import{S as zw,i as Fw,s as Yw,e as a,k as i,w as T,t as n,M as Vw,c as r,d as t,m as h,a as o,x as y,h as l,b as s,G as e,g as f,y as b,L as qw,q as w,o as A,B as k,v as Kw}from"../chunks/vendor-hf-doc-builder.js";import{I as G}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as a_}from"../chunks/CodeBlock-hf-doc-builder.js";function Xw(r_){let we,Fh,Ae,$e,Ho,ma,nc,jo,lc,Yh,Go,sc,Vh,Pe,ic,Ea,hc,dc,qh,S,_a,cc,Jo,fc,uc,gc,Ta,pc,Wo,vc,mc,Ec,ya,_c,Uo,Tc,yc,bc,ba,wc,Zo,Ac,kc,Kh,R,Dc,wa,Sc,Rc,Co,Mc,xc,Aa,Lc,Gc,Xh,Oe,Cc,ka,Ic,Nc,Qh,ke,He,zo,Da,Bc,Fo,$c,ed,je,Pc,Yo,Oc,Hc,td,Je,Sa,Ra,Vo,jc,Jc,Ma,qo,Wc,Uc,xa,Ko,La,Zc,Xo,Ga,ad,We,zc,Qo,Fc,Yc,rd,Ca,od,De,Ue,en,Ia,Vc,tn,qc,nd,Io,Kc,ld,M,No,an,Xc,Qc,ef,Bo,rn,tf,af,rf,$o,on,of,nf,lf,Po,nn,sf,hf,sd,Se,Ze,ln,Na,df,sn,cf,id,ze,ff,Ba,uf,gf,hd,Te,pf,$a,vf,mf,Pa,Ef,_f,dd,Re,Fe,hn,Oa,Tf,dn,yf,cd,Me,Ye,cn,Ha,bf,fn,wf,fd,Ve,un,C,gn,Af,kf,pn,Df,Sf,vn,Rf,Mf,mn,xf,Lf,p,I,En,ja,Gf,Cf,_n,Tn,If,Nf,yn,Bf,$f,bn,Pf,Of,N,wn,Ja,Hf,jf,An,kn,Jf,Wf,Dn,Uf,Zf,Sn,zf,Ff,B,Rn,Wa,Yf,Vf,Mn,xn,qf,Kf,Ln,Xf,Qf,Gn,eu,tu,$,Cn,Ua,au,ru,In,Nn,ou,nu,Bn,lu,su,$n,iu,hu,P,Pn,Za,du,cu,On,Hn,fu,uu,jn,gu,pu,Jn,vu,mu,O,Wn,za,Eu,_u,Un,Zn,Tu,yu,zn,bu,wu,Fn,Au,ku,H,Yn,Fa,Du,Su,Ya,Vn,Ru,Mu,qn,xu,Lu,Kn,Gu,Cu,Xn,Iu,Nu,j,Qn,Va,Bu,$u,el,tl,Pu,Ou,al,Hu,ju,rl,Ju,Wu,J,ol,qa,Uu,Zu,nl,ll,zu,Fu,sl,Yu,Vu,il,qu,Ku,W,hl,Ka,Xu,Qu,Xa,dl,eg,tg,cl,ag,rg,fl,og,ng,ul,lg,sg,U,gl,Qa,ig,hg,pl,vl,dg,cg,ml,fg,ug,El,gg,pg,Z,_l,er,vg,mg,Tl,yl,Eg,_g,bl,Tg,yg,wl,bg,ud,xe,qe,Al,tr,wg,kl,Ag,gd,Ke,Dl,z,Sl,kg,Dg,Rl,Sg,Rg,Ml,Mg,xg,xl,Lg,Gg,m,F,Ll,ar,Cg,Ig,Gl,Cl,Ng,Bg,Il,$g,Pg,Nl,Og,Hg,Y,Bl,rr,jg,Jg,$l,Pl,Wg,Ug,Ol,Zg,zg,Hl,Fg,Yg,V,jl,or,Vg,qg,Jl,Wl,Kg,Xg,Ul,Qg,ep,Zl,tp,ap,q,zl,nr,rp,op,Fl,Yl,np,lp,Vl,sp,ip,ql,hp,dp,K,Kl,lr,cp,fp,Xl,Ql,up,gp,es,pp,vp,ts,mp,Ep,X,as,sr,_p,Tp,rs,os,yp,bp,ns,wp,Ap,ls,kp,Dp,Q,ss,ir,Sp,Rp,is,Mp,xp,hs,Lp,Gp,ds,Cp,Ip,ee,cs,hr,Np,Bp,fs,$p,Pp,us,Op,Hp,gs,jp,pd,Le,Xe,ps,dr,Jp,vs,Wp,vd,Qe,ms,te,Es,Up,Zp,_s,zp,Fp,Ts,Yp,Vp,ys,qp,Kp,ae,re,bs,cr,Xp,Qp,ws,As,ev,tv,ks,av,rv,Ds,ov,nv,oe,Ss,fr,lv,sv,Rs,iv,hv,Ms,dv,cv,xs,fv,uv,ne,Ls,ur,gv,pv,Gs,Cs,vv,mv,Is,Ev,_v,Ns,Tv,yv,le,Bs,gr,bv,wv,$s,Av,kv,Ps,Dv,Sv,Os,Rv,md,Ge,et,Hs,pr,Mv,js,xv,Ed,tt,Js,se,Ws,Lv,Gv,Us,Cv,Iv,Zs,Nv,Bv,zs,$v,Pv,vr,ie,Fs,mr,Ov,Hv,Ys,jv,Jv,Vs,Wv,Uv,qs,Zv,zv,he,Ks,Er,Fv,Yv,Xs,Vv,qv,Qs,Kv,Xv,ei,Qv,_d,Ce,at,ti,_r,em,ai,tm,Td,rt,ri,de,oi,am,rm,ni,om,nm,li,lm,sm,si,im,hm,_,ce,ii,Tr,dm,cm,hi,di,fm,um,ci,gm,pm,fi,vm,mm,fe,ui,yr,Em,_m,gi,pi,Tm,ym,vi,bm,wm,mi,Am,km,ue,Ei,br,Dm,Sm,_i,Ti,Rm,Mm,yi,xm,Lm,bi,Gm,Cm,ge,wi,wr,Im,Nm,Ai,ki,Bm,$m,Di,Pm,Om,Si,Hm,jm,pe,Ri,Ar,Jm,Wm,Mi,xi,Um,Zm,Li,zm,Fm,Gi,Ym,Vm,ve,Ci,kr,qm,Km,Ii,Ni,Xm,Qm,Bi,e1,t1,$i,a1,yd,Ie,ot,Pi,Dr,r1,Oi,o1,bd,nt,Hi,me,ji,n1,l1,Ji,s1,i1,Wi,h1,d1,Ui,c1,f1,Zi,Ee,zi,Sr,u1,g1,Fi,p1,v1,Yi,m1,E1,Vi,_1,wd,Ne,lt,qi,Rr,T1,Ki,y1,Ad,u,st,Xi,Mr,b1,w1,xr,A1,k1,D1,it,Qi,Lr,S1,R1,Gr,M1,x1,L1,ht,eh,Cr,G1,C1,Ir,I1,N1,B1,dt,th,Nr,$1,P1,Br,O1,H1,j1,ct,ah,$r,J1,W1,Pr,U1,Z1,z1,ft,rh,Or,F1,Y1,Hr,V1,q1,K1,D,oh,jr,X1,Q1,Jr,eE,tE,Wr,aE,rE,Ur,oE,nE,Zr,lE,sE,iE,ut,nh,zr,hE,dE,Fr,cE,fE,uE,gt,lh,Yr,gE,pE,Vr,vE,mE,EE,x,sh,qr,_E,TE,Kr,yE,bE,ih,wE,AE,hh,kE,DE,SE,pt,dh,Xr,RE,ME,Qr,xE,LE,GE,vt,ch,eo,CE,IE,to,NE,BE,$E,ye,fh,ao,PE,OE,ro,HE,jE,oo,JE,WE,UE,mt,uh,no,ZE,zE,lo,FE,YE,VE,Et,gh,so,qE,KE,io,XE,QE,e2,_t,ph,ho,t2,a2,co,r2,o2,n2,Tt,vh,fo,l2,s2,uo,i2,h2,d2,yt,mh,go,c2,f2,po,u2,g2,p2,bt,Eh,vo,v2,m2,mo,E2,_2,T2,wt,_h,Eo,y2,b2,_o,w2,A2,k2,At,Th,To,D2,S2,yo,R2,M2,x2,kt,yh,bo,L2,G2,wo,C2,I2,N2,Dt,bh,Ao,B2,$2,ko,P2,O2,H2,St,wh,Do,j2,J2,So,W2,U2,Z2,be,Ah,Ro,z2,F2,Mo,Y2,V2,kh,q2,K2,kd;return ma=new G({}),Da=new G({}),La=new a_({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBJTIzJTIwQWxsb2NhdGUlMjBhJTIwcGlwZWxpbmUlMjBmb3IlMjBzZW50aW1lbnQtYW5hbHlzaXMlMEFwaXBlJTIwJTNEJTIwcGlwZWxpbmUoJ3NlbnRpbWVudC1hbmFseXNpcycpJTBBJTBBb3V0JTIwJTNEJTIwcGlwZSgnSSUyMGxvdmUlMjB0cmFuc2Zvcm1lcnMhJyklMEElMjMlMjAlNUIlN0InbGFiZWwnJTNBJTIwJ1BPU0lUSVZFJyUyQyUyMCdzY29yZSclM0ElMjAwLjk5OTgwNjk0MSU3RCU1RA==",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-comment"># Allocate a pipeline for sentiment-analysis</span>
pipe = pipeline(<span class="hljs-string">&#x27;sentiment-analysis&#x27;</span>)

out = pipe(<span class="hljs-string">&#x27;I love transformers!&#x27;</span>)
<span class="hljs-comment"># [{&#x27;label&#x27;: &#x27;POSITIVE&#x27;, &#x27;score&#x27;: 0.999806941}]</span>`}}),Ga=new a_({props:{code:"aW1wb3J0JTIwJTdCJTIwcGlwZWxpbmUlMjAlN0QlMjBmcm9tJTIwJyU0MHhlbm92YSUyRnRyYW5zZm9ybWVycyclM0IlMEElMEElMkYlMkYlMjBBbGxvY2F0ZSUyMGElMjBwaXBlbGluZSUyMGZvciUyMHNlbnRpbWVudC1hbmFseXNpcyUwQWxldCUyMHBpcGUlMjAlM0QlMjBhd2FpdCUyMHBpcGVsaW5lKCdzZW50aW1lbnQtYW5hbHlzaXMnKSUzQiUwQSUwQWxldCUyMG91dCUyMCUzRCUyMGF3YWl0JTIwcGlwZSgnSSUyMGxvdmUlMjB0cmFuc2Zvcm1lcnMhJyklM0IlMEElMkYlMkYlMjAlNUIlN0InbGFiZWwnJTNBJTIwJ1BPU0lUSVZFJyUyQyUyMCdzY29yZSclM0ElMjAwLjk5OTgxNzY4NiU3RCU1RA==",highlighted:`<span class="hljs-keyword">import</span> { pipeline } <span class="hljs-keyword">from</span> <span class="hljs-string">&#x27;@xenova/transformers&#x27;</span>;

<span class="hljs-comment">// Allocate a pipeline for sentiment-analysis</span>
<span class="hljs-keyword">let</span> pipe = <span class="hljs-keyword">await</span> <span class="hljs-title function_">pipeline</span>(<span class="hljs-string">&#x27;sentiment-analysis&#x27;</span>);

<span class="hljs-keyword">let</span> out = <span class="hljs-keyword">await</span> <span class="hljs-title function_">pipe</span>(<span class="hljs-string">&#x27;I love transformers!&#x27;</span>);
<span class="hljs-comment">// [{&#x27;label&#x27;: &#x27;POSITIVE&#x27;, &#x27;score&#x27;: 0.999817686}]</span>`}}),Ca=new a_({props:{code:"JTJGJTJGJTIwVXNlJTIwYSUyMGRpZmZlcmVudCUyMG1vZGVsJTIwZm9yJTIwc2VudGltZW50LWFuYWx5c2lzJTBBbGV0JTIwcGlwZSUyMCUzRCUyMGF3YWl0JTIwcGlwZWxpbmUoJ3NlbnRpbWVudC1hbmFseXNpcyclMkMlMjAnbmxwdG93biUyRmJlcnQtYmFzZS1tdWx0aWxpbmd1YWwtdW5jYXNlZC1zZW50aW1lbnQnKSUzQg==",highlighted:`<span class="hljs-comment">// Use a different model for sentiment-analysis</span>
<span class="hljs-keyword">let</span> pipe = <span class="hljs-keyword">await</span> <span class="hljs-title function_">pipeline</span>(<span class="hljs-string">&#x27;sentiment-analysis&#x27;</span>, <span class="hljs-string">&#x27;nlptown/bert-base-multilingual-uncased-sentiment&#x27;</span>);`}}),Ia=new G({}),Na=new G({}),Oa=new G({}),Ha=new G({}),tr=new G({}),dr=new G({}),pr=new G({}),_r=new G({}),Dr=new G({}),Rr=new G({}),{c(){we=a("meta"),Fh=i(),Ae=a("h1"),$e=a("a"),Ho=a("span"),T(ma.$$.fragment),nc=i(),jo=a("span"),lc=n("Transformers.js"),Yh=i(),Go=a("p"),sc=n("State-of-the-art Machine Learning for the web. Run \u{1F917} Transformers directly in your browser, with no need for a server!"),Vh=i(),Pe=a("p"),ic=n("Transformers.js is designed to be functionally equivalent to Hugging Face\u2019s "),Ea=a("a"),hc=n("transformers"),dc=n(" python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as:"),qh=i(),S=a("ul"),_a=a("li"),cc=n("\u{1F4DD} "),Jo=a("strong"),fc=n("Natural Language Processing"),uc=n(": text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation."),gc=i(),Ta=a("li"),pc=n("\u{1F5BC}\uFE0F "),Wo=a("strong"),vc=n("Computer Vision"),mc=n(": image classification, object detection, and segmentation."),Ec=i(),ya=a("li"),_c=n("\u{1F5E3}\uFE0F "),Uo=a("strong"),Tc=n("Audio"),yc=n(": automatic speech recognition and audio classification."),bc=i(),ba=a("li"),wc=n("\u{1F419} "),Zo=a("strong"),Ac=n("Multimodal"),kc=n(": zero-shot image classification."),Kh=i(),R=a("p"),Dc=n("Transformers.js uses "),wa=a("a"),Sc=n("ONNX Runtime"),Rc=n(" to run models in the browser. The best part about it, is that you can easily "),Co=a("a"),Mc=n("convert"),xc=n(" your pretrained PyTorch, TensorFlow, or JAX models to ONNX using "),Aa=a("a"),Lc=n("\u{1F917} Optimum"),Gc=n("."),Xh=i(),Oe=a("p"),Cc=n("For more information, check out the full "),ka=a("a"),Ic=n("documentation"),Nc=n("."),Qh=i(),ke=a("h2"),He=a("a"),zo=a("span"),T(Da.$$.fragment),Bc=i(),Fo=a("span"),$c=n("Quick tour"),ed=i(),je=a("p"),Pc=n("It\u2019s super simple to translate from existing code! Just like the python library, we support the "),Yo=a("code"),Oc=n("pipeline"),Hc=n(" API. Pipelines group together a pretrained model with preprocessing of inputs and postprocessing of outputs, making it the easiest way to run models with the library."),td=i(),Je=a("table"),Sa=a("tr"),Ra=a("th"),Vo=a("b"),jc=n("Python (original)"),Jc=i(),Ma=a("th"),qo=a("b"),Wc=n("Javascript (ours)"),Uc=i(),xa=a("tr"),Ko=a("td"),T(La.$$.fragment),Zc=i(),Xo=a("td"),T(Ga.$$.fragment),ad=i(),We=a("p"),zc=n("You can also use a different model by specifying the model id or path as the second argument to the "),Qo=a("code"),Fc=n("pipeline"),Yc=n(" function. For example:"),rd=i(),T(Ca.$$.fragment),od=i(),De=a("h2"),Ue=a("a"),en=a("span"),T(Ia.$$.fragment),Vc=i(),tn=a("span"),qc=n("Contents"),nd=i(),Io=a("p"),Kc=n("The documentation is organized into 4 sections:"),ld=i(),M=a("ol"),No=a("li"),an=a("strong"),Xc=n("GET STARTED"),Qc=n(" provides a quick tour of the library and installation instructions to get up and running."),ef=i(),Bo=a("li"),rn=a("strong"),tf=n("TUTORIALS"),af=n(" are a great place to start if you\u2019re a beginner! We also include sample applications for you to play around with!"),rf=i(),$o=a("li"),on=a("strong"),of=n("HOW-TO GUIDES"),nf=n(" show you how to use the library to achieve a specific goal."),lf=i(),Po=a("li"),nn=a("strong"),sf=n("API REFERENCE"),hf=n(" describes all classes and functions, as well as their available parameters and types."),sd=i(),Se=a("h2"),Ze=a("a"),ln=a("span"),T(Na.$$.fragment),df=i(),sn=a("span"),cf=n("Supported tasks/models"),id=i(),ze=a("p"),ff=n(`Here is the list of all tasks and architectures currently supported by Transformers.js.
If you don\u2019t see your task/model listed here or it is not yet supported, feel free
to open up a feature request `),Ba=a("a"),uf=n("here"),gf=n("."),hd=i(),Te=a("p"),pf=n("To find compatible models on the Hub, select the \u201Ctransformers.js\u201D library tag in the filter menu (or visit "),$a=a("a"),vf=n("this link"),mf=n(`).
You can refine your search by selecting the task you\u2019re interested in (e.g., `),Pa=a("a"),Ef=n("text-classification"),_f=n(")."),dd=i(),Re=a("h3"),Fe=a("a"),hn=a("span"),T(Oa.$$.fragment),Tf=i(),dn=a("span"),yf=n("Tasks"),cd=i(),Me=a("h4"),Ye=a("a"),cn=a("span"),T(Ha.$$.fragment),bf=i(),fn=a("span"),wf=n("Natual Language Processing"),fd=i(),Ve=a("table"),un=a("thead"),C=a("tr"),gn=a("th"),Af=n("Task"),kf=i(),pn=a("th"),Df=n("ID"),Sf=i(),vn=a("th"),Rf=n("Description"),Mf=i(),mn=a("th"),xf=n("Supported?"),Lf=i(),p=a("tbody"),I=a("tr"),En=a("td"),ja=a("a"),Gf=n("Conversational"),Cf=i(),_n=a("td"),Tn=a("code"),If=n("conversational"),Nf=i(),yn=a("td"),Bf=n("Generating conversational text that is relevant, coherent and knowledgable given a prompt."),$f=i(),bn=a("td"),Pf=n("\u274C"),Of=i(),N=a("tr"),wn=a("td"),Ja=a("a"),Hf=n("Fill-Mask"),jf=i(),An=a("td"),kn=a("code"),Jf=n("fill-mask"),Wf=i(),Dn=a("td"),Uf=n("Masking some of the words in a sentence and predicting which words should replace those masks."),Zf=i(),Sn=a("td"),zf=n("\u2705"),Ff=i(),B=a("tr"),Rn=a("td"),Wa=a("a"),Yf=n("Question Answering"),Vf=i(),Mn=a("td"),xn=a("code"),qf=n("question-answering"),Kf=i(),Ln=a("td"),Xf=n("Retrieve the answer to a question from a given text."),Qf=i(),Gn=a("td"),eu=n("\u2705"),tu=i(),$=a("tr"),Cn=a("td"),Ua=a("a"),au=n("Sentence Similarity"),ru=i(),In=a("td"),Nn=a("code"),ou=n("sentence-similarity"),nu=i(),Bn=a("td"),lu=n("Determining how similar two texts are."),su=i(),$n=a("td"),iu=n("\u2705"),hu=i(),P=a("tr"),Pn=a("td"),Za=a("a"),du=n("Summarization"),cu=i(),On=a("td"),Hn=a("code"),fu=n("summarization"),uu=i(),jn=a("td"),gu=n("Producing a shorter version of a document while preserving its important information."),pu=i(),Jn=a("td"),vu=n("\u2705"),mu=i(),O=a("tr"),Wn=a("td"),za=a("a"),Eu=n("Table Question Answering"),_u=i(),Un=a("td"),Zn=a("code"),Tu=n("table-question-answering"),yu=i(),zn=a("td"),bu=n("Answering a question about information from a given table."),wu=i(),Fn=a("td"),Au=n("\u274C"),ku=i(),H=a("tr"),Yn=a("td"),Fa=a("a"),Du=n("Text Classification"),Su=i(),Ya=a("td"),Vn=a("code"),Ru=n("text-classification"),Mu=n(" or "),qn=a("code"),xu=n("sentiment-analysis"),Lu=i(),Kn=a("td"),Gu=n("Assigning a label or class to a given text."),Cu=i(),Xn=a("td"),Iu=n("\u2705"),Nu=i(),j=a("tr"),Qn=a("td"),Va=a("a"),Bu=n("Text Generation"),$u=i(),el=a("td"),tl=a("code"),Pu=n("text-generation"),Ou=i(),al=a("td"),Hu=n("Producing new text by predicting the next word in a sequence."),ju=i(),rl=a("td"),Ju=n("\u2705"),Wu=i(),J=a("tr"),ol=a("td"),qa=a("a"),Uu=n("Text-to-text Generation"),Zu=i(),nl=a("td"),ll=a("code"),zu=n("text2text-generation"),Fu=i(),sl=a("td"),Yu=n("Converting one text sequence into another text sequence."),Vu=i(),il=a("td"),qu=n("\u2705"),Ku=i(),W=a("tr"),hl=a("td"),Ka=a("a"),Xu=n("Token Classification"),Qu=i(),Xa=a("td"),dl=a("code"),eg=n("token-classification"),tg=n(" or "),cl=a("code"),ag=n("ner"),rg=i(),fl=a("td"),og=n("Assigning a label to each token in a text."),ng=i(),ul=a("td"),lg=n("\u2705"),sg=i(),U=a("tr"),gl=a("td"),Qa=a("a"),ig=n("Translation"),hg=i(),pl=a("td"),vl=a("code"),dg=n("translation"),cg=i(),ml=a("td"),fg=n("Converting text from one language to another."),ug=i(),El=a("td"),gg=n("\u2705"),pg=i(),Z=a("tr"),_l=a("td"),er=a("a"),vg=n("Zero-Shot Classification"),mg=i(),Tl=a("td"),yl=a("code"),Eg=n("zero-shot-classification"),_g=i(),bl=a("td"),Tg=n("Classifying text into classes that are unseen during training."),yg=i(),wl=a("td"),bg=n("\u2705"),ud=i(),xe=a("h4"),qe=a("a"),Al=a("span"),T(tr.$$.fragment),wg=i(),kl=a("span"),Ag=n("Vision"),gd=i(),Ke=a("table"),Dl=a("thead"),z=a("tr"),Sl=a("th"),kg=n("Task"),Dg=i(),Rl=a("th"),Sg=n("ID"),Rg=i(),Ml=a("th"),Mg=n("Description"),xg=i(),xl=a("th"),Lg=n("Supported?"),Gg=i(),m=a("tbody"),F=a("tr"),Ll=a("td"),ar=a("a"),Cg=n("Depth Estimation"),Ig=i(),Gl=a("td"),Cl=a("code"),Ng=n("depth-estimation"),Bg=i(),Il=a("td"),$g=n("Predicting the depth of objects present in an image."),Pg=i(),Nl=a("td"),Og=n("\u274C"),Hg=i(),Y=a("tr"),Bl=a("td"),rr=a("a"),jg=n("Image Classification"),Jg=i(),$l=a("td"),Pl=a("code"),Wg=n("image-classification"),Ug=i(),Ol=a("td"),Zg=n("Assigning a label or class to an entire image."),zg=i(),Hl=a("td"),Fg=n("\u2705"),Yg=i(),V=a("tr"),jl=a("td"),or=a("a"),Vg=n("Image Segmentation"),qg=i(),Jl=a("td"),Wl=a("code"),Kg=n("image-segmentation"),Xg=i(),Ul=a("td"),Qg=n("Divides an image into segments where each pixel is mapped to an object. This task has multiple variants such as instance segmentation, panoptic segmentation and semantic segmentation."),ep=i(),Zl=a("td"),tp=n("\u2705"),ap=i(),q=a("tr"),zl=a("td"),nr=a("a"),rp=n("Image-to-Image"),op=i(),Fl=a("td"),Yl=a("code"),np=n("image-to-image"),lp=i(),Vl=a("td"),sp=n("Transforming a source image to match the characteristics of a target image or a target image domain."),ip=i(),ql=a("td"),hp=n("\u274C"),dp=i(),K=a("tr"),Kl=a("td"),lr=a("a"),cp=n("Mask Generation"),fp=i(),Xl=a("td"),Ql=a("code"),up=n("mask-generation"),gp=i(),es=a("td"),pp=n("Generate masks for the objects in an image."),vp=i(),ts=a("td"),mp=n("\u274C"),Ep=i(),X=a("tr"),as=a("td"),sr=a("a"),_p=n("Object Detection"),Tp=i(),rs=a("td"),os=a("code"),yp=n("object-detection"),bp=i(),ns=a("td"),wp=n("Identify objects of certain defined classes within an image."),Ap=i(),ls=a("td"),kp=n("\u2705"),Dp=i(),Q=a("tr"),ss=a("td"),ir=a("a"),Sp=n("Video 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Le, and Jason Wei"),uE=i(),gt=a("li"),lh=a("strong"),Yr=a("a"),gE=n("GPT Neo"),pE=n(" (from EleutherAI) released in the repository "),Vr=a("a"),vE=n("EleutherAI/gpt-neo"),mE=n(" by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy."),EE=i(),x=a("li"),sh=a("strong"),qr=a("a"),_E=n("GPT-2"),TE=n(" (from OpenAI) released with the paper "),Kr=a("a"),yE=n("Language Models are Unsupervised Multitask Learners"),bE=n(" by Alec Radford"),ih=a("em"),wE=n(", Jeffrey Wu"),AE=n(", Rewon Child, David Luan, Dario Amodei"),hh=a("strong"),kE=n("and Ilya Sutskever"),DE=n("."),SE=i(),pt=a("li"),dh=a("strong"),Xr=a("a"),RE=n("GPTBigCode"),ME=n(" (from BigCode) released with the paper "),Qr=a("a"),xE=n("SantaCoder: don\u2019t reach for the stars!"),LE=n(" by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo Garc\xEDa del R\xEDo, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra."),GE=i(),vt=a("li"),ch=a("strong"),eo=a("a"),CE=n("M2M100"),IE=n(" (from Facebook) released with the paper "),to=a("a"),NE=n("Beyond English-Centric Multilingual Machine Translation"),BE=n(" by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin."),$E=i(),ye=a("li"),fh=a("strong"),ao=a("a"),PE=n("MarianMT"),OE=n(" Machine translation models trained using "),ro=a("a"),HE=n("OPUS"),jE=n(" data by J\xF6rg Tiedemann. 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