Buckets:
| import{s as B,n as U,o as j}from"../chunks/scheduler.37c15a92.js";import{S as G,i as I,g as p,s as o,r as q,A as O,h as f,f as a,c as r,j as k,u as w,x as T,k as N,y as R,a as s,v as D,d as E,t as L,w as M}from"../chunks/index.2bf4358c.js";import{C as J}from"../chunks/CourseFloatingBanner.6add7356.js";import{H as K}from"../chunks/Heading.8ada512a.js";import{E as Q}from"../chunks/getInferenceSnippets.5cee47d1.js";function V(F){let n,g,$,_,i,b,l,v,u,S="Bueno, ese fue un gran tour de la librería 🤗 Datasets. ¡Felicitaciones por llegar hasta aquí! Con el conocimiento que adquiriste en este capítulo, deberías ser capaz de:",C,m,z="<li>Cargar datasets de cualquier parte, sea del Hub de Hugging Face, tu computador o un servidor remoto en tu compañía.</li> <li>Preparar tus datos usando una combinación de las funciones <code>Dataset.map()</code> y <code>Dataset.filter()</code>.</li> <li>Cambiar rápidamente entre formatos de datos como Pandas y NumPy usando <code>Dataset.set_format()</code>.</li> <li>Crear tu propio dataset y subirlo al Hub de Hugging Face.</li> <li>Procesar tus documentos usando un modelo de Transformer y construir un motor de búsqueda semántica usando FAISS.</li>",P,c,A='En el <a href="/course/chapter7">Capítulo 7</a> pondremos todo esto en práctica cuando veamos a profundidad las tareas de PLN en las que son buenos los modelos de Transformers. Antes de seguir, ¡es hora de poner a prueba tu conocimiento de 🤗 Datasets con un quiz!',x,d,y,h,H;return i=new K({props:{title:"🤗 Datasets, ¡listo!",local:"-datasets-listo",headingTag:"h1"}}),l=new J({props:{chapter:5,classNames:"absolute z-10 right-0 top-0"}}),d=new Q({props:{source:"https://github.com/huggingface/course/blob/main/chapters/es/chapter5/7.mdx"}}),{c(){n=p("meta"),g=o(),$=p("p"),_=o(),q(i.$$.fragment),b=o(),q(l.$$.fragment),v=o(),u=p("p"),u.textContent=S,C=o(),m=p("ul"),m.innerHTML=z,P=o(),c=p("p"),c.innerHTML=A,x=o(),q(d.$$.fragment),y=o(),h=p("p"),this.h()},l(e){const t=O("svelte-u9bgzb",document.head);n=f(t,"META",{name:!0,content:!0}),t.forEach(a),g=r(e),$=f(e,"P",{}),k($).forEach(a),_=r(e),w(i.$$.fragment,e),b=r(e),w(l.$$.fragment,e),v=r(e),u=f(e,"P",{"data-svelte-h":!0}),T(u)!=="svelte-1k0ykas"&&(u.textContent=S),C=r(e),m=f(e,"UL",{"data-svelte-h":!0}),T(m)!=="svelte-cvhtws"&&(m.innerHTML=z),P=r(e),c=f(e,"P",{"data-svelte-h":!0}),T(c)!=="svelte-fr2qs2"&&(c.innerHTML=A),x=r(e),w(d.$$.fragment,e),y=r(e),h=f(e,"P",{}),k(h).forEach(a),this.h()},h(){N(n,"name","hf:doc:metadata"),N(n,"content",W)},m(e,t){R(document.head,n),s(e,g,t),s(e,$,t),s(e,_,t),D(i,e,t),s(e,b,t),D(l,e,t),s(e,v,t),s(e,u,t),s(e,C,t),s(e,m,t),s(e,P,t),s(e,c,t),s(e,x,t),D(d,e,t),s(e,y,t),s(e,h,t),H=!0},p:U,i(e){H||(E(i.$$.fragment,e),E(l.$$.fragment,e),E(d.$$.fragment,e),H=!0)},o(e){L(i.$$.fragment,e),L(l.$$.fragment,e),L(d.$$.fragment,e),H=!1},d(e){e&&(a(g),a($),a(_),a(b),a(v),a(u),a(C),a(m),a(P),a(c),a(x),a(y),a(h)),a(n),M(i,e),M(l,e),M(d,e)}}}const W='{"title":"🤗 Datasets, ¡listo!","local":"-datasets-listo","sections":[],"depth":1}';function X(F){return j(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class se extends G{constructor(n){super(),I(this,n,X,V,B,{})}}export{se as component}; | |
Xet Storage Details
- Size:
- 3.22 kB
- Xet hash:
- a2392d703ebec5e60ee8c4bde2fb89fa38349d68873339846955c0759f8a5393
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.