import{s as K,n as V,o as W}from"../chunks/scheduler.cc52f4b9.js";import{S as X,i as Z,e as i,s as a,c as C,h as tt,a as r,d as l,b as o,f as Q,g as v,j as w,k as I,l as et,m as n,n as E,t as T,o as P,p as y}from"../chunks/index.ccfa71ad.js";import{C as lt,H as nt,E as at}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.bf6e17b3.js";import{Y as ot}from"../chunks/Youtube.6232240b.js";import{C as st}from"../chunks/CourseFloatingBanner.23e5afab.js";function it(Y){let s,L,x,M,m,R,f,H,p,z,u,B,$,D="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.",k,c,F="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.",S,h,G="인코더 모델은 문장 분류, 개체명 인식(더 넓은 범위에서 단어 분류), 추출 질의 응답 등과 같이 전체 문장에 대한 이해를 요구하는 작업에 특화되어 있습니다.",A,g,J="이 계열을 대표하는 모델들은 아래와 같습니다:",U,_,O='<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>',j,d,q,b,N;return m=new lt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),f=new nt({props:{title:"인코더 모델",local:"인코더-모델",headingTag:"h1"}}),p=new st({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),u=new ot({props:{id:"MUqNwgPjJvQ"}}),d=new at({props:{source:"https://github.com/huggingface/course/blob/main/chapters/ko/chapter1/5.mdx"}}),{c(){s=i("meta"),L=a(),x=i("p"),M=a(),C(m.$$.fragment),R=a(),C(f.$$.fragment),H=a(),C(p.$$.fragment),z=a(),C(u.$$.fragment),B=a(),$=i("p"),$.innerHTML=D,k=a(),c=i("p"),c.textContent=F,S=a(),h=i("p"),h.textContent=G,A=a(),g=i("p"),g.textContent=J,U=a(),_=i("ul"),_.innerHTML=O,j=a(),C(d.$$.fragment),q=a(),b=i("p"),this.h()},l(t){const e=tt("svelte-u9bgzb",document.head);s=r(e,"META",{name:!0,content:!0}),e.forEach(l),L=o(t),x=r(t,"P",{}),Q(x).forEach(l),M=o(t),v(m.$$.fragment,t),R=o(t),v(f.$$.fragment,t),H=o(t),v(p.$$.fragment,t),z=o(t),v(u.$$.fragment,t),B=o(t),$=r(t,"P",{"data-svelte-h":!0}),w($)!=="svelte-fcsfy5"&&($.innerHTML=D),k=o(t),c=r(t,"P",{"data-svelte-h":!0}),w(c)!=="svelte-5s30te"&&(c.textContent=F),S=o(t),h=r(t,"P",{"data-svelte-h":!0}),w(h)!=="svelte-1xwlvty"&&(h.textContent=G),A=o(t),g=r(t,"P",{"data-svelte-h":!0}),w(g)!=="svelte-hk1lgc"&&(g.textContent=J),U=o(t),_=r(t,"UL",{"data-svelte-h":!0}),w(_)!=="svelte-18kzzol"&&(_.innerHTML=O),j=o(t),v(d.$$.fragment,t),q=o(t),b=r(t,"P",{}),Q(b).forEach(l),this.h()},h(){I(s,"name","hf:doc:metadata"),I(s,"content",rt)},m(t,e){et(document.head,s),n(t,L,e),n(t,x,e),n(t,M,e),E(m,t,e),n(t,R,e),E(f,t,e),n(t,H,e),E(p,t,e),n(t,z,e),E(u,t,e),n(t,B,e),n(t,$,e),n(t,k,e),n(t,c,e),n(t,S,e),n(t,h,e),n(t,A,e),n(t,g,e),n(t,U,e),n(t,_,e),n(t,j,e),E(d,t,e),n(t,q,e),n(t,b,e),N=!0},p:V,i(t){N||(T(m.$$.fragment,t),T(f.$$.fragment,t),T(p.$$.fragment,t),T(u.$$.fragment,t),T(d.$$.fragment,t),N=!0)},o(t){P(m.$$.fragment,t),P(f.$$.fragment,t),P(p.$$.fragment,t),P(u.$$.fragment,t),P(d.$$.fragment,t),N=!1},d(t){t&&(l(L),l(x),l(M),l(R),l(H),l(z),l(B),l($),l(k),l(c),l(S),l(h),l(A),l(g),l(U),l(_),l(j),l(q),l(b)),l(s),y(m,t),y(f,t),y(p,t),y(u,t),y(d,t)}}}const rt='{"title":"인코더 모델","local":"인코더-모델","sections":[],"depth":1}';function mt(Y){return W(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ht extends X{constructor(s){super(),Z(this,s,mt,it,K,{})}}export{ht as component};
