import{S as Gr,i as Jr,s as Ur,e as o,k as h,w as f,t as g,M as Yr,c as a,d as t,m as p,a as i,x as u,h as $,b as n,G as r,g as l,y as c,L as Vr,q as m,o as d,B as w,v as Xr}from"../../chunks/vendor-hf-doc-builder.js";import{I as v}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as nr}from"../../chunks/CodeBlock-hf-doc-builder.js";import{Q as y}from"../../chunks/Question-hf-doc-builder.js";function Zr(lr){let b,Qe,x,H,be,R,zt,xe,Ht,Re,ve,jt,Ge,ye,Ft,Je,k,j,ke,G,Ct,J,It,_e,Mt,Dt,Ue,U,Ye,_,F,Ee,Y,Lt,Ae,Bt,Ve,V,Xe,X,Ze,E,C,qe,Z,Kt,Te,Ot,et,ee,tt,te,rt,A,I,Se,re,Qt,Pe,Rt,st,se,ot,oe,at,q,M,Ne,ae,Gt,We,Jt,it,ie,nt,T,D,ze,ne,Ut,He,Yt,lt,le,ht,S,L,je,he,Vt,Fe,Xt,pt,pe,ft,P,B,Ce,fe,Zt,Ie,er,ut,ue,ct,N,K,Me,ce,tr,De,rr,mt,me,dt,W,O,Le,de,sr,Be,or,wt,we,gt,z,Q,Ke,ge,ar,Oe,ir,$t,$e,vt;return R=new v({}),G=new v({}),U=new y({props:{choices:[{text:"Summarization",explain:'Look again on the <a href="https://huggingface.co/roberta-large-mnli">roberta-large-mnli page</a>.'},{text:"Text classification",explain:"More precisely, it classifies if two sentences are logically linked across three labels (contradiction, neutral, entailment) \u2014 a task also called <em>natural language inference</em>.",correct:!0},{text:"Text generation",explain:'Look again on the <a href="https://huggingface.co/roberta-large-mnli">roberta-large-mnli page</a>.'}]}}),Y=new v({}),V=new nr({props:{code:`from transformers import pipeline

ner = pipeline("ner", grouped_entities=True)
ner("My name is Sylvain and I work at Hugging Face in Brooklyn.")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

ner = pipeline(<span class="hljs-string">&quot;ner&quot;</span>, grouped_entities=<span class="hljs-literal">True</span>)
ner(<span class="hljs-string">&quot;My name is Sylvain and I work at Hugging Face in Brooklyn.&quot;</span>)`}}),X=new y({props:{choices:[{text:'It will return classification scores for this sentence, with labels "positive" or "negative".',explain:"This is incorrect \u2014 this would be a <code>sentiment-analysis</code> pipeline."},{text:"It will return a generated text completing this sentence.",explain:"This is incorrect \u2014 it would be a <code>text-generation</code> pipeline."},{text:"It will return the words representing persons, organizations or locations.",explain:'Furthermore, with <code>grouped_entities=True</code>, it will group together the words belonging to the same entity, like "Hugging Face".',correct:!0}]}}),Z=new v({}),ee=new nr({props:{code:`from transformers import pipeline

filler = pipeline("fill-mask", model="bert-base-cased")
result = filler("...")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

filler = pipeline(<span class="hljs-string">&quot;fill-mask&quot;</span>, model=<span class="hljs-string">&quot;bert-base-cased&quot;</span>)
result = filler(<span class="hljs-string">&quot;...&quot;</span>)`}}),te=new y({props:{choices:[{text:"This &#60;mask> has been waiting for you.",explain:"This is incorrect. Check out the <code>bert-base-cased</code> model card and try to spot your mistake."},{text:"This [MASK] has been waiting for you.",explain:"Correct! This model's mask token is [MASK].",correct:!0},{text:"This man has been waiting for you.",explain:"This is incorrect. This pipeline fills in masked words, so it needs a mask token somewhere."}]}}),re=new v({}),se=new nr({props:{code:`from transformers import pipeline

classifier = pipeline("zero-shot-classification")
result = classifier("This is a course about the Transformers library")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

classifier = pipeline(<span class="hljs-string">&quot;zero-shot-classification&quot;</span>)
result = classifier(<span class="hljs-string">&quot;This is a course about the Transformers library&quot;</span>)`}}),oe=new y({props:{choices:[{text:"This pipeline requires that labels be given to classify this text.",explain:"Right \u2014 the correct code needs to include <code>candidate_labels=[...]</code>.",correct:!0},{text:"This pipeline requires several sentences, not just one.",explain:"This is incorrect, though when properly used, this pipeline can take a list of sentences to process (like all other pipelines)."},{text:"The \u{1F917} Transformers library is broken, as usual.",explain:"We won't dignify this answer with a comment!"},{text:"This pipeline requires longer inputs; this one is too short.",explain:"This is incorrect. Note that a very long text will be truncated when processed by this pipeline."}]}}),ae=new v({}),ie=new y({props:{choices:[{text:"Transferring the knowledge of a pretrained model to a new model by training it on the same dataset.",explain:"No, that would be two versions of the same model."},{text:"Transferring the knowledge of a pretrained model to a new model by initializing the second model with the first model's weights.",explain:"Correct: when the second model is trained on a new task, it *transfers* the knowledge of the first model.",correct:!0},{text:"Transferring the knowledge of a pretrained model to a new model by building the second model with the same architecture as the first model.",explain:"The architecture is just the way the model is built; there is no knowledge shared or transferred in this case."}]}}),ne=new v({}),le=new y({props:{choices:[{text:"True",explain:"The pretraining is usually <em>self-supervised</em>, which means the labels are created automatically from the inputs (like predicting the next word or filling in some masked words).",correct:!0},{text:"False",explain:"This is not the correct answer."}]}}),he=new v({}),pe=new y({props:{choices:[{text:"If a model is a building, its architecture is the blueprint and the weights are the people living inside.",explain:"Following this metaphor, the weights would be the bricks and other materials used to construct the building."},{text:"An architecture is a map to build a model and its weights are the cities represented on the map.",explain:"The problem with this metaphor is that a map usually represents one existing reality (there is only one city in France named Paris). For a given architecture, multiple weights are possible."},{text:"An architecture is a succession of mathematical functions to build a model and its weights are those functions parameters.",explain:"The same set of mathematical functions (architecture) can be used to build different models by using different parameters (weights).",correct:!0}]}}),fe=new v({}),ue=new y({props:{choices:[{text:"An encoder model",explain:"An encoder model generates a representation of the whole sentence that is better suited for tasks like classification."},{text:"A decoder model",explain:"Decoder models are perfectly suited for text generation from a prompt.",correct:!0},{text:"A sequence-to-sequence model",explain:"Sequence-to-sequence models are better suited for tasks where you want to generate sentences in relation to the input sentences, not a given prompt."}]}}),ce=new v({}),me=new y({props:{choices:[{text:"An encoder model",explain:"An encoder model generates a representation of the whole sentence that is better suited for tasks like classification."},{text:"A decoder model",explain:"Decoder models are good for generating output text (like summaries), but they don't have the ability to exploit a context like the whole text to summarize."},{text:"A sequence-to-sequence model",explain:"Sequence-to-sequence models are perfectly suited for a summarization task.",correct:!0}]}}),de=new v({}),we=new y({props:{choices:[{text:"An encoder model",explain:"An encoder model generates a representation of the whole sentence which is perfectly suited for a task like classification.",correct:!0},{text:"A decoder model",explain:"Decoder models are good for generating output texts, not extracting a label out of a sentence."},{text:"A sequence-to-sequence model",explain:"Sequence-to-sequence models are better suited for tasks where you want to generate text based on an input sentence, not a label."}]}}),ge=new v({}),$e=new y({props:{choices:[{text:"The model is a fine-tuned version of a pretrained model and it picked up its bias from it.",explain:"When applying Transfer Learning, the bias in the pretrained model used perspires in the fine-tuned model.",correct:!0},{text:"The data the model was trained on is biased.",explain:"This is the most obvious source of bias, but not the only one.",correct:!0},{text:"The metric the model was optimizing for is biased.",explain:"A less obvious source of bias is the way the model is trained. Your model will blindly optimize for whatever metric you chose, without any second thoughts.",correct:!0}]}}),{c(){b=o("meta"),Qe=h(),x=o("h1"),H=o("a"),be=o("span"),f(R.$$.fragment),zt=h(),xe=o("span"),Ht=g("End-of-chapter quiz"),Re=h(),ve=o("p"),jt=g("This chapter covered a lot of ground! Don\u2019t worry if you didn\u2019t grasp all the details; the next chapters will help you understand how things work under the hood."),Ge=h(),ye=o("p"),Ft=g("First, though, let\u2019s test what you learned in this chapter!"),Je=h(),k=o("h3"),j=o("a"),ke=o("span"),f(G.$$.fragment),Ct=h(),J=o("span"),It=g("1. Explore the Hub and look for the "),_e=o("code"),Mt=g("roberta-large-mnli"),Dt=g(" checkpoint. What task does it perform?"),Ue=h(),f(U.$$.fragment),Ye=h(),_=o("h3"),F=o("a"),Ee=o("span"),f(Y.$$.fragment),Lt=h(),Ae=o("span"),Bt=g("2. 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Select the sentence that best describes the terms \u201Cmodel,\u201D \u201Carchitecture,\u201D and \u201Cweights.\u201D"),pt=h(),f(pe.$$.fragment),ft=h(),P=o("h3"),B=o("a"),Ce=o("span"),f(fe.$$.fragment),Zt=h(),Ie=o("span"),er=g("8. Which of these types of models would you use for completing prompts with generated text?"),ut=h(),f(ue.$$.fragment),ct=h(),N=o("h3"),K=o("a"),Me=o("span"),f(ce.$$.fragment),tr=h(),De=o("span"),rr=g("9. Which of those types of models would you use for summarizing texts?"),mt=h(),f(me.$$.fragment),dt=h(),W=o("h3"),O=o("a"),Le=o("span"),f(de.$$.fragment),sr=h(),Be=o("span"),or=g("10. Which of these types of models would you use for classifying text inputs according to certain labels?"),wt=h(),f(we.$$.fragment),gt=h(),z=o("h3"),Q=o("a"),Ke=o("span"),f(ge.$$.fragment),ar=h(),Oe=o("span"),ir=g("11. What possible source can the bias observed in a model have?"),$t=h(),f($e.$$.fragment),this.h()},l(e){const s=Yr('[data-svelte="svelte-1phssyn"]',document.head);b=a(s,"META",{name:!0,content:!0}),s.forEach(t),Qe=p(e),x=a(e,"H1",{class:!0});var yt=i(x);H=a(yt,"A",{id:!0,class:!0,href:!0});var hr=i(H);be=a(hr,"SPAN",{});var pr=i(be);u(R.$$.fragment,pr),pr.forEach(t),hr.forEach(t),zt=p(yt),xe=a(yt,"SPAN",{});var fr=i(xe);Ht=$(fr,"End-of-chapter quiz"),fr.forEach(t),yt.forEach(t),Re=p(e),ve=a(e,"P",{});var ur=i(ve);jt=$(ur,"This chapter covered a lot of ground! Don\u2019t worry if you didn\u2019t grasp all the details; the next chapters will help you understand how things work under the hood."),ur.forEach(t),Ge=p(e),ye=a(e,"P",{});var cr=i(ye);Ft=$(cr,"First, though, let\u2019s test what you learned in this chapter!"),cr.forEach(t),Je=p(e),k=a(e,"H3",{class:!0});var bt=i(k);j=a(bt,"A",{id:!0,class:!0,href:!0});var mr=i(j);ke=a(mr,"SPAN",{});var dr=i(ke);u(G.$$.fragment,dr),dr.forEach(t),mr.forEach(t),Ct=p(bt),J=a(bt,"SPAN",{});var xt=i(J);It=$(xt,"1. Explore the Hub and look for the "),_e=a(xt,"CODE",{});var wr=i(_e);Mt=$(wr,"roberta-large-mnli"),wr.forEach(t),Dt=$(xt," checkpoint. What task does it perform?"),xt.forEach(t),bt.forEach(t),Ue=p(e),u(U.$$.fragment,e),Ye=p(e),_=a(e,"H3",{class:!0});var kt=i(_);F=a(kt,"A",{id:!0,class:!0,href:!0});var gr=i(F);Ee=a(gr,"SPAN",{});var $r=i(Ee);u(Y.$$.fragment,$r),$r.forEach(t),gr.forEach(t),Lt=p(kt),Ae=a(kt,"SPAN",{});var vr=i(Ae);Bt=$(vr,"2. 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Which of these types of models would you use for classifying text inputs according to certain labels?"),Kr.forEach(t),Nt.forEach(t),wt=p(e),u(we.$$.fragment,e),gt=p(e),z=a(e,"H3",{class:!0});var Wt=i(z);Q=a(Wt,"A",{id:!0,class:!0,href:!0});var Or=i(Q);Ke=a(Or,"SPAN",{});var Qr=i(Ke);u(ge.$$.fragment,Qr),Qr.forEach(t),Or.forEach(t),ar=p(Wt),Oe=a(Wt,"SPAN",{});var Rr=i(Oe);ir=$(Rr,"11. 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