import{s as N,o as G}from"../chunks/scheduler.37c15a92.js";import{S as O,i as R,g as _,s as c,r as P,A as B,h as g,f as r,c as m,j as U,u as x,x as T,k as K,y as D,a as n,v as y,d as E,t as q,w as A}from"../chunks/index.2bf4358c.js";import{F as J}from"../chunks/FrameworkSwitchCourse.8d4d4ab6.js";import{H as Q,E as V}from"../chunks/getInferenceSnippets.648f38f6.js";function W(p){let t,l="<li>Come preparare un grande dataset dall’Hub</li> <li>Come usare Keras per affinare un modello</li> <li>Come usare Keras per ottenere delle predizioni</li> <li>Come usare una metrica personalizzata</li>";return{c(){t=_("ul"),t.innerHTML=l},l(i){t=g(i,"UL",{"data-svelte-h":!0}),T(t)!=="svelte-mrwdvj"&&(t.innerHTML=l)},m(i,o){n(i,t,o)},d(i){i&&r(t)}}}function X(p){let t,l="<li>Come preparare un grande dataset dall’Hub</li> <li>Come usare l’API di alto livello <code>Trainer</code> per affinare un modello</li> <li>Come usare un ciclo di addestramento personalizzato</li> <li>Come utilizzare la libreria 🤗 Accelerate per eseguire facilmente quel ciclo di addestramento personalizzato su qualsiasi sistema distribuito</li>";return{c(){t=_("ul"),t.innerHTML=l},l(i){t=g(i,"UL",{"data-svelte-h":!0}),T(t)!=="svelte-15bggv2"&&(t.innerHTML=l)},m(i,o){n(i,t,o)},d(i){i&&r(t)}}}function Y(p){let t,l,i,o,u,z,f,v,d,I='Nel <a href="/course/chapter2">Capitolo 2</a> abbiamo scoperto come utilizzare i tokenizzatori e i modelli preaddestrati per effettuare delle predizioni. Ma cosa fare se si vuole affinare un modello preaddestrato col tuo dataset? Lo scopriremo in questo capitolo! Impareremo:',C,b,h,S='Per caricare i checkpoint di addestramento sull’Hub di Hugging Face è necessario un account huggingface.co: <a href="https://huggingface.co/join" rel="nofollow">creare un account</a>',H,$,L,w,k;u=new J({props:{fw:p[0]}}),f=new Q({props:{title:"Introduzione",local:"introduzione",headingTag:"h1"}});function j(e,a){return e[0]==="pt"?X:W}let M=j(p),s=M(p);return $=new V({props:{source:"https://github.com/huggingface/course/blob/main/chapters/it/chapter3/1.mdx"}}),{c(){t=_("meta"),l=c(),i=_("p"),o=c(),P(u.$$.fragment),z=c(),P(f.$$.fragment),v=c(),d=_("p"),d.innerHTML=I,C=c(),s.c(),b=c(),h=_("p"),h.innerHTML=S,H=c(),P($.$$.fragment),L=c(),w=_("p"),this.h()},l(e){const a=B("svelte-u9bgzb",document.head);t=g(a,"META",{name:!0,content:!0}),a.forEach(r),l=m(e),i=g(e,"P",{}),U(i).forEach(r),o=m(e),x(u.$$.fragment,e),z=m(e),x(f.$$.fragment,e),v=m(e),d=g(e,"P",{"data-svelte-h":!0}),T(d)!=="svelte-10rihi9"&&(d.innerHTML=I),C=m(e),s.l(e),b=m(e),h=g(e,"P",{"data-svelte-h":!0}),T(h)!=="svelte-1xwv836"&&(h.innerHTML=S),H=m(e),x($.$$.fragment,e),L=m(e),w=g(e,"P",{}),U(w).forEach(r),this.h()},h(){K(t,"name","hf:doc:metadata"),K(t,"content",Z)},m(e,a){D(document.head,t),n(e,l,a),n(e,i,a),n(e,o,a),y(u,e,a),n(e,z,a),y(f,e,a),n(e,v,a),n(e,d,a),n(e,C,a),s.m(e,a),n(e,b,a),n(e,h,a),n(e,H,a),y($,e,a),n(e,L,a),n(e,w,a),k=!0},p(e,[a]){const F={};a&1&&(F.fw=e[0]),u.$set(F),M!==(M=j(e))&&(s.d(1),s=M(e),s&&(s.c(),s.m(b.parentNode,b)))},i(e){k||(E(u.$$.fragment,e),E(f.$$.fragment,e),E($.$$.fragment,e),k=!0)},o(e){q(u.$$.fragment,e),q(f.$$.fragment,e),q($.$$.fragment,e),k=!1},d(e){e&&(r(l),r(i),r(o),r(z),r(v),r(d),r(C),r(b),r(h),r(H),r(L),r(w)),r(t),A(u,e),A(f,e),s.d(e),A($,e)}}}const Z='{"title":"Introduzione","local":"introduzione","sections":[],"depth":1}';function ee(p,t,l){let i="pt";return G(()=>{const o=new URLSearchParams(window.location.search);l(0,i=o.get("fw")||"pt")}),[i]}class ne extends O{constructor(t){super(),R(this,t,ee,Y,N,{})}}export{ne as component};
