course/pr_1114/ro/_app/immutable/nodes/8.60fbb27f.js

import{s as K,n as V,o as W}from"../chunks/scheduler.893fe8c9.js";import{S as X,i as Z,e as o,s as n,c as b,h as ee,a as s,d as a,b as r,f as O,g as C,j as E,k as I,l as te,m as i,n as w,t as z,o as M,p as P}from"../chunks/index.6ee278c6.js";import{C as ae,H as ie,E as ne}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.543db1b8.js";import{Y as re}from"../chunks/Youtube.59d04f41.js";import{C as le}from"../chunks/CourseFloatingBanner.b902b8d5.js";function oe(Y){let l,T,x,L,m,y,p,R,f,H,c,A,u,k="Modelele Encoder utilizează doar encoderul unui model Transformer. La fiecare etapă, layer-urile de atenție pot accesa toate cuvintele din propoziția inițială. Aceste modele sunt adesea caracterizate ca având o atenție „bidirecțională” și sunt adesea numite <em>modele auto-encoding</em>.",B,d,D="Preantrenarea acestor modele se bazează, de obicei, pe alterarea unei propoziții date (de exemplu, prin mascarea unor cuvinte aleatorii) și pe sarcina modelului de a găsi sau reconstrui propoziția inițială.",S,$,F="Modelele Encoder sunt cele mai potrivite pentru sarcinile care necesită înțelegerea întregii propoziții, cum ar fi clasificarea propozițiilor, recunoașterea entităților numite (și, mai general, clasificarea cuvintelor) și Extractive QA.",j,g,G="Printre reprezentanții acestei familii de modele se numără:",U,h,J='<li><a href="https://huggingface.co/docs/transformers/model_doc/albert" rel="nofollow">ALBERT</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/bert" rel="nofollow">BERT</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/distilbert" rel="nofollow">DistilBERT</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/electra" rel="nofollow">ELECTRA</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/roberta" rel="nofollow">RoBERTa</a></li>',q,_,N,v,Q;return m=new ae({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),p=new ie({props:{title:"Modele Encoder",local:"modele-encoder",headingTag:"h1"}}),f=new le({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),c=new re({props:{id:"MUqNwgPjJvQ"}}),_=new ne({props:{source:"https://github.com/huggingface/course/blob/main/chapters/ro/chapter1/5.mdx"}}),{c(){l=o("meta"),T=n(),x=o("p"),L=n(),b(m.$$.fragment),y=n(),b(p.$$.fragment),R=n(),b(f.$$.fragment),H=n(),b(c.$$.fragment),A=n(),u=o("p"),u.innerHTML=k,B=n(),d=o("p"),d.textContent=D,S=n(),$=o("p"),$.textContent=F,j=n(),g=o("p"),g.textContent=G,U=n(),h=o("ul"),h.innerHTML=J,q=n(),b(_.$$.fragment),N=n(),v=o("p"),this.h()},l(e){const t=ee("svelte-u9bgzb",document.head);l=s(t,"META",{name:!0,content:!0}),t.forEach(a),T=r(e),x=s(e,"P",{}),O(x).forEach(a),L=r(e),C(m.$$.fragment,e),y=r(e),C(p.$$.fragment,e),R=r(e),C(f.$$.fragment,e),H=r(e),C(c.$$.fragment,e),A=r(e),u=s(e,"P",{"data-svelte-h":!0}),E(u)!=="svelte-7cte6f"&&(u.innerHTML=k),B=r(e),d=s(e,"P",{"data-svelte-h":!0}),E(d)!=="svelte-1ube0l3"&&(d.textContent=D),S=r(e),$=s(e,"P",{"data-svelte-h":!0}),E($)!=="svelte-jpa42"&&($.textContent=F),j=r(e),g=s(e,"P",{"data-svelte-h":!0}),E(g)!=="svelte-dd7odx"&&(g.textContent=G),U=r(e),h=s(e,"UL",{"data-svelte-h":!0}),E(h)!=="svelte-17o0nd4"&&(h.innerHTML=J),q=r(e),C(_.$$.fragment,e),N=r(e),v=s(e,"P",{}),O(v).forEach(a),this.h()},h(){I(l,"name","hf:doc:metadata"),I(l,"content",se)},m(e,t){te(document.head,l),i(e,T,t),i(e,x,t),i(e,L,t),w(m,e,t),i(e,y,t),w(p,e,t),i(e,R,t),w(f,e,t),i(e,H,t),w(c,e,t),i(e,A,t),i(e,u,t),i(e,B,t),i(e,d,t),i(e,S,t),i(e,$,t),i(e,j,t),i(e,g,t),i(e,U,t),i(e,h,t),i(e,q,t),w(_,e,t),i(e,N,t),i(e,v,t),Q=!0},p:V,i(e){Q||(z(m.$$.fragment,e),z(p.$$.fragment,e),z(f.$$.fragment,e),z(c.$$.fragment,e),z(_.$$.fragment,e),Q=!0)},o(e){M(m.$$.fragment,e),M(p.$$.fragment,e),M(f.$$.fragment,e),M(c.$$.fragment,e),M(_.$$.fragment,e),Q=!1},d(e){e&&(a(T),a(x),a(L),a(y),a(R),a(H),a(A),a(u),a(B),a(d),a(S),a($),a(j),a(g),a(U),a(h),a(q),a(N),a(v)),a(l),P(m,e),P(p,e),P(f,e),P(c,e),P(_,e)}}}const se='{"title":"Modele Encoder","local":"modele-encoder","sections":[],"depth":1}';function me(Y){return W(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class $e extends X{constructor(l){super(),Z(this,l,me,oe,K,{})}}export{$e as component};