import{s as fa,n as Ma,o as ba}from"../chunks/scheduler.d586627e.js";import{S as va,i as ya,g as s,s as o,u,A as Ja,h as n,f as t,c as i,j as p,v as c,y as r,k as m,o as d,a,w as g,d as h,t as f,x as M}from"../chunks/index.b757c891.js";import{I}from"../chunks/IconCopyLink.c074b82b.js";import{C as b}from"../chunks/CodeBlock.aa8a1a9b.js";import{D as Ta}from"../chunks/DocNotebookDropdown.889126d3.js";function wa(gl){let v,Ke,He,Oe,y,_,qe,R,ol,ze,hl="Modelos multilinguísticos para inferência",et,W,tt,V,fl=`Existem vários modelos multilinguísticos no 🤗 Transformers e seus usos para inferência diferem dos modelos monolíngues.
No entanto, nem <em>todos</em> os usos dos modelos multilíngues são tão diferentes.
Alguns modelos, como o <a href="https://huggingface.co/bert-base-multilingual-uncased" rel="nofollow">bert-base-multilingual-uncased</a>,
podem ser usados como se fossem monolíngues. Este guia irá te ajudar a usar modelos multilíngues cujo uso difere
para o propósito de inferência.`,lt,J,j,Ne,L,il,Ge,Ml="XLM",at,B,bl=`O XLM tem dez checkpoints diferentes dos quais apenas um é monolíngue.
Os nove checkpoints restantes do modelo são subdivididos em duas categorias:
checkpoints que usam de language embeddings e os que não.`,st,T,C,Fe,H,rl,Ie,vl="XLM com language embeddings",nt,q,yl="Os seguintes modelos de XLM usam language embeddings para especificar a linguagem utilizada para a inferência.",ot,N,Jl="<li><code>xlm-mlm-ende-1024</code> (Masked language modeling, English-German)</li> <li><code>xlm-mlm-enfr-1024</code> (Masked language modeling, English-French)</li> <li><code>xlm-mlm-enro-1024</code> (Masked language modeling, English-Romanian)</li> <li><code>xlm-mlm-xnli15-1024</code> (Masked language modeling, XNLI languages)</li> <li><code>xlm-mlm-tlm-xnli15-1024</code> (Masked language modeling + translation, XNLI languages)</li> <li><code>xlm-clm-enfr-1024</code> (Causal language modeling, English-French)</li> <li><code>xlm-clm-ende-1024</code> (Causal language modeling, English-German)</li>",it,F,Tl=`Os language embeddings são representados por um tensor de mesma dimensão que os <code>input_ids</code> passados ao modelo.
Os valores destes tensores dependem do idioma utilizado e se identificam pelos atributos <code>lang2id</code> e <code>id2lang</code> do tokenizador.`,rt,P,wl="Neste exemplo, carregamos o checkpoint <code>xlm-clm-enfr-1024</code>(Causal language modeling, English-French):",mt,Q,dt,A,Ul="O atributo <code>lang2id</code> do tokenizador mostra os idiomas deste modelo e seus ids:",pt,Y,ut,S,xl="Em seguida, cria-se um input de exemplo:",ct,D,gt,K,kl=`Estabelece-se o id do idioma, por exemplo <code>&quot;en&quot;</code>, e utiliza-se o mesmo para definir a language embedding.
A language embedding é um tensor preenchido com <code>0</code>, que é o id de idioma para o inglês.
Este tensor deve ser do mesmo tamanho que os <code>input_ids</code>.`,ht,O,ft,ee,$l="Agora você pode passar os <code>input_ids</code> e a language embedding ao modelo:",Mt,te,bt,le,_l='O script <a href="https://github.com/huggingface/transformers/tree/master/examples/pytorch/text-generation/run_generation.py" rel="nofollow">run_generation.py</a> pode gerar um texto com language embeddings utilizando os checkpoints <code>xlm-clm</code>.',vt,w,Z,Pe,ae,ml,Re,jl="XLM sem language embeddings",yt,se,Cl="Os seguintes modelos XLM não requerem o uso de language embeddings durante a inferência:",Jt,ne,Zl="<li><code>xlm-mlm-17-1280</code> (Modelagem de linguagem com máscara, 17 idiomas)</li> <li><code>xlm-mlm-100-1280</code> (Modelagem de linguagem com máscara, 100 idiomas)</li>",Tt,oe,Xl="Estes modelos são utilizados para representações genéricas de frase diferentemente dos checkpoints XLM anteriores.",wt,U,X,Qe,ie,dl,We,El="BERT",Ut,re,zl="Os seguintes modelos do BERT podem ser utilizados para tarefas multilinguísticas:",xt,me,Gl="<li><code>bert-base-multilingual-uncased</code> (Modelagem de linguagem com máscara + Previsão de frases, 102 idiomas)</li> <li><code>bert-base-multilingual-cased</code> (Modelagem de linguagem com máscara + Previsão de frases, 104 idiomas)</li>",kt,de,Il=`Estes modelos não requerem language embeddings durante a inferência. Devem identificar a linguagem a partir
do contexto e realizar a inferência em sequência.`,$t,x,E,Ae,pe,pl,Ve,Rl="XLM-RoBERTa",_t,ue,Wl="Os seguintes modelos do XLM-RoBERTa podem ser utilizados para tarefas multilinguísticas:",jt,ce,Vl="<li><code>xlm-roberta-base</code> (Modelagem de linguagem com máscara, 100 idiomas)</li> <li><code>xlm-roberta-large</code> Modelagem de linguagem com máscara, 100 idiomas)</li>",Ct,ge,Ll=`O XLM-RoBERTa foi treinado com 2,5 TB de dados do CommonCrawl recém-criados e testados em 100 idiomas.
Proporciona fortes vantagens sobre os modelos multilinguísticos publicados anteriormente como o mBERT e o XLM em tarefas
subsequentes como a classificação, a rotulagem de sequências e à respostas a perguntas.`,Zt,k,z,Ye,he,ul,Le,Bl="M2M100",Xt,fe,Hl="Os seguintes modelos de M2M100 podem ser utilizados para traduções multilinguísticas:",Et,Me,ql="<li><code>facebook/m2m100_418M</code> (Tradução)</li> <li><code>facebook/m2m100_1.2B</code> (Tradução)</li>",zt,be,Nl=`Neste exemplo, o checkpoint <code>facebook/m2m100_418M</code> é carregado para traduzir do mandarim ao inglês. É possível
estabelecer o idioma de origem no tokenizador:`,Gt,ve,It,ye,Fl="Tokenização do texto:",Rt,Je,Wt,Te,Pl=`O M2M100 força o id do idioma de destino como o primeiro token gerado para traduzir ao idioma de destino.
É definido o <code>forced_bos_token_id</code> como <code>en</code> no método <code>generate</code> para traduzir ao inglês.`,Vt,we,Lt,$,G,Se,Ue,cl,Be,Ql="MBart",Bt,xe,Al="Os seguintes modelos do MBart podem ser utilizados para tradução multilinguística:",Ht,ke,Yl="<li><code>facebook/mbart-large-50-one-to-many-mmt</code> (Tradução automática multilinguística de um a vários, 50 idiomas)</li> <li><code>facebook/mbart-large-50-many-to-many-mmt</code> (Tradução automática multilinguística de vários a vários, 50 idiomas)</li> <li><code>facebook/mbart-large-50-many-to-one-mmt</code> (Tradução automática multilinguística vários a um, 50 idiomas)</li> <li><code>facebook/mbart-large-50</code> (Tradução multilinguística, 50 idiomas)</li> <li><code>facebook/mbart-large-cc25</code></li>",qt,$e,Sl=`Neste exemplo, carrega-se o checkpoint <code>facebook/mbart-large-50-many-to-many-mmt</code> para traduzir do finlandês ao inglês.
Pode-se definir o idioma de origem no tokenizador:`,Nt,_e,Ft,je,Dl="Tokenizando o texto:",Pt,Ce,Qt,Ze,Kl=`O MBart força o id do idioma de destino como o primeiro token gerado para traduzir ao idioma de destino.
É definido o <code>forced_bos_token_id</code> como <code>en</code> no método <code>generate</code> para traduzir ao inglês.`,At,Xe,Yt,Ee,Ol=`Se estiver usando o checkpoint <code>facebook/mbart-large-50-many-to-one-mmt</code> não será necessário forçar o id do idioma de destino
como sendo o primeiro token generado, caso contrário a usagem é a mesma.`,St,De,Dt;return R=new I({}),W=new Ta({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/multilingual.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/pytorch/multilingual.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/pt/tensorflow/multilingual.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/multilingual.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/pytorch/multilingual.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/pt/tensorflow/multilingual.ipynb"}]}}),L=new I({}),H=new I({}),Q=new b({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwWExNVG9rZW5pemVyJTJDJTIwWExNV2l0aExNSGVhZE1vZGVsJTBBJTBBdG9rZW5pemVyJTIwJTNEJTIwWExNVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJ4bG0tY2xtLWVuZnItMTAyNCUyMiklMEFtb2RlbCUyMCUzRCUyMFhMTVdpdGhMTUhlYWRNb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyeGxtLWNsbS1lbmZyLTEwMjQlMjIp",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> XLMTokenizer, XLMWithLMHeadModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = XLMTokenizer.from_pretrained(<span class="hljs-string">&quot;xlm-clm-enfr-1024&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = XLMWithLMHeadModel.from_pretrained(<span class="hljs-string">&quot;xlm-clm-enfr-1024&quot;</span>)`,wrap:!1}}),Y=new b({props:{code:"cHJpbnQodG9rZW5pemVyLmxhbmcyaWQp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(tokenizer.lang2id)
{<span class="hljs-string">&#x27;en&#x27;</span>: <span class="hljs-number">0</span>, <span class="hljs-string">&#x27;fr&#x27;</span>: <span class="hljs-number">1</span>}`,wrap:!1}}),D=new b({props:{code:"aW5wdXRfaWRzJTIwJTNEJTIwdG9yY2gudGVuc29yKCU1QnRva2VuaXplci5lbmNvZGUoJTIyV2lraXBlZGlhJTIwd2FzJTIwdXNlZCUyMHRvJTIyKSU1RCklMjAlMjAlMjMlMjBiYXRjaCUyMHNpemUlMjBvZiUyMDE=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>input_ids = torch.tensor([tokenizer.encode(<span class="hljs-string">&quot;Wikipedia was used to&quot;</span>)])  <span class="hljs-comment"># batch size of 1</span>',wrap:!1}}),O=new b({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>language_id = tokenizer.lang2id[<span class="hljs-string">&quot;en&quot;</span>]  <span class="hljs-comment"># 0</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>langs = torch.tensor([language_id] * input_ids.shape[<span class="hljs-number">1</span>])  <span class="hljs-comment"># torch.tensor([0, 0, 0, ..., 0])</span>

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># We reshape it to be of size (batch_size, sequence_length)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>langs = langs.view(<span class="hljs-number">1</span>, -<span class="hljs-number">1</span>)  <span class="hljs-comment"># is now of shape [1, sequence_length] (we have a batch size of 1)</span>`,wrap:!1}}),te=new b({props:{code:"b3V0cHV0cyUyMCUzRCUyMG1vZGVsKGlucHV0X2lkcyUyQyUyMGxhbmdzJTNEbGFuZ3Mp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(input_ids, langs=langs)',wrap:!1}}),ae=new I({}),ie=new I({}),pe=new I({}),he=new I({}),ve=new b({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> M2M100ForConditionalGeneration, M2M100Tokenizer

<span class="hljs-meta">&gt;&gt;&gt; </span>en_text = <span class="hljs-string">&quot;Do not meddle in the affairs of wizards, for they are subtle and quick to anger.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>chinese_text = <span class="hljs-string">&quot;不要插手巫師的事務, 因為他們是微妙的, 很快就會發怒.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = M2M100Tokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/m2m100_418M&quot;</span>, src_lang=<span class="hljs-string">&quot;zh&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = M2M100ForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;facebook/m2m100_418M&quot;</span>)`,wrap:!1}}),Je=new b({props:{code:"ZW5jb2RlZF96aCUyMCUzRCUyMHRva2VuaXplcihjaGluZXNlX3RleHQlMkMlMjByZXR1cm5fdGVuc29ycyUzRCUyMnB0JTIyKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_zh = tokenizer(chinese_text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)',wrap:!1}}),we=new b({props:{code:"Z2VuZXJhdGVkX3Rva2VucyUyMCUzRCUyMG1vZGVsLmdlbmVyYXRlKCoqZW5jb2RlZF96aCUyQyUyMGZvcmNlZF9ib3NfdG9rZW5faWQlM0R0b2tlbml6ZXIuZ2V0X2xhbmdfaWQoJTIyZW4lMjIpKSUwQXRva2VuaXplci5iYXRjaF9kZWNvZGUoZ2VuZXJhdGVkX3Rva2VucyUyQyUyMHNraXBfc3BlY2lhbF90b2tlbnMlM0RUcnVlKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generated_tokens = model.generate(**encoded_zh, forced_bos_token_id=tokenizer.get_lang_id(<span class="hljs-string">&quot;en&quot;</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">&#x27;Do not interfere with the matters of the witches, because they are delicate and will soon be angry.&#x27;</span>`,wrap:!1}}),Ue=new I({}),_e=new b({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForSeq2SeqLM

<span class="hljs-meta">&gt;&gt;&gt; </span>en_text = <span class="hljs-string">&quot;Do not meddle in the affairs of wizards, for they are subtle and quick to anger.&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>fi_text = <span class="hljs-string">&quot;Älä sekaannu velhojen asioihin, sillä ne ovat hienovaraisia ja nopeasti vihaisia.&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;facebook/mbart-large-50-many-to-many-mmt&quot;</span>, src_lang=<span class="hljs-string">&quot;fi_FI&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;facebook/mbart-large-50-many-to-many-mmt&quot;</span>)`,wrap:!1}}),Ce=new b({props:{code:"ZW5jb2RlZF9lbiUyMCUzRCUyMHRva2VuaXplcihlbl90ZXh0JTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMik=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>encoded_en = tokenizer(en_text, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)',wrap:!1}}),Xe=new b({props:{code:"Z2VuZXJhdGVkX3Rva2VucyUyMCUzRCUyMG1vZGVsLmdlbmVyYXRlKCoqZW5jb2RlZF9lbiUyQyUyMGZvcmNlZF9ib3NfdG9rZW5faWQlM0R0b2tlbml6ZXIubGFuZ19jb2RlX3RvX2lkKCUyMmVuX1hYJTIyKSklMEF0b2tlbml6ZXIuYmF0Y2hfZGVjb2RlKGdlbmVyYXRlZF90b2tlbnMlMkMlMjBza2lwX3NwZWNpYWxfdG9rZW5zJTNEVHJ1ZSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id(<span class="hljs-string">&quot;en_XX&quot;</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generated_tokens, skip_special_tokens=<span class="hljs-literal">True</span>)
<span class="hljs-string">&quot;Don&#x27;t interfere with the wizard&#x27;s affairs, because they are subtle, will soon get 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