import{s as Ke,o as Oe,n as es}from"../chunks/scheduler.d586627e.js";import{S as ss,i as as,g as i,s as l,r,A as ts,h as p,f as a,c as n,j as De,u as m,x as h,k as Be,y as ls,a as t,v as c,d,t as u,w as f}from"../chunks/index.8589a59c.js";import{T as ns}from"../chunks/Tip.84e2336e.js";import{C as M}from"../chunks/CodeBlock.47c46d2c.js";import{H as O,E as is}from"../chunks/EditOnGithub.073dfa26.js";function ps(ee){let o,$="Acesse a documentação do <code>pipeline()</code> para obter uma lista completa de tarefas possíveis.";return{c(){o=i("p"),o.innerHTML=$},l(g){o=p(g,"P",{"data-svelte-h":!0}),h(o)!=="svelte-18yhfdg"&&(o.innerHTML=$)},m(g,D){t(g,o,D)},p:es,d(g){g&&a(o)}}}function os(ee){let o,$,g,D,b,se,U,He=`Um [pipeline] simplifica o uso dos modelos no <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a> para a inferência de uma diversidade de tarefas,
como a geração de texto, a segmentação de imagens e a classificação de áudio.
Inclusive, se não tem experiência com alguma modalidade específica ou não compreende o código que forma os modelos,
pode usar eles mesmo assim com o [pipeline]! Este tutorial te ensinará a:`,ae,x,Ve="<li>Utilizar um <code>pipeline()</code> para inferência.</li> <li>Utilizar um tokenizador ou model específico.</li> <li>Utilizar um <code>pipeline()</code> para tarefas de áudio e visão computacional.</li>",te,y,le,T,ne,w,qe=`Mesmo que cada tarefa tenha um <code>pipeline()</code> associado, é mais simples usar a abstração geral do <code>pipeline()</code> que
contém todos os pipelines das tarefas mais específicas.
O <code>pipeline()</code> carrega automaticamenta um modelo predeterminado e um tokenizador com capacidade de inferência para sua
tarefa.`,ie,J,_e="<li>Comece carregando um <code>pipeline()</code> e especifique uma tarefa de inferência:</li>",pe,v,oe,j,Re="<li>Passe seu dado de entrada, no caso um texto, ao <code>pipeline()</code>:</li>",re,Z,me,k,Le="Se tiver mais de uma entrada, passe-a como uma lista:",ce,G,de,I,ze=`Qualquer parâmetro adicional para a sua tarefa também pode ser incluído no <code>pipeline()</code>. A tarefa <code>text-generation</code> tem um método
<code>generate()</code> com vários parâmetros para controlar a saída.
Por exemplo, se quiser gerar mais de uma saída, defina-a no parâmetro <code>num_return_sequences</code>:`,ue,W,fe,C,he,B,Ee=`O <code>pipeline()</code> aceita qualquer modelo do <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a>. Há rótulos adicionais no Model Hub
que te permitem filtrar pelo modelo que gostaria de usar para sua tarefa. Uma vez que tiver escolhido o modelo apropriado,
carregue-o com as classes <code>AutoModelFor</code> e <code>AutoTokenizer</code> correspondentes. Por exemplo, carregue a classe <code>AutoModelForCausalLM</code>
para uma tarefa de modelagem de linguagem causal:`,ge,H,Me,V,Xe="Crie uma <code>pipeline()</code> para a sua tarefa e especifíque o modelo e o tokenizador que foram carregados:",ye,q,je,_,Pe="Passe seu texto de entrada ao <code>pipeline()</code> para gerar algum texto:",$e,R,be,L,Ue,z,Fe=`A flexibilidade do <code>pipeline()</code> significa que também pode-se extender às tarefas de áudio.
La flexibilidad de <code>pipeline()</code> significa que también se puede extender a tareas de audio.`,xe,E,Se=`Por exemplo, classifiquemos a emoção de um breve fragmento do famoso discurso de John F. Kennedy /home/rzimmerdev/dev/transformers/docs/source/pt/pipeline_tutorial.md
Encontre um modelo de <a href="https://huggingface.co/models?pipeline_tag=audio-classification" rel="nofollow">audio classification</a> para
reconhecimento de emoções no Model Hub e carregue-o usando o <code>pipeline()</code>:`,Te,X,we,P,Ye="Passe o arquivo de áudio ao <code>pipeline()</code>:",Je,F,ve,S,Ze,Y,Qe=`Finalmente, utilizar um <code>pipeline()</code> para tarefas de visão é praticamente a mesma coisa.
Especifique a sua tarefa de visão e passe a sua imagem ao classificador.
A imagem pode ser um link ou uma rota local à imagem. Por exemplo, que espécie de gato está presente na imagem?`,ke,Q,Ae='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" alt="pipeline-cat-chonk"/>',Ge,A,Ie,N,We,K,Ce;return b=new O({props:{title:"Pipelines para inferência",local:"pipelines-para-inferência",headingTag:"h1"}}),y=new ns({props:{$$slots:{default:[ps]},$$scope:{ctx:ee}}}),T=new O({props:{title:"Uso do pipeline",local:"uso-do-pipeline",headingTag:"h2"}}),v=new M({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBZ2VuZXJhdG9yJTIwJTNEJTIwcGlwZWxpbmUodGFzayUzRCUyMnRleHQtZ2VuZXJhdGlvbiUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = pipeline(task=<span class="hljs-string">&quot;text-generation&quot;</span>)`,wrap:!1}}),Z=new M({props:{code:"Z2VuZXJhdG9yKCUyMlRocmVlJTIwUmluZ3MlMjBmb3IlMjB0aGUlMjBFbHZlbi1raW5ncyUyMHVuZGVyJTIwdGhlJTIwc2t5JTJDJTIwU2V2ZW4lMjBmb3IlMjB0aGUlMjBEd2FyZi1sb3JkcyUyMGluJTIwdGhlaXIlMjBoYWxscyUyMG9mJTIwc3RvbmUlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generator(<span class="hljs-string">&quot;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone&quot;</span>)
[{<span class="hljs-string">&#x27;generated_text&#x27;</span>: <span class="hljs-string">&#x27;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain&#x27;</span>}]`,wrap:!1}}),G=new M({props:{code:"Z2VuZXJhdG9yKCUwQSUyMCUyMCUyMCUyMCU1QiUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMlRocmVlJTIwUmluZ3MlMjBmb3IlMjB0aGUlMjBFbHZlbi1raW5ncyUyMHVuZGVyJTIwdGhlJTIwc2t5JTJDJTIwU2V2ZW4lMjBmb3IlMjB0aGUlMjBEd2FyZi1sb3JkcyUyMGluJTIwdGhlaXIlMjBoYWxscyUyMG9mJTIwc3RvbmUlMjIlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJOaW5lJTIwZm9yJTIwTW9ydGFsJTIwTWVuJTJDJTIwZG9vbWVkJTIwdG8lMjBkaWUlMkMlMjBPbmUlMjBmb3IlMjB0aGUlMjBEYXJrJTIwTG9yZCUyMG9uJTIwaGlzJTIwZGFyayUyMHRocm9uZSUyMiUyQyUwQSUyMCUyMCUyMCUyMCU1RCUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generator(
<span class="hljs-meta">... </span>    [
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone&quot;</span>,
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne&quot;</span>,
<span class="hljs-meta">... </span>    ]
<span class="hljs-meta">... </span>)`,wrap:!1}}),W=new M({props:{code:"Z2VuZXJhdG9yKCUwQSUyMCUyMCUyMCUyMCUyMlRocmVlJTIwUmluZ3MlMjBmb3IlMjB0aGUlMjBFbHZlbi1raW5ncyUyMHVuZGVyJTIwdGhlJTIwc2t5JTJDJTIwU2V2ZW4lMjBmb3IlMjB0aGUlMjBEd2FyZi1sb3JkcyUyMGluJTIwdGhlaXIlMjBoYWxscyUyMG9mJTIwc3RvbmUlMjIlMkMlMEElMjAlMjAlMjAlMjBudW1fcmV0dXJuX3NlcXVlbmNlcyUzRDIlMkMlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generator(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone&quot;</span>,
<span class="hljs-meta">... </span>    num_return_sequences=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>)`,wrap:!1}}),C=new O({props:{title:"Selecionando um modelo e um tokenizador",local:"selecionando-um-modelo-e-um-tokenizador",headingTag:"h3"}}),H=new M({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTSUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQlMkZkaXN0aWxncHQyJTIyKSUwQW1vZGVsJTIwJTNEJTIwQXV0b01vZGVsRm9yQ2F1c2FsTE0uZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGJlcnQlMkZkaXN0aWxncHQyJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, AutoModelForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;distilbert/distilgpt2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;distilbert/distilgpt2&quot;</span>)`,wrap:!1}}),q=new M({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBZ2VuZXJhdG9yJTIwJTNEJTIwcGlwZWxpbmUodGFzayUzRCUyMnRleHQtZ2VuZXJhdGlvbiUyMiUyQyUyMG1vZGVsJTNEbW9kZWwlMkMlMjB0b2tlbml6ZXIlM0R0b2tlbml6ZXIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = pipeline(task=<span class="hljs-string">&quot;text-generation&quot;</span>, model=model, tokenizer=tokenizer)`,wrap:!1}}),R=new M({props:{code:"Z2VuZXJhdG9yKCUyMlRocmVlJTIwUmluZ3MlMjBmb3IlMjB0aGUlMjBFbHZlbi1raW5ncyUyMHVuZGVyJTIwdGhlJTIwc2t5JTJDJTIwU2V2ZW4lMjBmb3IlMjB0aGUlMjBEd2FyZi1sb3JkcyUyMGluJTIwdGhlaXIlMjBoYWxscyUyMG9mJTIwc3RvbmUlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generator(<span class="hljs-string">&quot;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone&quot;</span>)
[{<span class="hljs-string">&#x27;generated_text&#x27;</span>: <span class="hljs-string">&#x27;Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm&#x27;</span>}]`,wrap:!1}}),L=new O({props:{title:"Pipeline de audio",local:"pipeline-de-audio",headingTag:"h2"}}),X=new M({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBYXVkaW9fY2xhc3NpZmllciUyMCUzRCUyMHBpcGVsaW5lKCUwQSUyMCUyMCUyMCUyMHRhc2slM0QlMjJhdWRpby1jbGFzc2lmaWNhdGlvbiUyMiUyQyUyMG1vZGVsJTNEJTIyZWhjYWxhYnJlcyUyRndhdjJ2ZWMyLWxnLXhsc3ItZW4tc3BlZWNoLWVtb3Rpb24tcmVjb2duaXRpb24lMjIlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>audio_classifier = pipeline(
<span class="hljs-meta">... </span>    task=<span class="hljs-string">&quot;audio-classification&quot;</span>, model=<span class="hljs-string">&quot;ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition&quot;</span>
<span class="hljs-meta">... </span>)`,wrap:!1}}),F=new M({props:{code:"YXVkaW9fY2xhc3NpZmllciglMjJqZmtfbW9vbl9zcGVlY2gud2F2JTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>audio_classifier(<span class="hljs-string">&quot;jfk_moon_speech.wav&quot;</span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;calm&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.13856211304664612</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;disgust&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.13148026168346405</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;happy&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.12635163962841034</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;angry&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.12439591437578201</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;fearful&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.12404385954141617</span>}]`,wrap:!1}}),S=new O({props:{title:"Pipeline de visão computacional",local:"pipeline-de-visão-computacional",headingTag:"h2"}}),A=new M({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBdmlzaW9uX2NsYXNzaWZpZXIlMjAlM0QlMjBwaXBlbGluZSh0YXNrJTNEJTIyaW1hZ2UtY2xhc3NpZmljYXRpb24lMjIpJTBBdmlzaW9uX2NsYXNzaWZpZXIoJTBBJTIwJTIwJTIwJTIwaW1hZ2VzJTNEJTIyaHR0cHMlM0ElMkYlMkZodWdnaW5nZmFjZS5jbyUyRmRhdGFzZXRzJTJGaHVnZ2luZ2ZhY2UlMkZkb2N1bWVudGF0aW9uLWltYWdlcyUyRnJlc29sdmUlMkZtYWluJTJGcGlwZWxpbmUtY2F0LWNob25rLmpwZWclMjIlMEEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>vision_classifier = pipeline(task=<span class="hljs-string">&quot;image-classification&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>vision_classifier(
<span class="hljs-meta">... </span>    images=<span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg&quot;</span>
<span class="hljs-meta">... </span>)
[{<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;lynx, catamount&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.4403027892112732</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;cougar, puma, catamount, mountain lion, painter, panther, Felis concolor&#x27;</span>,
  <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.03433405980467796</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;snow leopard, ounce, Panthera uncia&#x27;</span>,
  <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.032148055732250214</span>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;Egyptian cat&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.02353910356760025</span>},
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