import{s as Q,n as I,o as K}from"../chunks/scheduler.37c15a92.js";import{S as V,i as W,g as r,s as a,r as k,A as X,h as i,f as l,c as o,j as J,u as z,x as b,k as O,y as Z,a as n,v as A,d as S,t as U,w as j}from"../chunks/index.2bf4358c.js";import{Y as tt}from"../chunks/Youtube.1e50a667.js";import{C as et}from"../chunks/CourseFloatingBanner.9ff4c771.js";import{H as lt,E as nt}from"../chunks/getInferenceSnippets.24b50994.js";function at(q){let s,v,d,w,m,C,f,E,p,P,u,N="인코더 모델(Encoder models)은 트랜스포머 모델의 인코더만 사용합니다. 각각의 단계에서 어텐션 레이어는 초기 문장의 모든 단어에 액세스 할 수 있습니다. 이러한 모델은 “양방향성(bi-directional)” 어텐션을 지닌 특성이 있다고도 하며 <em>자동 인코딩(auto-enoding) 모델</em>이라고 부릅니다.",T,c,Y="이러한 모델은 주어진 문장을 훼손시킨 후(랜덤으로 단어에 마스킹을 하는 방식 등으로) 모델이 원본 문장을 찾아 재구성하게끔 하는 과정을 반복시키는 방식으로 사전 학습을 진행 합니다.",L,h,D="인코더 모델은 문장 분류, 개체명 인식(더 넓은 범위에서 단어 분류), 추출 질의 응답 등과 같이 전체 문장에 대한 이해를 요구하는 작업에 특화되어 있습니다.",y,$,F="이 계열을 대표하는 모델들은 아래와 같습니다:",M,g,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>',R,_,H,x,B;return m=new lt({props:{title:"인코더 모델",local:"인코더-모델",headingTag:"h1"}}),f=new et({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),p=new tt({props:{id:"MUqNwgPjJvQ"}}),_=new nt({props:{source:"https://github.com/huggingface/course/blob/main/chapters/ko/chapter1/5.mdx"}}),{c(){s=r("meta"),v=a(),d=r("p"),w=a(),k(m.$$.fragment),C=a(),k(f.$$.fragment),E=a(),k(p.$$.fragment),P=a(),u=r("p"),u.innerHTML=N,T=a(),c=r("p"),c.textContent=Y,L=a(),h=r("p"),h.textContent=D,y=a(),$=r("p"),$.textContent=F,M=a(),g=r("ul"),g.innerHTML=G,R=a(),k(_.$$.fragment),H=a(),x=r("p"),this.h()},l(t){const e=X("svelte-u9bgzb",document.head);s=i(e,"META",{name:!0,content:!0}),e.forEach(l),v=o(t),d=i(t,"P",{}),J(d).forEach(l),w=o(t),z(m.$$.fragment,t),C=o(t),z(f.$$.fragment,t),E=o(t),z(p.$$.fragment,t),P=o(t),u=i(t,"P",{"data-svelte-h":!0}),b(u)!=="svelte-fcsfy5"&&(u.innerHTML=N),T=o(t),c=i(t,"P",{"data-svelte-h":!0}),b(c)!=="svelte-5s30te"&&(c.textContent=Y),L=o(t),h=i(t,"P",{"data-svelte-h":!0}),b(h)!=="svelte-1xwlvty"&&(h.textContent=D),y=o(t),$=i(t,"P",{"data-svelte-h":!0}),b($)!=="svelte-hk1lgc"&&($.textContent=F),M=o(t),g=i(t,"UL",{"data-svelte-h":!0}),b(g)!=="svelte-18kzzol"&&(g.innerHTML=G),R=o(t),z(_.$$.fragment,t),H=o(t),x=i(t,"P",{}),J(x).forEach(l),this.h()},h(){O(s,"name","hf:doc:metadata"),O(s,"content",ot)},m(t,e){Z(document.head,s),n(t,v,e),n(t,d,e),n(t,w,e),A(m,t,e),n(t,C,e),A(f,t,e),n(t,E,e),A(p,t,e),n(t,P,e),n(t,u,e),n(t,T,e),n(t,c,e),n(t,L,e),n(t,h,e),n(t,y,e),n(t,$,e),n(t,M,e),n(t,g,e),n(t,R,e),A(_,t,e),n(t,H,e),n(t,x,e),B=!0},p:I,i(t){B||(S(m.$$.fragment,t),S(f.$$.fragment,t),S(p.$$.fragment,t),S(_.$$.fragment,t),B=!0)},o(t){U(m.$$.fragment,t),U(f.$$.fragment,t),U(p.$$.fragment,t),U(_.$$.fragment,t),B=!1},d(t){t&&(l(v),l(d),l(w),l(C),l(E),l(P),l(u),l(T),l(c),l(L),l(h),l(y),l($),l(M),l(g),l(R),l(H),l(x)),l(s),j(m,t),j(f,t),j(p,t),j(_,t)}}}const ot='{"title":"인코더 모델","local":"인코더-모델","sections":[],"depth":1}';function st(q){return K(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ut extends V{constructor(s){super(),W(this,s,st,at,Q,{})}}export{ut as component};
