import{s as Q,n as G,o as I}from"../chunks/scheduler.df3b9db7.js";import{S as K,i as O,g as o,s as a,r as z,E as V,h as m,f as l,c as i,j as F,u as H,x as $,k as J,y as W,a as n,v as M,d as R,t as A,w as S}from"../chunks/index.b70c3ab0.js";import{Y as X}from"../chunks/Youtube.4beebb9d.js";import{C as Z}from"../chunks/CourseFloatingBanner.f2d73df4.js";import{H as ee}from"../chunks/Heading.542771dd.js";function te(U){let r,k,v,x,s,w,f,y,d,C,u,j="Encoder modelleri Transformer modellerinin sadece encoder kısmını kulanır.Her aşamada, attention katmanları ilk cümlenin bütün kelimelerine erişir. Bu modeller genellikle çift yönlü attention olarak nitelendirilir ve genellikle <em>auto-encoding models</em> olarak adlandırılır.",E,c,q="Bu modellerin öneğitimi genellikle verilen cümleyi bozmaya yöneliktir (örnek olarak, içindeki rastgele kelimeleri maskeleyerek) ve model ilk cümleyi bulma veya yeniden oluşturma ile görevlendirilir.",b,p,N="Encoder modelleri cümle sınıflandırma, varlık tanıma (daha spesifik olarak sözcük sınıflandırma) ve extractive soru yanıtlama gibi cümlenin tam anlaşılmasını gerektiren görevler için uygundur.",T,h,Y="Bu model ailesinin temsilcileri şunlardır:",P,g,D='<li><a href="https://huggingface.co/transformers/model_doc/albert.html" rel="nofollow">ALBERT</a></li> <li><a href="https://huggingface.co/transformers/model_doc/bert.html" rel="nofollow">BERT</a></li> <li><a href="https://huggingface.co/transformers/model_doc/distilbert.html" rel="nofollow">DistilBERT</a></li> <li><a href="https://huggingface.co/transformers/model_doc/electra.html" rel="nofollow">ELECTRA</a></li> <li><a href="https://huggingface.co/transformers/model_doc/roberta.html" rel="nofollow">RoBERTa</a></li>',B,_,L;return s=new ee({props:{title:"Encoder modelleri",local:"encoder-modelleri",headingTag:"h1"}}),f=new Z({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),d=new X({props:{id:"MUqNwgPjJvQ"}}),{c(){r=o("meta"),k=a(),v=o("p"),x=a(),z(s.$$.fragment),w=a(),z(f.$$.fragment),y=a(),z(d.$$.fragment),C=a(),u=o("p"),u.innerHTML=j,E=a(),c=o("p"),c.textContent=q,b=a(),p=o("p"),p.textContent=N,T=a(),h=o("p"),h.textContent=Y,P=a(),g=o("ul"),g.innerHTML=D,B=a(),_=o("p"),this.h()},l(e){const t=V("svelte-u9bgzb",document.head);r=m(t,"META",{name:!0,content:!0}),t.forEach(l),k=i(e),v=m(e,"P",{}),F(v).forEach(l),x=i(e),H(s.$$.fragment,e),w=i(e),H(f.$$.fragment,e),y=i(e),H(d.$$.fragment,e),C=i(e),u=m(e,"P",{"data-svelte-h":!0}),$(u)!=="svelte-14wzi0m"&&(u.innerHTML=j),E=i(e),c=m(e,"P",{"data-svelte-h":!0}),$(c)!=="svelte-1fvndt4"&&(c.textContent=q),b=i(e),p=m(e,"P",{"data-svelte-h":!0}),$(p)!=="svelte-870v45"&&(p.textContent=N),T=i(e),h=m(e,"P",{"data-svelte-h":!0}),$(h)!=="svelte-12f0id4"&&(h.textContent=Y),P=i(e),g=m(e,"UL",{"data-svelte-h":!0}),$(g)!=="svelte-18kzzol"&&(g.innerHTML=D),B=i(e),_=m(e,"P",{}),F(_).forEach(l),this.h()},h(){J(r,"name","hf:doc:metadata"),J(r,"content",le)},m(e,t){W(document.head,r),n(e,k,t),n(e,v,t),n(e,x,t),M(s,e,t),n(e,w,t),M(f,e,t),n(e,y,t),M(d,e,t),n(e,C,t),n(e,u,t),n(e,E,t),n(e,c,t),n(e,b,t),n(e,p,t),n(e,T,t),n(e,h,t),n(e,P,t),n(e,g,t),n(e,B,t),n(e,_,t),L=!0},p:G,i(e){L||(R(s.$$.fragment,e),R(f.$$.fragment,e),R(d.$$.fragment,e),L=!0)},o(e){A(s.$$.fragment,e),A(f.$$.fragment,e),A(d.$$.fragment,e),L=!1},d(e){e&&(l(k),l(v),l(x),l(w),l(y),l(C),l(u),l(E),l(c),l(b),l(p),l(T),l(h),l(P),l(g),l(B),l(_)),l(r),S(s,e),S(f,e),S(d,e)}}}const le='{"title":"Encoder modelleri","local":"encoder-modelleri","sections":[],"depth":1}';function ne(U){return I(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class se extends K{constructor(r){super(),O(this,r,ne,te,Q,{})}}export{se as component};
