course/pr_1069/de/_app/immutable/nodes/9.e280e019.js

import{s as Q,n as V,o as X}from"../chunks/scheduler.49e4e380.js";import{S as Z,i as I,g as i,s as r,r as S,A as J,h as o,f as n,c as s,j as N,u as q,x as v,k as O,y as K,a as l,v as A,d as G,t as W,w as j}from"../chunks/index.fb15006d.js";import{Y as ee}from"../chunks/Youtube.42918e4e.js";import{C as te}from"../chunks/CourseFloatingBanner.c832fd1e.js";import{H as ne,E as le}from"../chunks/getInferenceSnippets.233af260.js";function re(R){let a,x,_,w,m,C,f,M,u,P,d,k="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.",T,p,B="Beim Pretraining von Decoder-Modellen geht es in der Regel um die Vorhersage des nächsten Wortes im Satz.",L,c,U="Diese Modelle sind am besten für Aufgaben geeignet, bei denen es um die Generierung von Texten geht.",D,h,Y="Zu dieser Modellfamilie gehören unter anderem:",y,g,F='<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>',z,$,E,b,H;return m=new ne({props:{title:"Decoder-Modelle",local:"decoder-modelle",headingTag:"h1"}}),f=new te({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),u=new ee({props:{id:"d_ixlCubqQw"}}),$=new le({props:{source:"https://github.com/huggingface/course/blob/main/chapters/de/chapter1/6.mdx"}}),{c(){a=i("meta"),x=r(),_=i("p"),w=r(),S(m.$$.fragment),C=r(),S(f.$$.fragment),M=r(),S(u.$$.fragment),P=r(),d=i("p"),d.innerHTML=k,T=r(),p=i("p"),p.textContent=B,L=r(),c=i("p"),c.textContent=U,D=r(),h=i("p"),h.textContent=Y,y=r(),g=i("ul"),g.innerHTML=F,z=r(),S($.$$.fragment),E=r(),b=i("p"),this.h()},l(e){const t=J("svelte-u9bgzb",document.head);a=o(t,"META",{name:!0,content:!0}),t.forEach(n),x=s(e),_=o(e,"P",{}),N(_).forEach(n),w=s(e),q(m.$$.fragment,e),C=s(e),q(f.$$.fragment,e),M=s(e),q(u.$$.fragment,e),P=s(e),d=o(e,"P",{"data-svelte-h":!0}),v(d)!=="svelte-bh7cbg"&&(d.innerHTML=k),T=s(e),p=o(e,"P",{"data-svelte-h":!0}),v(p)!=="svelte-1bfcfsq"&&(p.textContent=B),L=s(e),c=o(e,"P",{"data-svelte-h":!0}),v(c)!=="svelte-1v540no"&&(c.textContent=U),D=s(e),h=o(e,"P",{"data-svelte-h":!0}),v(h)!=="svelte-cah3qi"&&(h.textContent=Y),y=s(e),g=o(e,"UL",{"data-svelte-h":!0}),v(g)!=="svelte-j4l2li"&&(g.innerHTML=F),z=s(e),q($.$$.fragment,e),E=s(e),b=o(e,"P",{}),N(b).forEach(n),this.h()},h(){O(a,"name","hf:doc:metadata"),O(a,"content",se)},m(e,t){K(document.head,a),l(e,x,t),l(e,_,t),l(e,w,t),A(m,e,t),l(e,C,t),A(f,e,t),l(e,M,t),A(u,e,t),l(e,P,t),l(e,d,t),l(e,T,t),l(e,p,t),l(e,L,t),l(e,c,t),l(e,D,t),l(e,h,t),l(e,y,t),l(e,g,t),l(e,z,t),A($,e,t),l(e,E,t),l(e,b,t),H=!0},p:V,i(e){H||(G(m.$$.fragment,e),G(f.$$.fragment,e),G(u.$$.fragment,e),G($.$$.fragment,e),H=!0)},o(e){W(m.$$.fragment,e),W(f.$$.fragment,e),W(u.$$.fragment,e),W($.$$.fragment,e),H=!1},d(e){e&&(n(x),n(_),n(w),n(C),n(M),n(P),n(d),n(T),n(p),n(L),n(c),n(D),n(h),n(y),n(g),n(z),n(E),n(b)),n(a),j(m,e),j(f,e),j(u,e),j($,e)}}}const se='{"title":"Decoder-Modelle","local":"decoder-modelle","sections":[],"depth":1}';function ae(R){return X(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class de extends Z{constructor(a){super(),I(this,a,ae,re,Q,{})}}export{de as component};