import{S as Ur,i as Yr,s as Vr,e as o,k as h,w as f,t as $,M as Xr,c as a,d as t,m as p,a as i,x as u,h as g,b as n,G as r,g as l,y as c,L as Zr,q as m,o as d,B as w,v as es}from"../../chunks/vendor-hf-doc-builder.js";import{I as v}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as hr}from"../../chunks/CodeBlock-hf-doc-builder.js";import{C as ts}from"../../chunks/CourseFloatingBanner-hf-doc-builder.js";import{Q as y}from"../../chunks/Question-hf-doc-builder.js";function rs(pr){let b,Re,x,H,xe,R,jt,ke,Ft,Ge,G,Je,ye,Ct,Ue,be,It,Ye,k,j,_e,J,Mt,U,Dt,Ee,Lt,Bt,Ve,Y,Xe,_,F,Ae,V,Kt,qe,Ot,Ze,X,et,Z,tt,E,C,Te,ee,Qt,Se,Rt,rt,te,st,re,ot,A,I,Pe,se,Gt,Ne,Jt,at,oe,it,ae,nt,q,M,ze,ie,Ut,We,Yt,lt,ne,ht,T,D,He,le,Vt,je,Xt,pt,he,ft,S,L,Fe,pe,Zt,Ce,er,ut,fe,ct,P,B,Ie,ue,tr,Me,rr,mt,ce,dt,N,K,De,me,sr,Le,or,wt,de,$t,z,O,Be,we,ar,Ke,ir,gt,$e,vt,W,Q,Oe,ge,nr,Qe,lr,yt,ve,bt;return R=new v({}),G=new ts({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),J=new v({}),Y=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>.'}]}}),V=new v({}),X=new hr({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>)`}}),Z=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}]}}),ee=new v({}),te=new hr({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>)`}}),re=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."}]}}),se=new v({}),oe=new hr({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>)`}}),ae=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."}]}}),ie=new v({}),ne=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."}]}}),le=new v({}),he=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."}]}}),pe=new v({}),fe=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}]}}),ue=new v({}),ce=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."}]}}),me=new v({}),de=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}]}}),we=new v({}),$e=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({}),ve=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"),Re=h(),x=o("h1"),H=o("a"),xe=o("span"),f(R.$$.fragment),jt=h(),ke=o("span"),Ft=$("End-of-chapter quiz"),Ge=h(),f(G.$$.fragment),Je=h(),ye=o("p"),Ct=$("This chapter covered a lot of ground! Don\u2019t worry if you didn\u2019t grasp all the details; the next chapters will help you understand how things work under the hood."),Ue=h(),be=o("p"),It=$("First, though, let\u2019s test what you learned in this chapter!"),Ye=h(),k=o("h3"),j=o("a"),_e=o("span"),f(J.$$.fragment),Mt=h(),U=o("span"),Dt=$("1. Explore the Hub and look for the "),Ee=o("code"),Lt=$("roberta-large-mnli"),Bt=$(" checkpoint. What task does it perform?"),Ve=h(),f(Y.$$.fragment),Xe=h(),_=o("h3"),F=o("a"),Ae=o("span"),f(V.$$.fragment),Kt=h(),qe=o("span"),Ot=$("2. 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