import{s as G,n as N,o as J}from"../chunks/scheduler.37c15a92.js";import{S as K,i as Q,g as l,s as i,r as k,A as V,h as r,f as a,c as s,j as R,u as E,x as y,k as U,y as X,a as n,v as I,d as j,t as q,w as z}from"../chunks/index.7cb9c9b8.js";import{C as Y}from"../chunks/CourseFloatingBanner.df82c153.js";import{H as Z,E as tt}from"../chunks/index.706e2c58.js";function et(D){let o,_,$,v,u,H,h,b,m,S='In <a href="/course/chapter3">Chapter 3</a> you got your first taste of the 🤗 Datasets library and saw that there were three main steps when it came to fine-tuning a model:',C,p,W="<li>Load a dataset from the Hugging Face Hub.</li> <li>Preprocess the data with <code>Dataset.map()</code>.</li> <li>Load and compute metrics.</li>",L,c,B="But this is just scratching the surface of what 🤗 Datasets can do! In this chapter, we will take a deep dive into the library. Along the way, we’ll find answers to the following questions:",x,f,F="<li>What do you do when your dataset is not on the Hub?</li> <li>How can you slice and dice a dataset? (And what if you <em>really</em> need to use Pandas?)</li> <li>What do you do when your dataset is huge and will melt your laptop’s RAM?</li> <li>What the heck are “memory mapping” and Apache Arrow?</li> <li>How can you create your own dataset and push it to the Hub?</li>",M,d,O='The techniques you learn here will prepare you for the advanced tokenization and fine-tuning tasks in <a href="/course/chapter6">Chapter 6</a> and <a href="/course/chapter7">Chapter 7</a> — so grab a coffee and let’s get started!',P,w,T,g,A;return u=new Z({props:{title:"Introduction",local:"introduction",headingTag:"h1"}}),h=new Y({props:{chapter:5,classNames:"absolute z-10 right-0 top-0"}}),w=new tt({props:{source:"https://github.com/huggingface/course/blob/main/chapters/en/chapter5/1.mdx"}}),{c(){o=l("meta"),_=i(),$=l("p"),v=i(),k(u.$$.fragment),H=i(),k(h.$$.fragment),b=i(),m=l("p"),m.innerHTML=S,C=i(),p=l("ol"),p.innerHTML=W,L=i(),c=l("p"),c.textContent=B,x=i(),f=l("ul"),f.innerHTML=F,M=i(),d=l("p"),d.innerHTML=O,P=i(),k(w.$$.fragment),T=i(),g=l("p"),this.h()},l(t){const e=V("svelte-u9bgzb",document.head);o=r(e,"META",{name:!0,content:!0}),e.forEach(a),_=s(t),$=r(t,"P",{}),R($).forEach(a),v=s(t),E(u.$$.fragment,t),H=s(t),E(h.$$.fragment,t),b=s(t),m=r(t,"P",{"data-svelte-h":!0}),y(m)!=="svelte-kgw5en"&&(m.innerHTML=S),C=s(t),p=r(t,"OL",{"data-svelte-h":!0}),y(p)!=="svelte-1ou2s90"&&(p.innerHTML=W),L=s(t),c=r(t,"P",{"data-svelte-h":!0}),y(c)!=="svelte-ow4a6"&&(c.textContent=B),x=s(t),f=r(t,"UL",{"data-svelte-h":!0}),y(f)!=="svelte-lj4ah0"&&(f.innerHTML=F),M=s(t),d=r(t,"P",{"data-svelte-h":!0}),y(d)!=="svelte-1n9tshc"&&(d.innerHTML=O),P=s(t),E(w.$$.fragment,t),T=s(t),g=r(t,"P",{}),R(g).forEach(a),this.h()},h(){U(o,"name","hf:doc:metadata"),U(o,"content",at)},m(t,e){X(document.head,o),n(t,_,e),n(t,$,e),n(t,v,e),I(u,t,e),n(t,H,e),I(h,t,e),n(t,b,e),n(t,m,e),n(t,C,e),n(t,p,e),n(t,L,e),n(t,c,e),n(t,x,e),n(t,f,e),n(t,M,e),n(t,d,e),n(t,P,e),I(w,t,e),n(t,T,e),n(t,g,e),A=!0},p:N,i(t){A||(j(u.$$.fragment,t),j(h.$$.fragment,t),j(w.$$.fragment,t),A=!0)},o(t){q(u.$$.fragment,t),q(h.$$.fragment,t),q(w.$$.fragment,t),A=!1},d(t){t&&(a(_),a($),a(v),a(H),a(b),a(m),a(C),a(p),a(L),a(c),a(x),a(f),a(M),a(d),a(P),a(T),a(g)),a(o),z(u,t),z(h,t),z(w,t)}}}const at='{"title":"Introduction","local":"introduction","sections":[],"depth":1}';function nt(D){return J(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class rt extends K{constructor(o){super(),Q(this,o,nt,et,G,{})}}export{rt as component};
