import{S as po,i as mo,s as wo,e as a,k as u,w as Ke,t as n,M as vo,c as o,d as r,m as f,a as i,x as Ve,h as s,b as l,Z as go,G as t,g as c,y as Ze,L as yo,q as Qe,o as Xe,B as et,v as bo}from"../../chunks/vendor-hf-doc-builder.js";import{Y as _o}from"../../chunks/Youtube-hf-doc-builder.js";import{I as Ft}from"../../chunks/IconCopyLink-hf-doc-builder.js";function Eo(pa){let y,tt,b,$,_e,x,xt,Ee,Gt,rt,_,S,ke,G,Ct,Pe,Mt,at,C,ot,g,Rt,M,Ot,qt,R,zt,Bt,O,Wt,Ut,q,Yt,jt,z,Jt,Kt,B,Vt,Zt,it,E,I,Le,W,Qt,Ae,Xt,nt,ge,er,st,k,U,ma,tr,Y,wa,lt,p,j,rr,J,ar,or,ir,He,nr,sr,Te,lr,ht,de,hr,ct,m,Ne,cr,ur,w,fr,K,gr,dr,V,pr,mr,Z,wr,vr,P,yr,Q,br,_r,X,Er,kr,ut,D,Pr,ee,Lr,Ar,ft,L,F,$e,te,Hr,Se,Tr,gt,pe,Nr,dt,A,Ie,$r,Sr,re,Ir,Dr,pt,ae,De,Fr,xr,mt,oe,Fe,Gr,Cr,wt,H,xe,Mr,Rr,Ge,ie,Or,qr,vt,ne,Ce,zr,Br,yt,se,Me,Wr,Ur,bt,le,Re,Yr,jr,_t,T,Oe,Jr,Kr,he,Vr,Zr,Et,N,qe,Qr,Xr,ce,ea,ta,kt,me,ra,Pt,v,ue,aa,ze,oa,ia,na,Be,sa,la,We,ha,Lt;return x=new Ft({}),G=new Ft({}),C=new _o({props:{id:"00GKzGyWFEs"}}),W=new Ft({}),te=new Ft({}),{c(){y=a("meta"),tt=u(),b=a("h1"),$=a("a"),_e=a("span"),Ke(x.$$.fragment),xt=u(),Ee=a("span"),Gt=n("Introduction"),rt=u(),_=a("h2"),S=a("a"),ke=a("span"),Ke(G.$$.fragment),Ct=u(),Pe=a("span"),Mt=n("Welcome to the \u{1F917} Course!"),at=u(),Ke(C.$$.fragment),ot=u(),g=a("p"),Rt=n("This course will teach you about natural language processing (NLP) using libraries from the "),M=a("a"),Ot=n("Hugging Face"),qt=n(" ecosystem \u2014 "),R=a("a"),zt=n("\u{1F917} Transformers"),Bt=n(", "),O=a("a"),Wt=n("\u{1F917} Datasets"),Ut=n(", "),q=a("a"),Yt=n("\u{1F917} Tokenizers"),jt=n(", and "),z=a("a"),Jt=n("\u{1F917} Accelerate"),Kt=n(" \u2014 as well as the "),B=a("a"),Vt=n("Hugging Face Hub"),Zt=n(". It\u2019s completely free and without ads."),it=u(),E=a("h2"),I=a("a"),Le=a("span"),Ke(W.$$.fragment),Qt=u(),Ae=a("span"),Xt=n("What to expect?"),nt=u(),ge=a("p"),er=n("Here is a brief overview of the course:"),st=u(),k=a("div"),U=a("img"),tr=u(),Y=a("img"),lt=u(),p=a("ul"),j=a("li"),rr=n("Chapters 1 to 4 provide an introduction to the main concepts of the \u{1F917} Transformers library. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the "),J=a("a"),ar=n("Hugging Face Hub"),or=n(", fine-tune it on a dataset, and share your results on the Hub!"),ir=u(),He=a("li"),nr=n("Chapters 5 to 8 teach the basics of \u{1F917} Datasets and \u{1F917} Tokenizers before diving into classic NLP tasks. By the end of this part, you will be able to tackle the most common NLP problems by yourself."),sr=u(),Te=a("li"),lr=n("Chapters 9 to 12 go beyond NLP, and explore how Transformer models can be used tackle tasks in speech processing and computer vision. Along the way, you\u2019ll learn how to build and share demos of your models, and optimize them for production environments. By the end of this part, you will be ready to apply \u{1F917} Transformers to (almost) any machine learning problem!"),ht=u(),de=a("p"),hr=n("This course:"),ct=u(),m=a("ul"),Ne=a("li"),cr=n("Requires a good knowledge of Python"),ur=u(),w=a("li"),fr=n("Is better taken after an introductory deep learning course, such as "),K=a("a"),gr=n("fast.ai\u2019s"),dr=u(),V=a("a"),pr=n("Practical Deep Learning for Coders"),mr=n(" or one of the programs developed by "),Z=a("a"),wr=n("DeepLearning.AI"),vr=u(),P=a("li"),yr=n("Does not expect prior "),Q=a("a"),br=n("PyTorch"),_r=n(" or "),X=a("a"),Er=n("TensorFlow"),kr=n(" knowledge, though some familiarity with either of those will help"),ut=u(),D=a("p"),Pr=n("After you\u2019ve completed this course, we recommend checking out DeepLearning.AI\u2019s "),ee=a("a"),Lr=n("Natural Language Processing Specialization"),Ar=n(", which covers a wide range of traditional NLP models like naive Bayes and LSTMs that are well worth knowing about!"),ft=u(),L=a("h2"),F=a("a"),$e=a("span"),Ke(te.$$.fragment),Hr=u(),Se=a("span"),Tr=n("Who are we?"),gt=u(),pe=a("p"),Nr=n("About the authors:"),dt=u(),A=a("p"),Ie=a("strong"),$r=n("Abubakar Abid"),Sr=n(" completed his PhD at Stanford in applied machine learning. During his PhD, he founded "),re=a("a"),Ir=n("Gradio"),Dr=n(", an open-source Python library that has been used to build over 600,000 machine learning demos. Gradio was acquired by Hugging Face, which is where Abubakar now serves as a machine learning team lead."),pt=u(),ae=a("p"),De=a("strong"),Fr=n("Matthew Carrigan"),xr=n(" is a Machine Learning Engineer at Hugging Face. He lives in Dublin, Ireland and previously worked as an ML engineer at Parse.ly and before that as a post-doctoral researcher at Trinity College Dublin. He does not believe we\u2019re going to get to AGI by scaling existing architectures, but has high hopes for robot immortality regardless."),mt=u(),oe=a("p"),Fe=a("strong"),Gr=n("Lysandre Debut"),Cr=n(" is a Machine Learning Engineer at Hugging Face and has been working on the \u{1F917} Transformers library since the very early development stages. His aim is to make NLP accessible for everyone by developing tools with a very simple API."),wt=u(),H=a("p"),xe=a("strong"),Mr=n("Sylvain Gugger"),Rr=n(" is a Research Engineer at Hugging Face and one of the core maintainers of the \u{1F917} Transformers library. Previously he was a Research Scientist at fast.ai, and he co-wrote "),Ge=a("em"),ie=a("a"),Or=n("Deep Learning for Coders with fastai and PyTorch"),qr=n(" with Jeremy Howard. The main focus of his research is on making deep learning more accessible, by designing and improving techniques that allow models to train fast on limited resources."),vt=u(),ne=a("p"),Ce=a("strong"),zr=n("Dawood Khan"),Br=n(" is a Machine Learning Engineer at Hugging Face. He\u2019s from NYC and graduated from New York University studying Computer Science. After working as an iOS Engineer for a few years, Dawood quit to start Gradio with his fellow co-founders. Gradio was eventually acquired by Hugging Face."),yt=u(),se=a("p"),Me=a("strong"),Wr=n("Merve Noyan"),Ur=n(" is a developer advocate at Hugging Face, working on developing tools and building content around them to democratize machine learning for everyone."),bt=u(),le=a("p"),Re=a("strong"),Yr=n("Lucile Saulnier"),jr=n(" is a machine learning engineer at Hugging Face, developing and supporting the use of open source tools. She is also actively involved in many research projects in the field of Natural Language Processing such as collaborative training and BigScience."),_t=u(),T=a("p"),Oe=a("strong"),Jr=n("Lewis Tunstall"),Kr=n("  is a machine learning engineer at Hugging Face, focused on developing open-source tools and making them accessible to the wider community. He is also a co-author of the O\u2019Reilly book "),he=a("a"),Vr=n("Natural Language Processing with Transformers"),Zr=n("."),Et=u(),N=a("p"),qe=a("strong"),Qr=n("Leandro von Werra"),Xr=n("  is a machine learning engineer in the open-source team at Hugging Face and also a co-author of the O\u2019Reilly book "),ce=a("a"),ea=n("Natural Language Processing with Transformers"),ta=n(". He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack.."),kt=u(),me=a("p"),ra=n("Are you ready to roll? In this chapter, you will learn:"),Pt=u(),v=a("ul"),ue=a("li"),aa=n("How to use the "),ze=a("code"),oa=n("pipeline()"),ia=n(" function to solve NLP tasks such as text generation and classification"),na=u(),Be=a("li"),sa=n("About the Transformer architecture"),la=u(),We=a("li"),ha=n("How to distinguish between encoder, decoder, and encoder-decoder architectures and use cases"),this.h()},l(e){const h=vo('[data-svelte="svelte-1phssyn"]',document.head);y=o(h,"META",{name:!0,content:!0}),h.forEach(r),tt=f(e),b=o(e,"H1",{class:!0});var At=i(b);$=o(At,"A",{id:!0,class:!0,href:!0});var va=i($);_e=o(va,"SPAN",{});var ya=i(_e);Ve(x.$$.fragment,ya),ya.forEach(r),va.forEach(r),xt=f(At),Ee=o(At,"SPAN",{});var ba=i(Ee);Gt=s(ba,"Introduction"),ba.forEach(r),At.forEach(r),rt=f(e),_=o(e,"H2",{class:!0});var Ht=i(_);S=o(Ht,"A",{id:!0,class:!0,href:!0});var _a=i(S);ke=o(_a,"SPAN",{});var Ea=i(ke);Ve(G.$$.fragment,Ea),Ea.forEach(r),_a.forEach(r),Ct=f(Ht),Pe=o(Ht,"SPAN",{});var ka=i(Pe);Mt=s(ka,"Welcome to the \u{1F917} Course!"),ka.forEach(r),Ht.forEach(r),at=f(e),Ve(C.$$.fragment,e),ot=f(e),g=o(e,"P",{});var d=i(g);Rt=s(d,"This course will teach you about natural language processing (NLP) using libraries from the "),M=o(d,"A",{href:!0,rel:!0});var Pa=i(M);Ot=s(Pa,"Hugging Face"),Pa.forEach(r),qt=s(d," ecosystem \u2014 "),R=o(d,"A",{href:!0,rel:!0});var La=i(R);zt=s(La,"\u{1F917} Transformers"),La.forEach(r),Bt=s(d,", "),O=o(d,"A",{href:!0,rel:!0});var Aa=i(O);Wt=s(Aa,"\u{1F917} Datasets"),Aa.forEach(r),Ut=s(d,", "),q=o(d,"A",{href:!0,rel:!0});var Ha=i(q);Yt=s(Ha,"\u{1F917} Tokenizers"),Ha.forEach(r),jt=s(d,", and "),z=o(d,"A",{href:!0,rel:!0});var Ta=i(z);Jt=s(Ta,"\u{1F917} Accelerate"),Ta.forEach(r),Kt=s(d," \u2014 as well as the "),B=o(d,"A",{href:!0,rel:!0});var Na=i(B);Vt=s(Na,"Hugging Face Hub"),Na.forEach(r),Zt=s(d,". It\u2019s completely free and without ads."),d.forEach(r),it=f(e),E=o(e,"H2",{class:!0});var Tt=i(E);I=o(Tt,"A",{id:!0,class:!0,href:!0});var $a=i(I);Le=o($a,"SPAN",{});var Sa=i(Le);Ve(W.$$.fragment,Sa),Sa.forEach(r),$a.forEach(r),Qt=f(Tt),Ae=o(Tt,"SPAN",{});var Ia=i(Ae);Xt=s(Ia,"What to expect?"),Ia.forEach(r),Tt.forEach(r),nt=f(e),ge=o(e,"P",{});var Da=i(ge);er=s(Da,"Here is a brief overview of the course:"),Da.forEach(r),st=f(e),k=o(e,"DIV",{class:!0});var Nt=i(k);U=o(Nt,"IMG",{class:!0,src:!0,alt:!0}),tr=f(Nt),Y=o(Nt,"IMG",{class:!0,src:!0,alt:!0}),Nt.forEach(r),lt=f(e),p=o(e,"UL",{});var we=i(p);j=o(we,"LI",{});var $t=i(j);rr=s($t,"Chapters 1 to 4 provide an introduction to the main concepts of the \u{1F917} Transformers library. By the end of this part of the course, you will be familiar with how Transformer models work and will know how to use a model from the "),J=o($t,"A",{href:!0,rel:!0});var Fa=i(J);ar=s(Fa,"Hugging Face Hub"),Fa.forEach(r),or=s($t,", fine-tune it on a dataset, and share your results on the Hub!"),$t.forEach(r),ir=f(we),He=o(we,"LI",{});var xa=i(He);nr=s(xa,"Chapters 5 to 8 teach the basics of \u{1F917} Datasets and \u{1F917} Tokenizers before diving into classic NLP tasks. By the end of this part, you will be able to tackle the most common NLP problems by yourself."),xa.forEach(r),sr=f(we),Te=o(we,"LI",{});var Ga=i(Te);lr=s(Ga,"Chapters 9 to 12 go beyond NLP, and explore how Transformer models can be used tackle tasks in speech processing and computer vision. Along the way, you\u2019ll learn how to build and share demos of your models, and optimize them for production environments. By the end of this part, you will be ready to apply \u{1F917} Transformers to (almost) any machine learning problem!"),Ga.forEach(r),we.forEach(r),ht=f(e),de=o(e,"P",{});var Ca=i(de);hr=s(Ca,"This course:"),Ca.forEach(r),ct=f(e),m=o(e,"UL",{});var ve=i(m);Ne=o(ve,"LI",{});var Ma=i(Ne);cr=s(Ma,"Requires a good knowledge of Python"),Ma.forEach(r),ur=f(ve),w=o(ve,"LI",{});var fe=i(w);fr=s(fe,"Is better taken after an introductory deep learning course, such as "),K=o(fe,"A",{href:!0,rel:!0});var Ra=i(K);gr=s(Ra,"fast.ai\u2019s"),Ra.forEach(r),dr=f(fe),V=o(fe,"A",{href:!0,rel:!0});var Oa=i(V);pr=s(Oa,"Practical Deep Learning for Coders"),Oa.forEach(r),mr=s(fe," or one of the programs developed by "),Z=o(fe,"A",{href:!0,rel:!0});var qa=i(Z);wr=s(qa,"DeepLearning.AI"),qa.forEach(r),fe.forEach(r),vr=f(ve),P=o(ve,"LI",{});var ye=i(P);yr=s(ye,"Does not expect prior "),Q=o(ye,"A",{href:!0,rel:!0});var za=i(Q);br=s(za,"PyTorch"),za.forEach(r),_r=s(ye," or "),X=o(ye,"A",{href:!0,rel:!0});var Ba=i(X);Er=s(Ba,"TensorFlow"),Ba.forEach(r),kr=s(ye," knowledge, though some familiarity with either of those will help"),ye.forEach(r),ve.forEach(r),ut=f(e),D=o(e,"P",{});var St=i(D);Pr=s(St,"After you\u2019ve completed this course, we recommend checking out DeepLearning.AI\u2019s "),ee=o(St,"A",{href:!0,rel:!0});var Wa=i(ee);Lr=s(Wa,"Natural Language Processing Specialization"),Wa.forEach(r),Ar=s(St,", which covers a wide range of traditional NLP models like naive Bayes and LSTMs that are well worth knowing about!"),St.forEach(r),ft=f(e),L=o(e,"H2",{class:!0});var It=i(L);F=o(It,"A",{id:!0,class:!0,href:!0});var Ua=i(F);$e=o(Ua,"SPAN",{});var Ya=i($e);Ve(te.$$.fragment,Ya),Ya.forEach(r),Ua.forEach(r),Hr=f(It),Se=o(It,"SPAN",{});var ja=i(Se);Tr=s(ja,"Who are we?"),ja.forEach(r),It.forEach(r),gt=f(e),pe=o(e,"P",{});var Ja=i(pe);Nr=s(Ja,"About the authors:"),Ja.forEach(r),dt=f(e),A=o(e,"P",{});var Ue=i(A);Ie=o(Ue,"STRONG",{});var Ka=i(Ie);$r=s(Ka,"Abubakar Abid"),Ka.forEach(r),Sr=s(Ue," completed his PhD at Stanford in applied machine learning. During his PhD, he founded "),re=o(Ue,"A",{href:!0,rel:!0});var Va=i(re);Ir=s(Va,"Gradio"),Va.forEach(r),Dr=s(Ue,", an open-source Python library that has been used to build over 600,000 machine learning demos. Gradio was acquired by Hugging Face, which is where Abubakar now serves as a machine learning team lead."),Ue.forEach(r),pt=f(e),ae=o(e,"P",{});var ca=i(ae);De=o(ca,"STRONG",{});var Za=i(De);Fr=s(Za,"Matthew Carrigan"),Za.forEach(r),xr=s(ca," is a Machine Learning Engineer at Hugging Face. He lives in Dublin, Ireland and previously worked as an ML engineer at Parse.ly and before that as a post-doctoral researcher at Trinity College Dublin. He does not believe we\u2019re going to get to AGI by scaling existing architectures, but has high hopes for robot immortality regardless."),ca.forEach(r),mt=f(e),oe=o(e,"P",{});var ua=i(oe);Fe=o(ua,"STRONG",{});var Qa=i(Fe);Gr=s(Qa,"Lysandre Debut"),Qa.forEach(r),Cr=s(ua," is a Machine Learning Engineer at Hugging Face and has been working on the \u{1F917} Transformers library since the very early development stages. His aim is to make NLP accessible for everyone by developing tools with a very simple API."),ua.forEach(r),wt=f(e),H=o(e,"P",{});var Ye=i(H);xe=o(Ye,"STRONG",{});var Xa=i(xe);Mr=s(Xa,"Sylvain Gugger"),Xa.forEach(r),Rr=s(Ye," is a Research Engineer at Hugging Face and one of the core maintainers of the \u{1F917} Transformers library. Previously he was a Research Scientist at fast.ai, and he co-wrote "),Ge=o(Ye,"EM",{});var eo=i(Ge);ie=o(eo,"A",{href:!0,rel:!0});var to=i(ie);Or=s(to,"Deep Learning for Coders with fastai and PyTorch"),to.forEach(r),eo.forEach(r),qr=s(Ye," with Jeremy Howard. The main focus of his research is on making deep learning more accessible, by designing and improving techniques that allow models to train fast on limited resources."),Ye.forEach(r),vt=f(e),ne=o(e,"P",{});var fa=i(ne);Ce=o(fa,"STRONG",{});var ro=i(Ce);zr=s(ro,"Dawood Khan"),ro.forEach(r),Br=s(fa," is a Machine Learning Engineer at Hugging Face. He\u2019s from NYC and graduated from New York University studying Computer Science. After working as an iOS Engineer for a few years, Dawood quit to start Gradio with his fellow co-founders. Gradio was eventually acquired by Hugging Face."),fa.forEach(r),yt=f(e),se=o(e,"P",{});var ga=i(se);Me=o(ga,"STRONG",{});var ao=i(Me);Wr=s(ao,"Merve Noyan"),ao.forEach(r),Ur=s(ga," is a developer advocate at Hugging Face, working on developing tools and building content around them to democratize machine learning for everyone."),ga.forEach(r),bt=f(e),le=o(e,"P",{});var da=i(le);Re=o(da,"STRONG",{});var oo=i(Re);Yr=s(oo,"Lucile Saulnier"),oo.forEach(r),jr=s(da," is a machine learning engineer at Hugging Face, developing and supporting the use of open source tools. She is also actively involved in many research projects in the field of Natural Language Processing such as collaborative training and BigScience."),da.forEach(r),_t=f(e),T=o(e,"P",{});var je=i(T);Oe=o(je,"STRONG",{});var io=i(Oe);Jr=s(io,"Lewis Tunstall"),io.forEach(r),Kr=s(je,"  is a machine learning engineer at Hugging Face, focused on developing open-source tools and making them accessible to the wider community. He is also a co-author of the O\u2019Reilly book "),he=o(je,"A",{href:!0,rel:!0});var no=i(he);Vr=s(no,"Natural Language Processing with Transformers"),no.forEach(r),Zr=s(je,"."),je.forEach(r),Et=f(e),N=o(e,"P",{});var Je=i(N);qe=o(Je,"STRONG",{});var so=i(qe);Qr=s(so,"Leandro von Werra"),so.forEach(r),Xr=s(Je,"  is a machine learning engineer in the open-source team at Hugging Face and also a co-author of the O\u2019Reilly book "),ce=o(Je,"A",{href:!0,rel:!0});var lo=i(ce);ea=s(lo,"Natural Language Processing with Transformers"),lo.forEach(r),ta=s(Je,". He has several years of industry experience bringing NLP projects to production by working across the whole machine learning stack.."),Je.forEach(r),kt=f(e),me=o(e,"P",{});var ho=i(me);ra=s(ho,"Are you ready to roll? In this chapter, you will learn:"),ho.forEach(r),Pt=f(e),v=o(e,"UL",{});var be=i(v);ue=o(be,"LI",{});var Dt=i(ue);aa=s(Dt,"How to use the "),ze=o(Dt,"CODE",{});var co=i(ze);oa=s(co,"pipeline()"),co.forEach(r),ia=s(Dt," function to solve NLP tasks such as text generation and classification"),Dt.forEach(r),na=f(be),Be=o(be,"LI",{});var uo=i(Be);sa=s(uo,"About the Transformer architecture"),uo.forEach(r),la=f(be),We=o(be,"LI",{});var fo=i(We);ha=s(fo,"How to distinguish between encoder, decoder, and encoder-decoder architectures and use cases"),fo.forEach(r),be.forEach(r),this.h()},h(){l(y,"name","hf:doc:metadata"),l(y,"content",JSON.stringify(ko)),l($,"id","introduction"),l($,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),l($,"href","#introduction"),l(b,"class","relative group"),l(S,"id","welcome-to-the-course"),l(S,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),l(S,"href","#welcome-to-the-course"),l(_,"class","relative group"),l(M,"href","https://huggingface.co/"),l(M,"rel","nofollow"),l(R,"href","https://github.com/huggingface/transformers"),l(R,"rel","nofollow"),l(O,"href","https://github.com/huggingface/datasets"),l(O,"rel","nofollow"),l(q,"href","https://github.com/huggingface/tokenizers"),l(q,"rel","nofollow"),l(z,"href","https://github.com/huggingface/accelerate"),l(z,"rel","nofollow"),l(B,"href","https://huggingface.co/models"),l(B,"rel","nofollow"),l(I,"id","what-to-expect"),l(I,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),l(I,"href","#what-to-expect"),l(E,"class","relative group"),l(U,"class","block dark:hidden"),go(U.src,ma="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/summary.svg")||l(U,"src",ma),l(U,"alt","Brief overview of the chapters of the course."),l(Y,"class","hidden dark:block"),go(Y.src,wa="https://huggingface.co/datasets/huggingface-course/documentation-images/resolve/main/en/chapter1/summary-dark.svg")||l(Y,"src",wa),l(Y,"alt","Brief overview of the chapters of the course."),l(k,"class","flex justify-center"),l(J,"href","https://huggingface.co/models"),l(J,"rel","nofollow"),l(K,"href","https://www.fast.ai/"),l(K,"rel","nofollow"),l(V,"href","https://course.fast.ai/"),l(V,"rel","nofollow"),l(Z,"href","https://www.deeplearning.ai/"),l(Z,"rel","nofollow"),l(Q,"href","https://pytorch.org/"),l(Q,"rel","nofollow"),l(X,"href","https://www.tensorflow.org/"),l(X,"rel","nofollow"),l(ee,"href","https://www.coursera.org/specializations/natural-language-processing?utm_source=deeplearning-ai&utm_medium=institutions&utm_campaign=20211011-nlp-2-hugging_face-page-nlp-refresh"),l(ee,"rel","nofollow"),l(F,"id","who-are-we"),l(F,"class","header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full"),l(F,"href","#who-are-we"),l(L,"class","relative group"),l(re,"href","https://github.com/gradio-app/gradio"),l(re,"rel","nofollow"),l(ie,"href","https://learning.oreilly.com/library/view/deep-learning-for/9781492045519/"),l(ie,"rel","nofollow"),l(he,"href","https://www.oreilly.com/library/view/natural-language-processing/9781098103231/"),l(he,"rel","nofollow"),l(ce,"href","https://www.oreilly.com/library/view/natural-language-processing/9781098103231/"),l(ce,"rel","nofollow")},m(e,h){t(document.head,y),c(e,tt,h),c(e,b,h),t(b,$),t($,_e),Ze(x,_e,null),t(b,xt),t(b,Ee),t(Ee,Gt),c(e,rt,h),c(e,_,h),t(_,S),t(S,ke),Ze(G,ke,null),t(_,Ct),t(_,Pe),t(Pe,Mt),c(e,at,h),Ze(C,e,h),c(e,ot,h),c(e,g,h),t(g,Rt),t(g,M),t(M,Ot),t(g,qt),t(g,R),t(R,zt),t(g,Bt),t(g,O),t(O,Wt),t(g,Ut),t(g,q),t(q,Yt),t(g,jt),t(g,z),t(z,Jt),t(g,Kt),t(g,B),t(B,Vt),t(g,Zt),c(e,it,h),c(e,E,h),t(E,I),t(I,Le),Ze(W,Le,null),t(E,Qt),t(E,Ae),t(Ae,Xt),c(e,nt,h),c(e,ge,h),t(ge,er),c(e,st,h),c(e,k,h),t(k,U),t(k,tr),t(k,Y),c(e,lt,h),c(e,p,h),t(p,j),t(j,rr),t(j,J),t(J,ar),t(j,or),t(p,ir),t(p,He),t(He,nr),t(p,sr),t(p,Te),t(Te,lr),c(e,ht,h),c(e,de,h),t(de,hr),c(e,ct,h),c(e,m,h),t(m,Ne),t(Ne,cr),t(m,ur),t(m,w),t(w,fr),t(w,K),t(K,gr),t(w,dr),t(w,V),t(V,pr),t(w,mr),t(w,Z),t(Z,wr),t(m,vr),t(m,P),t(P,yr),t(P,Q),t(Q,br),t(P,_r),t(P,X),t(X,Er),t(P,kr),c(e,ut,h),c(e,D,h),t(D,Pr),t(D,ee),t(ee,Lr),t(D,Ar),c(e,ft,h),c(e,L,h),t(L,F),t(F,$e),Ze(te,$e,null),t(L,Hr),t(L,Se),t(Se,Tr),c(e,gt,h),c(e,pe,h),t(pe,Nr),c(e,dt,h),c(e,A,h),t(A,Ie),t(Ie,$r),t(A,Sr),t(A,re),t(re,Ir),t(A,Dr),c(e,pt,h),c(e,ae,h),t(ae,De),t(De,Fr),t(ae,xr),c(e,mt,h),c(e,oe,h),t(oe,Fe),t(Fe,Gr),t(oe,Cr),c(e,wt,h),c(e,H,h),t(H,xe),t(xe,Mr),t(H,Rr),t(H,Ge),t(Ge,ie),t(ie,Or),t(H,qr),c(e,vt,h),c(e,ne,h),t(ne,Ce),t(Ce,zr),t(ne,Br),c(e,yt,h),c(e,se,h),t(se,Me),t(Me,Wr),t(se,Ur),c(e,bt,h),c(e,le,h),t(le,Re),t(Re,Yr),t(le,jr),c(e,_t,h),c(e,T,h),t(T,Oe),t(Oe,Jr),t(T,Kr),t(T,he),t(he,Vr),t(T,Zr),c(e,Et,h),c(e,N,h),t(N,qe),t(qe,Qr),t(N,Xr),t(N,ce),t(ce,ea),t(N,ta),c(e,kt,h),c(e,me,h),t(me,ra),c(e,Pt,h),c(e,v,h),t(v,ue),t(ue,aa),t(ue,ze),t(ze,oa),t(ue,ia),t(v,na),t(v,Be),t(Be,sa),t(v,la),t(v,We),t(We,ha),Lt=!0},p:yo,i(e){Lt||(Qe(x.$$.fragment,e),Qe(G.$$.fragment,e),Qe(C.$$.fragment,e),Qe(W.$$.fragment,e),Qe(te.$$.fragment,e),Lt=!0)},o(e){Xe(x.$$.fragment,e),Xe(G.$$.fragment,e),Xe(C.$$.fragment,e),Xe(W.$$.fragment,e),Xe(te.$$.fragment,e),Lt=!1},d(e){r(y),e&&r(tt),e&&r(b),et(x),e&&r(rt),e&&r(_),et(G),e&&r(at),et(C,e),e&&r(ot),e&&r(g),e&&r(it),e&&r(E),et(W),e&&r(nt),e&&r(ge),e&&r(st),e&&r(k),e&&r(lt),e&&r(p),e&&r(ht),e&&r(de),e&&r(ct),e&&r(m),e&&r(ut),e&&r(D),e&&r(ft),e&&r(L),et(te),e&&r(gt),e&&r(pe),e&&r(dt),e&&r(A),e&&r(pt),e&&r(ae),e&&r(mt),e&&r(oe),e&&r(wt),e&&r(H),e&&r(vt),e&&r(ne),e&&r(yt),e&&r(se),e&&r(bt),e&&r(le),e&&r(_t),e&&r(T),e&&r(Et),e&&r(N),e&&r(kt),e&&r(me),e&&r(Pt),e&&r(v)}}}const ko={local:"introduction",sections:[{local:"welcome-to-the-course",title:"Welcome to the \u{1F917} Course!"},{local:"what-to-expect",title:"What to expect?"},{local:"who-are-we",title:"Who are we?"}],title:"Introduction"};function Po(pa){return bo(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class To extends po{constructor(y){super();mo(this,y,Po,Eo,wo,{})}}export{To as default,ko as metadata};
