import{s as it,o as lt,n as mt}from"../chunks/scheduler.9bc65507.js";import{S as dt,i as ct,g as a,s as o,r as p,A as pt,h as i,f as s,c as r,j as T,u as g,x as _,k as v,y as t,a as d,v as f,d as u,t as h,w as k}from"../chunks/index.707bf1b6.js";import{T as gt}from"../chunks/Tip.c2ecdbf4.js";import{D as I}from"../chunks/Docstring.570ddb64.js";import{C as ft}from"../chunks/CodeBlock.54a9f38d.js";import{H as Be,E as ut}from"../chunks/EditOnGithub.922df6ba.js";function ht(me){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}),_(c)!=="svelte-19gvqi2"&&(c.innerHTML=q)},m(w,N){d(w,c,N)},p:mt,d(w){w&&s(c)}}}function kt(me){let c,q,w,N,C,de,U,ce,E,Se='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。',pe,j,Ae="論文の要約は次のとおりです。",ge,L,Ge=`<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>`,fe,R,ue,V,he,$,ke,Z,Ne='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。',_e,F,we,l,H,Ie,Y,Ye="Constructs a BERTweet tokenizer, using Byte-Pair-Encoding.",qe,O,Oe=`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.`,Ce,y,X,Ue,K,Ke="Loads a pre-existing dictionary from a text file and adds its symbols to this instance.",Ee,b,D,je,ee,et=`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:`,Le,te,tt="<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>",Re,J,P,Ve,ne,nt="Converts a sequence of tokens (string) in a single string.",Ze,M,Q,Fe,se,st=`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.`,He,z,W,Xe,oe,ot=`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.`,De,x,S,Pe,re,rt="Normalize tokens in a Tweet",Qe,B,A,We,ae,at="Normalize a raw Tweet",be,G,Te,le,ve;return C=new Be({props:{title:"BERTweet",local:"bertweet",headingTag:"h1"}}),U=new Be({props:{title:"Overview",local:"overview",headingTag:"h2"}}),R=new Be({props:{title:"Usage example",local:"usage-example",headingTag:"h2"}}),V=new ft({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}}),$=new gt({props:{$$slots:{default:[ht]},$$scope:{ctx:me}}}),F=new Be({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#L54"}}),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#L402"}}),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#L167",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#L368"}}),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#L221",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#L193",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#L341"}}),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#L307"}}),G=new ut({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/ja/model_doc/bertweet.md"}}),{c(){c=a("meta"),q=o(),w=a("p"),N=o(),p(C.$$.fragment),de=o(),p(U.$$.fragment),ce=o(),E=a("p"),E.innerHTML=Se,pe=o(),j=a("p"),j.textContent=Ae,ge=o(),L=a("p"),L.innerHTML=Ge,fe=o(),p(R.$$.fragment),ue=o(),p(V.$$.fragment),he=o(),p($.$$.fragment),ke=o(),Z=a("p"),Z.innerHTML=Ne,_e=o(),p(F.$$.fragment),we=o(),l=a("div"),p(H.$$.fragment),Ie=o(),Y=a("p"),Y.textContent=Ye,qe=o(),O=a("p"),O.innerHTML=Oe,Ce=o(),y=a("div"),p(X.$$.fragment),Ue=o(),K=a("p"),K.textContent=Ke,Ee=o(),b=a("div"),p(D.$$.fragment),je=o(),ee=a("p"),ee.textContent=et,Le=o(),te=a("ul"),te.innerHTML=tt,Re=o(),J=a("div"),p(P.$$.fragment),Ve=o(),ne=a("p"),ne.textContent=nt,Ze=o(),M=a("div"),p(Q.$$.fragment),Fe=o(),se=a("p"),se.textContent=st,He=o(),z=a("div"),p(W.$$.fragment),Xe=o(),oe=a("p"),oe.innerHTML=ot,De=o(),x=a("div"),p(S.$$.fragment),Pe=o(),re=a("p"),re.textContent=rt,Qe=o(),B=a("div"),p(A.$$.fragment),We=o(),ae=a("p"),ae.textContent=at,be=o(),p(G.$$.fragment),Te=o(),le=a("p"),this.h()},l(e){const 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