course/pr_1069/es/_app/immutable/nodes/9.a34df134.js

import{s as D,n as I,o as J}from"../chunks/scheduler.37c15a92.js";import{S as K,i as V,g as r,s as n,r as j,A as W,h as i,f as a,c as s,j as Q,u as G,x,k as X,y as Z,a as o,v as S,d as A,t as R,w as U}from"../chunks/index.2bf4358c.js";import{Y as ee}from"../chunks/Youtube.1e50a667.js";import{C as te}from"../chunks/CourseFloatingBanner.6add7356.js";import{H as ae}from"../chunks/Heading.8ada512a.js";import{E as oe}from"../chunks/getInferenceSnippets.b37612c0.js";function ne(Y){let l,v,_,w,m,C,d,P,f,T,p,k="Los modelos de decodificadores usan únicamente el decodificador del Transformador. En cada etapa, para una palabra dada las capas de atención pueden acceder solamente a las palabras que se ubican antes en la oración. Estos modelos se suelen llamar modelos <em>auto-regressive</em>.",E,c,B="El preentrenamiento de los modelos de decodificadores generalmente gira en torno a la predicción de la siguiente palabra en la oración.",L,u,F="Estos modelos son más adecuados para tareas que implican la generación de texto.",M,$,N="Los miembros de esta familia de modelos incluyen:",y,h,O='<li><a href="https://huggingface.co/transformers/model_doc/ctrl.html" rel="nofollow">CTRL</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/openai-gpt" rel="nofollow">GPT</a></li> <li><a href="https://huggingface.co/transformers/model_doc/gpt2.html" rel="nofollow">GPT-2</a></li> <li><a href="https://huggingface.co/transformers/model_doc/transformerxl.html" rel="nofollow">Transformer XL</a></li>',H,g,q,b,z;return m=new ae({props:{title:"Modelos de decodificadores",local:"modelos-de-decodificadores",headingTag:"h1"}}),d=new te({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),f=new ee({props:{id:"d_ixlCubqQw"}}),g=new oe({props:{source:"https://github.com/huggingface/course/blob/main/chapters/es/chapter1/6.mdx"}}),{c(){l=r("meta"),v=n(),_=r("p"),w=n(),j(m.$$.fragment),C=n(),j(d.$$.fragment),P=n(),j(f.$$.fragment),T=n(),p=r("p"),p.innerHTML=k,E=n(),c=r("p"),c.textContent=B,L=n(),u=r("p"),u.textContent=F,M=n(),$=r("p"),$.textContent=N,y=n(),h=r("ul"),h.innerHTML=O,H=n(),j(g.$$.fragment),q=n(),b=r("p"),this.h()},l(e){const t=W("svelte-u9bgzb",document.head);l=i(t,"META",{name:!0,content:!0}),t.forEach(a),v=s(e),_=i(e,"P",{}),Q(_).forEach(a),w=s(e),G(m.$$.fragment,e),C=s(e),G(d.$$.fragment,e),P=s(e),G(f.$$.fragment,e),T=s(e),p=i(e,"P",{"data-svelte-h":!0}),x(p)!=="svelte-l2w6zy"&&(p.innerHTML=k),E=s(e),c=i(e,"P",{"data-svelte-h":!0}),x(c)!=="svelte-tswbj6"&&(c.textContent=B),L=s(e),u=i(e,"P",{"data-svelte-h":!0}),x(u)!=="svelte-oocvh6"&&(u.textContent=F),M=s(e),$=i(e,"P",{"data-svelte-h":!0}),x($)!=="svelte-umsuzu"&&($.textContent=N),y=s(e),h=i(e,"UL",{"data-svelte-h":!0}),x(h)!=="svelte-jabf5n"&&(h.innerHTML=O),H=s(e),G(g.$$.fragment,e),q=s(e),b=i(e,"P",{}),Q(b).forEach(a),this.h()},h(){X(l,"name","hf:doc:metadata"),X(l,"content",se)},m(e,t){Z(document.head,l),o(e,v,t),o(e,_,t),o(e,w,t),S(m,e,t),o(e,C,t),S(d,e,t),o(e,P,t),S(f,e,t),o(e,T,t),o(e,p,t),o(e,E,t),o(e,c,t),o(e,L,t),o(e,u,t),o(e,M,t),o(e,$,t),o(e,y,t),o(e,h,t),o(e,H,t),S(g,e,t),o(e,q,t),o(e,b,t),z=!0},p:I,i(e){z||(A(m.$$.fragment,e),A(d.$$.fragment,e),A(f.$$.fragment,e),A(g.$$.fragment,e),z=!0)},o(e){R(m.$$.fragment,e),R(d.$$.fragment,e),R(f.$$.fragment,e),R(g.$$.fragment,e),z=!1},d(e){e&&(a(v),a(_),a(w),a(C),a(P),a(T),a(p),a(E),a(c),a(L),a(u),a(M),a($),a(y),a(h),a(H),a(q),a(b)),a(l),U(m,e),U(d,e),U(f,e),U(g,e)}}}const se='{"title":"Modelos de decodificadores","local":"modelos-de-decodificadores","sections":[],"depth":1}';function le(Y){return J(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ce extends K{constructor(l){super(),V(this,l,le,ne,D,{})}}export{ce as component};