import{s as Q,n as I,o as K}from"../chunks/scheduler.df3b9db7.js";import{S as V,i as W,g as o,s as a,r as M,E as X,h as m,f as l,c as i,j as J,u as R,x as k,k as O,y as Z,a as n,v as A,d as S,t as U,w as j}from"../chunks/index.b70c3ab0.js";import{Y as ee}from"../chunks/Youtube.4beebb9d.js";import{C as te}from"../chunks/CourseFloatingBanner.690209cd.js";import{H as le,E as ne}from"../chunks/getInferenceSnippets.b72fc54d.js";function ae(q){let r,b,v,x,s,E,f,w,u,y,d,N="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.",C,c,Y="Bu modellerin öneğitimi genellikle verilen cümleyi bozmaya yöneliktir (örnek olarak, içindeki rastgele kelimeleri maskeleyerek) ve model ilk cümleyi bulma veya yeniden oluşturma ile görevlendirilir.",T,p,D="Encoder modelleri cümle sınıflandırma, varlık tanıma (daha spesifik olarak sözcük sınıflandırma) ve extractive soru yanıtlama gibi cümlenin tam anlaşılmasını gerektiren görevler için uygundur.",P,g,F="Bu model ailesinin temsilcileri şunlardır:",B,h,G='<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>',L,$,z,_,H;return s=new le({props:{title:"Encoder modelleri",local:"encoder-modelleri",headingTag:"h1"}}),f=new te({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),u=new ee({props:{id:"MUqNwgPjJvQ"}}),$=new ne({props:{source:"https://github.com/huggingface/course/blob/main/chapters/tr/chapter1/5.mdx"}}),{c(){r=o("meta"),b=a(),v=o("p"),x=a(),M(s.$$.fragment),E=a(),M(f.$$.fragment),w=a(),M(u.$$.fragment),y=a(),d=o("p"),d.innerHTML=N,C=a(),c=o("p"),c.textContent=Y,T=a(),p=o("p"),p.textContent=D,P=a(),g=o("p"),g.textContent=F,B=a(),h=o("ul"),h.innerHTML=G,L=a(),M($.$$.fragment),z=a(),_=o("p"),this.h()},l(e){const t=X("svelte-u9bgzb",document.head);r=m(t,"META",{name:!0,content:!0}),t.forEach(l),b=i(e),v=m(e,"P",{}),J(v).forEach(l),x=i(e),R(s.$$.fragment,e),E=i(e),R(f.$$.fragment,e),w=i(e),R(u.$$.fragment,e),y=i(e),d=m(e,"P",{"data-svelte-h":!0}),k(d)!=="svelte-14wzi0m"&&(d.innerHTML=N),C=i(e),c=m(e,"P",{"data-svelte-h":!0}),k(c)!=="svelte-1fvndt4"&&(c.textContent=Y),T=i(e),p=m(e,"P",{"data-svelte-h":!0}),k(p)!=="svelte-870v45"&&(p.textContent=D),P=i(e),g=m(e,"P",{"data-svelte-h":!0}),k(g)!=="svelte-12f0id4"&&(g.textContent=F),B=i(e),h=m(e,"UL",{"data-svelte-h":!0}),k(h)!=="svelte-18kzzol"&&(h.innerHTML=G),L=i(e),R($.$$.fragment,e),z=i(e),_=m(e,"P",{}),J(_).forEach(l),this.h()},h(){O(r,"name","hf:doc:metadata"),O(r,"content",ie)},m(e,t){Z(document.head,r),n(e,b,t),n(e,v,t),n(e,x,t),A(s,e,t),n(e,E,t),A(f,e,t),n(e,w,t),A(u,e,t),n(e,y,t),n(e,d,t),n(e,C,t),n(e,c,t),n(e,T,t),n(e,p,t),n(e,P,t),n(e,g,t),n(e,B,t),n(e,h,t),n(e,L,t),A($,e,t),n(e,z,t),n(e,_,t),H=!0},p:I,i(e){H||(S(s.$$.fragment,e),S(f.$$.fragment,e),S(u.$$.fragment,e),S($.$$.fragment,e),H=!0)},o(e){U(s.$$.fragment,e),U(f.$$.fragment,e),U(u.$$.fragment,e),U($.$$.fragment,e),H=!1},d(e){e&&(l(b),l(v),l(x),l(E),l(w),l(y),l(d),l(C),l(c),l(T),l(p),l(P),l(g),l(B),l(h),l(L),l(z),l(_)),l(r),j(s,e),j(f,e),j(u,e),j($,e)}}}const ie='{"title":"Encoder modelleri","local":"encoder-modelleri","sections":[],"depth":1}';function re(q){return K(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class de extends V{constructor(r){super(),W(this,r,re,ae,Q,{})}}export{de as component};
