import{s as X,n as D,o as J}from"../chunks/scheduler.37c15a92.js";import{S as K,i as V,g as i,s as o,r as A,A as W,h as r,f as l,c as n,j as N,u as G,x,k as O,y as Z,a,v as Q,d as S,t as j,w as I}from"../chunks/index.2bf4358c.js";import{Y as ee}from"../chunks/Youtube.1e50a667.js";import{C as te}from"../chunks/CourseFloatingBanner.9ff4c771.js";import{H as le,E as ae}from"../chunks/getInferenceSnippets.80a69898.js";function oe(R){let s,w,_,C,m,b,p,P,f,T,u,U="I modelli decoder utilizzano solo il decoder di un modello Transformer. Ad ogni passaggio e per una data parola, gli attention layer hanno accesso solo alle parole che la precedono nella frase. Questi modelli sono spesso detti <em>auto-regressive models</em>.",L,d,Y="Il pre-addestramento dei modelli decoder ha spesso a che fare con la previsione della parola successiva in un contesto frasale.",M,c,k="Questi modelli sono particolarmente adatti a compiti di generazione testuale.",y,$,B="Alcuni rappresentanti di questa famiglia includono:",z,h,F='<li><a href="https://huggingface.co/transformers/model_doc/ctrl.html" rel="nofollow">CTRL</a></li> <li><a href="https://huggingface.co/docs/transformers/model_doc/openai-gpt" rel="nofollow">GPT</a></li> <li><a href="https://huggingface.co/transformers/model_doc/gpt2.html" rel="nofollow">GPT-2</a></li> <li><a href="https://huggingface.co/transformers/model_doc/transfo-xl.html" rel="nofollow">Transformer XL</a></li>',E,g,H,v,q;return m=new le({props:{title:"Modelli decoder",local:"modelli-decoder",headingTag:"h1"}}),p=new te({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),f=new ee({props:{id:"d_ixlCubqQw"}}),g=new ae({props:{source:"https://github.com/huggingface/course/blob/main/chapters/it/chapter1/6.mdx"}}),{c(){s=i("meta"),w=o(),_=i("p"),C=o(),A(m.$$.fragment),b=o(),A(p.$$.fragment),P=o(),A(f.$$.fragment),T=o(),u=i("p"),u.innerHTML=U,L=o(),d=i("p"),d.textContent=Y,M=o(),c=i("p"),c.textContent=k,y=o(),$=i("p"),$.textContent=B,z=o(),h=i("ul"),h.innerHTML=F,E=o(),A(g.$$.fragment),H=o(),v=i("p"),this.h()},l(e){const t=W("svelte-u9bgzb",document.head);s=r(t,"META",{name:!0,content:!0}),t.forEach(l),w=n(e),_=r(e,"P",{}),N(_).forEach(l),C=n(e),G(m.$$.fragment,e),b=n(e),G(p.$$.fragment,e),P=n(e),G(f.$$.fragment,e),T=n(e),u=r(e,"P",{"data-svelte-h":!0}),x(u)!=="svelte-rw3568"&&(u.innerHTML=U),L=n(e),d=r(e,"P",{"data-svelte-h":!0}),x(d)!=="svelte-1p3tbn1"&&(d.textContent=Y),M=n(e),c=r(e,"P",{"data-svelte-h":!0}),x(c)!=="svelte-17zbr6q"&&(c.textContent=k),y=n(e),$=r(e,"P",{"data-svelte-h":!0}),x($)!=="svelte-8vvjx9"&&($.textContent=B),z=n(e),h=r(e,"UL",{"data-svelte-h":!0}),x(h)!=="svelte-1tiql5w"&&(h.innerHTML=F),E=n(e),G(g.$$.fragment,e),H=n(e),v=r(e,"P",{}),N(v).forEach(l),this.h()},h(){O(s,"name","hf:doc:metadata"),O(s,"content",ne)},m(e,t){Z(document.head,s),a(e,w,t),a(e,_,t),a(e,C,t),Q(m,e,t),a(e,b,t),Q(p,e,t),a(e,P,t),Q(f,e,t),a(e,T,t),a(e,u,t),a(e,L,t),a(e,d,t),a(e,M,t),a(e,c,t),a(e,y,t),a(e,$,t),a(e,z,t),a(e,h,t),a(e,E,t),Q(g,e,t),a(e,H,t),a(e,v,t),q=!0},p:D,i(e){q||(S(m.$$.fragment,e),S(p.$$.fragment,e),S(f.$$.fragment,e),S(g.$$.fragment,e),q=!0)},o(e){j(m.$$.fragment,e),j(p.$$.fragment,e),j(f.$$.fragment,e),j(g.$$.fragment,e),q=!1},d(e){e&&(l(w),l(_),l(C),l(b),l(P),l(T),l(u),l(L),l(d),l(M),l(c),l(y),l($),l(z),l(h),l(E),l(H),l(v)),l(s),I(m,e),I(p,e),I(f,e),I(g,e)}}}const ne='{"title":"Modelli decoder","local":"modelli-decoder","sections":[],"depth":1}';function se(R){return J(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ue extends K{constructor(s){super(),V(this,s,se,oe,X,{})}}export{ue as component};
