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	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"커뮤니티","local":"community","sections":[{"title":"커뮤니티 리소스:","local":"community-resources","sections":[],"depth":2},{"title":"커뮤니티 노트북:","local":"community-notebooks","sections":[],"depth":2}],"depth":1}">
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	<link rel="modulepreload" href="/docs/transformers/main/ko/_app/immutable/chunks/EditOnGithub.922df6ba.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"커뮤니티","local":"community","sections":[{"title":"커뮤니티 리소스:","local":"community-resources","sections":[],"depth":2},{"title":"커뮤니티 노트북:","local":"community-notebooks","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="community" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#community"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>커뮤니티</span></h1> <p data-svelte-h="svelte-1dhb61b">이 페이지는 커뮤니티에서 개발한 🤗 Transformers 리소스를 재구성한 페이지입니다.</p> <h2 class="relative group"><a id="community-resources" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#community-resources"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>커뮤니티 리소스:</span></h2> <table data-svelte-h="svelte-geq5ms"><thead><tr><th align="left">리소스</th> <th align="left">설명</th> <th align="right">만든이</th></tr></thead> <tbody><tr><td align="left"><a href="https://www.darigovresearch.com/huggingface-transformers-glossary-flashcards" rel="nofollow">Hugging Face Transformers 용어집 플래시카드</a></td> <td align="left"><a href="glossary">Transformers 문서 용어집</a>을 기반으로 한 플래시카드 세트로, 지식을 장기적으로 유지하기 위해 특별히 설계된 오픈소스 크로스 플랫폼 앱인 <a href="https://apps.ankiweb.net/" rel="nofollow">Anki</a>를 사용하여 쉽게 학습/수정할 수 있는 형태로 제작되었습니다. <a href="https://www.youtube.com/watch?v=Dji_h7PILrw" rel="nofollow">플래시카드 사용법에 대한 소개 동영상</a>을 참조하세요.</td> <td align="right"><a href="https://www.darigovresearch.com/" rel="nofollow">Darigov 리서치</a></td></tr></tbody></table> <h2 class="relative group"><a id="community-notebooks" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#community-notebooks"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>커뮤니티 노트북:</span></h2> <table data-svelte-h="svelte-1368aku"><thead><tr><th align="left">노트북</th> <th align="left">설명</th> <th align="left">만든이</th> <th align="right"></th></tr></thead> <tbody><tr><td align="left"><a href="https://github.com/AlekseyKorshuk/huggingartists" rel="nofollow">가사를 생성하기 위해 사전훈련된 트랜스포머를 미세 조정하기</a></td> <td align="left">GPT-2 모델을 미세 조정하여 좋아하는 아티스트의 스타일로 가사를 생성하는 방법</td> <td align="left"><a href="https://github.com/AlekseyKorshuk" rel="nofollow">Aleksey Korshuk</a></td> <td align="right"><a href="https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/snapthat/TF-T5-text-to-text" rel="nofollow">Tensorflow 2로 T5 훈련하기</a></td> <td align="left">Tensorflow 2를 사용하여 T5를 훈련시키는 방법. 이 노트북은 Tensorflow 2로 SQUAD를 사용하여 구현한 질의응답 작업을 보여줍니다.</td> <td align="left"><a href="https://github.com/HarrisDePerceptron" rel="nofollow">Muhammad Harris</a></td> <td align="right"><a href="https://colab.research.google.com/github/snapthat/TF-T5-text-to-text/blob/master/snapthatT5/notebooks/TF-T5-Datasets%20Training.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb" rel="nofollow">TPU에서 T5 훈련하기</a></td> <td align="left">Transformers와 Nlp를 사용하여 SQUAD로 T5를 훈련하는 방법</td> <td align="left"><a href="https://github.com/patil-suraj" rel="nofollow">Suraj Patil</a></td> <td align="right"><a href="https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/T5_on_TPU.ipynb#scrollTo=QLGiFCDqvuil" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb" rel="nofollow">분류 및 객관식 문제를 위해 T5 미세 조정하기</a></td> <td align="left">분류 및 객관식 문제에 맞게 텍스트-텍스트 형식을 사용하여 PyTorch Lightning으로 T5를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/patil-suraj" rel="nofollow">Suraj Patil</a></td> <td align="right"><a href="https://colab.research.google.com/github/patil-suraj/exploring-T5/blob/master/t5_fine_tuning.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb" rel="nofollow">새로운 데이터 세트와 언어로 DialoGPT 미세 조정하기</a></td> <td align="left">자유 대화형 챗봇을 만들기 위해 새로운 데이터 세트로 DialoGPT 모델을 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/ncoop57" rel="nofollow">Nathan Cooper</a></td> <td align="right"><a href="https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb" rel="nofollow">Reformer로 긴 시퀀스 모델링하기</a></td> <td align="left">Reformer로 최대 50만 토큰의 시퀀스를 훈련하는 방법</td> <td align="left"><a href="https://github.com/patrickvonplaten" rel="nofollow">Patrick von Platen</a></td> <td align="right"><a href="https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/ohmeow/ohmeow_website/blob/master/posts/2021-05-25-mbart-sequence-classification-with-blurr.ipynb" rel="nofollow">요약을 위해 BART 미세 조정하기</a></td> <td align="left">blurr를 사용하여 fastai로 요약하기 위해 BART를 미세 조정하는 방법</td> <td align="left"><a href="https://ohmeow.com/" rel="nofollow">Wayde Gilliam</a></td> <td align="right"><a href="https://colab.research.google.com/github/ohmeow/ohmeow_website/blob/master/posts/2021-05-25-mbart-sequence-classification-with-blurr.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb" rel="nofollow">다른 사람의 트윗으로 사전훈련된 트랜스포머 미세 조정하기</a></td> <td align="left">GPT-2 모델을 미세 조정하여 좋아하는 트위터 계정 스타일로 트윗을 생성하는 방법</td> <td align="left"><a href="https://github.com/borisdayma" rel="nofollow">Boris Dayma</a></td> <td align="right"><a href="https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb" rel="nofollow">Weights & Biases로 🤗 Hugging Face 모델 최적화하기</a></td> <td align="left">W&B와 Hugging Face의 통합을 보여주는 전체 튜토리얼</td> <td align="left"><a href="https://github.com/borisdayma" rel="nofollow">Boris Dayma</a></td> <td align="right"><a href="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb" rel="nofollow">Longformer 사전훈련하기</a></td> <td align="left">기존 사전훈련된 모델의 “긴” 버전을 빌드하는 방법</td> <td align="left"><a href="https://beltagy.net" rel="nofollow">Iz Beltagy</a></td> <td align="right"><a href="https://colab.research.google.com/github/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb" rel="nofollow">QA를 위해 Longformer 미세 조정하기</a></td> <td align="left">QA 작업을 위해 Longformer를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/patil-suraj" rel="nofollow">Suraj Patil</a></td> <td align="right"><a href="https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patrickvonplaten/notebooks/blob/master/How_to_evaluate_Longformer_on_TriviaQA_using_NLP.ipynb" rel="nofollow">🤗 Nlp로 모델 평가하기</a></td> <td align="left"><code>Nlp</code>로 TriviaQA에서 Longformer를 평가하는 방법</td> <td align="left"><a href="https://github.com/patrickvonplaten" rel="nofollow">Patrick von Platen</a></td> <td align="right"><a href="https://colab.research.google.com/drive/1m7eTGlPmLRgoPkkA7rkhQdZ9ydpmsdLE?usp=sharing" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb" rel="nofollow">감정 범위 추출을 위해 T5 미세 조정하기</a></td> <td align="left">감정 범위 추출을 위해 텍스트-텍스트 형식을 사용하여 PyTorch Lightning으로 T5를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/enzoampil" rel="nofollow">Lorenzo Ampil</a></td> <td align="right"><a href="https://colab.research.google.com/github/enzoampil/t5-intro/blob/master/t5_qa_training_pytorch_span_extraction.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb" rel="nofollow">다중 클래스 분류를 위해 DistilBert 미세 조정하기</a></td> <td align="left">다중 클래스 분류를 위해 PyTorch를 사용하여 DistilBert를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/abhimishra91" rel="nofollow">Abhishek Kumar Mishra</a></td> <td align="right"><a href="https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multiclass_classification.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb" rel="nofollow">다중 레이블 분류를 위해 BERT 미세 조정하기</a></td> <td align="left">다중 레이블 분류를 위해 PyTorch를 사용하여 BERT를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/abhimishra91" rel="nofollow">Abhishek Kumar Mishra</a></td> <td align="right"><a href="https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_multi_label_classification.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb" rel="nofollow">요약을 위해 T5 미세 조정하기</a></td> <td align="left">요약을 위해 PyTorch로 T5를 미세 조정하고 WandB로 실험을 추적하는 방법</td> <td align="left"><a href="https://github.com/abhimishra91" rel="nofollow">Abhishek Kumar Mishra</a></td> <td align="right"><a href="https://colab.research.google.com/github/abhimishra91/transformers-tutorials/blob/master/transformers_summarization_wandb.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/ELS-RD/transformers-notebook/blob/master/Divide_Hugging_Face_Transformers_training_time_by_2_or_more.ipynb" rel="nofollow">동적 패딩/버켓팅으로 Transformers 미세 조정 속도 높이기</a></td> <td align="left">동적 패딩/버켓팅을 사용하여 미세 조정 속도를 2배로 높이는 방법</td> <td align="left"><a href="https://github.com/pommedeterresautee" rel="nofollow">Michael Benesty</a></td> <td align="right"><a href="https://colab.research.google.com/drive/1CBfRU1zbfu7-ijiOqAAQUA-RJaxfcJoO?usp=sharing" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patrickvonplaten/notebooks/blob/master/Reformer_For_Masked_LM.ipynb" rel="nofollow">마스킹된 언어 모델링을 위해 Reformer 사전훈련하기</a></td> <td align="left">양방향 셀프 어텐션 레이어를 이용해서 Reformer 모델을 훈련하는 방법</td> <td align="left"><a href="https://github.com/patrickvonplaten" rel="nofollow">Patrick von Platen</a></td> <td align="right"><a href="https://colab.research.google.com/drive/1tzzh0i8PgDQGV3SMFUGxM7_gGae3K-uW?usp=sharing" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/lordtt13/word-embeddings/blob/master/COVID-19%20Research%20Data/COVID-SciBERT.ipynb" rel="nofollow">Sci-BERT 확장 및 미세 조정하기</a></td> <td align="left">CORD 데이터 세트로 AllenAI에서 사전훈련된 SciBERT 모델의 어휘를 늘리고 파이프라인을 구축하는 방법</td> <td align="left"><a href="https://github.com/lordtt13" rel="nofollow">Tanmay Thakur</a></td> <td align="right"><a href="https://colab.research.google.com/drive/1rqAR40goxbAfez1xvF3hBJphSCsvXmh8" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/lordtt13/transformers-experiments/blob/master/Custom%20Tasks/fine-tune-blenderbot_small-for-summarization.ipynb" rel="nofollow">요약을 위해 Trainer API로 BlenderBotSmall 미세 조정하기</a></td> <td align="left">요약을 위해 Trainer API를 사용하여 사용자 지정 데이터 세트로 BlenderBotSmall 미세 조정하기</td> <td align="left"><a href="https://github.com/lordtt13" rel="nofollow">Tanmay Thakur</a></td> <td align="right"><a href="https://colab.research.google.com/drive/19Wmupuls7mykSGyRN_Qo6lPQhgp56ymq?usp=sharing" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/elsanns/xai-nlp-notebooks/blob/master/electra_fine_tune_interpret_captum_ig.ipynb" rel="nofollow">통합 기울기(Integrated Gradient)를 이용하여 Electra 미세 조정하고 해석하기</a></td> <td align="left">감정 분석을 위해 Electra를 미세 조정하고 Captum 통합 기울기로 예측을 해석하는 방법</td> <td align="left"><a href="https://elsanns.github.io" rel="nofollow">Eliza Szczechla</a></td> <td align="right"><a href="https://colab.research.google.com/github/elsanns/xai-nlp-notebooks/blob/master/electra_fine_tune_interpret_captum_ig.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb" rel="nofollow">Trainer 클래스로 비영어권 GPT-2 모델 미세 조정하기</a></td> <td align="left">Trainer 클래스로 비영어권 GPT-2 모델을 미세 조정하는 방법</td> <td align="left"><a href="https://www.philschmid.de" rel="nofollow">Philipp Schmid</a></td> <td align="right"><a href="https://colab.research.google.com/github/philschmid/fine-tune-GPT-2/blob/master/Fine_tune_a_non_English_GPT_2_Model_with_Huggingface.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb" rel="nofollow">다중 라벨 분류 작업을 위해 DistilBERT 모델 미세 조정하기</a></td> <td align="left">다중 라벨 분류 작업을 위해 DistilBERT 모델을 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/DhavalTaunk08" rel="nofollow">Dhaval Taunk</a></td> <td align="right"><a href="https://colab.research.google.com/github/DhavalTaunk08/Transformers_scripts/blob/master/Transformers_multilabel_distilbert.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb" rel="nofollow">문장쌍 분류를 위해 ALBERT 미세 조정하기</a></td> <td align="left">문장쌍 분류 작업을 위해 ALBERT 모델 또는 다른 BERT 기반 모델을 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/NadirEM" rel="nofollow">Nadir El Manouzi</a></td> <td align="right"><a href="https://colab.research.google.com/github/NadirEM/nlp-notebooks/blob/master/Fine_tune_ALBERT_sentence_pair_classification.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb" rel="nofollow">감정 분석을 위해 Roberta 미세 조정하기</a></td> <td align="left">감정 분석을 위해 Roberta 모델을 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/DhavalTaunk08" rel="nofollow">Dhaval Taunk</a></td> <td align="right"><a href="https://colab.research.google.com/github/DhavalTaunk08/NLP_scripts/blob/master/sentiment_analysis_using_roberta.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/flexudy-pipe/qugeev" rel="nofollow">질문 생성 모델 평가하기</a></td> <td align="left">seq2seq 트랜스포머 모델이 생성한 질문과 이에 대한 답변이 얼마나 정확한가요?</td> <td align="left"><a href="https://github.com/zolekode" rel="nofollow">Pascal Zoleko</a></td> <td align="right"><a href="https://colab.research.google.com/drive/1bpsSqCQU-iw_5nNoRm_crPq6FRuJthq_?usp=sharing" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb" rel="nofollow">DistilBERT와 Tensorflow로 텍스트 분류하기</a></td> <td align="left">텍스트 분류를 위해 TensorFlow로 DistilBERT를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/peterbayerle" rel="nofollow">Peter Bayerle</a></td> <td align="right"><a href="https://colab.research.google.com/github/peterbayerle/huggingface_notebook/blob/main/distilbert_tf.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb" rel="nofollow">CNN/Dailail 요약을 위해 인코더-디코더 모델에 BERT 활용하기</a></td> <td align="left">CNN/Dailail 요약을 위해 <em>google-bert/bert-base-uncased</em> 체크포인트를 활용하여 <em>EncoderDecoderModel</em>을 워밍업하는 방법</td> <td align="left"><a href="https://github.com/patrickvonplaten" rel="nofollow">Patrick von Platen</a></td> <td align="right"><a href="https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb" rel="nofollow">BBC XSum 요약을 위해 인코더-디코더 모델에 RoBERTa 활용하기</a></td> <td align="left">BBC/XSum 요약을 위해 <em>FacebookAI/roberta-base</em> 체크포인트를 활용하여 공유 <em>EncoderDecoderModel</em>을 워밍업하는 방법</td> <td align="left"><a href="https://github.com/patrickvonplaten" rel="nofollow">Patrick von Platen</a></td> <td align="right"><a href="https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/RoBERTaShared_for_BBC_XSum.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb" rel="nofollow">순차적 질문 답변(SQA)을 위해 TAPAS 미세 조정하기</a></td> <td align="left"><em>tapas-base</em> 체크포인트를 활용하여 순차적 질문 답변(SQA) 데이터 세트로 <em>TapasForQuestionAnswering</em>을 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/nielsrogge" rel="nofollow">Niels Rogge</a></td> <td align="right"><a href="https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Fine_tuning_TapasForQuestionAnswering_on_SQA.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb" rel="nofollow">표 사실 검사(TabFact)로 TAPAS 평가하기</a></td> <td align="left">🤗 Datasets와 🤗 Transformer 라이브러리를 함께 사용하여 <em>tapas-base-finetuned-tabfact</em> 체크포인트로 미세 조정된 <em>TapasForSequenceClassification</em>을 평가하는 방법</td> <td align="left"><a href="https://github.com/nielsrogge" rel="nofollow">Niels Rogge</a></td> <td align="right"><a href="https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/TAPAS/Evaluating_TAPAS_on_the_Tabfact_test_set.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb" rel="nofollow">번역을 위해 mBART 미세 조정하기</a></td> <td align="left">힌디어에서 영어로 번역하기 위해 Seq2SeqTrainer를 사용하여 mBART를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/vasudevgupta7" rel="nofollow">Vasudev Gupta</a></td> <td align="right"><a href="https://colab.research.google.com/github/vasudevgupta7/huggingface-tutorials/blob/main/translation_training.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb" rel="nofollow">FUNSD(양식 이해 데이터 세트)로 LayoutLM 미세 조정하기</a></td> <td align="left">스캔한 문서에서 정보 추출을 위해 FUNSD 데이터 세트로 <em>LayoutLMForTokenClassification</em>을 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/nielsrogge" rel="nofollow">Niels Rogge</a></td> <td align="right"><a href="https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForTokenClassification_on_FUNSD.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://colab.research.google.com/github/tripathiaakash/DistilGPT2-Tutorial/blob/main/distilgpt2_fine_tuning.ipynb" rel="nofollow">DistilGPT2 미세 조정하고 및 텍스트 생성하기</a></td> <td align="left">DistilGPT2를 미세 조정하고 텍스트를 생성하는 방법</td> <td align="left"><a href="https://github.com/tripathiaakash" rel="nofollow">Aakash Tripathi</a></td> <td align="right"><a href="https://colab.research.google.com/github/tripathiaakash/DistilGPT2-Tutorial/blob/main/distilgpt2_fine_tuning.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb" rel="nofollow">최대 8K 토큰에서 LED 미세 조정하기</a></td> <td align="left">긴 범위를 요약하기 위해 PubMed로 LED를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/patrickvonplaten" rel="nofollow">Patrick von Platen</a></td> <td align="right"><a href="https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_tune_Longformer_Encoder_Decoder_(LED)_for_Summarization_on_pubmed.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patrickvonplaten/notebooks/blob/master/LED_on_Arxiv.ipynb" rel="nofollow">Arxiv로 LED 평가하기</a></td> <td align="left">긴 범위 요약에 대해 LED를 효과적으로 평가하는 방법</td> <td align="left"><a href="https://github.com/patrickvonplaten" rel="nofollow">Patrick von Platen</a></td> <td align="right"><a href="https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/LED_on_Arxiv.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb" rel="nofollow">RVL-CDIP(문서 이미지 분류 데이터 세트)로 LayoutLM 미세 조정하기)</a></td> <td align="left">스캔 문서 분류를 위해 RVL-CDIP 데이터 세트로 <em>LayoutLMForSequenceClassification</em>을 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/nielsrogge" rel="nofollow">Niels Rogge</a></td> <td align="right"><a href="https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLM/Fine_tuning_LayoutLMForSequenceClassification_on_RVL_CDIP.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/voidful/huggingface_notebook/blob/main/xlsr_gpt.ipynb" rel="nofollow">GPT2 조정을 통한 Wav2Vec2 CTC 디코딩</a></td> <td align="left">언어 모델 조정을 통해 CTC 시퀀스를 디코딩하는 방법</td> <td align="left"><a href="https://github.com/voidful" rel="nofollow">Eric Lam</a></td> <td align="right"><a href="https://colab.research.google.com/drive/1e_z5jQHYbO2YKEaUgzb1ww1WwiAyydAj?usp=sharing" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb" rel="nofollow">Trainer 클래스로 두 개 언어로 요약하기 위해 BART 미세 조정하기</a></td> <td align="left">Trainer 클래스로 두 개 언어로 요약하기 위해 BART 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/elsanns" rel="nofollow">Eliza Szczechla</a></td> <td align="right"><a href="https://colab.research.google.com/github/elsanns/xai-nlp-notebooks/blob/master/fine_tune_bart_summarization_two_langs.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb" rel="nofollow">Trivia QA로 Big Bird 평가하기</a></td> <td align="left">Trivia QA로 긴 문서 질문에 대한 답변에 대해 BigBird를 평가하는 방법</td> <td align="left"><a href="https://github.com/patrickvonplaten" rel="nofollow">Patrick von Platen</a></td> <td align="right"><a href="https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Evaluating_Big_Bird_on_TriviaQA.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb" rel="nofollow">Wav2Vec2를 사용하여 동영상 캡션 만들기</a></td> <td align="left">Wav2Vec으로 오디오를 텍스트로 변환하여 모든 동영상에서 YouTube 캡션 만드는 방법</td> <td align="left"><a href="https://github.com/Muennighoff" rel="nofollow">Niklas Muennighoff</a></td> <td align="right"><a href="https://colab.research.google.com/github/Muennighoff/ytclipcc/blob/main/wav2vec_youtube_captions.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb" rel="nofollow">PyTorch Lightning을 사용하여 CIFAR-10으로 비전 트랜스포머 미세 조정하기</a></td> <td align="left">HuggingFace Transformers, Datasets, PyTorch Lightning을 사용하여 CIFAR-10으로 비전 트랜스포머(ViT)를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/nielsrogge" rel="nofollow">Niels Rogge</a></td> <td align="right"><a href="https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_PyTorch_Lightning.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb" rel="nofollow">🤗 Trainer를 사용하여 CIFAR-10에서 비전 트랜스포머 미세 조정하기</a></td> <td align="left">Datasets, 🤗 Trainer를 사용하여 CIFAR-10에서 비전 트랜스포머(ViT)를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/nielsrogge" rel="nofollow">Niels Rogge</a></td> <td align="right"><a href="https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/VisionTransformer/Fine_tuning_the_Vision_Transformer_on_CIFAR_10_with_the_%F0%9F%A4%97_Trainer.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb" rel="nofollow">개체 입력 데이터 세트인 Open Entity로 LUKE 평가하기</a></td> <td align="left">Open Entity 데이터 세트로 <em>LukeForEntityClassification</em>을 평가하는 방법</td> <td align="left"><a href="https://github.com/ikuyamada" rel="nofollow">Ikuya Yamada</a></td> <td align="right"><a href="https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_open_entity.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb" rel="nofollow">관계 추출 데이터 세트인 TACRED로 LUKE 평가하기</a></td> <td align="left">TACRED 데이터 세트로 <em>LukeForEntityPairClassification</em>을 평가하는 방법</td> <td align="left"><a href="https://github.com/ikuyamada" rel="nofollow">Ikuya Yamada</a></td> <td align="right"><a href="https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_tacred.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb" rel="nofollow">중요 NER 벤치마크인 CoNLL-2003으로 LUKE 평가하기</a></td> <td align="left">CoNLL-2003 데이터 세트로 <em>LukeForEntitySpanClassification</em>를 평가하는 방법</td> <td align="left"><a href="https://github.com/ikuyamada" rel="nofollow">Ikuya Yamada</a></td> <td align="right"><a href="https://colab.research.google.com/github/studio-ousia/luke/blob/master/notebooks/huggingface_conll_2003.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb" rel="nofollow">PubMed 데이터 세트로 BigBird-Pegasus 평가하기</a></td> <td align="left">PubMed 데이터 세트로 <em>BigBirdPegasusForConditionalGeneration</em>를 평가하는 방법</td> <td align="left"><a href="https://github.com/vasudevgupta7" rel="nofollow">Vasudev Gupta</a></td> <td align="right"><a href="https://colab.research.google.com/github/vasudevgupta7/bigbird/blob/main/notebooks/bigbird_pegasus_evaluation.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb" rel="nofollow">Wav2Vec2를 사용해서 음성 감정 분류하기</a></td> <td align="left">감정 분류를 위해 사전훈련된 Wav2Vec2 모델을 MEGA 데이터 세트에 활용하는 방법</td> <td align="left"><a href="https://github.com/m3hrdadfi" rel="nofollow">Mehrdad Farahani</a></td> <td align="right"><a href="https://colab.research.google.com/github/m3hrdadfi/soxan/blob/main/notebooks/Emotion_recognition_in_Greek_speech_using_Wav2Vec2.ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_minimal_example_(with_DetrFeatureExtractor).ipynb" rel="nofollow">DETR로 이미지에서 객체 탐지하기</a></td> <td align="left">훈련된 <em>DetrForObjectDetection</em> 모델을 사용하여 이미지에서 객체를 탐지하고 어텐션을 시각화하는 방법</td> <td align="left"><a href="https://github.com/NielsRogge" rel="nofollow">Niels Rogge</a></td> <td align="right"><a href="https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_minimal_example_(with_DetrFeatureExtractor).ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/NielsRogge/Transformers-Tutorials/blob/master/DETR/Fine_tuning_DetrForObjectDetection_on_custom_dataset_(balloon).ipynb" rel="nofollow">사용자 지정 객체 탐지 데이터 세트로 DETR 미세 조정하기</a></td> <td align="left">사용자 지정 객체 탐지 데이터 세트로 <em>DetrForObjectDetection</em>을 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/NielsRogge" rel="nofollow">Niels Rogge</a></td> <td align="right"><a href="https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/Fine_tuning_DetrForObjectDetection_on_custom_dataset_(balloon).ipynb" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr> <tr><td align="left"><a href="https://github.com/ToluClassics/Notebooks/blob/main/T5_Ner_Finetuning.ipynb" rel="nofollow">개체명 인식을 위해 T5 미세 조정하기</a></td> <td align="left">개체명 인식 작업을 위해 <em>T5</em>를 미세 조정하는 방법</td> <td align="left"><a href="https://github.com/ToluClassics" rel="nofollow">Ogundepo Odunayo</a></td> <td align="right"><a href="https://colab.research.google.com/drive/1obr78FY_cBmWY5ODViCmzdY6O1KB65Vc?usp=sharing" rel="nofollow"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></td></tr></tbody></table> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/transformers/blob/main/docs/source/ko/community.md" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p>

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