Buckets:
| import{s as G,o as O}from"../chunks/scheduler.37c15a92.js";import{S as R,i as D,g,s as u,r as T,A as J,h as b,f as r,c,j as N,u as y,x as S,k as B,y as Q,a as o,v as x,d as E,t as A,w as F}from"../chunks/index.2bf4358c.js";import{C as V}from"../chunks/CourseFloatingBanner.6add7356.js";import{F as W}from"../chunks/FrameworkSwitchCourse.8d4d4ab6.js";import{H as X}from"../chunks/Heading.8ada512a.js";import{E as Y}from"../chunks/getInferenceSnippets.b37612c0.js";function Z(p){let t,i="<li>Cómo preparar un conjunto de datos grande desde el Hub.</li> <li>Cómo usar Keras para ajustar un modelo.</li> <li>Cómo usar Keras para obtener predicciones.</li> <li>Cómo usar una métrica personalizada.</li>";return{c(){t=g("ul"),t.innerHTML=i},l(n){t=b(n,"UL",{"data-svelte-h":!0}),S(t)!=="svelte-1bptjzn"&&(t.innerHTML=i)},m(n,s){o(n,t,s)},d(n){n&&r(t)}}}function ee(p){let t,i="<li>Cómo preparar un conjunto de datos grande desde el Hub.</li> <li>Cómo usar la API de alto nivel del entrenador para ajustar un modelo.</li> <li>Cómo usar un bucle personalizado de entrenamiento.</li> <li>Cómo aprovechar la librería 🤗 Accelerate para fácilmente ejecutar el bucle personalizado de entrenamiento en cualquier configuración distribuida.</li>";return{c(){t=g("ul"),t.innerHTML=i},l(n){t=b(n,"UL",{"data-svelte-h":!0}),S(t)!=="svelte-ctzwo8"&&(t.innerHTML=i)},m(n,s){o(n,t,s)},d(n){n&&r(t)}}}function te(p){let t,i,n,s,m,v,f,H,d,j,$,q='En el <a href="/course/chapter2">Capítulo 2</a> exploramos cómo usar los tokenizadores y modelos preentrenados para realizar predicciones. Pero, ¿qué pasa si deseas ajustar un modelo preentrenado con tu propio conjunto de datos?',k,w,h,I='Para subir tus puntos de control (<em>checkpoints</em>) en el Hub de Hugging Face, necesitas una cuenta en huggingface.co: <a href="https://huggingface.co/join" rel="nofollow">crea una cuenta</a>',z,_,L,C,M;m=new W({props:{fw:p[0]}}),f=new X({props:{title:"Introducción",local:"introducción",headingTag:"h1"}}),d=new V({props:{chapter:3,classNames:"absolute z-10 right-0 top-0"}});function U(e,a){return e[0]==="pt"?ee:Z}let P=U(p),l=P(p);return _=new Y({props:{source:"https://github.com/huggingface/course/blob/main/chapters/es/chapter3/1.mdx"}}),{c(){t=g("meta"),i=u(),n=g("p"),s=u(),T(m.$$.fragment),v=u(),T(f.$$.fragment),H=u(),T(d.$$.fragment),j=u(),$=g("p"),$.innerHTML=q,k=u(),l.c(),w=u(),h=g("p"),h.innerHTML=I,z=u(),T(_.$$.fragment),L=u(),C=g("p"),this.h()},l(e){const a=J("svelte-u9bgzb",document.head);t=b(a,"META",{name:!0,content:!0}),a.forEach(r),i=c(e),n=b(e,"P",{}),N(n).forEach(r),s=c(e),y(m.$$.fragment,e),v=c(e),y(f.$$.fragment,e),H=c(e),y(d.$$.fragment,e),j=c(e),$=b(e,"P",{"data-svelte-h":!0}),S($)!=="svelte-ojtfmi"&&($.innerHTML=q),k=c(e),l.l(e),w=c(e),h=b(e,"P",{"data-svelte-h":!0}),S(h)!=="svelte-6zatsz"&&(h.innerHTML=I),z=c(e),y(_.$$.fragment,e),L=c(e),C=b(e,"P",{}),N(C).forEach(r),this.h()},h(){B(t,"name","hf:doc:metadata"),B(t,"content",ae)},m(e,a){Q(document.head,t),o(e,i,a),o(e,n,a),o(e,s,a),x(m,e,a),o(e,v,a),x(f,e,a),o(e,H,a),x(d,e,a),o(e,j,a),o(e,$,a),o(e,k,a),l.m(e,a),o(e,w,a),o(e,h,a),o(e,z,a),x(_,e,a),o(e,L,a),o(e,C,a),M=!0},p(e,[a]){const K={};a&1&&(K.fw=e[0]),m.$set(K),P!==(P=U(e))&&(l.d(1),l=P(e),l&&(l.c(),l.m(w.parentNode,w)))},i(e){M||(E(m.$$.fragment,e),E(f.$$.fragment,e),E(d.$$.fragment,e),E(_.$$.fragment,e),M=!0)},o(e){A(m.$$.fragment,e),A(f.$$.fragment,e),A(d.$$.fragment,e),A(_.$$.fragment,e),M=!1},d(e){e&&(r(i),r(n),r(s),r(v),r(H),r(j),r($),r(k),r(w),r(h),r(z),r(L),r(C)),r(t),F(m,e),F(f,e),F(d,e),l.d(e),F(_,e)}}}const ae='{"title":"Introducción","local":"introducción","sections":[],"depth":1}';function ne(p,t,i){let n="pt";return O(()=>{const s=new URLSearchParams(window.location.search);i(0,n=s.get("fw")||"pt")}),[n]}class ce extends R{constructor(t){super(),D(this,t,ne,te,G,{})}}export{ce as component}; | |
Xet Storage Details
- Size:
- 3.86 kB
- Xet hash:
- 9b39f7c32fb086e486cc79bcc46c712754322c01dff229a4f04da942998d10d7
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.