import{s as rt,o as at,n as it}from"../chunks/scheduler.9bc65507.js";import{S as lt,i as dt,g as a,s as o,r as g,A as mt,h as i,f as n,c as r,j as T,u as f,x as p,k as v,y as t,a as m,v as u,d as h,t as k,w as _}from"../chunks/index.707bf1b6.js";import{T as ct}from"../chunks/Tip.c2ecdbf4.js";import{D as I}from"../chunks/Docstring.17db21ae.js";import{C as pt}from"../chunks/CodeBlock.54a9f38d.js";import{H as ze}from"../chunks/Heading.342b1fa6.js";function gt(le){let c,q=`この実装は、トークン化方法を除いて BERT と同じです。詳細については、<a href="bert">BERT 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(){c=a("p"),c.innerHTML=q},l(w){c=i(w,"P",{"data-svelte-h":!0}),p(c)!=="svelte-19gvqi2"&&(c.innerHTML=q)},m(w,N){m(w,c,N)},p:it,d(w){w&&n(c)}}}function ft(le){let c,q,w,N,C,de,U,me,j,Qe='BERTweet モデルは、Dat Quoc Nguyen、Thanh Vu によって <a href="https://www.aclweb.org/anthology/2020.emnlp-demos.2.pdf" rel="nofollow">BERTweet: A pre-trained language model for English Tweets</a> で提案されました。アン・トゥアン・グエンさん。',ce,E,We="論文の要約は次のとおりです。",pe,L,Se=`<em>私たちは、英語ツイート用に初めて公開された大規模な事前トレーニング済み言語モデルである BERTweet を紹介します。私たちのBERTweetは、
BERT ベースと同じアーキテクチャ (Devlin et al., 2019) は、RoBERTa 事前トレーニング手順 (Liu et al.) を使用してトレーニングされます。
al.、2019）。実験では、BERTweet が強力なベースラインである RoBERTa ベースおよび XLM-R ベースを上回るパフォーマンスを示すことが示されています (Conneau et al.,
2020)、3 つのツイート NLP タスクにおいて、以前の最先端モデルよりも優れたパフォーマンス結果が得られました。
品詞タグ付け、固有表現認識およびテキスト分類。</em>`,ge,R,fe,V,ue,y,he,Z,Ae='このモデルは <a href="https://huggingface.co/dqnguyen" rel="nofollow">dqnguyen</a> によって提供されました。元のコードは <a href="https://github.com/VinAIResearch/BERTweet" rel="nofollow">ここ</a> にあります。',ke,F,_e,l,H,xe,G,Ne="Constructs a BERTweet tokenizer, using Byte-Pair-Encoding.",Be,Y,Ge=`This tokenizer inherits from <a href="/docs/transformers/main/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.`,Ie,$,X,qe,O,Ye="Loads a pre-existing dictionary from a text file and adds its symbols to this instance.",Ce,b,D,Ue,K,Oe=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A BERTweet sequence has the following format:`,je,ee,Ke="<li>single sequence: <code>&lt;s&gt; X &lt;/s&gt;</code></li> <li>pair of sequences: <code>&lt;s&gt; A &lt;/s&gt;&lt;/s&gt; B &lt;/s&gt;</code></li>",Ee,J,P,Le,te,et="Converts a sequence of tokens (string) in a single string.",Re,M,Q,Ve,ne,tt=`Create a mask from the two sequences passed to be used in a sequence-pair classification task. BERTweet does
not make use of token type ids, therefore a list of zeros is returned.`,Ze,z,W,Fe,se,nt=`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.`,He,x,S,Xe,oe,st="Normalize tokens in a Tweet",De,B,A,Pe,re,ot="Normalize a raw Tweet",we,ie,be;return C=new ze({props:{title:"BERTweet",local:"bertweet",headingTag:"h1"}}),U=new ze({props:{title:"Overview",local:"overview",headingTag:"h2"}}),R=new ze({props:{title:"Usage example",local:"usage-example",headingTag:"h2"}}),V=new pt({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> AutoModel, AutoTokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>bertweet = AutoModel.from_pretrained(<span class="hljs-string">&quot;vinai/bertweet-base&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># For transformers v4.x+:</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;vinai/bertweet-base&quot;</span>, use_fast=<span class="hljs-literal">False</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># For transformers v3.x:</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># tokenizer = AutoTokenizer.from_pretrained(&quot;vinai/bertweet-base&quot;)</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># INPUT TWEET IS ALREADY NORMALIZED!</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>line = <span class="hljs-string">&quot;SC has first two presumptive cases of coronavirus , DHEC confirms HTTPURL via @USER :cry:&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = torch.tensor([tokenizer.encode(line)])

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    features = bertweet(input_ids)  <span class="hljs-comment"># Models outputs are now tuples</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># With TensorFlow 2.0+:</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># from transformers import TFAutoModel</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># bertweet = TFAutoModel.from_pretrained(&quot;vinai/bertweet-base&quot;)</span>`,wrap:!1}}),y=new ct({props:{$$slots:{default:[gt]},$$scope:{ctx:le}}}),F=new ze({props:{title:"BertweetTokenizer",local:"transformers.BertweetTokenizer",headingTag:"h2"}}),H=new I({props:{name:"class transformers.BertweetTokenizer",anchor:"transformers.BertweetTokenizer",parameters:[{name:"vocab_file",val:""},{name:"merges_file",val:""},{name:"normalization",val:" = False"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"sep_token",val:" = '</s>'"},{name:"cls_token",val:" = '<s>'"},{name:"unk_token",val:" = '<unk>'"},{name:"pad_token",val:" = '<pad>'"},{name:"mask_token",val:" = '<mask>'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BertweetTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.BertweetTokenizer.merges_file",description:`<strong>merges_file</strong> (<code>str</code>) &#x2014;
Path to the merges file.`,name:"merges_file"},{anchor:"transformers.BertweetTokenizer.normalization",description:`<strong>normalization</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to apply a normalization preprocess.`,name:"normalization"},{anchor:"transformers.BertweetTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&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.BertweetTokenizer.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.</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.BertweetTokenizer.sep_token",description:`<strong>sep_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&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.BertweetTokenizer.cls_token",description:`<strong>cls_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&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.BertweetTokenizer.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.BertweetTokenizer.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.BertweetTokenizer.mask_token",description:`<strong>mask_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;mask&gt;&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/main/src/transformers/models/bertweet/tokenization_bertweet.py#L68"}}),X=new I({props:{name:"add_from_file",anchor:"transformers.BertweetTokenizer.add_from_file",parameters:[{name:"f",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/bertweet/tokenization_bertweet.py#L418"}}),D=new I({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.BertweetTokenizer.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.BertweetTokenizer.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.BertweetTokenizer.build_inputs_with_special_tokens.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/bertweet/tokenization_bertweet.py#L183",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>
`}}),P=new I({props:{name:"convert_tokens_to_string",anchor:"transformers.BertweetTokenizer.convert_tokens_to_string",parameters:[{name:"tokens",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/bertweet/tokenization_bertweet.py#L384"}}),Q=new I({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.BertweetTokenizer.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.BertweetTokenizer.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.BertweetTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/bertweet/tokenization_bertweet.py#L237",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of zeros.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),W=new I({props:{name:"get_special_tokens_mask",anchor:"transformers.BertweetTokenizer.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.BertweetTokenizer.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.BertweetTokenizer.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.BertweetTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/bertweet/tokenization_bertweet.py#L209",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>
`}}),S=new I({props:{name:"normalizeToken",anchor:"transformers.BertweetTokenizer.normalizeToken",parameters:[{name:"token",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/bertweet/tokenization_bertweet.py#L357"}}),A=new I({props:{name:"normalizeTweet",anchor:"transformers.BertweetTokenizer.normalizeTweet",parameters:[{name:"tweet",val:""}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/bertweet/tokenization_bertweet.py#L323"}}),{c(){c=a("meta"),q=o(),w=a("p"),N=o(),g(C.$$.fragment),de=o(),g(U.$$.fragment),me=o(),j=a("p"),j.innerHTML=Qe,ce=o(),E=a("p"),E.textContent=We,pe=o(),L=a("p"),L.innerHTML=Se,ge=o(),g(R.$$.fragment),fe=o(),g(V.$$.fragment),ue=o(),g(y.$$.fragment),he=o(),Z=a("p"),Z.innerHTML=Ae,ke=o(),g(F.$$.fragment),_e=o(),l=a("div"),g(H.$$.fragment),xe=o(),G=a("p"),G.textContent=Ne,Be=o(),Y=a("p"),Y.innerHTML=Ge,Ie=o(),$=a("div"),g(X.$$.fragment),qe=o(),O=a("p"),O.textContent=Ye,Ce=o(),b=a("div"),g(D.$$.fragment),Ue=o(),K=a("p"),K.textContent=Oe,je=o(),ee=a("ul"),ee.innerHTML=Ke,Ee=o(),J=a("div"),g(P.$$.fragment),Le=o(),te=a("p"),te.textContent=et,Re=o(),M=a("div"),g(Q.$$.fragment),Ve=o(),ne=a("p"),ne.textContent=tt,Ze=o(),z=a("div"),g(W.$$.fragment),Fe=o(),se=a("p"),se.innerHTML=nt,He=o(),x=a("div"),g(S.$$.fragment),Xe=o(),oe=a("p"),oe.textContent=st,De=o(),B=a("div"),g(A.$$.fragment),Pe=o(),re=a("p"),re.textContent=ot,we=o(),ie=a("p"),this.h()},l(e){const 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