import{s as ht,o as $t,n as Qe}from"../chunks/scheduler.9bc65507.js";import{S as vt,i as bt,g as l,s as o,r as _,A as xt,h as p,f as n,c as a,j as q,u,x as f,k as z,y as t,a as m,v as g,d as k,t as h,w as $}from"../chunks/index.707bf1b6.js";import{T as Ct}from"../chunks/Tip.c2ecdbf4.js";import{D as V}from"../chunks/Docstring.86474e80.js";import{C as kt}from"../chunks/CodeBlock.54a9f38d.js";import{E as gt}from"../chunks/ExampleCodeBlock.034f3a73.js";import{H as De,E as wt}from"../chunks/EditOnGithub.922df6ba.js";function Tt(P){let i,w=`CPM のアーキテクチャは、トークン化方法を除いて GPT-2 と同じです。詳細については、<a href="openai-community/gpt2">GPT-2 ドキュメント</a> を参照してください。
API リファレンス情報。`;return{c(){i=l("p"),i.innerHTML=w},l(d){i=p(d,"P",{"data-svelte-h":!0}),f(i)!=="svelte-hrrftm"&&(i.innerHTML=w)},m(d,c){m(d,i,c)},p:Qe,d(d){d&&n(i)}}}function Mt(P){let i,w="sequence pair mask has the following format:",d,c,v;return c=new kt({props:{code:"MCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMEElN0MlMjBmaXJzdCUyMHNlcXVlbmNlJTIwJTIwJTIwJTIwJTdDJTIwc2Vjb25kJTIwc2VxdWVuY2UlMjAlN0M=",highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`,wrap:!1}}),{c(){i=l("p"),i.textContent=w,d=o(),_(c.$$.fragment)},l(r){i=p(r,"P",{"data-svelte-h":!0}),f(i)!=="svelte-16klr56"&&(i.textContent=w),d=a(r),u(c.$$.fragment,r)},m(r,x){m(r,i,x),m(r,d,x),g(c,r,x),v=!0},p:Qe,i(r){v||(k(c.$$.fragment,r),v=!0)},o(r){h(c.$$.fragment,r),v=!1},d(r){r&&(n(i),n(d)),$(c,r)}}}function Lt(P){let i,w="sequence pair mask has the following format:",d,c,v;return c=new kt({props:{code:"MCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMEElN0MlMjBmaXJzdCUyMHNlcXVlbmNlJTIwJTIwJTIwJTIwJTdDJTIwc2Vjb25kJTIwc2VxdWVuY2UlMjAlN0M=",highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`,wrap:!1}}),{c(){i=l("p"),i.textContent=w,d=o(),_(c.$$.fragment)},l(r){i=p(r,"P",{"data-svelte-h":!0}),f(i)!=="svelte-16klr56"&&(i.textContent=w),d=a(r),u(c.$$.fragment,r)},m(r,x){m(r,i,x),m(r,d,x),g(c,r,x),v=!0},p:Qe,i(r){v||(k(c.$$.fragment,r),v=!0)},o(r){h(c.$$.fragment,r),v=!1},d(r){r&&(n(i),n(d)),$(c,r)}}}function yt(P){let i,w,d,c,v,r,x,$e,G,et=`CPM モデルは、Zhengyan Zhang、Xu Han、Hao Zhou、Pei Ke、Yuxian Gu によって <a href="https://arxiv.org/abs/2012.00413" rel="nofollow">CPM: A Large-scale Generative Chinese Pre-trained Language Model</a> で提案されました。葉徳明、秦裕佳、
Yusheng Su、Haozhe Ji、Jian Guan、Fanchao Qi、Xiaozi Wang、Yanan Zheng、Guoyang Zeng、Huanqi Cao、Shengqi Chen、
Daixuan Li、Zhenbo Sun、Zhiyuan Liu、Minlie Huang、Wentao Han、Jie Tang、Juanzi Li、Xiaoyan Zhu、Maosong Sun。`,ve,O,tt="論文の要約は次のとおりです。",be,S,nt=`<em>事前トレーニングされた言語モデル (PLM) は、さまざまな下流の NLP タスクに有益であることが証明されています。最近ではGPT-3、
1,750億個のパラメータと570GBの学習データを備え、数回の撮影（1枚でも）の容量で大きな注目を集めました
ゼロショット）学習。ただし、GPT-3 を適用して中国語の NLP タスクに対処することは依然として困難です。
GPT-3 の言語は主に英語であり、パラメーターは公開されていません。この技術レポートでは、
大規模な中国語トレーニング データに対する生成的事前トレーニングを備えた中国語事前トレーニング済み言語モデル (CPM)。最高に
私たちの知識の限りでは、26 億のパラメータと 100GB の中国語トレーニング データを備えた CPM は、事前トレーニングされた中国語としては最大のものです。
言語モデルは、会話、エッセイの作成、
クローゼテストと言語理解。広範な実験により、CPM が多くの環境で優れたパフォーマンスを達成できることが実証されています。
少数ショット (ゼロショットでも) 学習の設定での NLP タスク。</em>`,xe,X,st=`このモデルは <a href="https://huggingface.co/canwenxu" rel="nofollow">canwenxu</a> によって提供されました。オリジナルの実装が見つかります
ここ: <a href="https://github.com/TsinghuaAI/CPM-Generate" rel="nofollow">https://github.com/TsinghuaAI/CPM-Generate</a>`,Ce,j,we,B,Te,b,Z,Pe,ae,ot="Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models.",je,y,W,Ae,re,at=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLNet sequence has the following format:`,Je,ie,rt="<li>single sequence: <code>X &lt;sep&gt; &lt;cls&gt;</code></li> <li>pair of sequences: <code>A &lt;sep&gt; B &lt;sep&gt; &lt;cls&gt;</code></li>",He,A,Y,Ee,le,it="Converts a sequence of tokens (strings for sub-words) in a single string.",Ne,M,R,Ue,pe,lt="Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet",Fe,J,Ve,me,pt="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",Ge,H,K,Oe,ce,mt=`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> method.`,Me,Q,Le,T,ee,Se,de,ct="Runs pre-tokenization with Jieba segmentation tool. It is used in CPM models.",Xe,I,te,Be,fe,dt=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLNet sequence has the following format:`,Ze,_e,ft="<li>single sequence: <code>X &lt;sep&gt; &lt;cls&gt;</code></li> <li>pair of sequences: <code>A &lt;sep&gt; B &lt;sep&gt; &lt;cls&gt;</code></li>",We,L,ne,Ye,ue,_t="Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet",Re,E,Ke,ge,ut="If <code>token_ids_1</code> is <code>None</code>, this method only returns the first portion of the mask (0s).",ye,se,Ie,he,qe;return v=new De({props:{title:"CPM",local:"cpm",headingTag:"h1"}}),x=new De({props:{title:"Overview",local:"overview",headingTag:"h2"}}),j=new Ct({props:{$$slots:{default:[Tt]},$$scope:{ctx:P}}}),B=new De({props:{title:"CpmTokenizer",local:"transformers.CpmTokenizer",headingTag:"h2"}}),Z=new V({props:{name:"class transformers.CpmTokenizer",anchor:"transformers.CpmTokenizer",parameters:[{name:"vocab_file",val:""},{name:"do_lower_case",val:" = False"},{name:"remove_space",val:" = True"},{name:"keep_accents",val:" = False"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{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:"additional_special_tokens",val:" = ['<eop>', '<eod>']"},{name:"sp_model_kwargs",val:": Optional = None"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.43.0/src/transformers/models/cpm/tokenization_cpm.py#L33"}}),W=new V({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.CpmTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.CpmTokenizer.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.CpmTokenizer.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/v4.43.0/src/transformers/models/cpm/tokenization_cpm.py#L239",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>List[int]</code></p>
`}}),Y=new V({props:{name:"convert_tokens_to_string",anchor:"transformers.CpmTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.43.0/src/transformers/models/cpm/tokenization_cpm.py#L233"}}),R=new V({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.CpmTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.CpmTokenizer.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.CpmTokenizer.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/v4.43.0/src/transformers/models/cpm/tokenization_cpm.py#L294",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),J=new gt({props:{anchor:"transformers.CpmTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[Mt]},$$scope:{ctx:P}}}),K=new V({props:{name:"get_special_tokens_mask",anchor:"transformers.CpmTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.CpmTokenizer.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.CpmTokenizer.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.CpmTokenizer.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/v4.43.0/src/transformers/models/cpm/tokenization_cpm.py#L265",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),Q=new De({props:{title:"CpmTokenizerFast",local:"transformers.CpmTokenizerFast",headingTag:"h2"}}),ee=new V({props:{name:"class transformers.CpmTokenizerFast",anchor:"transformers.CpmTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"do_lower_case",val:" = False"},{name:"remove_space",val:" = True"},{name:"keep_accents",val:" = False"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{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:"additional_special_tokens",val:" = ['<eop>', '<eod>']"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.43.0/src/transformers/models/cpm/tokenization_cpm_fast.py#L30"}}),te=new V({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.CpmTokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.CpmTokenizerFast.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.CpmTokenizerFast.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/v4.43.0/src/transformers/models/cpm/tokenization_cpm_fast.py#L152",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


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


<p><code>List[int]</code></p>
`}}),ne=new V({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.CpmTokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.CpmTokenizerFast.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.CpmTokenizerFast.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/v4.43.0/src/transformers/models/cpm/tokenization_cpm_fast.py#L178",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),E=new gt({props:{anchor:"transformers.CpmTokenizerFast.create_token_type_ids_from_sequences.example",$$slots:{default:[Lt]},$$scope:{ctx:P}}}),se=new wt({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/ja/model_doc/cpm.md"}}),{c(){i=l("meta"),w=o(),d=l("p"),c=o(),_(v.$$.fragment),r=o(),_(x.$$.fragment),$e=o(),G=l("p"),G.innerHTML=et,ve=o(),O=l("p"),O.textContent=tt,be=o(),S=l("p"),S.innerHTML=nt,xe=o(),X=l("p"),X.innerHTML=st,Ce=o(),_(j.$$.fragment),we=o(),_(B.$$.fragment),Te=o(),b=l("div"),_(Z.$$.fragment),Pe=o(),ae=l("p"),ae.textContent=ot,je=o(),y=l("div"),_(W.$$.fragment),Ae=o(),re=l("p"),re.textContent=at,Je=o(),ie=l("ul"),ie.innerHTML=rt,He=o(),A=l("div"),_(Y.$$.fragment),Ee=o(),le=l("p"),le.textContent=it,Ne=o(),M=l("div"),_(R.$$.fragment),Ue=o(),pe=l("p"),pe.textContent=lt,Fe=o(),_(J.$$.fragment),Ve=o(),me=l("p"),me.innerHTML=pt,Ge=o(),H=l("div"),_(K.$$.fragment),Oe=o(),ce=l("p"),ce.innerHTML=mt,Me=o(),_(Q.$$.fragment),Le=o(),T=l("div"),_(ee.$$.fragment),Se=o(),de=l("p"),de.textContent=ct,Xe=o(),I=l("div"),_(te.$$.fragment),Be=o(),fe=l("p"),fe.textContent=dt,Ze=o(),_e=l("ul"),_e.innerHTML=ft,We=o(),L=l("div"),_(ne.$$.fragment),Ye=o(),ue=l("p"),ue.textContent=_t,Re=o(),_(E.$$.fragment),Ke=o(),ge=l("p"),ge.innerHTML=ut,ye=o(),_(se.$$.fragment),Ie=o(),he=l("p"),this.h()},l(e){const 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