import{s as j,n as B,o as U}from"../chunks/scheduler.37c15a92.js";import{S as G,i as I,g as p,s as o,r as T,A as O,h as f,f as a,c as s,j as N,u as z,x as D,k as A,y as R,a as i,v as L,d as k,t as q,w as y}from"../chunks/index.2bf4358c.js";import{C as J}from"../chunks/CourseFloatingBanner.9ff4c771.js";import{H as K,E as Q}from"../chunks/index.b8688d36.js";function V(E){let n,$,g,v,r,_,l,b,u,M="Beh, abbiamo fatto un bel giro nella libreria 🤗 Datasets: complimenti per aver raggiunto quest’obiettivo! Con le conoscenze che hai ottenuto da questo capitolo, sei in grado di:",C,m,F="<li>Caricare dataset da ogni luogo, sia esso l’Hub di Hugging Face, il tuo portatile, o un server in remoto della tua compagnia.</li> <li>Fare data-wrangling usando un mix delle funzioni <code>Dataset.map()</code> e <code>Dataset.filter()</code>.</li> <li>Passare velocemente tra diversi formati di dati domce Pandas e NumPy usando <code>Dataset.set_format()</code>.</li> <li>Creare il tuo dataset e condividerlo sull’Hub Hugging Face.</li> <li>Creare embedding dei tuoi documenti usando un modello Transformer, e costruire un motore di ricerca semantico usando FAISS.</li>",P,c,S='Nel <a href="/course/chapter7">Capitolo 7</a>, faremo buon uso di tutto questo mentre ci avventureremo nei task principali NLP, per i quali i modelli Transformer sono un’ottima soluzione. Prima di andare oltre, però, metti alla prova la tua conoscenza di 🤗 Datasets con un quiz!',x,d,w,h,H;return r=new K({props:{title:"🤗 Datasets, check!",local:"-datasets-check",headingTag:"h1"}}),l=new J({props:{chapter:5,classNames:"absolute z-10 right-0 top-0"}}),d=new Q({props:{source:"https://github.com/huggingface/course/blob/main/chapters/it/chapter5/7.mdx"}}),{c(){n=p("meta"),$=o(),g=p("p"),v=o(),T(r.$$.fragment),_=o(),T(l.$$.fragment),b=o(),u=p("p"),u.textContent=M,C=o(),m=p("ul"),m.innerHTML=F,P=o(),c=p("p"),c.innerHTML=S,x=o(),T(d.$$.fragment),w=o(),h=p("p"),this.h()},l(e){const t=O("svelte-u9bgzb",document.head);n=f(t,"META",{name:!0,content:!0}),t.forEach(a),$=s(e),g=f(e,"P",{}),N(g).forEach(a),v=s(e),z(r.$$.fragment,e),_=s(e),z(l.$$.fragment,e),b=s(e),u=f(e,"P",{"data-svelte-h":!0}),D(u)!=="svelte-r0yujb"&&(u.textContent=M),C=s(e),m=f(e,"UL",{"data-svelte-h":!0}),D(m)!=="svelte-1ccexcl"&&(m.innerHTML=F),P=s(e),c=f(e,"P",{"data-svelte-h":!0}),D(c)!=="svelte-wevowg"&&(c.innerHTML=S),x=s(e),z(d.$$.fragment,e),w=s(e),h=f(e,"P",{}),N(h).forEach(a),this.h()},h(){A(n,"name","hf:doc:metadata"),A(n,"content",W)},m(e,t){R(document.head,n),i(e,$,t),i(e,g,t),i(e,v,t),L(r,e,t),i(e,_,t),L(l,e,t),i(e,b,t),i(e,u,t),i(e,C,t),i(e,m,t),i(e,P,t),i(e,c,t),i(e,x,t),L(d,e,t),i(e,w,t),i(e,h,t),H=!0},p:B,i(e){H||(k(r.$$.fragment,e),k(l.$$.fragment,e),k(d.$$.fragment,e),H=!0)},o(e){q(r.$$.fragment,e),q(l.$$.fragment,e),q(d.$$.fragment,e),H=!1},d(e){e&&(a($),a(g),a(v),a(_),a(b),a(u),a(C),a(m),a(P),a(c),a(x),a(w),a(h)),a(n),y(r,e),y(l,e),y(d,e)}}}const W='{"title":"🤗 Datasets, check!","local":"-datasets-check","sections":[],"depth":1}';function X(E){return U(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ae extends G{constructor(n){super(),I(this,n,X,V,j,{})}}export{ae as component};
