import{s as At,o as Yt,n as Ce}from"../chunks/scheduler.9bc65507.js";import{S as Ot,i as Kt,g as d,s as o,r as u,A as en,h as c,f as n,c as s,j as R,u as f,x as h,k as q,y as l,a as i,v as g,d as _,t as b,w as y}from"../chunks/index.707bf1b6.js";import{T as Qt}from"../chunks/Tip.c2ecdbf4.js";import{D as me}from"../chunks/Docstring.86474e80.js";import{C as Ze}from"../chunks/CodeBlock.54a9f38d.js";import{E as Bt}from"../chunks/ExampleCodeBlock.034f3a73.js";import{H as he,E as tn}from"../chunks/EditOnGithub.922df6ba.js";function nn(G){let a,k="Examples:",p,m,M;return m=new Ze({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> BertGenerationConfig, BertGenerationEncoder

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

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){a=d("p"),a.textContent=k,p=o(),u(m.$$.fragment)},l(r){a=c(r,"P",{"data-svelte-h":!0}),h(a)!=="svelte-kvfsh7"&&(a.textContent=k),p=s(r),f(m.$$.fragment,r)},m(r,v){i(r,a,v),i(r,p,v),g(m,r,v),M=!0},p:Ce,i(r){M||(_(m.$$.fragment,r),M=!0)},o(r){b(m.$$.fragment,r),M=!1},d(r){r&&(n(a),n(p)),y(m,r)}}}function on(G){let a,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){a=d("p"),a.innerHTML=k},l(p){a=c(p,"P",{"data-svelte-h":!0}),h(a)!=="svelte-fincs2"&&(a.innerHTML=k)},m(p,m){i(p,a,m)},p:Ce,d(p){p&&n(a)}}}function sn(G){let a,k="Example:",p,m,M;return m=new Ze({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBCZXJ0R2VuZXJhdGlvbkVuY29kZXIlMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZSUyRmJlcnRfZm9yX3NlcV9nZW5lcmF0aW9uX0wtMjRfYmJjX2VuY29kZXIlMjIpJTBBbW9kZWwlMjAlM0QlMjBCZXJ0R2VuZXJhdGlvbkVuY29kZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZSUyRmJlcnRfZm9yX3NlcV9nZW5lcmF0aW9uX0wtMjRfYmJjX2VuY29kZXIlMjIpJTBBJTBBaW5wdXRzJTIwJTNEJTIwdG9rZW5pemVyKCUyMkhlbGxvJTJDJTIwbXklMjBkb2clMjBpcyUyMGN1dGUlMjIlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKSUwQW91dHB1dHMlMjAlM0QlMjBtb2RlbCgqKmlucHV0cyklMEElMEFsYXN0X2hpZGRlbl9zdGF0ZXMlMjAlM0QlMjBvdXRwdXRzLmxhc3RfaGlkZGVuX3N0YXRl",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, BertGenerationEncoder
<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/bert_for_seq_generation_L-24_bbc_encoder&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = BertGenerationEncoder.from_pretrained(<span class="hljs-string">&quot;google/bert_for_seq_generation_L-24_bbc_encoder&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){a=d("p"),a.textContent=k,p=o(),u(m.$$.fragment)},l(r){a=c(r,"P",{"data-svelte-h":!0}),h(a)!=="svelte-11lpom8"&&(a.textContent=k),p=s(r),f(m.$$.fragment,r)},m(r,v){i(r,a,v),i(r,p,v),g(m,r,v),M=!0},p:Ce,i(r){M||(_(m.$$.fragment,r),M=!0)},o(r){b(m.$$.fragment,r),M=!1},d(r){r&&(n(a),n(p)),y(m,r)}}}function an(G){let a,k=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){a=d("p"),a.innerHTML=k},l(p){a=c(p,"P",{"data-svelte-h":!0}),h(a)!=="svelte-fincs2"&&(a.innerHTML=k)},m(p,m){i(p,a,m)},p:Ce,d(p){p&&n(a)}}}function rn(G){let a,k="Example:",p,m,M;return m=new Ze({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBCZXJ0R2VuZXJhdGlvbkRlY29kZXIlMkMlMjBCZXJ0R2VuZXJhdGlvbkNvbmZpZyUwQWltcG9ydCUyMHRvcmNoJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGYmVydF9mb3Jfc2VxX2dlbmVyYXRpb25fTC0yNF9iYmNfZW5jb2RlciUyMiklMEFjb25maWclMjAlM0QlMjBCZXJ0R2VuZXJhdGlvbkNvbmZpZy5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlJTJGYmVydF9mb3Jfc2VxX2dlbmVyYXRpb25fTC0yNF9iYmNfZW5jb2RlciUyMiklMEFjb25maWcuaXNfZGVjb2RlciUyMCUzRCUyMFRydWUlMEFtb2RlbCUyMCUzRCUyMEJlcnRHZW5lcmF0aW9uRGVjb2Rlci5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIyZ29vZ2xlJTJGYmVydF9mb3Jfc2VxX2dlbmVyYXRpb25fTC0yNF9iYmNfZW5jb2RlciUyMiUyQyUyMGNvbmZpZyUzRGNvbmZpZyUwQSklMEElMEFpbnB1dHMlMjAlM0QlMjB0b2tlbml6ZXIoJTIySGVsbG8lMkMlMjBteSUyMGRvZyUyMGlzJTIwY3V0ZSUyMiUyQyUyMHJldHVybl90b2tlbl90eXBlX2lkcyUzREZhbHNlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEFvdXRwdXRzJTIwJTNEJTIwbW9kZWwoKippbnB1dHMpJTBBJTBBcHJlZGljdGlvbl9sb2dpdHMlMjAlM0QlMjBvdXRwdXRzLmxvZ2l0cw==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, BertGenerationDecoder, BertGenerationConfig
<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/bert_for_seq_generation_L-24_bbc_encoder&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>config = BertGenerationConfig.from_pretrained(<span class="hljs-string">&quot;google/bert_for_seq_generation_L-24_bbc_encoder&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 = BertGenerationDecoder.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;google/bert_for_seq_generation_L-24_bbc_encoder&quot;</span>, config=config
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_token_type_ids=<span class="hljs-literal">False</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`,wrap:!1}}),{c(){a=d("p"),a.textContent=k,p=o(),u(m.$$.fragment)},l(r){a=c(r,"P",{"data-svelte-h":!0}),h(a)!=="svelte-11lpom8"&&(a.textContent=k),p=s(r),f(m.$$.fragment,r)},m(r,v){i(r,a,v),i(r,p,v),g(m,r,v),M=!0},p:Ce,i(r){M||(_(m.$$.fragment,r),M=!0)},o(r){b(m.$$.fragment,r),M=!1},d(r){r&&(n(a),n(p)),y(m,r)}}}function ln(G){let a,k,p,m,M,r,v,Ue,N,jt=`BertGeneration モデルは、次を使用してシーケンス間のタスクに利用できる BERT モデルです。
<a href="https://arxiv.org/abs/1907.12461" rel="nofollow">Leveraging Pre-trained Checkpoints for Sequence Generation Tasks</a> で提案されている <code>EncoderDecoderModel</code>
タスク、Sascha Rothe、Sishi Nagayan、Aliaksei Severyn 著。`,Re,F,$t="論文の要約は次のとおりです。",qe,L,Gt=`<em>大規模なニューラル モデルの教師なし事前トレーニングは、最近、自然言語処理に革命をもたらしました。による
NLP 実践者は、公開されたチェックポイントからウォームスタートして、複数の項目で最先端の技術を推進してきました。
コンピューティング時間を大幅に節約しながらベンチマークを実行します。これまでのところ、主に自然言語に焦点を当ててきました。
タスクを理解する。この論文では、シーケンス生成のための事前トレーニングされたチェックポイントの有効性を実証します。私たちは
公開されている事前トレーニング済み BERT と互換性のある Transformer ベースのシーケンス間モデルを開発しました。
GPT-2 および RoBERTa チェックポイントを使用し、モデルの初期化の有用性について広範な実証研究を実施しました。
エンコーダとデコーダ、これらのチェックポイント。私たちのモデルは、機械翻訳に関する新しい最先端の結果をもたらします。
テキストの要約、文の分割、および文の融合。</em>`,Ee,P,We,D,Jt=`<li>モデルを <code>EncoderDecoderModel</code> と組み合わせて使用​​して、2 つの事前トレーニングされたモデルを活用できます。
後続の微調整のための BERT チェックポイント。</li>`,Ie,S,Xe,Q,zt="<li>事前トレーニングされた <code>EncoderDecoderModel</code> もモデル ハブで直接利用できます。</li>",Ve,A,He,Y,xt="チップ：",Ne,O,Zt=`<li><a href="/docs/transformers/pr_30674/ja/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> と <a href="/docs/transformers/pr_30674/ja/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> は、
<code>EncoderDecoder</code> と組み合わせます。</li> <li>要約、文の分割、文の融合、および翻訳の場合、入力に特別なトークンは必要ありません。
したがって、入力の末尾に EOS トークンを追加しないでください。</li>`,Fe,K,Ct=`このモデルは、<a href="https://huggingface.co/patrickvonplaten" rel="nofollow">patrickvonplaten</a> によって提供されました。元のコードは次のとおりです
<a href="https://tfhub.dev/s?module-type=text-generation&amp;subtype=module,placeholder" rel="nofollow">ここ</a> があります。`,Le,ee,Pe,j,te,nt,ue,Ut=`This is the configuration class to store the configuration of a <code>BertGenerationPreTrainedModel</code>. It is used to
instantiate a BertGeneration model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the BertGeneration
<a href="https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder" rel="nofollow">google/bert_for_seq_generation_L-24_bbc_encoder</a>
architecture.`,ot,fe,Rt=`Configuration objects inherit from <a href="/docs/transformers/pr_30674/ja/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/pr_30674/ja/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,st,E,De,ne,Se,$,oe,at,ge,qt='Construct a BertGeneration tokenizer. Based on <a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a>.',rt,_e,Et=`This tokenizer inherits from <a href="/docs/transformers/pr_30674/ja/main_classes/tokenizer#transformers.PreTrainedTokenizer">PreTrainedTokenizer</a> which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.`,it,be,se,Qe,ae,Ae,T,re,lt,ye,Wt="The bare BertGeneration model transformer outputting raw hidden-states without any specific head on top.",dt,Me,It=`This model inherits from <a href="/docs/transformers/pr_30674/ja/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,ct,Te,Xt=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,pt,ke,Vt=`The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
cross-attention is added between the self-attention layers, following the architecture described in <a href="https://arxiv.org/abs/1706.03762" rel="nofollow">Attention is
all you need</a> by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.`,mt,ve,Ht=`This model should be used when leveraging Bert or Roberta checkpoints for the <code>EncoderDecoderModel</code> class as
described in <a href="https://arxiv.org/abs/1907.12461" rel="nofollow">Leveraging Pre-trained Checkpoints for Sequence Generation Tasks</a>
by Sascha Rothe, Shashi Narayan, and Aliaksei Severyn.`,ht,we,Nt=`To behave as an decoder the model needs to be initialized with the <code>is_decoder</code> argument of the configuration set
to <code>True</code>. To be used in a Seq2Seq model, the model needs to initialized with both <code>is_decoder</code> argument and
<code>add_cross_attention</code> set to <code>True</code>; an <code>encoder_hidden_states</code> is then expected as an input to the forward pass.`,ut,J,ie,ft,Be,Ft='The <a href="/docs/transformers/pr_30674/ja/model_doc/bert-generation#transformers.BertGenerationEncoder">BertGenerationEncoder</a> forward method, overrides the <code>__call__</code> special method.',gt,W,_t,I,Ye,le,Oe,B,de,bt,je,Lt="BertGeneration Model with a <code>language modeling</code> head on top for CLM fine-tuning.",yt,$e,Pt=`This model inherits from <a href="/docs/transformers/pr_30674/ja/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Mt,Ge,Dt=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Tt,z,ce,kt,Je,St='The <a href="/docs/transformers/pr_30674/ja/model_doc/bert-generation#transformers.BertGenerationDecoder">BertGenerationDecoder</a> forward method, overrides the <code>__call__</code> special method.',vt,X,wt,V,Ke,pe,et,ze,tt;return M=new he({props:{title:"BertGeneration",local:"bertgeneration",headingTag:"h1"}}),v=new he({props:{title:"Overview",local:"overview",headingTag:"h2"}}),P=new he({props:{title:"Usage examples and tips",local:"usage-examples-and-tips",headingTag:"h2"}}),S=new Ze({props:{code:"JTIzJTIwbGV2ZXJhZ2UlMjBjaGVja3BvaW50cyUyMGZvciUyMEJlcnQyQmVydCUyMG1vZGVsLi4uJTBBJTIzJTIwdXNlJTIwQkVSVCdzJTIwY2xzJTIwdG9rZW4lMjBhcyUyMEJPUyUyMHRva2VuJTIwYW5kJTIwc2VwJTIwdG9rZW4lMjBhcyUyMEVPUyUyMHRva2VuJTBBZW5jb2RlciUyMCUzRCUyMEJlcnRHZW5lcmF0aW9uRW5jb2Rlci5mcm9tX3ByZXRyYWluZWQoJTIyZ29vZ2xlLWJlcnQlMkZiZXJ0LWxhcmdlLXVuY2FzZWQlMjIlMkMlMjBib3NfdG9rZW5faWQlM0QxMDElMkMlMjBlb3NfdG9rZW5faWQlM0QxMDIpJTBBJTIzJTIwYWRkJTIwY3Jvc3MlMjBhdHRlbnRpb24lMjBsYXllcnMlMjBhbmQlMjB1c2UlMjBCRVJUJ3MlMjBjbHMlMjB0b2tlbiUyMGFzJTIwQk9TJTIwdG9rZW4lMjBhbmQlMjBzZXAlMjB0b2tlbiUyMGFzJTIwRU9TJTIwdG9rZW4lMEFkZWNvZGVyJTIwJTNEJTIwQmVydEdlbmVyYXRpb25EZWNvZGVyLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJnb29nbGUtYmVydCUyRmJlcnQtbGFyZ2UtdW5jYXNlZCUyMiUyQyUyMGFkZF9jcm9zc19hdHRlbnRpb24lM0RUcnVlJTJDJTIwaXNfZGVjb2RlciUzRFRydWUlMkMlMjBib3NfdG9rZW5faWQlM0QxMDElMkMlMjBlb3NfdG9rZW5faWQlM0QxMDIlMEEpJTBBYmVydDJiZXJ0JTIwJTNEJTIwRW5jb2RlckRlY29kZXJNb2RlbChlbmNvZGVyJTNEZW5jb2RlciUyQyUyMGRlY29kZXIlM0RkZWNvZGVyKSUwQSUwQSUyMyUyMGNyZWF0ZSUyMHRva2VuaXplci4uLiUwQXRva2VuaXplciUyMCUzRCUyMEJlcnRUb2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZS1iZXJ0JTJGYmVydC1sYXJnZS11bmNhc2VkJTIyKSUwQSUwQWlucHV0X2lkcyUyMCUzRCUyMHRva2VuaXplciglMEElMjAlMjAlMjAlMjAlMjJUaGlzJTIwaXMlMjBhJTIwbG9uZyUyMGFydGljbGUlMjB0byUyMHN1bW1hcml6ZSUyMiUyQyUyMGFkZF9zcGVjaWFsX3Rva2VucyUzREZhbHNlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiUwQSkuaW5wdXRfaWRzJTBBbGFiZWxzJTIwJTNEJTIwdG9rZW5pemVyKCUyMlRoaXMlMjBpcyUyMGElMjBzaG9ydCUyMHN1bW1hcnklMjIlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKS5pbnB1dF9pZHMlMEElMEElMjMlMjB0cmFpbi4uLiUwQWxvc3MlMjAlM0QlMjBiZXJ0MmJlcnQoaW5wdXRfaWRzJTNEaW5wdXRfaWRzJTJDJTIwZGVjb2Rlcl9pbnB1dF9pZHMlM0RsYWJlbHMlMkMlMjBsYWJlbHMlM0RsYWJlbHMpLmxvc3MlMEFsb3NzLmJhY2t3YXJkKCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># leverage checkpoints for Bert2Bert model...</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># use BERT&#x27;s cls token as BOS token and sep token as EOS token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>encoder = BertGenerationEncoder.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-large-uncased&quot;</span>, bos_token_id=<span class="hljs-number">101</span>, eos_token_id=<span class="hljs-number">102</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># add cross attention layers and use BERT&#x27;s cls token as BOS token and sep token as EOS token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>decoder = BertGenerationDecoder.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;google-bert/bert-large-uncased&quot;</span>, add_cross_attention=<span class="hljs-literal">True</span>, is_decoder=<span class="hljs-literal">True</span>, bos_token_id=<span class="hljs-number">101</span>, eos_token_id=<span class="hljs-number">102</span>
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>bert2bert = EncoderDecoderModel(encoder=encoder, decoder=decoder)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># create tokenizer...</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = BertTokenizer.from_pretrained(<span class="hljs-string">&quot;google-bert/bert-large-uncased&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;This is a long article to summarize&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>).input_ids
<span class="hljs-meta">&gt;&gt;&gt; </span>labels = tokenizer(<span class="hljs-string">&quot;This is a short summary&quot;</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># train...</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = bert2bert(input_ids=input_ids, decoder_input_ids=labels, labels=labels).loss
<span class="hljs-meta">&gt;&gt;&gt; </span>loss.backward()`,wrap:!1}}),A=new Ze({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># instantiate sentence fusion model</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>sentence_fuser = EncoderDecoderModel.from_pretrained(<span class="hljs-string">&quot;google/roberta2roberta_L-24_discofuse&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;google/roberta2roberta_L-24_discofuse&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = tokenizer(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;This is the first sentence. This is the second sentence.&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>).input_ids

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = sentence_fuser.generate(input_ids)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.decode(outputs[<span class="hljs-number">0</span>]))`,wrap:!1}}),ee=new he({props:{title:"BertGenerationConfig",local:"transformers.BertGenerationConfig",headingTag:"h2"}}),te=new me({props:{name:"class transformers.BertGenerationConfig",anchor:"transformers.BertGenerationConfig",parameters:[{name:"vocab_size",val:" = 50358"},{name:"hidden_size",val:" = 1024"},{name:"num_hidden_layers",val:" = 24"},{name:"num_attention_heads",val:" = 16"},{name:"intermediate_size",val:" = 4096"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_probs_dropout_prob",val:" = 0.1"},{name:"max_position_embeddings",val:" = 512"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-12"},{name:"pad_token_id",val:" = 0"},{name:"bos_token_id",val:" = 2"},{name:"eos_token_id",val:" = 1"},{name:"position_embedding_type",val:" = 'absolute'"},{name:"use_cache",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BertGenerationConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 50358) &#x2014;
Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <code>BertGeneration</code>.`,name:"vocab_size"},{anchor:"transformers.BertGenerationConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Dimensionality of the encoder layers and the pooler layer.`,name:"hidden_size"},{anchor:"transformers.BertGenerationConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 24) &#x2014;
Number of hidden layers in the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.BertGenerationConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 16) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.BertGenerationConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimensionality of the &#x201C;intermediate&#x201D; (often called feed-forward) layer in the Transformer encoder.`,name:"intermediate_size"},{anchor:"transformers.BertGenerationConfig.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;silu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.BertGenerationConfig.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.BertGenerationConfig.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.BertGenerationConfig.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.BertGenerationConfig.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.BertGenerationConfig.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.BertGenerationConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 0) &#x2014;
Padding token id.`,name:"pad_token_id"},{anchor:"transformers.BertGenerationConfig.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
Beginning of stream token id.`,name:"bos_token_id"},{anchor:"transformers.BertGenerationConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
End of stream token id.`,name:"eos_token_id"},{anchor:"transformers.BertGenerationConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;absolute&quot;</code>) &#x2014;
Type of position embedding. Choose one of <code>&quot;absolute&quot;</code>, <code>&quot;relative_key&quot;</code>, <code>&quot;relative_key_query&quot;</code>. For
positional embeddings use <code>&quot;absolute&quot;</code>. For more information on <code>&quot;relative_key&quot;</code>, please refer to
<a href="https://arxiv.org/abs/1803.02155" rel="nofollow">Self-Attention with Relative Position Representations (Shaw et al.)</a>.
For more information on <code>&quot;relative_key_query&quot;</code>, please refer to <em>Method 4</em> in <a href="https://arxiv.org/abs/2009.13658" rel="nofollow">Improve Transformer Models
with Better Relative Position Embeddings (Huang et al.)</a>.`,name:"position_embedding_type"},{anchor:"transformers.BertGenerationConfig.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/vr_30674/src/transformers/models/bert_generation/configuration_bert_generation.py#L20"}}),E=new Bt({props:{anchor:"transformers.BertGenerationConfig.example",$$slots:{default:[nn]},$$scope:{ctx:G}}}),ne=new he({props:{title:"BertGenerationTokenizer",local:"transformers.BertGenerationTokenizer",headingTag:"h2"}}),oe=new me({props:{name:"class transformers.BertGenerationTokenizer",anchor:"transformers.BertGenerationTokenizer",parameters:[{name:"vocab_file",val:""},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"sep_token",val:" = '<::::>'"},{name:"sp_model_kwargs",val:": Optional = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BertGenerationTokenizer.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.BertGenerationTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The begin of sequence token.`,name:"bos_token"},{anchor:"transformers.BertGenerationTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.BertGenerationTokenizer.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.BertGenerationTokenizer.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.BertGenerationTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt; --:::&gt;&quot;</code>):
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.BertGenerationTokenizer.sp_model_kwargs",description:`<strong>sp_model_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Will be passed to the <code>SentencePieceProcessor.__init__()</code> method. The <a href="https://github.com/google/sentencepiece/tree/master/python" rel="nofollow">Python wrapper for
SentencePiece</a> can be used, among other things,
to set:</p>
<ul>
<li>
<p><code>enable_sampling</code>: Enable subword regularization.</p>
</li>
<li>
<p><code>nbest_size</code>: Sampling parameters for unigram. Invalid for BPE-Dropout.</p>
<ul>
<li><code>nbest_size = {0,1}</code>: No sampling is performed.</li>
<li><code>nbest_size &gt; 1</code>: samples from the nbest_size results.</li>
<li><code>nbest_size &lt; 0</code>: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.</li>
</ul>
</li>
<li>
<p><code>alpha</code>: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.</p>
</li>
</ul>`,name:"sp_model_kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_30674/src/transformers/models/bert_generation/tokenization_bert_generation.py#L32"}}),se=new me({props:{name:"save_vocabulary",anchor:"transformers.BertGenerationTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": Optional = None"}],source:"https://github.com/huggingface/transformers/blob/vr_30674/src/transformers/models/bert_generation/tokenization_bert_generation.py#L157"}}),ae=new he({props:{title:"BertGenerationEncoder",local:"transformers.BertGenerationEncoder",headingTag:"h2"}}),re=new me({props:{name:"class transformers.BertGenerationEncoder",anchor:"transformers.BertGenerationEncoder",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BertGenerationEncoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30674/ja/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_30674/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_30674/src/transformers/models/bert_generation/modeling_bert_generation.py#L671"}}),ie=new me({props:{name:"forward",anchor:"transformers.BertGenerationEncoder.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"encoder_hidden_states",val:": Optional = None"},{name:"encoder_attention_mask",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BertGenerationEncoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_30674/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30674/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_30674/ja/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.BertGenerationEncoder.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.BertGenerationEncoder.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.BertGenerationEncoder.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.BertGenerationEncoder.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BertGenerationEncoder.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.BertGenerationEncoder.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.BertGenerationEncoder.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_30674/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BertGenerationEncoder.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.BertGenerationEncoder.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>: <code>1</code> for
tokens that are NOT MASKED, <code>0</code> for MASKED tokens.`,name:"encoder_attention_mask"},{anchor:"transformers.BertGenerationEncoder.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.</p>
<p>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.BertGenerationEncoder.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_30674/src/transformers/models/bert_generation/modeling_bert_generation.py#L716",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30674/ja/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/pr_30674/ja/model_doc/bert-generation#transformers.BertGenerationConfig"
>BertGenerationConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
<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>) — 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>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> and <code>config.add_cross_attention=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights of the decoder’s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_30674/ja/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions"
>transformers.modeling_outputs.BaseModelOutputWithPastAndCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),W=new Qt({props:{$$slots:{default:[on]},$$scope:{ctx:G}}}),I=new Bt({props:{anchor:"transformers.BertGenerationEncoder.forward.example",$$slots:{default:[sn]},$$scope:{ctx:G}}}),le=new he({props:{title:"BertGenerationDecoder",local:"transformers.BertGenerationDecoder",headingTag:"h2"}}),de=new me({props:{name:"class transformers.BertGenerationDecoder",anchor:"transformers.BertGenerationDecoder",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.BertGenerationDecoder.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_30674/ja/model_doc/bert-generation#transformers.BertGenerationConfig">BertGenerationConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_30674/ja/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_30674/src/transformers/models/bert_generation/modeling_bert_generation.py#L862"}}),ce=new me({props:{name:"forward",anchor:"transformers.BertGenerationDecoder.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"encoder_hidden_states",val:": Optional = None"},{name:"encoder_attention_mask",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.BertGenerationDecoder.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_30674/ja/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_30674/ja/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> and
<a href="/docs/transformers/pr_30674/ja/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.BertGenerationDecoder.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.BertGenerationDecoder.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.BertGenerationDecoder.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.BertGenerationDecoder.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.BertGenerationDecoder.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.BertGenerationDecoder.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.BertGenerationDecoder.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_30674/ja/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.BertGenerationDecoder.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.BertGenerationDecoder.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.BertGenerationDecoder.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 in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.BertGenerationDecoder.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.</p>
<p>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.BertGenerationDecoder.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_30674/src/transformers/models/bert_generation/modeling_bert_generation.py#L888",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_30674/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_30674/ja/model_doc/bert-generation#transformers.BertGenerationConfig"
>BertGenerationConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>cross_attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Cross attentions weights after the attention softmax, used to compute the weighted average in the
cross-attention heads.</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — Tuple of <code>torch.FloatTensor</code> tuples of length <code>config.n_layers</code>, with each tuple containing the cached key,
value states of the self-attention and the cross-attention layers if model is used in encoder-decoder
setting. Only relevant if <code>config.is_decoder = True</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_30674/ja/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithCrossAttentions"
>transformers.modeling_outputs.CausalLMOutputWithCrossAttentions</a> or <code>tuple(torch.FloatTensor)</code></p>
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