import{S as ol,i as pl,s as hl,e as r,k as f,w as m,t as n,M as fl,c as i,d as s,m as c,a as o,x as u,h as l,b as h,N as cl,G as a,g as p,y as d,q as g,o as v,B as _,v as ml}from"../chunks/vendor-hf-doc-builder.js";import{T as ul}from"../chunks/Tip-hf-doc-builder.js";import{I as ls}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as y}from"../chunks/CodeBlock-hf-doc-builder.js";function dl($s){let j,N,k,b,F;return{c(){j=r("p"),N=n("Take a look at the "),k=r("a"),b=n("pipeline()"),F=n(" documentation for a complete list of supported tasks and available parameters."),this.h()},l(w){j=i(w,"P",{});var P=o(j);N=l(P,"Take a look at the "),k=i(P,"A",{href:!0});var H=o(k);b=l(H,"pipeline()"),H.forEach(s),F=l(P," documentation for a complete list of supported tasks and available parameters."),P.forEach(s),this.h()},h(){h(k,"href","/docs/transformers/pr_19907/en/main_classes/pipelines#transformers.pipeline")},m(w,P){p(w,j,P),a(j,N),a(j,k),a(k,b),a(j,F)},d(w){w&&s(j)}}}function gl($s){let j,N,k,b,F,w,P,H,ya,xs,E,ba,Pe,Ea,qa,ae,Aa,Pa,Te,Ta,Sa,ws,T,te,Ma,Se,Fa,za,Ca,rs,La,Ia,ne,Da,Me,Na,Ha,ys,R,bs,z,U,is,le,Ra,os,Ua,Es,q,Oa,Fe,Ka,Va,ze,Wa,Ga,Ce,Ba,Qa,qs,Le,re,Ja,Ie,Xa,Ya,As,ie,Ps,oe,pe,Za,De,et,st,Ts,he,Ss,Ne,at,Ms,fe,Fs,x,tt,He,nt,lt,ps,rt,it,Re,ot,pt,hs,ht,ft,zs,ce,Cs,C,O,fs,me,ct,cs,mt,Ls,$,ut,Ue,dt,gt,ue,vt,_t,ms,jt,kt,Oe,$t,xt,Ke,wt,yt,Is,de,Ds,K,bt,Ve,Et,qt,Ns,ge,Hs,V,At,We,Pt,Tt,Rs,ve,Us,L,W,us,_e,St,ds,Mt,Os,G,Ft,Ge,zt,Ct,Ks,Be,Lt,Vs,je,Ws,S,It,ke,Dt,Nt,Qe,Ht,Rt,Gs,$e,Bs,B,Ut,Je,Ot,Kt,Qs,xe,Js,I,Q,gs,we,Vt,vs,Wt,Xs,J,Gt,Xe,Bt,Qt,Ys,Ye,Jt,Zs,Ze,es,rn,ea,ye,sa,D,X,_s,be,Xt,js,Yt,aa,Y,Zt,ss,en,sn,ta,as,an,na,Ee,la,Z,tn,ks,nn,ln,ra,qe,ia;return w=new ls({}),R=new ul({props:{$$slots:{default:[dl]},$$scope:{ctx:$s}}}),le=new ls({}),ie=new y({props:{code:`from transformers import pipeline

generator = pipeline(task="text-generation")`,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>)`}}),he=new y({props:{code:`generator(
    "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
)  # doctest: +SKIP`,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>)  <span class="hljs-comment"># doctest: +SKIP</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>}]`}}),fe=new y({props:{code:`generator(
    [
        "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
        "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
    ]
)  # doctest: +SKIP`,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>)  <span class="hljs-comment"># doctest: +SKIP</span>`}}),ce=new y({props:{code:`generator(
    "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
    num_return_sequences=2,
)  # doctest: +SKIP`,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>)  <span class="hljs-comment"># doctest: +SKIP</span>`}}),me=new ls({}),de=new y({props:{code:`from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
model = AutoModelForCausalLM.from_pretrained("distilgpt2")`,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>)`}}),ge=new y({props:{code:`from transformers import pipeline

generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer)`,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)`}}),ve=new y({props:{code:`generator(
    "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
)  # doctest: +SKIP`,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>)  <span class="hljs-comment"># doctest: +SKIP</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>}]`}}),_e=new ls({}),je=new y({props:{code:`from datasets import load_dataset
import torch

torch.manual_seed(42)
ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
audio_file = ds[0]["audio"]["path"]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>torch.manual_seed(<span class="hljs-number">42</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>ds = load_dataset(<span class="hljs-string">&quot;hf-internal-testing/librispeech_asr_demo&quot;</span>, <span class="hljs-string">&quot;clean&quot;</span>, split=<span class="hljs-string">&quot;validation&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>audio_file = ds[<span class="hljs-number">0</span>][<span class="hljs-string">&quot;audio&quot;</span>][<span class="hljs-string">&quot;path&quot;</span>]`}}),$e=new y({props:{code:`from transformers import pipeline

audio_classifier = pipeline(
    task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
)`,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>)`}}),xe=new y({props:{code:`preds = audio_classifier(audio_file)
preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
preds`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>preds = audio_classifier(audio_file)
<span class="hljs-meta">&gt;&gt;&gt; </span>preds = [{<span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-built_in">round</span>(pred[<span class="hljs-string">&quot;score&quot;</span>], <span class="hljs-number">4</span>), <span class="hljs-string">&quot;label&quot;</span>: pred[<span class="hljs-string">&quot;label&quot;</span>]} <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> preds]
<span class="hljs-meta">&gt;&gt;&gt; </span>preds
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1315</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">0.1307</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;neutral&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1274</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;sad&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.1261</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">0.1242</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;happy&#x27;</span>}]`}}),we=new ls({}),ye=new y({props:{code:`from transformers import pipeline

vision_classifier = pipeline(task="image-classification")
preds = vision_classifier(
    images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
)
preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
preds`,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>preds = 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-meta">&gt;&gt;&gt; </span>preds = [{<span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-built_in">round</span>(pred[<span class="hljs-string">&quot;score&quot;</span>], <span class="hljs-number">4</span>), <span class="hljs-string">&quot;label&quot;</span>: pred[<span class="hljs-string">&quot;label&quot;</span>]} <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> preds]
<span class="hljs-meta">&gt;&gt;&gt; </span>preds
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.4335</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">0.0348</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">0.0324</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">0.0239</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">0.0229</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;tiger cat&#x27;</span>}]`}}),be=new ls({}),Ee=new y({props:{code:`image = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
question = "Where is the cat?"`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>image = <span class="hljs-string">&quot;https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>question = <span class="hljs-string">&quot;Where is the cat?&quot;</span>`}}),qe=new y({props:{code:`from transformers import pipeline

vqa = pipeline(task="vqa")
preds = vqa(image=image, question=question)
preds = [{"score": round(pred["score"], 4), "answer": pred["answer"]} for pred in preds]
preds`,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>vqa = pipeline(task=<span class="hljs-string">&quot;vqa&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>preds = vqa(image=image, question=question)
<span class="hljs-meta">&gt;&gt;&gt; </span>preds = [{<span class="hljs-string">&quot;score&quot;</span>: <span class="hljs-built_in">round</span>(pred[<span class="hljs-string">&quot;score&quot;</span>], <span class="hljs-number">4</span>), <span class="hljs-string">&quot;answer&quot;</span>: pred[<span class="hljs-string">&quot;answer&quot;</span>]} <span class="hljs-keyword">for</span> pred <span class="hljs-keyword">in</span> preds]
<span class="hljs-meta">&gt;&gt;&gt; </span>preds
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.911</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;snow&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.8786</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;in snow&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.6714</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;outside&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0293</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;on ground&#x27;</span>}, {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0272</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;ground&#x27;</span>}]`}}),{c(){j=r("meta"),N=f(),k=r("h1"),b=r("a"),F=r("span"),m(w.$$.fragment),P=f(),H=r("span"),ya=n("Pipelines for inference"),xs=f(),E=r("p"),ba=n("The "),Pe=r("a"),Ea=n("pipeline()"),qa=n(" makes it simple to use any model from the "),ae=r("a"),Aa=n("Hub"),Pa=n(" for inference on any language, computer vision, speech, and multimodal tasks. 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