course/pr_1207/es/_app/immutable/nodes/9.bd330c19.js

import{s as J,n as K,o as V}from"../chunks/scheduler.505acc25.js";import{S as W,i as Z,e as i,s as o,c as C,h as ee,a as r,d as a,b as l,f as D,g as v,j as w,k as I,l as te,m as n,n as L,t as P,o as T,p as E}from"../chunks/index.ccc5f2c1.js";import{C as ae,E as ne}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.d60d5764.js";import{Y as oe}from"../chunks/Youtube.ed0cfdd7.js";import{C as le}from"../chunks/CourseFloatingBanner.edb13094.js";import{H as se}from"../chunks/Heading.666af0ac.js";function ie(B){let s,M,x,y,m,H,f,q,p,z,d,S,u,F="Los modelos de decodificadores usan únicamente el decodificador del Transformer. 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>.",j,c,N="El preentrenamiento de los modelos de decodificadores generalmente gira en torno a la predicción de la siguiente palabra en la oración.",G,$,O="Estos modelos son más adecuados para tareas que implican la generación de texto.",R,g,Q="Los miembros de esta familia de modelos incluyen:",U,h,X='<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>',Y,_,k,b,A;return m=new ae({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),f=new se({props:{title:"Modelos de decodificadores",local:"modelos-de-decodificadores",headingTag:"h1"}}),p=new le({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),d=new oe({props:{id:"d_ixlCubqQw"}}),_=new ne({props:{source:"https://github.com/huggingface/course/blob/main/chapters/es/chapter1/6.mdx"}}),{c(){s=i("meta"),M=o(),x=i("p"),y=o(),C(m.$$.fragment),H=o(),C(f.$$.fragment),q=o(),C(p.$$.fragment),z=o(),C(d.$$.fragment),S=o(),u=i("p"),u.innerHTML=F,j=o(),c=i("p"),c.textContent=N,G=o(),$=i("p"),$.textContent=O,R=o(),g=i("p"),g.textContent=Q,U=o(),h=i("ul"),h.innerHTML=X,Y=o(),C(_.$$.fragment),k=o(),b=i("p"),this.h()},l(e){const t=ee("svelte-u9bgzb",document.head);s=r(t,"META",{name:!0,content:!0}),t.forEach(a),M=l(e),x=r(e,"P",{}),D(x).forEach(a),y=l(e),v(m.$$.fragment,e),H=l(e),v(f.$$.fragment,e),q=l(e),v(p.$$.fragment,e),z=l(e),v(d.$$.fragment,e),S=l(e),u=r(e,"P",{"data-svelte-h":!0}),w(u)!=="svelte-1i6a7dh"&&(u.innerHTML=F),j=l(e),c=r(e,"P",{"data-svelte-h":!0}),w(c)!=="svelte-tswbj6"&&(c.textContent=N),G=l(e),$=r(e,"P",{"data-svelte-h":!0}),w($)!=="svelte-oocvh6"&&($.textContent=O),R=l(e),g=r(e,"P",{"data-svelte-h":!0}),w(g)!=="svelte-umsuzu"&&(g.textContent=Q),U=l(e),h=r(e,"UL",{"data-svelte-h":!0}),w(h)!=="svelte-jabf5n"&&(h.innerHTML=X),Y=l(e),v(_.$$.fragment,e),k=l(e),b=r(e,"P",{}),D(b).forEach(a),this.h()},h(){I(s,"name","hf:doc:metadata"),I(s,"content",re)},m(e,t){te(document.head,s),n(e,M,t),n(e,x,t),n(e,y,t),L(m,e,t),n(e,H,t),L(f,e,t),n(e,q,t),L(p,e,t),n(e,z,t),L(d,e,t),n(e,S,t),n(e,u,t),n(e,j,t),n(e,c,t),n(e,G,t),n(e,$,t),n(e,R,t),n(e,g,t),n(e,U,t),n(e,h,t),n(e,Y,t),L(_,e,t),n(e,k,t),n(e,b,t),A=!0},p:K,i(e){A||(P(m.$$.fragment,e),P(f.$$.fragment,e),P(p.$$.fragment,e),P(d.$$.fragment,e),P(_.$$.fragment,e),A=!0)},o(e){T(m.$$.fragment,e),T(f.$$.fragment,e),T(p.$$.fragment,e),T(d.$$.fragment,e),T(_.$$.fragment,e),A=!1},d(e){e&&(a(M),a(x),a(y),a(H),a(q),a(z),a(S),a(u),a(j),a(c),a(G),a($),a(R),a(g),a(U),a(h),a(Y),a(k),a(b)),a(s),E(m,e),E(f,e),E(p,e),E(d,e),E(_,e)}}}const re='{"title":"Modelos de decodificadores","local":"modelos-de-decodificadores","sections":[],"depth":1}';function me(B){return V(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ge extends W{constructor(s){super(),Z(this,s,me,ie,J,{})}}export{ge as component};