course/pr_1110/ro/_app/immutable/nodes/10.dbce5fa3.js

import{s as Q,n as V,o as W}from"../chunks/scheduler.893fe8c9.js";import{S as X,i as Z,e as o,s as i,c as b,h as ee,a as s,d as n,b as l,f as I,g as q,j as w,k as J,l as te,m as a,n as M,t as C,o as T,p as z}from"../chunks/index.6ee278c6.js";import{C as ne,H as ae,E as ie}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.278b228a.js";import{Y as le}from"../chunks/Youtube.59d04f41.js";import{C as re}from"../chunks/CourseFloatingBanner.b51f5100.js";function oe(k){let r,P,_,y,u,L,m,E,c,H,p,S,f,F="Modelele Encoder-Decoder (denumite și <em>modele sequence-to-sequence</em>) utilizează ambele părți ale arhitecturii Transformer. În fiecare etapă, layerele de atenție ale encoder-ului pot accesa toate cuvintele din propoziția inițială, în timp ce layerele de atenție ale decoder-ului pot accesa doar cuvintele poziționate înaintea unui anumit cuvânt din intrare.",A,d,G='Preantrenarea acestor modele se poate face folosind obiectivele modelelor de codificare sau de decodificare, dar de obicei implică ceva un pic mai complex. De exemplu, <a href="https://huggingface.co/t5-base" rel="nofollow">T5</a> este prenatrenat prin înlocuirea unor intervale aleatorii de text (care pot conține mai multe cuvinte) cu un singur cuvânt special mascat, iar obiectivul este apoi de a prezice textul pe care îl înlocuiește acest cuvânt mascat.',B,$,K="Modelele Sequence-to-sequence sunt cele mai potrivite pentru sarcinile care se învârt în jurul generării de noi propoziții în funcție de o intrare dată, cum ar fi rezumarea, traducerea sau răspunsul generativ la întrebări.",R,g,N="Printre reprezentanții acestei familii de modele se numără:",j,h,O='<li><a href="https://huggingface.co/transformers/model_doc/bart" rel="nofollow">BART</a></li> <li><a href="https://huggingface.co/transformers/model_doc/mbart" rel="nofollow">mBART</a></li> <li><a href="https://huggingface.co/transformers/model_doc/marian" rel="nofollow">Marian</a></li> <li><a href="https://huggingface.co/transformers/model_doc/t5" rel="nofollow">T5</a></li>',D,v,U,x,Y;return u=new ne({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),m=new ae({props:{title:"Modele Sequence-to-sequence modele-sequence-to-sequence",local:"modele-sequence-to-sequence-modele-sequence-to-sequence",headingTag:"h1"}}),c=new re({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),p=new le({props:{id:"0_4KEb08xrE"}}),v=new ie({props:{source:"https://github.com/huggingface/course/blob/main/chapters/ro/chapter1/7.mdx"}}),{c(){r=o("meta"),P=i(),_=o("p"),y=i(),b(u.$$.fragment),L=i(),b(m.$$.fragment),E=i(),b(c.$$.fragment),H=i(),b(p.$$.fragment),S=i(),f=o("p"),f.innerHTML=F,A=i(),d=o("p"),d.innerHTML=G,B=i(),$=o("p"),$.textContent=K,R=i(),g=o("p"),g.textContent=N,j=i(),h=o("ul"),h.innerHTML=O,D=i(),b(v.$$.fragment),U=i(),x=o("p"),this.h()},l(e){const t=ee("svelte-u9bgzb",document.head);r=s(t,"META",{name:!0,content:!0}),t.forEach(n),P=l(e),_=s(e,"P",{}),I(_).forEach(n),y=l(e),q(u.$$.fragment,e),L=l(e),q(m.$$.fragment,e),E=l(e),q(c.$$.fragment,e),H=l(e),q(p.$$.fragment,e),S=l(e),f=s(e,"P",{"data-svelte-h":!0}),w(f)!=="svelte-50lumr"&&(f.innerHTML=F),A=l(e),d=s(e,"P",{"data-svelte-h":!0}),w(d)!=="svelte-11ocaip"&&(d.innerHTML=G),B=l(e),$=s(e,"P",{"data-svelte-h":!0}),w($)!=="svelte-i9wbac"&&($.textContent=K),R=l(e),g=s(e,"P",{"data-svelte-h":!0}),w(g)!=="svelte-dd7odx"&&(g.textContent=N),j=l(e),h=s(e,"UL",{"data-svelte-h":!0}),w(h)!=="svelte-cz4hgg"&&(h.innerHTML=O),D=l(e),q(v.$$.fragment,e),U=l(e),x=s(e,"P",{}),I(x).forEach(n),this.h()},h(){J(r,"name","hf:doc:metadata"),J(r,"content",se)},m(e,t){te(document.head,r),a(e,P,t),a(e,_,t),a(e,y,t),M(u,e,t),a(e,L,t),M(m,e,t),a(e,E,t),M(c,e,t),a(e,H,t),M(p,e,t),a(e,S,t),a(e,f,t),a(e,A,t),a(e,d,t),a(e,B,t),a(e,$,t),a(e,R,t),a(e,g,t),a(e,j,t),a(e,h,t),a(e,D,t),M(v,e,t),a(e,U,t),a(e,x,t),Y=!0},p:V,i(e){Y||(C(u.$$.fragment,e),C(m.$$.fragment,e),C(c.$$.fragment,e),C(p.$$.fragment,e),C(v.$$.fragment,e),Y=!0)},o(e){T(u.$$.fragment,e),T(m.$$.fragment,e),T(c.$$.fragment,e),T(p.$$.fragment,e),T(v.$$.fragment,e),Y=!1},d(e){e&&(n(P),n(_),n(y),n(L),n(E),n(H),n(S),n(f),n(A),n(d),n(B),n($),n(R),n(g),n(j),n(h),n(D),n(U),n(x)),n(r),z(u,e),z(m,e),z(c,e),z(p,e),z(v,e)}}}const se='{"title":"Modele Sequence-to-sequence modele-sequence-to-sequence","local":"modele-sequence-to-sequence-modele-sequence-to-sequence","sections":[],"depth":1}';function ue(k){return W(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class $e extends X{constructor(r){super(),Z(this,r,ue,oe,Q,{})}}export{$e as component};