course/pr_1189/ko/_app/immutable/nodes/12.8ad00a4b.js

import{s as D,n as F,o as I}from"../chunks/scheduler.505acc25.js";import{S as O,i as U,e as $,s as r,c as M,h as X,a as c,d as n,b as i,f as j,g as R,j as k,k as q,l as J,m as a,n as w,t as B,o as A,p as H}from"../chunks/index.821724d0.js";import{C as K,H as Q,E as V}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.7483318d.js";import{C as W}from"../chunks/CourseFloatingBanner.a3154b9b.js";function Y(z){let s,_,h,g,l,b,m,x,o,E,p,S="이번 단원에서는 🤗 Transformers의 하이레벨 함수인 <code>pipeline()</code> 를 사용하여 다양한 NLP 문제에 대한 접근 방식을 배웠습니다. 그리고 Hub에서 모델을 검색하여 사용하는 방법, 추론 API를 이용해 브라우저 상에서 바로 모델을 테스트 하는 방법 또한 알아보았습니다.",L,f,G="지금까지 트랜스포머 모델의 대략작인 동작 방식과, 전이 학습(transfer learning) 및 미세 조정(fine-tuning)의 중요성에 알아보았습니다. 핵심은 어떤 문제를 풀고싶냐에 따라 전체 모델 구조를 다 사용하거나 인코더, 디코더만 사용할 수도 있다는 것입니다. 아래 표는 이를 요약해서 보여주고 있습니다:",C,d,N="<thead><tr><th>Model</th> <th>Examples</th> <th>Tasks</th></tr></thead> <tbody><tr><td>인코더</td> <td>ALBERT, BERT, DistilBERT, ELECTRA, RoBERTa</td> <td>문장 분류, 개체명 인식, 추출 질의 응답</td></tr> <tr><td>디코더</td> <td>CTRL, GPT, GPT-2, Transformer XL</td> <td>텍스트 생성</td></tr> <tr><td>인코더-디코더</td> <td>BART, T5, Marian, mBART</td> <td>요약, 번역, 생성 질의 응답</td></tr></tbody>",P,u,v,T,y;return l=new K({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),m=new Q({props:{title:"단원 정리",local:"단원-정리",headingTag:"h1"}}),o=new W({props:{chapter:1,classNames:"absolute z-10 right-0 top-0"}}),u=new V({props:{source:"https://github.com/huggingface/course/blob/main/chapters/ko/chapter1/9.mdx"}}),{c(){s=$("meta"),_=r(),h=$("p"),g=r(),M(l.$$.fragment),b=r(),M(m.$$.fragment),x=r(),M(o.$$.fragment),E=r(),p=$("p"),p.innerHTML=S,L=r(),f=$("p"),f.textContent=G,C=r(),d=$("table"),d.innerHTML=N,P=r(),M(u.$$.fragment),v=r(),T=$("p"),this.h()},l(t){const e=X("svelte-u9bgzb",document.head);s=c(e,"META",{name:!0,content:!0}),e.forEach(n),_=i(t),h=c(t,"P",{}),j(h).forEach(n),g=i(t),R(l.$$.fragment,t),b=i(t),R(m.$$.fragment,t),x=i(t),R(o.$$.fragment,t),E=i(t),p=c(t,"P",{"data-svelte-h":!0}),k(p)!=="svelte-y3e3wl"&&(p.innerHTML=S),L=i(t),f=c(t,"P",{"data-svelte-h":!0}),k(f)!=="svelte-17mdlkb"&&(f.textContent=G),C=i(t),d=c(t,"TABLE",{"data-svelte-h":!0}),k(d)!=="svelte-8dnz9a"&&(d.innerHTML=N),P=i(t),R(u.$$.fragment,t),v=i(t),T=c(t,"P",{}),j(T).forEach(n),this.h()},h(){q(s,"name","hf:doc:metadata"),q(s,"content",Z)},m(t,e){J(document.head,s),a(t,_,e),a(t,h,e),a(t,g,e),w(l,t,e),a(t,b,e),w(m,t,e),a(t,x,e),w(o,t,e),a(t,E,e),a(t,p,e),a(t,L,e),a(t,f,e),a(t,C,e),a(t,d,e),a(t,P,e),w(u,t,e),a(t,v,e),a(t,T,e),y=!0},p:F,i(t){y||(B(l.$$.fragment,t),B(m.$$.fragment,t),B(o.$$.fragment,t),B(u.$$.fragment,t),y=!0)},o(t){A(l.$$.fragment,t),A(m.$$.fragment,t),A(o.$$.fragment,t),A(u.$$.fragment,t),y=!1},d(t){t&&(n(_),n(h),n(g),n(b),n(x),n(E),n(p),n(L),n(f),n(C),n(d),n(P),n(v),n(T)),n(s),H(l,t),H(m,t),H(o,t),H(u,t)}}}const Z='{"title":"단원 정리","local":"단원-정리","sections":[],"depth":1}';function tt(z){return I(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class rt extends O{constructor(s){super(),U(this,s,tt,Y,D,{})}}export{rt as component};