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<h1 id="">단원 정리</h1>


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<p>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.</p>
<p>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:</p>
<table><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>오약, 번역, 생성 질의 응답</td></tr></tbody></table>


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