("code"),ie=o("model(input_ids)"),he=p(),z=s("li"),Je=o(`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),P=s("code"),ze=o("model([input_ids, attention_mask])"),ve=o(" or "),Z=s("code"),ue=o("model([input_ids, attention_mask, token_type_ids])"),de=p(),W=s("li"),Ue=o(`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),O=s("code"),ce=o('model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),se=p(),I=s("p"),fe=o(`Note that when creating models and layers with
`),x=s("a"),Ie=o("subclassing"),xe=o(` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),this.h()},l(f){d=r(f,"P",{});var F=a(d);b=n(F,"TensorFlow models and layers in "),h=r(F,"CODE",{});var Le=a(h);u=n(Le,"transformers"),Le.forEach(t),y=n(F," accept two formats as input:"),F.forEach(t),l=m(f),c=r(f,"UL",{});var re=a(c);j=r(re,"LI",{});var Ne=a(j);Me=n(Ne,"having all inputs as keyword arguments (like PyTorch models), or"),Ne.forEach(t),ge=m(re),N=r(re,"LI",{});var qe=a(N);ae=n(qe,"having all inputs as a list, tuple or dict in the first positional argument."),qe.forEach(t),re.forEach(t),te=m(f),k=r(f,"P",{});var E=a(k);$e=n(E,`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 `),D=r(E,"CODE",{});var Xe=a(D);Re=n(Xe,"model.fit()"),Xe.forEach(t),_e=n(E,` things should \u201Cjust work\u201D for you - just
pass your inputs and labels in any format that `),S=r(E,"CODE",{});var Ae=a(S);Be=n(Ae,"model.fit()"),Ae.forEach(t),le=n(E,` supports! If, however, you want to use the second
format outside of Keras methods like `),Q=r(E,"CODE",{});var We=a(Q);Fe=n(We,"fit()"),We.forEach(t),be=n(E," and "),V=r(E,"CODE",{});var De=a(V);Ee=n(De,"predict()"),De.forEach(t),ye=n(E,`, such as when creating your own layers or models with
the Keras `),L=r(E,"CODE",{});var Ve=a(L);me=n(Ve,"Functional"),Ve.forEach(t),Ce=n(E,` API, there are three possibilities you can use to gather all the input Tensors in the first
positional argument:`),E.forEach(t),oe=m(f),J=r(f,"UL",{});var q=a(J);U=r(q,"LI",{});var G=a(U);Te=n(G,"a single Tensor with "),X=r(G,"CODE",{});var Se=a(X);je=n(Se,"input_ids"),Se.forEach(t),ke=n(G," only and nothing else: "),A=r(G,"CODE",{});var Ze=a(A);ie=n(Ze,"model(input_ids)"),Ze.forEach(t),G.forEach(t),he=m(q),z=r(q,"LI",{});var H=a(z);Je=n(H,`a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`),P=r(H,"CODE",{});var Ge=a(P);ze=n(Ge,"model([input_ids, attention_mask])"),Ge.forEach(t),ve=n(H," or "),Z=r(H,"CODE",{});var Pe=a(Z);ue=n(Pe,"model([input_ids, attention_mask, token_type_ids])"),Pe.forEach(t),H.forEach(t),de=m(q),W=r(q,"LI",{});var we=a(W);Ue=n(we,`a dictionary with one or several input Tensors associated to the input names given in the docstring:
`),O=r(we,"CODE",{});var pe=a(O);ce=n(pe,'model({"input_ids": input_ids, "token_type_ids": token_type_ids})'),pe.forEach(t),we.forEach(t),q.forEach(t),se=m(f),I=r(f,"P",{});var K=a(I);fe=n(K,`Note that when creating models and layers with
`),x=r(K,"A",{href:!0,rel:!0});var Oe=a(x);Ie=n(Oe,"subclassing"),Oe.forEach(t),xe=n(K,` then you don\u2019t need to worry
about any of this, as you can just pass inputs like you would to any other Python function!`),K.forEach(t),this.h()},h(){g(x,"href","https://keras.io/guides/making_new_layers_and_models_via_subclassing/"),g(x,"rel","nofollow")},m(f,F){_(f,d,F),e(d,b),e(d,h),e(h,u),e(d,y),_(f,l,F),_(f,c,F),e(c,j),e(j,Me),e(c,ge),e(c,N),e(N,ae),_(f,te,F),_(f,k,F),e(k,$e),e(k,D),e(D,Re),e(k,_e),e(k,S),e(S,Be),e(k,le),e(k,Q),e(Q,Fe),e(k,be),e(k,V),e(V,Ee),e(k,ye),e(k,L),e(L,me),e(k,Ce),_(f,oe,F),_(f,J,F),e(J,U),e(U,Te),e(U,X),e(X,je),e(U,ke),e(U,A),e(A,ie),e(J,he),e(J,z),e(z,Je),e(z,P),e(P,ze),e(z,ve),e(z,Z),e(Z,ue),e(J,de),e(J,W),e(W,Ue),e(W,O),e(O,ce),_(f,se,F),_(f,I,F),e(I,fe),e(I,x),e(x,Ie),e(I,xe)},d(f){f&&t(d),f&&t(l),f&&t(c),f&&t(te),f&&t(k),f&&t(oe),f&&t(J),f&&t(se),f&&t(I)}}}function vw(C){let d,b,h,u,y;return{c(){d=s("p"),b=o("Although the recipe for forward pass needs to be defined within this function, one should call the "),h=s("code"),u=o("Module"),y=o(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function ww(C){let d,b,h,u,y;return u=new Ye({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, TFRemBertForMultipleChoice
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForMultipleChoice.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function Mw(C){let d,b,h,u,y,l,c,j,Me,ge,N,ae,te,k,$e,D,Re,_e,S,Be,le,Q,Fe,be,V,Ee,ye,L,me,Ce,oe,J,U,Te,X,je,ke,A,ie,he,z,Je,P,ze,ve,Z,ue,de,W,Ue,O,ce,se,I,fe,x,Ie,xe;return{c(){d=s("p"),b=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),y=o(" accept two formats as input:"),l=p(),c=s("ul"),j=s("li"),Me=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),k=s("p"),$e=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function Rw(C){let d,b,h,u,y;return u=new Ye({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, TFRemBertForTokenClassification
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForTokenClassification.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function Bw(C){let d,b;return d=new Ye({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(){v(d.$$.fragment)},l(h){w(d.$$.fragment,h)},m(h,u){M(d,h,u),b=!0},p:He,i(h){b||($(d.$$.fragment,h),b=!0)},o(h){R(d.$$.fragment,h),b=!1},d(h){B(d,h)}}}function Fw(C){let d,b,h,u,y,l,c,j,Me,ge,N,ae,te,k,$e,D,Re,_e,S,Be,le,Q,Fe,be,V,Ee,ye,L,me,Ce,oe,J,U,Te,X,je,ke,A,ie,he,z,Je,P,ze,ve,Z,ue,de,W,Ue,O,ce,se,I,fe,x,Ie,xe;return{c(){d=s("p"),b=o("TensorFlow models and layers in "),h=s("code"),u=o("transformers"),y=o(" accept two formats as input:"),l=p(),c=s("ul"),j=s("li"),Me=o("having all inputs as keyword arguments (like PyTorch models), or"),ge=p(),N=s("li"),ae=o("having all inputs as a list, tuple or dict in the first positional argument."),te=p(),k=s("p"),$e=o(`The reason the second format is supported is that Keras methods prefer this format when passing inputs to models
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the latter silently ignores them.`)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Although the recipe for forward pass needs to be defined within this function, one should call the "),h=r(c,"CODE",{});var j=a(h);u=n(j,"Module"),j.forEach(t),y=n(c,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),c.forEach(t)},m(l,c){_(l,d,c),e(d,b),e(d,h),e(h,u),e(d,y)},d(l){l&&t(d)}}}function Cw(C){let d,b,h,u,y;return u=new Ye({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, TFRemBertForQuestionAnswering
<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;google/rembert&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = TFRemBertForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;google/rembert&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(){d=s("p"),b=o("Example:"),h=p(),v(u.$$.fragment)},l(l){d=r(l,"P",{});var c=a(d);b=n(c,"Example:"),c.forEach(t),h=m(l),w(u.$$.fragment,l)},m(l,c){_(l,d,c),e(d,b),_(l,h,c),M(u,l,c),y=!0},p:He,i(l){y||($(u.$$.fragment,l),y=!0)},o(l){R(u.$$.fragment,l),y=!1},d(l){l&&t(d),l&&t(h),B(u,l)}}}function jw(C){let d,b;return d=new Ye({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(){v(d.$$.fragment)},l(h){w(d.$$.fragment,h)},m(h,u){M(d,h,u),b=!0},p:He,i(h){b||($(d.$$.fragment,h),b=!0)},o(h){R(d.$$.fragment,h),b=!1},d(h){B(d,h)}}}function Jw(C){let d,b,h,u,y,l,c,j,Me,ge,N,ae,te,k,$e,D,Re,_e,S,Be,le,Q,Fe,be,V,Ee,ye,L,me,Ce,oe,J,U,Te,X,je,ke,A,ie,he,z,Je,P,ze,ve,Z,ue,de,W,Ue,O,ce,se,I,fe,x,Ie,xe,f,F,Le,re,Ne,qe,E,Xe,Ae,We,De,Ve,q,G,Se,Ze,H,Ge,Pe,we,pe,K,Oe,so,bp,pa,yp,Tp,ps,kp,vp,wp,ro,Mp,ma,$p,Rp,ha,Bp,Fp,Ep,Go,Vd,ao,Vo,ci,ms,Cp,pi,jp,Ld,tt,hs,Jp,us,zp,fs,Up,Ip,xp,gs,Zp,ua,qp,Wp,Np,Ot,_s,Ap,mi,Sp,Gp,bs,fa,Vp,hi,Lp,Xp,ga,Dp,ui,Pp,Op,Lo,ys,Qp,Ts,Hp,fi,Yp,Kp,em,Bt,ks,tm,gi,om,nm,Xo,sm,io,rm,_i,am,im,bi,lm,dm,cm,_a,vs,Xd,lo,Do,yi,ws,pm,Ti,mm,Dd,nt,Ms,hm,Lt,um,ki,fm,gm,$s,_m,bm,ba,ym,Tm,km,Qt,Rs,vm,vi,wm,Mm,Bs,ya,$m,wi,Rm,Bm,Ta,Fm,Mi,Em,Cm,Po,Fs,jm,Es,Jm,$i,zm,Um,Im,Ft,Cs,xm,Ri,Zm,qm,Oo,Wm,Bi,Nm,Am,ka,js,Pd,co,Qo,Fi,Js,Sm,Ei,Gm,Od,ht,zs,Vm,Us,Lm,Is,Xm,Dm,Pm,xs,Om,Zs,Qm,Hm,Ym,st,Km,Ci,eh,th,ji,oh,nh,Ji,sh,rh,zi,ah,ih,Ui,lh,dh,Ii,ch,ph,mh,Et,qs,hh,po,uh,va,fh,gh,xi,_h,bh,yh,Ho,Th,Yo,Qd,mo,Ko,Zi,Ws,kh,qi,vh,Hd,Xt,Ns,wh,ho,Mh,Wi,$h,Rh,As,Bh,Fh,Eh,Ct,Ss,Ch,uo,jh,wa,Jh,zh,Ni,Uh,Ih,xh,en,Zh,tn,Yd,fo,on,Ai,Gs,qh,Si,Wh,Kd,Dt,Vs,Nh,go,Ah,Gi,Sh,Gh,Ls,Vh,Lh,Xh,jt,Xs,Dh,_o,Ph,Ma,Oh,Qh,Vi,Hh,Yh,Kh,nn,eu,sn,ec,bo,rn,Li,Ds,tu,Xi,ou,tc,wt,Ps,nu,Di,su,ru,Os,au,Qs,iu,lu,du,ft,Hs,cu,yo,pu,$a,mu,hu,Pi,uu,fu,gu,an,_u,ln,bu,dn,oc,To,cn,Oi,Ys,yu,Qi,Tu,nc,Mt,Ks,ku,Hi,vu,wu,er,Mu,tr,$u,Ru,Bu,Jt,or,Fu,ko,Eu,Ra,Cu,ju,Yi,Ju,zu,Uu,pn,Iu,mn,sc,vo,hn,Ki,nr,xu,el,Zu,rc,$t,sr,qu,tl,Wu,Nu,rr,Au,ar,Su,Gu,Vu,zt,ir,Lu,wo,Xu,Ba,Du,Pu,ol,Ou,Qu,Hu,un,Yu,fn,ac,Mo,gn,nl,lr,Ku,sl,ef,ic,Rt,dr,tf,$o,of,rl,nf,sf,al,rf,af,lf,cr,df,pr,cf,pf,mf,Ut,mr,hf,Ro,uf,Fa,ff,gf,il,_f,bf,yf,_n,Tf,bn,lc,Bo,yn,ll,hr,kf,dl,vf,dc,rt,ur,wf,cl,Mf,$f,fr,Rf,Ea,Bf,Ff,Ef,gr,Cf,_r,jf,Jf,zf,Tn,Uf,It,br,If,Fo,xf,Ca,Zf,qf,pl,Wf,Nf,Af,kn,Sf,vn,cc,Eo,wn,ml,yr,Gf,hl,Vf,pc,at,Tr,Lf,kr,Xf,ul,Df,Pf,Of,vr,Qf,ja,Hf,Yf,Kf,wr,eg,Mr,tg,og,ng,Mn,sg,gt,$r,rg,Co,ag,Ja,ig,lg,fl,dg,cg,pg,$n,mg,Rn,hg,Bn,mc,jo,Fn,gl,Rr,ug,_l,fg,hc,it,Br,gg,Fr,_g,bl,bg,yg,Tg,Er,kg,za,vg,wg,Mg,Cr,$g,jr,Rg,Bg,Fg,En,Eg,_t,Jr,Cg,ot,jg,yl,Jg,zg,Tl,Ug,Ig,kl,xg,Zg,vl,qg,Wg,wl,Ng,Ag,Ml,Sg,Gg,$l,Vg,Lg,Xg,zr,Ur,Dg,Rl,Pg,Og,Qg,Ir,Hg,Bl,Yg,Kg,e_,Y,t_,Fl,o_,n_,El,s_,r_,Cl,a_,i_,jl,l_,d_,Jl,c_,p_,zl,m_,h_,Ul,u_,f_,Il,g_,__,xl,b_,y_,Zl,T_,k_,ql,v_,w_,Wl,M_,$_,Nl,R_,B_,Al,F_,E_,Sl,C_,j_,Gl,J_,z_,Vl,U_,I_,Ll,x_,Z_,Xl,q_,W_,Dl,N_,A_,S_,Cn,uc,Jo,jn,Pl,xr,G_,Ol,V_,fc,lt,Zr,L_,Ql,X_,D_,qr,P_,Ua,O_,Q_,H_,Wr,Y_,Nr,K_,eb,tb,Jn,ob,bt,Ar,nb,zo,sb,Ia,rb,ab,Hl,ib,lb,db,zn,cb,Un,pb,In,gc,Uo,xn,Yl,Sr,mb,Kl,hb,_c,dt,Gr,ub,ed,fb,gb,Vr,_b,xa,bb,yb,Tb,Lr,kb,Xr,vb,wb,Mb,Zn,$b,xt,Dr,Rb,Io,Bb,Za,Fb,Eb,td,Cb,jb,Jb,qn,zb,Wn,bc,xo,Nn,od,Pr,Ub,nd,Ib,yc,ct,Or,xb,sd,Zb,qb,Qr,Wb,qa,Nb,Ab,Sb,Hr,Gb,Yr,Vb,Lb,Xb,An,Db,yt,Kr,Pb,Zo,Ob,Wa,Qb,Hb,rd,Yb,Kb,ey,Sn,ty,Gn,oy,Vn,Tc,qo,Ln,ad,ea,ny,id,sy,kc,pt,ta,ry,Wo,ay,ld,iy,ly,dd,dy,cy,py,oa,my,Na,hy,uy,fy,na,gy,sa,_y,by,yy,Xn,Ty,Tt,ra,ky,No,vy,Aa,wy,My,cd,$y,Ry,By,Dn,Fy,Pn,Ey,On,vc;return l=new et({}),k=new et({}),z=new et({}),Ze=new et({}),K=new ne({props:{name:"class transformers.RemBertConfig",anchor:"transformers.RemBertConfig",parameters:[{name:"vocab_size",val:" = 250300"},{name:"hidden_size",val:" = 1152"},{name:"num_hidden_layers",val:" = 32"},{name:"num_attention_heads",val:" = 18"},{name:"input_embedding_size",val:" = 256"},{name:"output_embedding_size",val:" = 1664"},{name:"intermediate_size",val:" = 4608"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"classifier_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 512"},{name:"type_vocab_size",val:" = 2"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"use_cache",val:" = True"},{name:"pad_token_id",val:" = 0"},{name:"bos_token_id",val:" = 312"},{name:"eos_token_id",val:" = 313"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RemBertConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 250300) &#x2014;
Vocabulary size of the RemBERT model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> or <a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a>. Vocabulary size of the model.
Defines the different tokens that can be represented by the <em>inputs_ids</em> passed to the forward method of
<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a>.`,name:"vocab_size"},{anchor:"transformers.RemBertConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1152) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.RemBertConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.RemBertConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 18) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.RemBertConfig.input_embedding_size",description:`<strong>input_embedding_size</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
Dimensionality of the input embeddings.`,name:"input_embedding_size"},{anchor:"transformers.RemBertConfig.output_embedding_size",description:`<strong>output_embedding_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1664) &#x2014;
Dimensionality of the output embeddings.`,name:"output_embedding_size"},{anchor:"transformers.RemBertConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4608) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.RemBertConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</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;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.RemBertConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.RemBertConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.RemBertConfig.classifier_dropout_prob",description:`<strong>classifier_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the classifier layer when fine-tuning.`,name:"classifier_dropout_prob"},{anchor:"transformers.RemBertConfig.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.RemBertConfig.type_vocab_size",description:`<strong>type_vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The vocabulary size of the <code>token_type_ids</code> passed when calling <a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertModel">RemBertModel</a> or <a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.TFRemBertModel">TFRemBertModel</a>.`,name:"type_vocab_size"},{anchor:"transformers.RemBertConfig.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.RemBertConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.RemBertConfig.is_decoder",description:`<strong>is_decoder</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether the model is used as a decoder or not. If <code>False</code>, the model is used as an encoder.`,name:"is_decoder"},{anchor:"transformers.RemBertConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if <code>config.is_decoder=True</code>.`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/configuration_rembert.py#L32"}}),Go=new Qe({props:{anchor:"transformers.RemBertConfig.example",$$slots:{default:[Vv]},$$scope:{ctx:C}}}),ms=new et({}),hs=new ne({props:{name:"class transformers.RemBertTokenizer",anchor:"transformers.RemBertTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = False"},{name:"remove_space",val:" = True"},{name:"keep_accents",val:" = True"},{name:"bos_token",val:" = '[CLS]'"},{name:"eos_token",val:" = '[SEP]'"},{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:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RemBertTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.RemBertTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the <code>cls_token</code>.</p>

					</div>`,name:"bos_token"},{anchor:"transformers.RemBertTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The end of sequence token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the end of sequence.
The token used is the <code>sep_token</code>.</p>

					</div>`,name:"eos_token"},{anchor:"transformers.RemBertTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.RemBertTokenizer.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.RemBertTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.RemBertTokenizer.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.RemBertTokenizer.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.RemBertTokenizer.sp_model",description:`<strong>sp_model</strong> (<code>SentencePieceProcessor</code>) &#x2014;
The <em>SentencePiece</em> processor that is used for every conversion (string, tokens and IDs).`,name:"sp_model"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L43"}}),_s=new ne({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.RemBertTokenizer.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.RemBertTokenizer.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.RemBertTokenizer.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/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L172",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>
`}}),ys=new ne({props:{name:"get_special_tokens_mask",anchor:"transformers.RemBertTokenizer.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.RemBertTokenizer.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.RemBertTokenizer.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.RemBertTokenizer.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/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L197",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>
`}}),ks=new ne({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.RemBertTokenizer.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.RemBertTokenizer.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.RemBertTokenizer.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/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L228",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>
`}}),Xo=new Qe({props:{anchor:"transformers.RemBertTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[Lv]},$$scope:{ctx:C}}}),vs=new ne({props:{name:"save_vocabulary",anchor:"transformers.RemBertTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert.py#L258"}}),ws=new et({}),Ms=new ne({props:{name:"class transformers.RemBertTokenizerFast",anchor:"transformers.RemBertTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = True"},{name:"remove_space",val:" = True"},{name:"keep_accents",val:" = False"},{name:"bos_token",val:" = '[CLS]'"},{name:"eos_token",val:" = '[SEP]'"},{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:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.RemBertTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a <em>.spm</em> extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.RemBertTokenizerFast.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.RemBertTokenizerFast.remove_space",description:`<strong>remove_space</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to strip the text when tokenizing (removing excess spaces before and after the string).`,name:"remove_space"},{anchor:"transformers.RemBertTokenizerFast.keep_accents",description:`<strong>keep_accents</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to keep accents when tokenizing.`,name:"keep_accents"},{anchor:"transformers.RemBertTokenizerFast.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[CLS]&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.</p>
<div class="course-tip  bg-gradient-to-br dark:bg-gradient-to-r before:border-green-500 dark:before:border-green-800 from-green-50 dark:from-gray-900 to-white dark:to-gray-950 border border-green-50 text-green-700 dark:text-gray-400">
						
<p>When building a sequence using special tokens, this is not the token that is used for the beginning of
sequence. The token used is the <code>cls_token</code>.</p>

					</div>`,name:"bos_token"},{anchor:"transformers.RemBertTokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[SEP]&quot;</code>) &#x2014;
The end of sequence token. .. note:: When building a sequence using special tokens, this is not the token
that is used for the end of sequence. The token used is the <code>sep_token</code>.`,name:"eos_token"},{anchor:"transformers.RemBertTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.RemBertTokenizerFast.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.RemBertTokenizerFast.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding, for example when batching sequences of different lengths.`,name:"pad_token"},{anchor:"transformers.RemBertTokenizerFast.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.RemBertTokenizerFast.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;[MASK]&quot;</code>) &#x2014;
The token used for masking values. This is the token used when training this model with masked language
modeling. This is the token which the model will try to predict.`,name:"mask_token"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L51"}}),Rs=new ne({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.RemBertTokenizerFast.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.RemBertTokenizerFast.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.RemBertTokenizerFast.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L144",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>
`}}),Fs=new ne({props:{name:"get_special_tokens_mask",anchor:"transformers.RemBertTokenizerFast.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.RemBertTokenizerFast.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.RemBertTokenizerFast.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.RemBertTokenizerFast.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Set to True if the token list is already formatted with special tokens for the model`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L169",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>
`}}),Cs=new ne({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.RemBertTokenizerFast.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.RemBertTokenizerFast.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.RemBertTokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>None</code>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L200",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>
`}}),Oo=new Qe({props:{anchor:"transformers.RemBertTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[Xv]},$$scope:{ctx:C}}}),js=new ne({props:{name:"save_vocabulary",anchor:"transformers.RemBertTokenizerFast.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": typing.Optional[str] = None"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/tokenization_rembert_fast.py#L230"}}),Js=new et({}),zs=new ne({props:{name:"class transformers.RemBertModel",anchor:"transformers.RemBertModel",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"}],parametersDescription:[{anchor:"transformers.RemBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L745"}}),qs=new ne({props:{name:"forward",anchor:"transformers.RemBertModel.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"use_cache",val:": typing.Optional[bool] = 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.RemBertModel.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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__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.RemBertModel.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.RemBertModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RemBertModel.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.RemBertModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertModel.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.RemBertModel.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.RemBertModel.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/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RemBertModel.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.RemBertModel.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"encoder_attention_mask"},{anchor:"transformers.RemBertModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.RemBertModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L784",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions</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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>) and optionally if
<code>config.is_encoder_decoder=True</code> 2 additional tensors of shape <code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
input) to speed up sequential decoding.</p>
</li>
<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>) \u2014 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Ho=new Ke({props:{$$slots:{default:[Dv]},$$scope:{ctx:C}}}),Yo=new Qe({props:{anchor:"transformers.RemBertModel.forward.example",$$slots:{default:[Pv]},$$scope:{ctx:C}}}),Ws=new et({}),Ns=new ne({props:{name:"class transformers.RemBertForCausalLM",anchor:"transformers.RemBertForCausalLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1016"}}),Ss=new ne({props:{name:"forward",anchor:"transformers.RemBertForCausalLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.FloatTensor]]] = None"},{name:"labels",val:": typing.Optional[torch.LongTensor] = None"},{name:"use_cache",val:": typing.Optional[bool] = 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.RemBertForCausalLM.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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__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.RemBertForCausalLM.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.RemBertForCausalLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForCausalLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RemBertForCausalLM.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.RemBertForCausalLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForCausalLM.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.RemBertForCausalLM.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.RemBertForCausalLM.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/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RemBertForCausalLM.forward.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.RemBertForCausalLM.forward.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"encoder_attention_mask"},{anchor:"transformers.RemBertForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code> of length <code>config.n_layers</code> with each tuple having 4 tensors of shape <code>(batch_size, num_heads, sequence_length - 1, embed_size_per_head)</code>) &#x2014;
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.RemBertForCausalLM.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
<code>[-100, 0, ..., config.vocab_size]</code> (see <code>input_ids</code> docstring) Tokens with indices set to <code>-100</code> are
ignored (masked), the loss is only computed for the tokens with labels n <code>[0, ..., config.vocab_size]</code>.`,name:"labels"},{anchor:"transformers.RemBertForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1037",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 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>) \u2014 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),en=new Ke({props:{$$slots:{default:[Ov]},$$scope:{ctx:C}}}),tn=new Qe({props:{anchor:"transformers.RemBertForCausalLM.forward.example",$$slots:{default:[Qv]},$$scope:{ctx:C}}}),Gs=new et({}),Vs=new ne({props:{name:"class transformers.RemBertForMaskedLM",anchor:"transformers.RemBertForMaskedLM",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L914"}}),Xs=new ne({props:{name:"forward",anchor:"transformers.RemBertForMaskedLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.LongTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_hidden_states",val:": typing.Optional[torch.FloatTensor] = None"},{name:"encoder_attention_mask",val:": typing.Optional[torch.FloatTensor] = 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.RemBertForMaskedLM.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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__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.RemBertForMaskedLM.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.RemBertForMaskedLM.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForMaskedLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RemBertForMaskedLM.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.RemBertForMaskedLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForMaskedLM.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.RemBertForMaskedLM.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.RemBertForMaskedLM.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/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RemBertForMaskedLM.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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L936",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_outputs.MaskedLMOutput"
>transformers.modeling_outputs.MaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),nn=new Ke({props:{$$slots:{default:[Hv]},$$scope:{ctx:C}}}),sn=new Qe({props:{anchor:"transformers.RemBertForMaskedLM.forward.example",$$slots:{default:[Yv]},$$scope:{ctx:C}}}),Ds=new et({}),Ps=new ne({props:{name:"class transformers.RemBertForSequenceClassification",anchor:"transformers.RemBertForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1168"}}),Hs=new ne({props:{name:"forward",anchor:"transformers.RemBertForSequenceClassification.forward",parameters:[{name:"input_ids",val:": FloatTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = 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.RemBertForSequenceClassification.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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__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.RemBertForSequenceClassification.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.RemBertForSequenceClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForSequenceClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RemBertForSequenceClassification.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.RemBertForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForSequenceClassification.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.RemBertForSequenceClassification.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.RemBertForSequenceClassification.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/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RemBertForSequenceClassification.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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1179",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
>transformers.modeling_outputs.SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),an=new Ke({props:{$$slots:{default:[Kv]},$$scope:{ctx:C}}}),ln=new Qe({props:{anchor:"transformers.RemBertForSequenceClassification.forward.example",$$slots:{default:[ew]},$$scope:{ctx:C}}}),dn=new Qe({props:{anchor:"transformers.RemBertForSequenceClassification.forward.example-2",$$slots:{default:[tw]},$$scope:{ctx:C}}}),Ys=new et({}),Ks=new ne({props:{name:"class transformers.RemBertForMultipleChoice",anchor:"transformers.RemBertForMultipleChoice",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1264"}}),or=new ne({props:{name:"forward",anchor:"transformers.RemBertForMultipleChoice.forward",parameters:[{name:"input_ids",val:": FloatTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = 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.RemBertForMultipleChoice.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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__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.RemBertForMultipleChoice.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.RemBertForMultipleChoice.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForMultipleChoice.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RemBertForMultipleChoice.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.RemBertForMultipleChoice.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForMultipleChoice.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.RemBertForMultipleChoice.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.RemBertForMultipleChoice.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/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RemBertForMultipleChoice.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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1275",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_outputs.MultipleChoiceModelOutput"
>transformers.modeling_outputs.MultipleChoiceModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),pn=new Ke({props:{$$slots:{default:[ow]},$$scope:{ctx:C}}}),mn=new Qe({props:{anchor:"transformers.RemBertForMultipleChoice.forward.example",$$slots:{default:[nw]},$$scope:{ctx:C}}}),nr=new et({}),sr=new ne({props:{name:"class transformers.RemBertForTokenClassification",anchor:"transformers.RemBertForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1355"}}),ir=new ne({props:{name:"forward",anchor:"transformers.RemBertForTokenClassification.forward",parameters:[{name:"input_ids",val:": FloatTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = 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.RemBertForTokenClassification.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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__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.RemBertForTokenClassification.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.RemBertForTokenClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForTokenClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RemBertForTokenClassification.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.RemBertForTokenClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForTokenClassification.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.RemBertForTokenClassification.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.RemBertForTokenClassification.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/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RemBertForTokenClassification.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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1367",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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)  \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_outputs.TokenClassifierOutput"
>transformers.modeling_outputs.TokenClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),un=new Ke({props:{$$slots:{default:[sw]},$$scope:{ctx:C}}}),fn=new Qe({props:{anchor:"transformers.RemBertForTokenClassification.forward.example",$$slots:{default:[rw]},$$scope:{ctx:C}}}),lr=new et({}),dr=new ne({props:{name:"class transformers.RemBertForQuestionAnswering",anchor:"transformers.RemBertForQuestionAnswering",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.RemBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1433"}}),mr=new ne({props:{name:"forward",anchor:"transformers.RemBertForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": FloatTensor = None"},{name:"attention_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"token_type_ids",val:": typing.Optional[torch.LongTensor] = None"},{name:"position_ids",val:": typing.Optional[torch.FloatTensor] = None"},{name:"head_mask",val:": typing.Optional[torch.FloatTensor] = None"},{name:"inputs_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"start_positions",val:": typing.Optional[torch.LongTensor] = None"},{name:"end_positions",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.RemBertForQuestionAnswering.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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__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.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.RemBertForQuestionAnswering.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <em>input_ids</em> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.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/v4.30.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.RemBertForQuestionAnswering.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.RemBertForQuestionAnswering.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/v4.30.0/src/transformers/models/rembert/modeling_rembert.py#L1445",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_outputs.QuestionAnsweringModelOutput"
>transformers.modeling_outputs.QuestionAnsweringModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),_n=new Ke({props:{$$slots:{default:[aw]},$$scope:{ctx:C}}}),bn=new Qe({props:{anchor:"transformers.RemBertForQuestionAnswering.forward.example",$$slots:{default:[iw]},$$scope:{ctx:C}}}),hr=new et({}),ur=new ne({props:{name:"class transformers.TFRemBertModel",anchor:"transformers.TFRemBertModel",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L916"}}),Tn=new Ke({props:{$$slots:{default:[lw]},$$scope:{ctx:C}}}),br=new ne({props:{name:"call",anchor:"transformers.TFRemBertModel.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"token_type_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",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:"encoder_hidden_states",val:": np.ndarray | tf.Tensor | None = None"},{name:"encoder_attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"past_key_values",val:": Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None"},{name:"use_cache",val:": Optional[bool] = 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.TFRemBertModel.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRemBertModel.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertModel.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRemBertModel.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRemBertModel.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertModel.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertModel.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.TFRemBertModel.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.TFRemBertModel.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/v4.30.0/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.TFRemBertModel.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertModel.call.encoder_hidden_states",description:`<strong>encoder_hidden_states</strong>  (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.`,name:"encoder_hidden_states"},{anchor:"transformers.TFRemBertModel.call.encoder_attention_mask",description:`<strong>encoder_attention_mask</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>`,name:"encoder_attention_mask"},{anchor:"transformers.TFRemBertModel.call.past_key_values",description:`<strong>past_key_values</strong> (<code>Tuple[Tuple[tf.Tensor]]</code> of length <code>config.n_layers</code>) &#x2014;
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_input_ids</code> (those that
don&#x2019;t have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all
<code>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.TFRemBertModel.call.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>). Set to <code>False</code> during training, <code>True</code> during generation`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L922",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions</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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, hidden_size)</code>) \u2014 Last layer hidden-state of the first token of the sequence (classification token) further processed by a
Linear layer and a Tanh activation function. The Linear layer weights are trained from the next sentence
prediction (classification) objective during pretraining.</p>
<p>This output is usually <em>not</em> a good summary of the semantic content of the input, you\u2019re often better with
averaging or pooling the sequence of hidden-states for the whole input sequence.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<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>) \u2014 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>) \u2014 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>
<li>
<p><strong>cross_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>) \u2014 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 of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions"
>transformers.modeling_tf_outputs.TFBaseModelOutputWithPoolingAndCrossAttentions</a> or <code>tuple(tf.Tensor)</code></p>
`}}),kn=new Ke({props:{$$slots:{default:[dw]},$$scope:{ctx:C}}}),vn=new Qe({props:{anchor:"transformers.TFRemBertModel.call.example",$$slots:{default:[cw]},$$scope:{ctx:C}}}),yr=new et({}),Tr=new ne({props:{name:"class transformers.TFRemBertForMaskedLM",anchor:"transformers.TFRemBertForMaskedLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForMaskedLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L987"}}),Mn=new Ke({props:{$$slots:{default:[pw]},$$scope:{ctx:C}}}),$r=new ne({props:{name:"call",anchor:"transformers.TFRemBertForMaskedLM.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"token_type_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",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.TFRemBertForMaskedLM.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRemBertForMaskedLM.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForMaskedLM.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRemBertForMaskedLM.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRemBertForMaskedLM.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForMaskedLM.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForMaskedLM.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.TFRemBertForMaskedLM.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.TFRemBertForMaskedLM.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/v4.30.0/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.TFRemBertForMaskedLM.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertForMaskedLM.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1003",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFMaskedLMOutput"
>transformers.modeling_tf_outputs.TFMaskedLMOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),$n=new Ke({props:{$$slots:{default:[mw]},$$scope:{ctx:C}}}),Rn=new Qe({props:{anchor:"transformers.TFRemBertForMaskedLM.call.example",$$slots:{default:[hw]},$$scope:{ctx:C}}}),Bn=new Qe({props:{anchor:"transformers.TFRemBertForMaskedLM.call.example-2",$$slots:{default:[uw]},$$scope:{ctx:C}}}),Rr=new et({}),Br=new ne({props:{name:"class transformers.TFRemBertForCausalLM",anchor:"transformers.TFRemBertForCausalLM",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1061"}}),En=new Ke({props:{$$slots:{default:[fw]},$$scope:{ctx:C}}}),Jr=new ne({props:{name:"call",anchor:"transformers.TFRemBertForCausalLM.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"token_type_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",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:"encoder_hidden_states",val:": np.ndarray | tf.Tensor | None = None"},{name:"encoder_attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"past_key_values",val:": Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None"},{name:"use_cache",val:": Optional[bool] = 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"}],source:"https://github.com/huggingface/transformers/blob/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1087",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions"
>transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions</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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) \u2014 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>) \u2014 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>) \u2014 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>
<li>
<p><strong>cross_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>) \u2014 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 of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>List[tf.Tensor]</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) \u2014 List of <code>tf.Tensor</code> of length <code>config.n_layers</code>, with each tensor of shape <code>(2, batch_size, num_heads, sequence_length, embed_size_per_head)</code>).</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions"
>transformers.modeling_tf_outputs.TFCausalLMOutputWithCrossAttentions</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Cn=new Qe({props:{anchor:"transformers.TFRemBertForCausalLM.call.example",$$slots:{default:[gw]},$$scope:{ctx:C}}}),xr=new et({}),Zr=new ne({props:{name:"class transformers.TFRemBertForSequenceClassification",anchor:"transformers.TFRemBertForSequenceClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1180"}}),Jn=new Ke({props:{$$slots:{default:[_w]},$$scope:{ctx:C}}}),Ar=new ne({props:{name:"call",anchor:"transformers.TFRemBertForSequenceClassification.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"token_type_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",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.TFRemBertForSequenceClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRemBertForSequenceClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForSequenceClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRemBertForSequenceClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRemBertForSequenceClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForSequenceClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForSequenceClassification.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.TFRemBertForSequenceClassification.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.TFRemBertForSequenceClassification.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/v4.30.0/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.TFRemBertForSequenceClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertForSequenceClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1194",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFSequenceClassifierOutput"
>transformers.modeling_tf_outputs.TFSequenceClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),zn=new Ke({props:{$$slots:{default:[bw]},$$scope:{ctx:C}}}),Un=new Qe({props:{anchor:"transformers.TFRemBertForSequenceClassification.call.example",$$slots:{default:[yw]},$$scope:{ctx:C}}}),In=new Qe({props:{anchor:"transformers.TFRemBertForSequenceClassification.call.example-2",$$slots:{default:[Tw]},$$scope:{ctx:C}}}),Sr=new et({}),Gr=new ne({props:{name:"class transformers.TFRemBertForMultipleChoice",anchor:"transformers.TFRemBertForMultipleChoice",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForMultipleChoice.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1257"}}),Zn=new Ke({props:{$$slots:{default:[kw]},$$scope:{ctx:C}}}),Dr=new ne({props:{name:"call",anchor:"transformers.TFRemBertForMultipleChoice.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"token_type_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",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.TFRemBertForMultipleChoice.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRemBertForMultipleChoice.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForMultipleChoice.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRemBertForMultipleChoice.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, num_choices, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRemBertForMultipleChoice.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForMultipleChoice.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForMultipleChoice.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.TFRemBertForMultipleChoice.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.TFRemBertForMultipleChoice.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/v4.30.0/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.TFRemBertForMultipleChoice.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertForMultipleChoice.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1267",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, num_choices)</code>) \u2014 <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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput"
>transformers.modeling_tf_outputs.TFMultipleChoiceModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),qn=new Ke({props:{$$slots:{default:[vw]},$$scope:{ctx:C}}}),Wn=new Qe({props:{anchor:"transformers.TFRemBertForMultipleChoice.call.example",$$slots:{default:[ww]},$$scope:{ctx:C}}}),Pr=new et({}),Or=new ne({props:{name:"class transformers.TFRemBertForTokenClassification",anchor:"transformers.TFRemBertForTokenClassification",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1353"}}),An=new Ke({props:{$$slots:{default:[Mw]},$$scope:{ctx:C}}}),Kr=new ne({props:{name:"call",anchor:"transformers.TFRemBertForTokenClassification.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"token_type_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",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.TFRemBertForTokenClassification.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRemBertForTokenClassification.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForTokenClassification.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRemBertForTokenClassification.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRemBertForTokenClassification.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForTokenClassification.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForTokenClassification.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.TFRemBertForTokenClassification.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.TFRemBertForTokenClassification.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/v4.30.0/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.TFRemBertForTokenClassification.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertForTokenClassification.call.labels",description:`<strong>labels</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1365",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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)  \u2014 Classification loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>tf.Tensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>) \u2014 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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFTokenClassifierOutput"
>transformers.modeling_tf_outputs.TFTokenClassifierOutput</a> or <code>tuple(tf.Tensor)</code></p>
`}}),Sn=new Ke({props:{$$slots:{default:[$w]},$$scope:{ctx:C}}}),Gn=new Qe({props:{anchor:"transformers.TFRemBertForTokenClassification.call.example",$$slots:{default:[Rw]},$$scope:{ctx:C}}}),Vn=new Qe({props:{anchor:"transformers.TFRemBertForTokenClassification.call.example-2",$$slots:{default:[Bw]},$$scope:{ctx:C}}}),ea=new et({}),ta=new ne({props:{name:"class transformers.TFRemBertForQuestionAnswering",anchor:"transformers.TFRemBertForQuestionAnswering",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.TFRemBertForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig">RemBertConfig</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/v4.30.0/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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1426"}}),Xn=new Ke({props:{$$slots:{default:[Fw]},$$scope:{ctx:C}}}),ra=new ne({props:{name:"call",anchor:"transformers.TFRemBertForQuestionAnswering.call",parameters:[{name:"input_ids",val:": TFModelInputType | None = None"},{name:"attention_mask",val:": np.ndarray | tf.Tensor | None = None"},{name:"token_type_ids",val:": np.ndarray | tf.Tensor | None = None"},{name:"position_ids",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.TFRemBertForQuestionAnswering.call.input_ids",description:`<strong>input_ids</strong> (<code>np.ndarray</code>, <code>tf.Tensor</code>, <code>List[tf.Tensor]</code> \`<code>Dict[str, tf.Tensor]</code> or <code>Dict[str, np.ndarray]</code> and each example must have the 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/v4.30.0/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.30.0/en/model_doc/speecht5#transformers.SpeechT5Tokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/v4.30.0/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.TFRemBertForQuestionAnswering.call.attention_mask",description:`<strong>attention_mask</strong> (<code>np.ndarray</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.TFRemBertForQuestionAnswering.call.token_type_ids",description:`<strong>token_type_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
<ul>
<li>0 corresponds to a <em>sentence A</em> token,</li>
<li>1 corresponds to a <em>sentence B</em> token.</li>
</ul>
<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.TFRemBertForQuestionAnswering.call.position_ids",description:`<strong>position_ids</strong> (<code>np.ndarray</code> or <code>tf.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.max_position_embeddings - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.TFRemBertForQuestionAnswering.call.head_mask",description:`<strong>head_mask</strong> (<code>np.ndarray</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.TFRemBertForQuestionAnswering.call.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>np.ndarray</code> or <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.TFRemBertForQuestionAnswering.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.TFRemBertForQuestionAnswering.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.TFRemBertForQuestionAnswering.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/v4.30.0/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.TFRemBertForQuestionAnswering.call.training",description:`<strong>training</strong> (<code>bool</code>, <em>optional</em>, defaults to \`False&#x201C;) &#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.TFRemBertForQuestionAnswering.call.start_positions",description:`<strong>start_positions</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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.TFRemBertForQuestionAnswering.call.end_positions",description:`<strong>end_positions</strong> (<code>tf.Tensor</code> or <code>np.ndarray</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/v4.30.0/src/transformers/models/rembert/modeling_tf_rembert.py#L1437",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.30.0/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/v4.30.0/en/model_doc/rembert#transformers.RemBertConfig"
>RemBertConfig</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) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 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>) \u2014 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/v4.30.0/en/main_classes/output#transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput"
>transformers.modeling_tf_outputs.TFQuestionAnsweringModelOutput</a> or <code>tuple(tf.Tensor)</code></p>
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standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that
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standard natural language understanding tasks with the same number of parameters during fine-tuning. We also show that
allocating additional capacity to the output embedding provides benefits to the model that persist through the
fine-tuning stage even though the output embedding is discarded after pre-training. Our analysis shows that larger
output embeddings prevent the model\u2019s last layers from overspecializing to the pre-training task and encourage
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cross-attention is added between the self-attention layers, following the architecture described in `),Zs=r(Ic,"A",{href:!0,rel:!0});var pT=a(Zs);Qm=n(pT,`Attention is
all you need`),pT.forEach(t),Hm=n(Ic,` by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.`),Ic.forEach(t),Ym=m(Ht),st=r(Ht,"P",{});var vt=a(st);Km=n(vt,"To behave as an decoder the model needs to be initialized with the "),Ci=r(vt,"CODE",{});var mT=a(Ci);eh=n(mT,"is_decoder"),mT.forEach(t),th=n(vt,` argument of the configuration set
to `),ji=r(vt,"CODE",{});var hT=a(ji);oh=n(hT,"True"),hT.forEach(t),nh=n(vt,". To be used in a Seq2Seq model, the model needs to initialized with both "),Ji=r(vt,"CODE",{});var uT=a(Ji);sh=n(uT,"is_decoder"),uT.forEach(t),rh=n(vt,` argument and
`),zi=r(vt,"CODE",{});var fT=a(zi);ah=n(fT,"add_cross_attention"),fT.forEach(t),ih=n(vt," set to "),Ui=r(vt,"CODE",{});var gT=a(Ui);lh=n(gT,"True"),gT.forEach(t),dh=n(vt,"; an "),Ii=r(vt,"CODE",{});var _T=a(Ii);ch=n(_T,"encoder_hidden_states"),_T.forEach(t),ph=n(vt," is then expected as an input to the forward pass."),vt.forEach(t),mh=m(Ht),Et=r(Ht,"DIV",{class:!0});var Kn=a(Et);w(qs.$$.fragment,Kn),hh=m(Kn),po=r(Kn,"P",{});var La=a(po);uh=n(La,"The "),va=r(La,"A",{href:!0});var bT=a(va);fh=n(bT,"RemBertModel"),bT.forEach(t),gh=n(La," forward method, overrides the "),xi=r(La,"CODE",{});var yT=a(xi);_h=n(yT,"__call__"),yT.forEach(t),bh=n(La," special method."),La.forEach(t),yh=m(Kn),w(Ho.$$.fragment,Kn),Th=m(Kn),w(Yo.$$.fragment,Kn),Kn.forEach(t),Ht.forEach(t),Qd=m(i),mo=r(i,"H2",{class:!0});var xc=a(mo);Ko=r(xc,"A",{id:!0,class:!0,href:!0});var TT=a(Ko);Zi=r(TT,"SPAN",{});var kT=a(Zi);w(Ws.$$.fragment,kT),kT.forEach(t),TT.forEach(t),kh=m(xc),qi=r(xc,"SPAN",{});var vT=a(qi);vh=n(vT,"RemBertForCausalLM"),vT.forEach(t),xc.forEach(t),Hd=m(i),Xt=r(i,"DIV",{class:!0});var Xa=a(Xt);w(Ns.$$.fragment,Xa),wh=m(Xa),ho=r(Xa,"P",{});var Da=a(ho);Mh=n(Da,"RemBERT Model with a "),Wi=r(Da,"CODE",{});var wT=a(Wi);$h=n(wT,"language modeling"),wT.forEach(t),Rh=n(Da,` head on top for CLM fine-tuning.
This model is a PyTorch `),As=r(Da,"A",{href:!0,rel:!0});var MT=a(As);Bh=n(MT,"torch.nn.Module"),MT.forEach(t),Fh=n(Da,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Da.forEach(t),Eh=m(Xa),Ct=r(Xa,"DIV",{class:!0});var es=a(Ct);w(Ss.$$.fragment,es),Ch=m(es),uo=r(es,"P",{});var Pa=a(uo);jh=n(Pa,"The "),wa=r(Pa,"A",{href:!0});var $T=a(wa);Jh=n($T,"RemBertForCausalLM"),$T.forEach(t),zh=n(Pa," forward method, overrides the "),Ni=r(Pa,"CODE",{});var RT=a(Ni);Uh=n(RT,"__call__"),RT.forEach(t),Ih=n(Pa," special method."),Pa.forEach(t),xh=m(es),w(en.$$.fragment,es),Zh=m(es),w(tn.$$.fragment,es),es.forEach(t),Xa.forEach(t),Yd=m(i),fo=r(i,"H2",{class:!0});var Zc=a(fo);on=r(Zc,"A",{id:!0,class:!0,href:!0});var BT=a(on);Ai=r(BT,"SPAN",{});var FT=a(Ai);w(Gs.$$.fragment,FT),FT.forEach(t),BT.forEach(t),qh=m(Zc),Si=r(Zc,"SPAN",{});var ET=a(Si);Wh=n(ET,"RemBertForMaskedLM"),ET.forEach(t),Zc.forEach(t),Kd=m(i),Dt=r(i,"DIV",{class:!0});var Oa=a(Dt);w(Vs.$$.fragment,Oa),Nh=m(Oa),go=r(Oa,"P",{});var Qa=a(go);Ah=n(Qa,"RemBERT Model with a "),Gi=r(Qa,"CODE",{});var CT=a(Gi);Sh=n(CT,"language modeling"),CT.forEach(t),Gh=n(Qa,` head on top.
This model is a PyTorch `),Ls=r(Qa,"A",{href:!0,rel:!0});var jT=a(Ls);Vh=n(jT,"torch.nn.Module"),jT.forEach(t),Lh=n(Qa,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Qa.forEach(t),Xh=m(Oa),jt=r(Oa,"DIV",{class:!0});var ts=a(jt);w(Xs.$$.fragment,ts),Dh=m(ts),_o=r(ts,"P",{});var Ha=a(_o);Ph=n(Ha,"The "),Ma=r(Ha,"A",{href:!0});var JT=a(Ma);Oh=n(JT,"RemBertForMaskedLM"),JT.forEach(t),Qh=n(Ha," forward method, overrides the "),Vi=r(Ha,"CODE",{});var zT=a(Vi);Hh=n(zT,"__call__"),zT.forEach(t),Yh=n(Ha," special method."),Ha.forEach(t),Kh=m(ts),w(nn.$$.fragment,ts),eu=m(ts),w(sn.$$.fragment,ts),ts.forEach(t),Oa.forEach(t),ec=m(i),bo=r(i,"H2",{class:!0});var qc=a(bo);rn=r(qc,"A",{id:!0,class:!0,href:!0});var UT=a(rn);Li=r(UT,"SPAN",{});var IT=a(Li);w(Ds.$$.fragment,IT),IT.forEach(t),UT.forEach(t),tu=m(qc),Xi=r(qc,"SPAN",{});var xT=a(Xi);ou=n(xT,"RemBertForSequenceClassification"),xT.forEach(t),qc.forEach(t),tc=m(i),wt=r(i,"DIV",{class:!0});var os=a(wt);w(Ps.$$.fragment,os),nu=m(os),Di=r(os,"P",{});var ZT=a(Di);su=n(ZT,`RemBERT Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.`),ZT.forEach(t),ru=m(os),Os=r(os,"P",{});var Wc=a(Os);au=n(Wc,"This model is a PyTorch "),Qs=r(Wc,"A",{href:!0,rel:!0});var qT=a(Qs);iu=n(qT,"torch.nn.Module"),qT.forEach(t),lu=n(Wc,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Wc.forEach(t),du=m(os),ft=r(os,"DIV",{class:!0});var Yt=a(ft);w(Hs.$$.fragment,Yt),cu=m(Yt),yo=r(Yt,"P",{});var Ya=a(yo);pu=n(Ya,"The "),$a=r(Ya,"A",{href:!0});var WT=a($a);mu=n(WT,"RemBertForSequenceClassification"),WT.forEach(t),hu=n(Ya," forward method, overrides the "),Pi=r(Ya,"CODE",{});var NT=a(Pi);uu=n(NT,"__call__"),NT.forEach(t),fu=n(Ya," special method."),Ya.forEach(t),gu=m(Yt),w(an.$$.fragment,Yt),_u=m(Yt),w(ln.$$.fragment,Yt),bu=m(Yt),w(dn.$$.fragment,Yt),Yt.forEach(t),os.forEach(t),oc=m(i),To=r(i,"H2",{class:!0});var Nc=a(To);cn=r(Nc,"A",{id:!0,class:!0,href:!0});var AT=a(cn);Oi=r(AT,"SPAN",{});var ST=a(Oi);w(Ys.$$.fragment,ST),ST.forEach(t),AT.forEach(t),yu=m(Nc),Qi=r(Nc,"SPAN",{});var GT=a(Qi);Tu=n(GT,"RemBertForMultipleChoice"),GT.forEach(t),Nc.forEach(t),nc=m(i),Mt=r(i,"DIV",{class:!0});var ns=a(Mt);w(Ks.$$.fragment,ns),ku=m(ns),Hi=r(ns,"P",{});var VT=a(Hi);vu=n(VT,`RemBERT Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
softmax) e.g. for RocStories/SWAG tasks.`),VT.forEach(t),wu=m(ns),er=r(ns,"P",{});var Ac=a(er);Mu=n(Ac,"This model is a PyTorch "),tr=r(Ac,"A",{href:!0,rel:!0});var LT=a(tr);$u=n(LT,"torch.nn.Module"),LT.forEach(t),Ru=n(Ac,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Ac.forEach(t),Bu=m(ns),Jt=r(ns,"DIV",{class:!0});var ss=a(Jt);w(or.$$.fragment,ss),Fu=m(ss),ko=r(ss,"P",{});var Ka=a(ko);Eu=n(Ka,"The "),Ra=r(Ka,"A",{href:!0});var XT=a(Ra);Cu=n(XT,"RemBertForMultipleChoice"),XT.forEach(t),ju=n(Ka," forward method, overrides the "),Yi=r(Ka,"CODE",{});var DT=a(Yi);Ju=n(DT,"__call__"),DT.forEach(t),zu=n(Ka," special method."),Ka.forEach(t),Uu=m(ss),w(pn.$$.fragment,ss),Iu=m(ss),w(mn.$$.fragment,ss),ss.forEach(t),ns.forEach(t),sc=m(i),vo=r(i,"H2",{class:!0});var Sc=a(vo);hn=r(Sc,"A",{id:!0,class:!0,href:!0});var PT=a(hn);Ki=r(PT,"SPAN",{});var OT=a(Ki);w(nr.$$.fragment,OT),OT.forEach(t),PT.forEach(t),xu=m(Sc),el=r(Sc,"SPAN",{});var QT=a(el);Zu=n(QT,"RemBertForTokenClassification"),QT.forEach(t),Sc.forEach(t),rc=m(i),$t=r(i,"DIV",{class:!0});var rs=a($t);w(sr.$$.fragment,rs),qu=m(rs),tl=r(rs,"P",{});var HT=a(tl);Wu=n(HT,`RemBERT 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.`),HT.forEach(t),Nu=m(rs),rr=r(rs,"P",{});var Gc=a(rr);Au=n(Gc,"This model is a PyTorch "),ar=r(Gc,"A",{href:!0,rel:!0});var YT=a(ar);Su=n(YT,"torch.nn.Module"),YT.forEach(t),Gu=n(Gc,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Gc.forEach(t),Vu=m(rs),zt=r(rs,"DIV",{class:!0});var as=a(zt);w(ir.$$.fragment,as),Lu=m(as),wo=r(as,"P",{});var ei=a(wo);Xu=n(ei,"The "),Ba=r(ei,"A",{href:!0});var KT=a(Ba);Du=n(KT,"RemBertForTokenClassification"),KT.forEach(t),Pu=n(ei," forward method, overrides the "),ol=r(ei,"CODE",{});var ek=a(ol);Ou=n(ek,"__call__"),ek.forEach(t),Qu=n(ei," special method."),ei.forEach(t),Hu=m(as),w(un.$$.fragment,as),Yu=m(as),w(fn.$$.fragment,as),as.forEach(t),rs.forEach(t),ac=m(i),Mo=r(i,"H2",{class:!0});var Vc=a(Mo);gn=r(Vc,"A",{id:!0,class:!0,href:!0});var tk=a(gn);nl=r(tk,"SPAN",{});var ok=a(nl);w(lr.$$.fragment,ok),ok.forEach(t),tk.forEach(t),Ku=m(Vc),sl=r(Vc,"SPAN",{});var nk=a(sl);ef=n(nk,"RemBertForQuestionAnswering"),nk.forEach(t),Vc.forEach(t),ic=m(i),Rt=r(i,"DIV",{class:!0});var is=a(Rt);w(dr.$$.fragment,is),tf=m(is),$o=r(is,"P",{});var ti=a($o);of=n(ti,`RemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `),rl=r(ti,"CODE",{});var sk=a(rl);nf=n(sk,"span start logits"),sk.forEach(t),sf=n(ti," and "),al=r(ti,"CODE",{});var rk=a(al);rf=n(rk,"span end logits"),rk.forEach(t),af=n(ti,")."),ti.forEach(t),lf=m(is),cr=r(is,"P",{});var Lc=a(cr);df=n(Lc,"This model is a PyTorch "),pr=r(Lc,"A",{href:!0,rel:!0});var ak=a(pr);cf=n(ak,"torch.nn.Module"),ak.forEach(t),pf=n(Lc,` sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`),Lc.forEach(t),mf=m(is),Ut=r(is,"DIV",{class:!0});var ls=a(Ut);w(mr.$$.fragment,ls),hf=m(ls),Ro=r(ls,"P",{});var oi=a(Ro);uf=n(oi,"The "),Fa=r(oi,"A",{href:!0});var ik=a(Fa);ff=n(ik,"RemBertForQuestionAnswering"),ik.forEach(t),gf=n(oi," forward method, overrides the "),il=r(oi,"CODE",{});var lk=a(il);_f=n(lk,"__call__"),lk.forEach(t),bf=n(oi," special method."),oi.forEach(t),yf=m(ls),w(_n.$$.fragment,ls),Tf=m(ls),w(bn.$$.fragment,ls),ls.forEach(t),is.forEach(t),lc=m(i),Bo=r(i,"H2",{class:!0});var Xc=a(Bo);yn=r(Xc,"A",{id:!0,class:!0,href:!0});var dk=a(yn);ll=r(dk,"SPAN",{});var ck=a(ll);w(hr.$$.fragment,ck),ck.forEach(t),dk.forEach(t),kf=m(Xc),dl=r(Xc,"SPAN",{});var pk=a(dl);vf=n(pk,"TFRemBertModel"),pk.forEach(t),Xc.forEach(t),dc=m(i),rt=r(i,"DIV",{class:!0});var qt=a(rt);w(ur.$$.fragment,qt),wf=m(qt),cl=r(qt,"P",{});var mk=a(cl);Mf=n(mk,"The bare RemBERT Model transformer outputing raw hidden-states without any specific head on top."),mk.forEach(t),$f=m(qt),fr=r(qt,"P",{});var Dc=a(fr);Rf=n(Dc,"This model inherits from "),Ea=r(Dc,"A",{href:!0});var hk=a(Ea);Bf=n(hk,"TFPreTrainedModel"),hk.forEach(t),Ff=n(Dc,`. 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.)`),Dc.forEach(t),Ef=m(qt),gr=r(qt,"P",{});var Pc=a(gr);Cf=n(Pc,"This model is also a "),_r=r(Pc,"A",{href:!0,rel:!0});var uk=a(_r);jf=n(uk,"tf.keras.Model"),uk.forEach(t),Jf=n(Pc,` 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.`),Pc.forEach(t),zf=m(qt),w(Tn.$$.fragment,qt),Uf=m(qt),It=r(qt,"DIV",{class:!0});var ds=a(It);w(br.$$.fragment,ds),If=m(ds),Fo=r(ds,"P",{});var ni=a(Fo);xf=n(ni,"The "),Ca=r(ni,"A",{href:!0});var fk=a(Ca);Zf=n(fk,"TFRemBertModel"),fk.forEach(t),qf=n(ni," forward method, overrides the "),pl=r(ni,"CODE",{});var gk=a(pl);Wf=n(gk,"__call__"),gk.forEach(t),Nf=n(ni," special method."),ni.forEach(t),Af=m(ds),w(kn.$$.fragment,ds),Sf=m(ds),w(vn.$$.fragment,ds),ds.forEach(t),qt.forEach(t),cc=m(i),Eo=r(i,"H2",{class:!0});var Oc=a(Eo);wn=r(Oc,"A",{id:!0,class:!0,href:!0});var _k=a(wn);ml=r(_k,"SPAN",{});var bk=a(ml);w(yr.$$.fragment,bk),bk.forEach(t),_k.forEach(t),Gf=m(Oc),hl=r(Oc,"SPAN",{});var yk=a(hl);Vf=n(yk,"TFRemBertForMaskedLM"),yk.forEach(t),Oc.forEach(t),pc=m(i),at=r(i,"DIV",{class:!0});var Wt=a(at);w(Tr.$$.fragment,Wt),Lf=m(Wt),kr=r(Wt,"P",{});var Qc=a(kr);Xf=n(Qc,"RemBERT Model with a "),ul=r(Qc,"CODE",{});var Tk=a(ul);Df=n(Tk,"language modeling"),Tk.forEach(t),Pf=n(Qc," head on top."),Qc.forEach(t),Of=m(Wt),vr=r(Wt,"P",{});var Hc=a(vr);Qf=n(Hc,"This model inherits from "),ja=r(Hc,"A",{href:!0});var kk=a(ja);Hf=n(kk,"TFPreTrainedModel"),kk.forEach(t),Yf=n(Hc,`. 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.)`),Hc.forEach(t),Kf=m(Wt),wr=r(Wt,"P",{});var Yc=a(wr);eg=n(Yc,"This model is also a "),Mr=r(Yc,"A",{href:!0,rel:!0});var vk=a(Mr);tg=n(vk,"tf.keras.Model"),vk.forEach(t),og=n(Yc,` 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.`),Yc.forEach(t),ng=m(Wt),w(Mn.$$.fragment,Wt),sg=m(Wt),gt=r(Wt,"DIV",{class:!0});var Kt=a(gt);w($r.$$.fragment,Kt),rg=m(Kt),Co=r(Kt,"P",{});var si=a(Co);ag=n(si,"The "),Ja=r(si,"A",{href:!0});var wk=a(Ja);ig=n(wk,"TFRemBertForMaskedLM"),wk.forEach(t),lg=n(si," forward method, overrides the "),fl=r(si,"CODE",{});var Mk=a(fl);dg=n(Mk,"__call__"),Mk.forEach(t),cg=n(si," special method."),si.forEach(t),pg=m(Kt),w($n.$$.fragment,Kt),mg=m(Kt),w(Rn.$$.fragment,Kt),hg=m(Kt),w(Bn.$$.fragment,Kt),Kt.forEach(t),Wt.forEach(t),mc=m(i),jo=r(i,"H2",{class:!0});var Kc=a(jo);Fn=r(Kc,"A",{id:!0,class:!0,href:!0});var $k=a(Fn);gl=r($k,"SPAN",{});var Rk=a(gl);w(Rr.$$.fragment,Rk),Rk.forEach(t),$k.forEach(t),ug=m(Kc),_l=r(Kc,"SPAN",{});var Bk=a(_l);fg=n(Bk,"TFRemBertForCausalLM"),Bk.forEach(t),Kc.forEach(t),hc=m(i),it=r(i,"DIV",{class:!0});var Nt=a(it);w(Br.$$.fragment,Nt),gg=m(Nt),Fr=r(Nt,"P",{});var ep=a(Fr);_g=n(ep,"RemBERT Model with a "),bl=r(ep,"CODE",{});var Fk=a(bl);bg=n(Fk,"language modeling"),Fk.forEach(t),yg=n(ep," head on top for CLM fine-tuning."),ep.forEach(t),Tg=m(Nt),Er=r(Nt,"P",{});var tp=a(Er);kg=n(tp,"This model inherits from "),za=r(tp,"A",{href:!0});var Ek=a(za);vg=n(Ek,"TFPreTrainedModel"),Ek.forEach(t),wg=n(tp,`. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`),tp.forEach(t),Mg=m(Nt),Cr=r(Nt,"P",{});var op=a(Cr);$g=n(op,"This model is also a "),jr=r(op,"A",{href:!0,rel:!0});var Ck=a(jr);Rg=n(Ck,"tf.keras.Model"),Ck.forEach(t),Bg=n(op,` subclass. Use it
as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
behavior.`),op.forEach(t),Fg=m(Nt),w(En.$$.fragment,Nt),Eg=m(Nt),_t=r(Nt,"DIV",{class:!0});var eo=a(_t);w(Jr.$$.fragment,eo),Cg=m(eo),ot=r(eo,"P",{});var mt=a(ot);jg=n(mt,"encoder_hidden_states  ("),yl=r(mt,"CODE",{});var jk=a(yl);Jg=n(jk,"tf.Tensor"),jk.forEach(t),zg=n(mt," of shape "),Tl=r(mt,"CODE",{});var Jk=a(Tl);Ug=n(Jk,"(batch_size, sequence_length, hidden_size)"),Jk.forEach(t),Ig=n(mt,", "),kl=r(mt,"EM",{});var zk=a(kl);xg=n(zk,"optional"),zk.forEach(t),Zg=n(mt,`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`),vl=r(mt,"CODE",{});var Uk=a(vl);qg=n(Uk,"tf.Tensor"),Uk.forEach(t),Wg=n(mt," of shape "),wl=r(mt,"CODE",{});var Ik=a(wl);Ng=n(Ik,"(batch_size, sequence_length)"),Ik.forEach(t),Ag=n(mt,", "),Ml=r(mt,"EM",{});var xk=a(Ml);Sg=n(xk,"optional"),xk.forEach(t),Gg=n(mt,`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
the cross-attention if the model is configured as a decoder. Mask values selected in `),$l=r(mt,"CODE",{});var Zk=a($l);Vg=n(Zk,"[0, 1]"),Zk.forEach(t),Lg=n(mt,":"),mt.forEach(t),Xg=m(eo),zr=r(eo,"UL",{});var np=a(zr);Ur=r(np,"LI",{});var sp=a(Ur);Dg=n(sp,"1 for tokens that are "),Rl=r(sp,"STRONG",{});var qk=a(Rl);Pg=n(qk,"not masked"),qk.forEach(t),Og=n(sp,","),sp.forEach(t),Qg=m(np),Ir=r(np,"LI",{});var rp=a(Ir);Hg=n(rp,"0 for tokens that are "),Bl=r(rp,"STRONG",{});var Wk=a(Bl);Yg=n(Wk,"masked"),Wk.forEach(t),Kg=n(rp,"."),rp.forEach(t),np.forEach(t),e_=m(eo),Y=r(eo,"P",{});var ee=a(Y);t_=n(ee,"past_key_values ("),Fl=r(ee,"CODE",{});var Nk=a(Fl);o_=n(Nk,"Tuple[Tuple[tf.Tensor]]"),Nk.forEach(t),n_=n(ee," of length "),El=r(ee,"CODE",{});var Ak=a(El);s_=n(Ak,"config.n_layers"),Ak.forEach(t),r_=n(ee,`)
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
If `),Cl=r(ee,"CODE",{});var Sk=a(Cl);a_=n(Sk,"past_key_values"),Sk.forEach(t),i_=n(ee," are used, the user can optionally input only the last "),jl=r(ee,"CODE",{});var Gk=a(jl);l_=n(Gk,"decoder_input_ids"),Gk.forEach(t),d_=n(ee,` (those that
don\u2019t have their past key value states given to this model) of shape `),Jl=r(ee,"CODE",{});var Vk=a(Jl);c_=n(Vk,"(batch_size, 1)"),Vk.forEach(t),p_=n(ee,` instead of all
`),zl=r(ee,"CODE",{});var Lk=a(zl);m_=n(Lk,"decoder_input_ids"),Lk.forEach(t),h_=n(ee," of shape "),Ul=r(ee,"CODE",{});var Xk=a(Ul);u_=n(Xk,"(batch_size, sequence_length)"),Xk.forEach(t),f_=n(ee,`.
use_cache (`),Il=r(ee,"CODE",{});var Dk=a(Il);g_=n(Dk,"bool"),Dk.forEach(t),__=n(ee,", "),xl=r(ee,"EM",{});var Pk=a(xl);b_=n(Pk,"optional"),Pk.forEach(t),y_=n(ee,", defaults to "),Zl=r(ee,"CODE",{});var Ok=a(Zl);T_=n(Ok,"True"),Ok.forEach(t),k_=n(ee,`):
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