import{s as Ls,o as Rs,n as Ps}from"../chunks/scheduler.d586627e.js";import{S as Ss,i as Xs,g as l,s as i,u,A as As,h as n,f as s,c as o,j as d,v as h,y as c,k as r,o as p,a as t,w as f,d as g,t as M,x as y}from"../chunks/index.b757c891.js";import{T as Ns}from"../chunks/Tip.4380e997.js";import{I as $e}from"../chunks/IconCopyLink.c074b82b.js";import{C as v}from"../chunks/CodeBlock.36019a2e.js";function Ys(we){let m,W="Acesse a documentação do <code>pipeline()</code> para obter uma lista completa de tarefas possíveis.";return{c(){m=l("p"),m.innerHTML=W},l(j){m=n(j,"P",{"data-svelte-h":!0}),c(m)!=="svelte-18yhfdg"&&(m.innerHTML=W)},m(j,ce){t(j,m,ce)},p:Ps,d(j){j&&s(m)}}}function Fs(we){let m,W,j,ce,x,T,ge,B,is,me,ms="Pipelines para inferência",be,_,ds=`Um [pipeline] simplifica o uso dos modelos no <a href="https://huggingface.co/models" rel="nofollow">Model Hub</a> para a inferência de uma diversidade de tarefas,
como a geração de texto, a segmentação de imagens e a classificação de áudio.
Inclusive, se não tem experiência com alguma modalidade específica ou não compreende o código que forma os modelos,
pode usar eles mesmo assim com o [pipeline]! Este tutorial te ensinará a:`,Ue,H,us="<li>Utilizar um <code>pipeline()</code> para inferência.</li> <li>Utilizar um tokenizador ou model específico.</li> <li>Utilizar um <code>pipeline()</code> para tarefas de áudio e visão computacional.</li>",Te,k,ke,$,J,Me,q,os,de,hs="Uso do pipeline",Je,V,fs=`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.`,Ze,E,gs="<li>Comece carregando um <code>pipeline()</code> e especifique uma tarefa de inferência:</li>",Ce,z,Ge,Z,Ms="<li>Passe seu dado de entrada, no caso um texto, ao <code>pipeline()</code>:</li>",Ie,L,We,R,ys="Se tiver mais de uma entrada, passe-a como uma lista:",Be,P,_e,S,js=`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>:`,He,X,qe,w,C,ye,A,rs,ue,vs="Selecionando um modelo e um tokenizador",Ve,N,xs=`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 [\`AutoTokenizer’] correspondentes. Por exemplo, carregue a classe <code>AutoModelForCausalLM</code>
para uma tarefa de modelagem de linguagem causal:`,Ee,Y,ze,F,$s="Crie uma <code>pipeline()</code> para a sua tarefa e especifíque o modelo e o tokenizador que foram carregados:",Le,Q,Re,D,ws="Passe seu texto de entrada ao <code>pipeline()</code> para gerar algum texto:",Pe,K,Se,b,G,je,O,ps,he,bs="Pipeline de audio",Xe,ee,Us=`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.`,Ae,se,Ts=`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>:`,Ne,ae,Ye,te,ks="Passe o arquivo de áudio ao <code>pipeline()</code>:",Fe,le,Qe,U,I,ve,ne,cs,fe,Js="Pipeline de visão computacional",De,ie,Zs=`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,oe,Cs='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg" alt="pipeline-cat-chonk"/>',Oe,re,es,xe,ss;return B=new $e({}),k=new Ns({props:{$$slots:{default:[Ys]},$$scope:{ctx:we}}}),q=new $e({}),z=new v({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>)`}}),L=new v({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>}]`}}),P=new v({props:{code:"Z2VuZXJhdG9yKCUwQSUyMCUyMCUyMCUyMCU1QiUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMlRocmVlJTIwUmluZ3MlMjBmb3IlMjB0aGUlMjBFbHZlbi1raW5ncyUyMHVuZGVyJTIwdGhlJTIwc2t5JTJDJTIwU2V2ZW4lMjBmb3IlMjB0aGUlMjBEd2FyZi1sb3JkcyUyMGluJTIwdGhlaXIlMjBoYWxscyUyMG9mJTIwc3RvbmUlMjIlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJOaW5lJTIwZm9yJTIwTW9ydGFsJTIwTWVuJTJDJTIwZG9vbWVkJTIwdG8lMjBkaWUlMkMlMjBPbmUlMjBmb3IlMjB0aGUlMjBEYXJrJTIwTG9yZCUyMG9uJTIwaGlzJTIwZGFyayUyMHRocm9uZSUyMiUyQyUwQSUyMCUyMCUyMCUyMCU1RCUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>generator(
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<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>)`}}),X=new v({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>)`}}),A=new $e({}),Y=new v({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMEF1dG9Ub2tlbml6ZXIlMkMlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTSUwQSUwQXRva2VuaXplciUyMCUzRCUyMEF1dG9Ub2tlbml6ZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmRpc3RpbGdwdDIlMjIpJTBBbW9kZWwlMjAlM0QlMjBBdXRvTW9kZWxGb3JDYXVzYWxMTS5mcm9tX3ByZXRyYWluZWQoJTIyZGlzdGlsZ3B0MiUyMik=",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;distilgpt2&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = AutoModelForCausalLM.from_pretrained(<span class="hljs-string">&quot;distilgpt2&quot;</span>)`}}),Q=new v({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)`}}),K=new v({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>}]`}}),O=new $e({}),ae=new v({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>)`}}),le=new v({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>}]`}}),ne=new $e({}),re=new v({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>},
 {<span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;tiger cat&#x27;</span>, <span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.023034192621707916</span>}]`}}),{c(){m=l("meta"),W=i(),j=l("p"),ce=i(),x=l("h1"),T=l("a"),ge=l("span"),u(B.$$.fragment),is=i(),me=l("span"),me.textContent=ms,be=i(),_=l("p"),_.innerHTML=ds,Ue=i(),H=l("ul"),H.innerHTML=us,Te=i(),u(k.$$.fragment),ke=i(),$=l("h2"),J=l("a"),Me=l("span"),u(q.$$.fragment),os=i(),de=l("span"),de.textContent=hs,Je=i(),V=l("p"),V.innerHTML=fs,Ze=i(),E=l("ol"),E.innerHTML=gs,Ce=i(),u(z.$$.fragment),Ge=i(),Z=l("ol"),Z.innerHTML=Ms,Ie=i(),u(L.$$.fragment),We=i(),R=l("p"),R.textContent=ys,Be=i(),u(P.$$.fragment),_e=i(),S=l("p"),S.innerHTML=js,He=i(),u(X.$$.fragment),qe=i(),w=l("h3"),C=l("a"),ye=l("span"),u(A.$$.fragment),rs=i(),ue=l("span"),ue.textContent=vs,Ve=i(),N=l("p"),N.innerHTML=xs,Ee=i(),u(Y.$$.fragment),ze=i(),F=l("p"),F.innerHTML=$s,Le=i(),u(Q.$$.fragment),Re=i(),D=l("p"),D.innerHTML=ws,Pe=i(),u(K.$$.fragment),Se=i(),b=l("h2"),G=l("a"),je=l("span"),u(O.$$.fragment),ps=i(),he=l("span"),he.textContent=bs,Xe=i(),ee=l("p"),ee.innerHTML=Us,Ae=i(),se=l("p"),se.innerHTML=Ts,Ne=i(),u(ae.$$.fragment),Ye=i(),te=l("p"),te.innerHTML=ks,Fe=i(),u(le.$$.fragment),Qe=i(),U=l("h2"),I=l("a"),ve=l("span"),u(ne.$$.fragment),cs=i(),fe=l("span"),fe.textContent=Js,De=i(),ie=l("p"),ie.innerHTML=Zs,Ke=i(),oe=l("p"),oe.innerHTML=Cs,Oe=i(),u(re.$$.fragment),es=i(),xe=l("p"),this.h()},l(e){const 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