import{s as N,n as Q,o as V}from"../chunks/scheduler.49e4e380.js";import{S as X,i as Z,g as s,s as r,r as H,A as I,h as i,f as n,c as o,j as Y,u as S,x as v,k as F,y as J,a as l,v as q,d as A,t as E,w as W}from"../chunks/index.fb15006d.js";import{Y as K}from"../chunks/Youtube.42918e4e.js";import{C as O}from"../chunks/CourseFloatingBanner.3ce48d00.js";import{H as ee}from"../chunks/Heading.5d1ab858.js";function te(j){let a,x,_,b,m,C,f,w,d,M,u,G="Decoder-Modelle verwenden nur den Decoder eines Transformer-Modells. Die Attention-Layer können bei jedem Schritt hinsichtlich eines bestimmten Wortes nur auf die Wörter zugreifen, die vor diesem Wort im Satz stehen. Diese Modelle werden oft als <em>autoregressive Modelle</em> bezeichnet.",P,c,R="Beim Pretraining von Decoder-Modellen geht es in der Regel um die Vorhersage des nächsten Wortes im Satz.",T,p,k="Diese Modelle sind am besten für Aufgaben geeignet, bei denen es um die Generierung von Texten geht.",L,h,B="Zu dieser Modellfamilie gehören unter anderem:",D,g,U='<li><a href="https://huggingface.co/transformers/model_doc/ctrl" 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" rel="nofollow">GPT-2</a></li> <li><a href="https://huggingface.co/transformers/model_doc/transformerxl" rel="nofollow">Transformer XL</a></li>',y,$,z;return m=new ee({props:{title:"Decoder-Modelle",local:"decoder-modelle",headingTag:"h1"}}),f=new O({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),d=new K({props:{id:"d_ixlCubqQw"}}),{c(){a=s("meta"),x=r(),_=s("p"),b=r(),H(m.$$.fragment),C=r(),H(f.$$.fragment),w=r(),H(d.$$.fragment),M=r(),u=s("p"),u.innerHTML=G,P=r(),c=s("p"),c.textContent=R,T=r(),p=s("p"),p.textContent=k,L=r(),h=s("p"),h.textContent=B,D=r(),g=s("ul"),g.innerHTML=U,y=r(),$=s("p"),this.h()},l(e){const t=I("svelte-u9bgzb",document.head);a=i(t,"META",{name:!0,content:!0}),t.forEach(n),x=o(e),_=i(e,"P",{}),Y(_).forEach(n),b=o(e),S(m.$$.fragment,e),C=o(e),S(f.$$.fragment,e),w=o(e),S(d.$$.fragment,e),M=o(e),u=i(e,"P",{"data-svelte-h":!0}),v(u)!=="svelte-bh7cbg"&&(u.innerHTML=G),P=o(e),c=i(e,"P",{"data-svelte-h":!0}),v(c)!=="svelte-1bfcfsq"&&(c.textContent=R),T=o(e),p=i(e,"P",{"data-svelte-h":!0}),v(p)!=="svelte-1v540no"&&(p.textContent=k),L=o(e),h=i(e,"P",{"data-svelte-h":!0}),v(h)!=="svelte-cah3qi"&&(h.textContent=B),D=o(e),g=i(e,"UL",{"data-svelte-h":!0}),v(g)!=="svelte-j4l2li"&&(g.innerHTML=U),y=o(e),$=i(e,"P",{}),Y($).forEach(n),this.h()},h(){F(a,"name","hf:doc:metadata"),F(a,"content",ne)},m(e,t){J(document.head,a),l(e,x,t),l(e,_,t),l(e,b,t),q(m,e,t),l(e,C,t),q(f,e,t),l(e,w,t),q(d,e,t),l(e,M,t),l(e,u,t),l(e,P,t),l(e,c,t),l(e,T,t),l(e,p,t),l(e,L,t),l(e,h,t),l(e,D,t),l(e,g,t),l(e,y,t),l(e,$,t),z=!0},p:Q,i(e){z||(A(m.$$.fragment,e),A(f.$$.fragment,e),A(d.$$.fragment,e),z=!0)},o(e){E(m.$$.fragment,e),E(f.$$.fragment,e),E(d.$$.fragment,e),z=!1},d(e){e&&(n(x),n(_),n(b),n(C),n(w),n(M),n(u),n(P),n(c),n(T),n(p),n(L),n(h),n(D),n(g),n(y),n($)),n(a),W(m,e),W(f,e),W(d,e)}}}const ne='{"title":"Decoder-Modelle","local":"decoder-modelle","sections":[],"depth":1}';function le(j){return V(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class me extends X{constructor(a){super(),Z(this,a,le,te,N,{})}}export{me as component};
