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Copilot/fix 967787a2 2dec 4fb0 a368 41efa5fb543a #1112

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9eb37dd
Initial plan
Copilot Sep 28, 2025
d1aa150
Fix Korean grammar typo: 버전을 → 버전이
Copilot Sep 28, 2025
755fb84
Merge pull request #1 from braintrue/copilot/fix-fbe7b15a-669d-44bf-9…
braintrue Sep 28, 2025
6853239
Initial plan
Copilot Sep 28, 2025
03cf605
Fix Korean course structure and notebook links
Copilot Sep 28, 2025
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7 changes: 2 additions & 5 deletions chapters/ko/_toctree.yml
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Expand Up @@ -51,13 +51,10 @@
sections:
- local: chapter3/2
title: 데이터 처리
- local: chapter3/5
title: 학습 곡선 이해하기

- title: 3. 🤗 사전훈련된 모델 미세조정
sections:
- local: chapter3/3
title: Trainer API를 사용한 모델 미세조정
- local: chapter3/5
title: 학습 곡선 이해하기

- title: 5. 🤗 Datasets 라이브러리
sections:
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2 changes: 1 addition & 1 deletion chapters/ko/chapter0/1.mdx
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Expand Up @@ -50,7 +50,7 @@ import transformers

파이썬 가상 환경 사용을 원하신다면 먼저 파이썬을 설치해야 합니다. 이 [가이드](https://realpython.com/installing-python/)를 따라 설치를 진행하실 수 있습니다.

파이썬 설치가 완료되면 터미널에서 파이썬 명령어를 실행할 수 있습니다. 다음 단계로 넘어가기 전에, 다음과 같은 명령어를 실행하여 설치가 잘 되었는지 확인하세요: `python --version`. 이 때 시스템에 사용할 수 있는 파이썬 버전을 출력되어야 합니다.
파이썬 설치가 완료되면 터미널에서 파이썬 명령어를 실행할 수 있습니다. 다음 단계로 넘어가기 전에, 다음과 같은 명령어를 실행하여 설치가 잘 되었는지 확인하세요: `python --version`. 이 때 시스템에 사용할 수 있는 파이썬 버전이 출력되어야 합니다.

터미널에서 `python --version` 과 같은 파이썬 명령어를 실행하면, 명령어를 실행하는 프로그램을 시스템의 “메인(main)” 파이썬으로 생각해야 합니다. 이 메인 파이썬은 어떤 패키지도 설치하지 않은 상태로 유지하면서, 작업 중인 각 어플리케이션마다 별도의 환경을 생성하여 이용하는 것을 권장합니다. 이렇게 하면, 각 어플리케이션은 각각의 의존성 및 패키지를 갖게 되어 다른 어플리케이션과의 잠재적 호환성 문제를 피할 수 있습니다.

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8 changes: 4 additions & 4 deletions chapters/ko/chapter2/2.mdx
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<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section2_pt.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section2_pt.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter2/section2_pt.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter2/section2_pt.ipynb"},
]} />

{:else}

<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section2_tf.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section2_tf.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter2/section2_tf.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter2/section2_tf.ipynb"},
]} />

{/if}
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8 changes: 4 additions & 4 deletions chapters/ko/chapter2/4.mdx
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<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section4_pt.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section4_pt.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter2/section4_pt.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter2/section4_pt.ipynb"},
]} />

{:else}

<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section4_tf.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section4_tf.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter2/section4_tf.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter2/section4_tf.ipynb"},
]} />

{/if}
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8 changes: 4 additions & 4 deletions chapters/ko/chapter2/5.mdx
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<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section5_pt.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section5_pt.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter2/section5_pt.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter2/section5_pt.ipynb"},
]} />

{:else}

<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section5_tf.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section5_tf.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter2/section5_tf.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter2/section5_tf.ipynb"},
]} />

{/if}
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8 changes: 4 additions & 4 deletions chapters/ko/chapter2/6.mdx
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<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section6_pt.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section6_pt.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter2/section6_pt.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter2/section6_pt.ipynb"},
]} />

{:else}

<CourseFloatingBanner chapter={2}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter2/section6_tf.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter2/section6_tf.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter2/section6_tf.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter2/section6_tf.ipynb"},
]} />

{/if}
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4 changes: 2 additions & 2 deletions chapters/ko/chapter3/2.mdx
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<CourseFloatingBanner chapter={3}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter3/section2.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter3/section2.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter3/section2.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter3/section2.ipynb"},
]} />

[이전 챕터](/course/chapter2)의 예제에 이어서, 한 배치에서 시퀀스 분류기를 훈련하는 방법은 다음과 같습니다.
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4 changes: 2 additions & 2 deletions chapters/ko/chapter3/3.mdx
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<CourseFloatingBanner chapter={3}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter3/section3.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter3/section3.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter3/section3.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter3/section3.ipynb"},
]} />

<Youtube id="nvBXf7s7vTI"/>
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4 changes: 2 additions & 2 deletions chapters/ko/chapter3/5.mdx
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<CourseFloatingBanner chapter={3}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter3/section7.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter3/section7.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter3/section7.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter3/section7.ipynb"},
]} />

이제 `Trainer` API와 사용자 정의 훈련 루프를 모두 사용하여 미세 조정을 구현하는 방법을 배웠으므로, 결과를 해석하는 방법을 이해하는 것이 중요합니다. 학습 곡선은 훈련 중 모델의 성능을 평가하고 성능 저하를 일으킬 수 있는 잠재적 문제를 미리 식별하는 데 도움이 되는 매우 유용한 도구입니다.
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4 changes: 2 additions & 2 deletions chapters/ko/chapter5/2.mdx
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<CourseFloatingBanner chapter={5}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter5/section2.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter5/section2.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter5/section2.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter5/section2.ipynb"},
]} />

[Hugging Face Hub](https://huggingface.co/datasets)를 통해 데이터셋을 다운로드하는 방법은 배웠지만, Hub가 아닌 로컬 환경이나 원격 서버에 저장된 데이터로 작업해야하는 경우도 많이 있을 것입니다. 이번 섹션에서는 🤗 Datasets를 이용하여 Hugging Face Hub에 없는 데이터셋을 로딩하는 방법을 알려드리도록 하겠습니다.
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4 changes: 2 additions & 2 deletions chapters/ko/chapter8/2.mdx
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<CourseFloatingBanner chapter={8}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter8/section2.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter8/section2.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter8/section2.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter8/section2.ipynb"},
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이번 장에서는 Transformer 모델을 새롭게 튜닝 후 예측을 하려고 할 때 발생할 수 있는 몇가지 일반적인 에러를 살펴보겠습니다.
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4 changes: 2 additions & 2 deletions chapters/ko/chapter8/3.mdx
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<CourseFloatingBanner chapter={8}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter8/section3.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/en/chapter8/section3.ipynb"},
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/ko/chapter8/section3.ipynb"},
{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter8/section3.ipynb"},
]} />

<Youtube id="S2EEG3JIt2A"/>
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4 changes: 2 additions & 2 deletions chapters/ko/chapter8/4.mdx
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<CourseFloatingBanner chapter={8}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter8/section4_pt.ipynb"},
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]} />

[단원 7](/course/chapter7)의 조언을 충실히 따라 주어진 작업에서 모델을 학습하거나 파인튜닝 하는 아름다운 스크립트를 작성했습니다. 하지만 `trainer.train()` 명령을 실행하면 끔찍한 일이 발생합니다. 에러가 발생합니다 😱! 또는 더 나쁜 것은 모든 것이 정상인 것처럼 보이고 학습이 에러 없이 실행되지만 결과 모델은 엉망입니다. 이 섹션에서는 이러한 종류의 문제를 디버그하기 위해 수행할 수 있는 것들을 보여줍니다.
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4 changes: 2 additions & 2 deletions chapters/ko/chapter8/4_tf.mdx
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<CourseFloatingBanner chapter={8}
classNames="absolute z-10 right-0 top-0"
notebooks={[
{label: "Google Colab", value: "https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter8/section4_tf.ipynb"},
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{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter8/section4_tf.ipynb"},
]} />

[단원 7](/course/chapter7)의 조언에 충실히 따라 주어진 작업에서 모델을 훈련하거나 파인튜닝하는 멋진 스크립트를 작성했습니다. 하지만 `model.fit()` 명령을 실행하면 끔찍한 일이 발생합니다. 오류가 발생했네요 😱! 또는 더 좋지 못한 일은 모든 것이 정상인 것처럼 보이고 훈련은 오류 없이 실행되었지만 결과 모델이 엉망인 경우입니다. 이 장에서는 이러한 종류의 이슈를 디버그하기 위해 수행할 수 있는 작업을 보여줍니다.
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4 changes: 2 additions & 2 deletions chapters/ko/chapter8/5.mdx
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<CourseFloatingBanner chapter={8}
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{label: "Aws Studio", value: "https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/master/course/ko/chapter8/section5.ipynb"},
]} />

Hugging Face 라이브러리 중에서 이상한 것을 발견하면 고칠 수 있도록 반드시 알려주셔야 합니다(해당 문제에 대해서는 모든 오픈 소스 라이브러리가 동일). 버그가 자신의 코드에 있는지 또는 라이브러리에 있는지 확실하지 않은 경우 가장 먼저 [포럼](https://discuss.huggingface.co/)에서 확인하세요. 커뮤니티에서 이를 파악하는 데 도움을 줄 것이며 Hugging Face 팀도 그곳에서 토론을 면밀히 관찰합니다.
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