import{s as Io,o as No,n as ml}from"../chunks/scheduler.d586627e.js";import{S as qo,i as So,g as o,s,u,A as Ao,h as n,f as t,c as i,j as d,v as c,y as p,k as f,o as r,a as l,w as h,d as v,t as g,x as $}from"../chunks/index.b757c891.js";import{T as pl}from"../chunks/Tip.4380e997.js";import{I as F}from"../chunks/IconCopyLink.c074b82b.js";import{C as w}from"../chunks/CodeBlock.36019a2e.js";function zo(_){let m,y="É necessário manter o diretório <code>transformers</code> se desejas continuar usando a biblioteca.";return{c(){m=o("p"),m.innerHTML=y},l(b){m=n(b,"P",{"data-svelte-h":!0}),p(m)!=="svelte-1bdt7ke"&&(m.innerHTML=y)},m(b,x){l(b,m,x)},p:ml,d(b){b&&t(m)}}}function Fo(_){let m,y=`O 🤗 Transformers usará as variáveis de ambiente do shell <code>PYTORCH_TRANSFORMERS_CACHE</code> ou <code>PYTORCH_PRETRAINED_BERT_CACHE</code>
se estiver vindo de uma versão anterior da biblioteca que tenha configurado essas variáveis de ambiente, a menos que
você especifique a variável de ambiente do shell <code>TRANSFORMERS_CACHE</code>.`;return{c(){m=o("p"),m.innerHTML=y},l(b){m=n(b,"P",{"data-svelte-h":!0}),p(m)!=="svelte-1tkrq2a"&&(m.innerHTML=y)},m(b,x){l(b,m,x)},p:ml,d(b){b&&t(m)}}}function Vo(_){let m,y=`Você pode adicionar o <a href="https://huggingface.co/docs/datasets/" rel="nofollow">🤗 Datasets</a> ao pipeline de treinamento offline declarando
a variável de ambiente <code>HF_DATASETS_OFFLINE=1</code>.`;return{c(){m=o("p"),m.innerHTML=y},l(b){m=n(b,"P",{"data-svelte-h":!0}),p(m)!=="svelte-56g52x"&&(m.innerHTML=y)},m(b,x){l(b,m,x)},p:ml,d(b){b&&t(m)}}}function Xo(_){let m,y='Para obter mais detalhes sobre como baixar arquivos armazenados no Hub, consulte a seção <a href="https://huggingface.co/docs/hub/how-to-downstream" rel="nofollow">How to download files from the Hub</a>.';return{c(){m=o("p"),m.innerHTML=y},l(b){m=n(b,"P",{"data-svelte-h":!0}),p(m)!=="svelte-1ws8uxc"&&(m.innerHTML=y)},m(b,x){l(b,m,x)},p:ml,d(b){b&&t(m)}}}function Yo(_){let m,y,b,x,M,U,Ct,V,dl,pt,kl="Guia de Instalação",Gt,X,Hl=`Neste guia poderá encontrar informações para a instalação do 🤗 Transformers para qualquer biblioteca de
Machine Learning com a qual esteja a trabalhar. Além disso, poderá encontrar informações sobre como gerar cachês e
configurar o 🤗 Transformers para execução em modo offline (opcional).`,It,Y,jl=`🤗 Transformers foi testado com Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, e Flax. Para instalar a biblioteca de
deep learning com que deseja trabalhar, siga as instruções correspondentes listadas a seguir:`,Nt,B,Zl='<li><a href="https://pytorch.org/get-started/locally/" rel="nofollow">PyTorch</a></li> <li><a href="https://www.tensorflow.org/install/pip" rel="nofollow">TensorFlow 2.0</a></li> <li><a href="https://flax.readthedocs.io/en/latest/" rel="nofollow">Flax</a></li>',qt,T,W,Jt,O,fl,mt,El="Instalação pelo Pip",St,Q,Ul=`É sugerido instalar o 🤗 Transformers num <a href="https://docs.python.org/3/library/venv.html" rel="nofollow">ambiente virtual</a>. Se precisar
de mais informações sobre ambientes virtuais em Python, consulte este <a href="https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/" rel="nofollow">guia</a>.
Um ambiente virtual facilitará a manipulação e organização de projetos e evita problemas de compatibilidade entre dependências.`,At,D,Wl="Comece criando um ambiente virtual no diretório do seu projeto:",zt,K,Ft,ee,Ll="E para ativar o ambiente virtual:",Vt,te,Xt,ae,Pl="Agora É possível instalar o 🤗 Transformers com o comando a seguir:",Yt,le,Bt,oe,Rl="Somente para a CPU, é possível instalar o 🤗 Transformers e a biblioteca de deep learning respectiva apenas numa linha.",Ot,ne,Gl="Por exemplo, para instalar o 🤗 Transformers e o PyTorch, digite:",Qt,se,Dt,ie,Il="🤗 Transformers e TensorFlow 2.0:",Kt,re,ea,pe,Nl="🤗 Transformers e Flax:",ta,me,aa,de,ql="Por último, verifique se o 🤗 Transformers foi instalado com sucesso usando o seguinte comando para baixar um modelo pré-treinado:",la,fe,oa,ue,Sl="Em seguida, imprima um rótulo e sua pontuação:",na,ce,sa,C,L,kt,he,ul,dt,Al="Instalação usando a fonte",ia,ve,zl="Para instalar o 🤗 Transformers a partir da fonte use o seguinte comando:",ra,ge,pa,$e,Fl=`O comando acima instalará a versão <code>master</code> mais atual em vez da última versão estável. A versão <code>master</code> é útil para
utilizar os últimos updates contidos em 🤗 Transformers. Por exemplo, um erro recente pode ter sido corrigido somente
após a última versão estável, antes que houvesse um novo lançamento. No entanto, há a possibilidade que a versão <code>master</code> não esteja estável.
A equipa trata de mantér a versão <code>master</code> operacional e a maioria dos erros são resolvidos em poucas horas ou dias.
Se encontrar quaisquer problemas, por favor abra um <a href="https://github.com/huggingface/transformers/issues" rel="nofollow">Issue</a> para que o
mesmo possa ser corrigido o mais rápido possível.`,ma,be,Vl="Verifique que o 🤗 Transformers está instalado corretamente usando o seguinte comando:",da,we,fa,J,P,Ht,ye,cl,ft,Xl="Instalação editável",ua,xe,Yl="Uma instalação editável será necessária caso desejas um dos seguintes:",ca,_e,Bl="<li>Usar a versão <code>master</code> do código fonte.</li> <li>Contribuir ao 🤗 Transformers e precisa testar mudanças ao código.</li>",ha,Me,Ol="Para tal, clone o repositório e instale o 🤗 Transformers com os seguintes comandos:",va,Te,ga,Ce,Ql=`Estes comandos vão ligar o diretório para o qual foi clonado o repositório ao caminho de bibliotecas do Python.
O Python agora buscará dentro dos arquivos que foram clonados além dos caminhos normais da biblioteca.
Por exemplo, se os pacotes do Python se encontram instalados no caminho <code>~/anaconda3/envs/main/lib/python3.7/site-packages/</code>,
o Python também buscará módulos no diretório onde clonamos o repositório <code>~/transformers/</code>.`,$a,R,ba,Je,Dl="Assim, É possível atualizar sua cópia local para com a última versão do 🤗 Transformers com o seguinte comando:",wa,ke,ya,He,Kl="O ambiente de Python que foi criado para a instalação do 🤗 Transformers encontrará a versão <code>master</code> em execuções seguintes.",xa,k,G,jt,je,hl,ut,eo="Instalação usando o Conda",_a,Ze,to="É possível instalar o 🤗 Transformers a partir do canal conda <code>huggingface</code> com o seguinte comando:",Ma,Ee,Ta,H,I,Zt,Ue,vl,ct,ao="Configuração do Cachê",Ca,We,lo=`Os modelos pré-treinados são baixados e armazenados no cachê local, encontrado em <code>~/.cache/huggingface/transformers/</code>.
Este é o diretório padrão determinado pela variável <code>TRANSFORMERS_CACHE</code> dentro do shell.
No Windows, este diretório pré-definido é dado por <code>C:\\Users\\username\\.cache\\huggingface\\transformers</code>.
É possível mudar as variáveis dentro do shell em ordem de prioridade para especificar um diretório de cachê diferente:`,Ja,Le,oo="<li>Variável de ambiente do shell (por padrão): <code>TRANSFORMERS_CACHE</code>.</li> <li>Variável de ambiente do shell:<code>HF_HOME</code> + <code>transformers/</code>.</li> <li>Variável de ambiente do shell: <code>XDG_CACHE_HOME</code> + <code>/huggingface/transformers</code>.</li>",ka,N,Ha,j,q,Et,Pe,gl,ht,no="Modo Offline",ja,Re,so=`O 🤗 Transformers também pode ser executado num ambiente de firewall ou fora da rede (offline) usando arquivos locais.
Para tal, configure a variável de ambiente de modo que <code>TRANSFORMERS_OFFLINE=1</code>.`,Za,S,Ea,Ge,io="Segue um exemplo de execução do programa numa rede padrão com firewall para instâncias externas, usando o seguinte comando:",Ua,Ie,Wa,Ne,ro="Execute esse mesmo programa numa instância offline com o seguinte comando:",La,qe,Pa,Se,po="O script agora deve ser executado sem travar ou expirar, pois procurará apenas por arquivos locais.",Ra,Z,A,Ut,Ae,$l,vt,mo="Obtendo modelos e tokenizers para uso offline",Ga,ze,fo="Outra opção para usar o 🤗 Transformers offline é baixar os arquivos antes e depois apontar para o caminho local onde estão localizados. Existem três maneiras de fazer isso:",Ia,Fe,uo='<li><p>Baixe um arquivo por meio da interface de usuário do <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a> clicando no ícone ↓.</p> <p><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/download-icon.png" alt="download-icon"/></p></li>',Na,gt,Ve,$t,co="Use o pipeline do <code>PreTrainedModel.from_pretrained()</code> e <code>PreTrainedModel.save_pretrained()</code>:",bl,Wt,Xe,bt,ho="Baixa os arquivos previamente com <code>PreTrainedModel.from_pretrained()</code>:",wl,Ye,qa,E,Be,wt,vo="Salve os arquivos em um diretório específico com <code>PreTrainedModel.save_pretrained()</code>:",yl,Oe,xl,Qe,yt,go="Quando estiver offline, acesse os arquivos com <code>PreTrainedModel.from_pretrained()</code> do diretório especificado:",_l,De,Sa,xt,Ke,_t,$o='Baixando arquivos programaticamente com a biblioteca <a href="https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub" rel="nofollow">huggingface_hub</a>:',Ml,et,tt,Mt,bo='Instale a biblioteca <a href="https://github.com/huggingface/huggingface_hub/tree/main/src/huggingface_hub" rel="nofollow">huggingface_hub</a> em seu ambiente virtual:',Tl,at,Cl,lt,Tt,wo='Utiliza a função <a href="https://huggingface.co/docs/hub/adding-a-library#download-files-from-the-hub" rel="nofollow"><code>hf_hub_download</code></a> para baixar um arquivo para um caminho específico. Por exemplo, o comando a seguir baixará o arquivo <code>config.json</code> para o modelo <a href="https://huggingface.co/bigscience/T0_3B" rel="nofollow">T0</a> no caminho desejado:',Jl,ot,Aa,nt,yo="Depois que o arquivo for baixado e armazenado no cachê local, especifique seu caminho local para carregá-lo e usá-lo:",za,st,Fa,z,Va,Lt,Xa;return V=new F({}),O=new F({}),K=new w({props:{code:"cHl0aG9uJTIwLW0lMjB2ZW52JTIwLmVudg==",highlighted:'python -m venv .<span class="hljs-built_in">env</span>'}}),te=new w({props:{code:"c291cmNlJTIwLmVudiUyRmJpbiUyRmFjdGl2YXRl",highlighted:'<span class="hljs-built_in">source</span> .<span class="hljs-built_in">env</span>/bin/activate'}}),le=new w({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycw==",highlighted:"pip install transformers"}}),se=new w({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyU1QnRvcmNoJTVE",highlighted:"pip install transformers[torch]"}}),re=new w({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyU1QnRmLWNwdSU1RA==",highlighted:"pip install transformers[tf-cpu]"}}),me=new w({props:{code:"cGlwJTIwaW5zdGFsbCUyMHRyYW5zZm9ybWVycyU1QmZsYXglNUQ=",highlighted:"pip install transformers[flax]"}}),fe=new w({props:{code:"cHl0aG9uJTIwLWMlMjAlMjJmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwcGlwZWxpbmUlM0IlMjBwcmludChwaXBlbGluZSgnc2VudGltZW50LWFuYWx5c2lzJykoJ3dlJTIwbG92ZSUyMHlvdScpKSUyMg==",highlighted:'python -c <span class="hljs-string">&quot;from transformers import pipeline; print(pipeline(&#x27;sentiment-analysis&#x27;)(&#x27;we love you&#x27;))&quot;</span>'}}),ce=new w({props:{code:"JTVCJTdCJ2xhYmVsJyUzQSUyMCdQT1NJVElWRSclMkMlMjAnc2NvcmUnJTNBJTIwMC45OTk4NzA0NzkxMDY5MDMxJTdEJTVE",highlighted:'[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;POSITIVE&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: 0.9998704791069031}]'}}),he=new F({}),ge=new w({props:{code:"cGlwJTIwaW5zdGFsbCUyMGdpdCUyQmh0dHBzJTNBJTJGJTJGZ2l0aHViLmNvbSUyRmh1Z2dpbmdmYWNlJTJGdHJhbnNmb3JtZXJz",highlighted:"pip install git+https://github.com/huggingface/transformers"}}),we=new w({props:{code:"cHl0aG9uJTIwLWMlMjAlMjJmcm9tJTIwdHJhbnNmb3JtZXJzJTIwaW1wb3J0JTIwcGlwZWxpbmUlM0IlMjBwcmludChwaXBlbGluZSgnc2VudGltZW50LWFuYWx5c2lzJykoJ0klMjBsb3ZlJTIweW91JykpJTIy",highlighted:'python -c <span class="hljs-string">&quot;from transformers import pipeline; print(pipeline(&#x27;sentiment-analysis&#x27;)(&#x27;I love you&#x27;))&quot;</span>'}}),ye=new F({}),Te=new w({props:{code:"Z2l0JTIwY2xvbmUlMjBodHRwcyUzQSUyRiUyRmdpdGh1Yi5jb20lMkZodWdnaW5nZmFjZSUyRnRyYW5zZm9ybWVycy5naXQlMEFjZCUyMHRyYW5zZm9ybWVycyUwQXBpcCUyMGluc3RhbGwlMjAtZSUyMC4=",highlighted:`git <span class="hljs-built_in">clone</span> https://github.com/huggingface/transformers.git
<span class="hljs-built_in">cd</span> transformers
pip install -e .`}}),R=new pl({props:{warning:!0,$$slots:{default:[zo]},$$scope:{ctx:_}}}),ke=new w({props:{code:"Y2QlMjB+JTJGdHJhbnNmb3JtZXJzJTJGJTBBZ2l0JTIwcHVsbA==",highlighted:`<span class="hljs-built_in">cd</span> ~/transformers/
git pull`}}),je=new F({}),Ee=new w({props:{code:"Y29uZGElMjBpbnN0YWxsJTIwLWMlMjBodWdnaW5nZmFjZSUyMHRyYW5zZm9ybWVycw==",highlighted:"conda install -c huggingface transformers"}}),Ue=new F({}),N=new pl({props:{$$slots:{default:[Fo]},$$scope:{ctx:_}}}),Pe=new F({}),S=new pl({props:{$$slots:{default:[Vo]},$$scope:{ctx:_}}}),Ie=new w({props:{code:"cHl0aG9uJTIwZXhhbXBsZXMlMkZweXRvcmNoJTJGdHJhbnNsYXRpb24lMkZydW5fdHJhbnNsYXRpb24ucHklMjAtLW1vZGVsX25hbWVfb3JfcGF0aCUyMHQ1LXNtYWxsJTIwLS1kYXRhc2V0X25hbWUlMjB3bXQxNiUyMC0tZGF0YXNldF9jb25maWclMjByby1lbiUyMC4uLg==",highlighted:"python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ..."}}),qe=new w({props:{code:"SEZfREFUQVNFVFNfT0ZGTElORSUzRDElMjBUUkFOU0ZPUk1FUlNfT0ZGTElORSUzRDElMjAlNUMlMEFweXRob24lMjBleGFtcGxlcyUyRnB5dG9yY2glMkZ0cmFuc2xhdGlvbiUyRnJ1bl90cmFuc2xhdGlvbi5weSUyMC0tbW9kZWxfbmFtZV9vcl9wYXRoJTIwdDUtc21hbGwlMjAtLWRhdGFzZXRfbmFtZSUyMHdtdDE2JTIwLS1kYXRhc2V0X2NvbmZpZyUyMHJvLWVuJTIwLi4u",highlighted:`HF_DATASETS_OFFLINE=1 TRANSFORMERS_OFFLINE=1 \\
python examples/pytorch/translation/run_translation.py --model_name_or_path t5-small --dataset_name wmt16 --dataset_config ro-en ...`}}),Ae=new F({}),Ye=new w({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0lMEElMEF0b2tlbml6ZXIlMjAlM0QlMjBBdXRvVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjJiaWdzY2llbmNlJTJGVDBfM0IlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JTZXEyU2VxTE0uZnJvbV9wcmV0cmFpbmVkKCUyMmJpZ3NjaWVuY2UlMkZUMF8zQiUyMik=",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>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;bigscience/T0_3B&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForSeq2SeqLM.from_pretrained(<span class="hljs-string">&quot;bigscience/T0_3B&quot;</span>)`}}),Oe=new w({props:{code:"dG9rZW5pemVyLnNhdmVfcHJldHJhaW5lZCglMjIuJTJGeW91ciUyRnBhdGglMkZiaWdzY2llbmNlX3QwJTIyKSUwQW1vZGVsLnNhdmVfcHJldHJhaW5lZCglMjIuJTJGeW91ciUyRnBhdGglMkZiaWdzY2llbmNlX3QwJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.save_pretrained(<span class="hljs-string">&quot;./your/path/bigscience_t0&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model.save_pretrained(<span class="hljs-string">&quot;./your/path/bigscience_t0&quot;</span>)`}}),De=new w({props:{code:"dG9rZW5pemVyJTIwJTNEJTIwQXV0b1Rva2VuaXplci5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnlvdXIlMkZwYXRoJTJGYmlnc2NpZW5jZV90MCUyMiklMEFtb2RlbCUyMCUzRCUyMEF1dG9Nb2RlbC5mcm9tX3ByZXRyYWluZWQoJTIyLiUyRnlvdXIlMkZwYXRoJTJGYmlnc2NpZW5jZV90MCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;./your/path/bigscience_t0&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModel.from_pretrained(<span class="hljs-string">&quot;./your/path/bigscience_t0&quot;</span>)`}}),at=new w({props:{code:"cHl0aG9uJTIwLW0lMjBwaXAlMjBpbnN0YWxsJTIwaHVnZ2luZ2ZhY2VfaHVi",highlighted:"python -m pip install huggingface_hub"}}),ot=new w({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGhmX2h1Yl9kb3dubG9hZCUwQSUwQWhmX2h1Yl9kb3dubG9hZChyZXBvX2lkJTNEJTIyYmlnc2NpZW5jZSUyRlQwXzNCJTIyJTJDJTIwZmlsZW5hbWUlM0QlMjJjb25maWcuanNvbiUyMiUyQyUyMGNhY2hlX2RpciUzRCUyMi4lMkZ5b3VyJTJGcGF0aCUyRmJpZ3NjaWVuY2VfdDAlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

<span class="hljs-meta">&gt;&gt;&gt; </span>hf_hub_download(repo_id=<span class="hljs-string">&quot;bigscience/T0_3B&quot;</span>, filename=<span class="hljs-string">&quot;config.json&quot;</span>, cache_dir=<span class="hljs-string">&quot;./your/path/bigscience_t0&quot;</span>)`}}),st=new w({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Db25maWclMEElMEFjb25maWclMjAlM0QlMjBBdXRvQ29uZmlnLmZyb21fcHJldHJhaW5lZCglMjIuJTJGeW91ciUyRnBhdGglMkZiaWdzY2llbmNlX3QwJTJGY29uZmlnLmpzb24lMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoConfig

<span class="hljs-meta">&gt;&gt;&gt; </span>config = AutoConfig.from_pretrained(<span class="hljs-string">&quot;./your/path/bigscience_t0/config.json&quot;</span>)`}}),z=new 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