course/pr_1138/es/_app/immutable/nodes/12.1ea5cef2.js

import{s as I,n as N,o as U}from"../chunks/scheduler.505acc25.js";import{S as F,i as O,e as f,s,c as P,h as X,a as $,d as a,b as i,f as D,g as M,j as z,k,l as J,m as n,n as w,t as B,o as A,p as H}from"../chunks/index.ccc5f2c1.js";import{C as K,E as Q}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.9ac22bda.js";import{C as V}from"../chunks/CourseFloatingBanner.edb13094.js";import{H as W}from"../chunks/Heading.666af0ac.js";function Y(q){let r,T,h,g,o,v,l,_,m,x,d,j="En este capítulo viste cómo abordar diferentes tareas de PLN usando la función de alto nivel <code>pipeline()</code> de 🤗 Transformers. También viste como buscar modelos en el Hub, así como usar la API de Inferencia para probar los modelos directamente en tu navegador.",C,p,G="Discutimos brevemente el funcionamiento de los Transformadores y hablamos sobre la importancia de la transferencia de aprendizaje y el ajuste. Un aspecto clave es que puedes usar la arquitectura completa o sólo el codificador o decodificador, dependiendo de qué tipo de tarea quieres resolver. La siguiente tabla resume lo anterior:",E,u,S="<thead><tr><th>Modelo</th> <th>Ejemplos</th> <th>Tareas</th></tr></thead> <tbody><tr><td>Codificador</td> <td>ALBERT, BERT, DistilBERT, ELECTRA, RoBERTa</td> <td>Clasificación de oraciones, reconocimiento de entidades nombradas, respuesta extractiva a preguntas</td></tr> <tr><td>Decodificador</td> <td>CTRL, GPT, GPT-2, Transformer XL</td> <td>Generación de texto</td></tr> <tr><td>Codificador-decodificador</td> <td>BART, T5, Marian, mBART</td> <td>Resumen, traducción, respuesta generativa a preguntas</td></tr></tbody>",L,c,R,b,y;return o=new K({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),l=new W({props:{title:"Resumen",local:"resumen",headingTag:"h1"}}),m=new V({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),c=new Q({props:{source:"https://github.com/huggingface/course/blob/main/chapters/es/chapter1/9.mdx"}}),{c(){r=f("meta"),T=s(),h=f("p"),g=s(),P(o.$$.fragment),v=s(),P(l.$$.fragment),_=s(),P(m.$$.fragment),x=s(),d=f("p"),d.innerHTML=j,C=s(),p=f("p"),p.textContent=G,E=s(),u=f("table"),u.innerHTML=S,L=s(),P(c.$$.fragment),R=s(),b=f("p"),this.h()},l(e){const t=X("svelte-u9bgzb",document.head);r=$(t,"META",{name:!0,content:!0}),t.forEach(a),T=i(e),h=$(e,"P",{}),D(h).forEach(a),g=i(e),M(o.$$.fragment,e),v=i(e),M(l.$$.fragment,e),_=i(e),M(m.$$.fragment,e),x=i(e),d=$(e,"P",{"data-svelte-h":!0}),z(d)!=="svelte-z7d3gl"&&(d.innerHTML=j),C=i(e),p=$(e,"P",{"data-svelte-h":!0}),z(p)!=="svelte-1ymi42z"&&(p.textContent=G),E=i(e),u=$(e,"TABLE",{"data-svelte-h":!0}),z(u)!=="svelte-15pk5w0"&&(u.innerHTML=S),L=i(e),M(c.$$.fragment,e),R=i(e),b=$(e,"P",{}),D(b).forEach(a),this.h()},h(){k(r,"name","hf:doc:metadata"),k(r,"content",Z)},m(e,t){J(document.head,r),n(e,T,t),n(e,h,t),n(e,g,t),w(o,e,t),n(e,v,t),w(l,e,t),n(e,_,t),w(m,e,t),n(e,x,t),n(e,d,t),n(e,C,t),n(e,p,t),n(e,E,t),n(e,u,t),n(e,L,t),w(c,e,t),n(e,R,t),n(e,b,t),y=!0},p:N,i(e){y||(B(o.$$.fragment,e),B(l.$$.fragment,e),B(m.$$.fragment,e),B(c.$$.fragment,e),y=!0)},o(e){A(o.$$.fragment,e),A(l.$$.fragment,e),A(m.$$.fragment,e),A(c.$$.fragment,e),y=!1},d(e){e&&(a(T),a(h),a(g),a(v),a(_),a(x),a(d),a(C),a(p),a(E),a(u),a(L),a(R),a(b)),a(r),H(o,e),H(l,e),H(m,e),H(c,e)}}}const Z='{"title":"Resumen","local":"resumen","sections":[],"depth":1}';function ee(q){return U(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ie extends F{constructor(r){super(),O(this,r,ee,Y,I,{})}}export{ie as component};