course/pr_1112/it/_app/immutable/nodes/9.d4e4d387.js

import{s as J,n as K,o as V}from"../chunks/scheduler.505acc25.js";import{S as W,i as Z,e as s,s as n,c as C,h as ee,a as r,d as l,b as o,f as X,g as w,j as b,k as D,l as te,m as a,n as P,t as T,o as L,p as M}from"../chunks/index.1238bded.js";import{C as le,H as ae,E as ne}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.07f35dbc.js";import{Y as oe}from"../chunks/Youtube.2fb63721.js";import{C as ie}from"../chunks/CourseFloatingBanner.588c2d8e.js";function se(Y){let i,y,x,z,m,E,p,H,f,q,u,S,d,k="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>.",A,c,B="Il pre-addestramento dei modelli decoder ha spesso a che fare con la previsione della parola successiva in un contesto frasale.",G,$,F="Questi modelli sono particolarmente adatti a compiti di generazione testuale.",Q,g,N="Alcuni rappresentanti di questa famiglia includono:",j,h,O='<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>',I,_,R,v,U;return m=new le({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),p=new ae({props:{title:"Modelli decoder",local:"modelli-decoder",headingTag:"h1"}}),f=new ie({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),u=new oe({props:{id:"d_ixlCubqQw"}}),_=new ne({props:{source:"https://github.com/huggingface/course/blob/main/chapters/it/chapter1/6.mdx"}}),{c(){i=s("meta"),y=n(),x=s("p"),z=n(),C(m.$$.fragment),E=n(),C(p.$$.fragment),H=n(),C(f.$$.fragment),q=n(),C(u.$$.fragment),S=n(),d=s("p"),d.innerHTML=k,A=n(),c=s("p"),c.textContent=B,G=n(),$=s("p"),$.textContent=F,Q=n(),g=s("p"),g.textContent=N,j=n(),h=s("ul"),h.innerHTML=O,I=n(),C(_.$$.fragment),R=n(),v=s("p"),this.h()},l(e){const t=ee("svelte-u9bgzb",document.head);i=r(t,"META",{name:!0,content:!0}),t.forEach(l),y=o(e),x=r(e,"P",{}),X(x).forEach(l),z=o(e),w(m.$$.fragment,e),E=o(e),w(p.$$.fragment,e),H=o(e),w(f.$$.fragment,e),q=o(e),w(u.$$.fragment,e),S=o(e),d=r(e,"P",{"data-svelte-h":!0}),b(d)!=="svelte-rw3568"&&(d.innerHTML=k),A=o(e),c=r(e,"P",{"data-svelte-h":!0}),b(c)!=="svelte-1p3tbn1"&&(c.textContent=B),G=o(e),$=r(e,"P",{"data-svelte-h":!0}),b($)!=="svelte-17zbr6q"&&($.textContent=F),Q=o(e),g=r(e,"P",{"data-svelte-h":!0}),b(g)!=="svelte-8vvjx9"&&(g.textContent=N),j=o(e),h=r(e,"UL",{"data-svelte-h":!0}),b(h)!=="svelte-1tiql5w"&&(h.innerHTML=O),I=o(e),w(_.$$.fragment,e),R=o(e),v=r(e,"P",{}),X(v).forEach(l),this.h()},h(){D(i,"name","hf:doc:metadata"),D(i,"content",re)},m(e,t){te(document.head,i),a(e,y,t),a(e,x,t),a(e,z,t),P(m,e,t),a(e,E,t),P(p,e,t),a(e,H,t),P(f,e,t),a(e,q,t),P(u,e,t),a(e,S,t),a(e,d,t),a(e,A,t),a(e,c,t),a(e,G,t),a(e,$,t),a(e,Q,t),a(e,g,t),a(e,j,t),a(e,h,t),a(e,I,t),P(_,e,t),a(e,R,t),a(e,v,t),U=!0},p:K,i(e){U||(T(m.$$.fragment,e),T(p.$$.fragment,e),T(f.$$.fragment,e),T(u.$$.fragment,e),T(_.$$.fragment,e),U=!0)},o(e){L(m.$$.fragment,e),L(p.$$.fragment,e),L(f.$$.fragment,e),L(u.$$.fragment,e),L(_.$$.fragment,e),U=!1},d(e){e&&(l(y),l(x),l(z),l(E),l(H),l(q),l(S),l(d),l(A),l(c),l(G),l($),l(Q),l(g),l(j),l(h),l(I),l(R),l(v)),l(i),M(m,e),M(p,e),M(f,e),M(u,e),M(_,e)}}}const re='{"title":"Modelli decoder","local":"modelli-decoder","sections":[],"depth":1}';function me(Y){return V(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class $e extends W{constructor(i){super(),Z(this,i,me,se,J,{})}}export{$e as component};