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🌐 [i18n-KO] Translating docs to Korean #20179
Description
Hi!
Let's bring the documentation to all the Korean-speaking community 🌏 (currently 9 out of 77 complete)
Would you want to translate? Please follow the 🤗 TRANSLATING guide. Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.
Some notes:
- Please translate using an informal tone (imagine you are talking with a friend about transformers 🤗).
- Please translate in a gender-neutral way.
- Add your translations to the folder called
koinside the source folder. - Register your translation in
ko/_toctree.yml; please follow the order of the English version. - Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @ArthurZucker, @sgugger and @eunseojo for review.
- 🙋 If you'd like others to help you with the translation, you can also post in the 🤗 forums.
- With the HuggingFace Documentation l10n initiative of Pseudo Lab, full translation will be done even faster. 🎉 Please give us your support! Cheers to our team 👍@0525hhgus, @kihoon71, @gabrielwithappy, @jungnerd, @sim-so, @HanNayeoniee, @wonhyeongseo
안녕하세요!
한국어를 사용하는 모두가 기술 문서를 읽을 수 있게 해보아요 🌏 (현재 77개 문서 중 9개 완료)
번역에 참여하고 싶으신가요? 🤗 번역 가이드를 먼저 읽어보시기 바랍니다. 끝 부분에 번역해야할 파일들이 나열되어 있습니다. 작업하고 계신 파일이 있다면 여기에 간단히 알려주세요. 중복되지 않도록 작업중으로 표시해둘게요.
참고 사항:
- 기술 문서이지만 (친구에게 설명 듣듯이) 쉽게 읽히면 좋겠습니다. 존댓말 로 써주시면 감사하겠습니다.
- 성별은 일부 언어(스페인어, 프랑스어 등)에만 적용되는 사항으로, 한국어의 경우 번역기를 사용하신 후 문장 기호와 조사 등이 알맞는지 확인해주시기 바랍니다.
- 소스 폴더 아래
ko폴더에 번역본을 넣어주세요. - 목차(
ko/_toctree.yml)도 함께 업데이트해주세요. 영어 목차와 순서가 동일해야 합니다. - 모두 마치셨다면, 기록이 원활하도록 PR을 여실 때 현재 이슈(
#20179)를 내용에 넣어주시기 바랍니다. 리뷰 요청은 @ArthurZucker님, @sgugger님, @eunseojo님께 요청해주세요. - 🙋 커뮤니티에 마음껏 홍보해주시기 바랍니다! 🤗 포럼에 올리셔도 좋아요.
- 가짜연구소의 이니셔티브로 번역이 더욱 빠르게 진행될 예정입니다. 🎉 많은 응원 부탁드려요! 우리팀 화이팅 👍
@0525hhgus, @kihoon71, @gabrielwithappy, @jungnerd, @sim-so, @HanNayeoniee, @wonhyeongseo
GET STARTED
- 🤗 Transformers [i18n-KO] Translated index page to Korean #20180
- Quick tour [i18n-KO] Translated quicktour page to Korean #20946
- Installation 🌐 [i18n-KO] Translated
installation.mdxto Korean #20948
TUTORIAL
- Pipelines for inference 🌐 [i18n-KO] Translated
pipeline_tutorial.mdxto Korean #22508 - Load pretrained instances with an AutoClass 🌐[i18n-KO] Translate
autoclass_tutorialto Korean and Fix the typo ofquicktour#22533 - Preprocess 🌐 [i18n-KO] Translated
tutorial/proprecssing.mdxto Korean #22578 - Fine-tune a pretrained model 🌐 [i18n-KO] Translated
training.mdxto Korean #22670 - Train with a script 🌐 [i18n-KO] Translated
run_scripts.mdxto Korean #22793 - Distributed training with 🤗 Accelerate [i18n-KO] Translated
accelerate.mdxto Korean #22830 - Load and train adapters with 🤗 PEFT 🌐 [i18n-KO] Translated peft.md to Korean #25706
- Share a model
- Agents 🌐 [i18n-KO] Translated
transformers_agents.mdto Korean #24881 - Generation with LLMs 🌐[i18n-KO] Translated
llm_tutorial.mdto Korean #25791
TASK GUIDES
NATURAL LANGUAGE PROCESSING
- Text classification 🌐 [i18n-KO] Translated
sequence_classification.mdxto Korean #22655 - Token classification 🌐 [i18n-KO] Translated
token_classification.mdxto Korean #22945 - Question answering
- Causal language modeling
- Masked language modeling 🌐 [i18n-KO] Translated
tasks/masked_language_modeling.mdxto Korean #22838 - Translation 🌐 [i18n-KO] Translated
tasks/translation.mdxto Korean #22805 - Summarization 🌐 [i18n-KO] Translated
tasks/summarization.mdxto Korean #22783 - Multiple choice
AUDIO
- Audio classification 🌐 [i18n-KO] Translated
audio_classification.mdxto Korean #26200 - Automatic speech recognition
COMPUTER VISION
- Image classification
- Semantic segmentation 🌐 [i18n-KO] Translated
semantic_segmentation.mdto Korean #26515 - Video classification
- Object detection
- Zero-shot object detection
- Zero-shot image classification
- Depth estimation
MULTIMODAL
- Image captioning
- Document Question Answering
- Visual Question Answering 🌐 [i18n-KO] Translated
visual_question_answering.mdto Korean #25679 - Text to speech
GENERATION
- Customize the generation strategy
DEVELOPER GUIDES
- Use tokenizers from 🤗 Tokenizers 🌐 [i18n-KO] Translated
fast_tokenizers.mdxto Korean #22956 - Inference for multilingual models
- Create a custom architecture 🌐 [i18n-KO] translate
create_a_modeldoc to Korean #22754 - Sharing custom models 🌐 [i18n-KO] Translated
custom_models.mdxto Korean #22534 - Run training on Amazon SageMaker docs: ko: sagemaker.mdx #22509
- Export to ONNX 🌐 [i18n-KO] Translated
serialization.mdxto Korean #22806 - Export to TFLite
- Export to TorchScript
- Benchmarks
- Notebooks with examples
- Community resources 🌐 [i18n-KO] Translated
community.mdto Korean #25674 - Custom Tools and Prompts
- Troubleshoot
PERFORMANCE AND SCALABILITY
- Overview
EFFICIENT TRAINING TECHNIQUES
- Training on one GPU Ko perf train gpu one #25250
- Training on many GPUs 🌐 [i18n-KO] Translated
perf_train_gpu_many.mdto Korean #26244 - Training on CPU 🌐 [i18n-KO] Translated
perf_train_cpu.mdto Korean #24911 - Training on many CPUs 🌐 [i18n-KO] Translated
perf_train_cpu_many.mdto Korean #24923 - Training on TPUs
- Training on TPU with TensorFlow
- Training on Specialized Hardware
- Custom hardware for training 🌐 [i18n-KO] Translated
perf_hardware.mdto Korean #24966 - Hyperparameter Search using Trainer API
OPTIMIZING INFERENCE
- Inference on CPU 🌐 [i18n-KO] Translated
perf_infer_cpu.mdto Korean #24920 - Inference on one GPU 🌐 [i18n-KO] Translated
perf_infer_gpu_one.mdto Korean #24978 - Inference on many GPUs 🌐 [i18n-KO] Translated
perf_infer_gpu_many.mdto Korean #24943 - Inference on Specialized Hardware
- Instantiating a big model 🌐 [i18n-KO] Translated
big_models.mdto Korean #26245 - Debugging 🌐 [i18n-KO] Translated
debugging.mdto Korean #26246 - XLA Integration for TensorFlow Models 🌐 [i18n-KO] Translated
tf_xla.mdto Korean #24904 - Optimize inference using
torch.compile
CONTRIBUTE
- How to contribute to transformers? 🌐 [i18n-KO] Translated
contributing.mdto Korean #25877 - How to add a model to 🤗 Transformers? 🌐 [i18n-KO] Translated
add_new_model.mdto Korean #24957 - How to convert a 🤗 Transformers model to TensorFlow? 🌐 [i18n-KO] Translated
add_tensorflow_model.mdto Korean #25017 - How to add a pipeline to 🤗 Transformers? 🌐 [i18n-KO] Translated
add_new_pipeline.mdto Korean #25498 - Testing 🌐 [i18n-KO] Translated
testing.mdto Korean #24900 - Checks on a Pull Request
CONCEPTUAL GUIDES
- Philosophy 🌐 [i18n-KO] Translated
philosophy.mdto Korean #25010 - Glossary
- What 🤗 Transformers can do
- How 🤗 Transformers solve tasks 🌐 [i18n-KO] Translated
tasks_explained.mdxto Korean #23844 - The Transformer model family 🌐 [i18n-KO] Translated
model_summary.mdto Korean #24625 - Summary of the tokenizers 🌐 [i18n-KO] Translated
tokenizer_summary.mdto Korean #26243 - Attention mechanisms
- Padding and truncation 🌐 [i18n-KO] Translated
pad_truncation.mdxto Korean #23823 - BERTology 🌐 [i18n-KO] Translated
bertology.mdxto Korean #23968 - Perplexity of fixed-length models 🌐 [i18n-KO] Translated
perplexity.mdxto Korean #23850 - Pipelines for webserver inference 🌐[i18n-KO] Translated pipeline_webserver.md to Korean #24828
- Model training anatomy 🌐 [i18n-KO]
model_memory_anatomy.mdto Korean #25755
Other relevant PRs along the way
- Enable easy Table of Contents editing docs: ko: complete
_toctree.yml#22581 - Added forgotten internal English anchors for
sagemaker.mdx[i18n-KO] fix: docs: ko: sagemaker anchors and_toctree.yml#22549 - Fixed anchor links for
auto_class,training🌐 [i18n-KO] Fix anchor links for docsauto_tutorial,training#22796 - Update ToC from upstream docs: ko: update
_toctree.yml#23112
Reacted by Nayeon Han, Hyeonseo Yun and Joosun Hwang
Reacted by Sylvain Gugger, Nayeon Han and Hyeonseo Yun
Activity
[-]Tranformers documentation translation to Korean[/-][+][i18n/ko] Translating docs to Korean[/+][-][i18n/ko] Translating docs to Korean[/-][+][i18n-KO] Translating docs to Korean[/+]github-actions commentedon Dec 12, 2022
This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.
Please note that issues that do not follow the contributing guidelines are likely to be ignored.
wonhyeongseo commentedon Dec 13, 2022
Hello @sgugger, may you please add the
WIPtag to this issue? Thank you so much.[-][i18n-KO] Translating docs to Korean[/-][+]🌐 [i18n-KO] Translating docs to Korean[/+]wonhyeongseo commentedon Apr 2, 2023
For contributors and PseudoLab team members, please see a PR template gist (raw) that could ease your first PR experience.
@0525hhgus, @kihoon71, @gabrielwithappy, @jungnerd, @sim-so, @HanNayeoniee, @wonhyeongseo
gabrielwithappy commentedon Apr 3, 2023
Dear @sgugger, would you add
documentlabel to this issue?I think other issues for the translation have a
documentlabel.Thank you in advance
@wonhyeongseo
I changed my PR with a new PR template. would you change
Load pretrained instances with an AutoClassto [WIP]🌐[i18n-KO] Translate autoclass_tutorial to Korean and Fix the typo of quicktour #22533gabrielwithappy commentedon Apr 3, 2023
@sgugger wow! Thank you a million! :-)
wonhyeongseo commentedon Apr 15, 2023
@sgugger
Dear HuggingFace Team,
I hope you are doing well. My name is Wonhyeong Seo from the Pseudo Lab team. As you may know, we are actively working on localizing the
huggingface/transformersrepository documentation into Korean. Our goal is to make this valuable resource more accessible to Korean-speaking users, thereby promoting the development of NLP and machine learning in Korea and beyond.We are currently in the process of applying for government sponsorship to support our localization efforts. To strengthen our application, we kindly request your permission to use the documentation's Google Analytics data to include in our reports. This data will help us demonstrate the impact of our work and the potential benefits of localizing the documentation.
Additionally, we would be grateful for any feedback or suggestions from the HuggingFace team regarding our localization project. Your insights will be invaluable in ensuring our efforts align with your vision and standards, and in fostering a successful collaboration.
Thank you for considering our request. We look forward to your response and the opportunity to work together to expand the reach of the
huggingface/transformersrepository.Best regards,
Hyunseo Yun, Kihoon Son, Gabriel Yang, Sohyun Sim,
Nayeon Han, Woojun Jung, Wonhyeong Seo
The Localization Initiative members of Pseudo Lab
LysandreJik commentedon Apr 17, 2023
Hey @wonhyeongseo, thanks for all you work on translating the documentation to Korean!
Do you mind contacting me at lysandre at hf.co so we may see how best to help you?
wonhyeongseo commentedon Apr 23, 2023
Welcome to a simple guide on how to use ChatGPT to speed up the translation process. By following these guidelines, you can create a first draft in less than an hour. Please note that it is essential to proofread your work thoroughly before sharing it with your colleagues.
(Optional) If you want to extract only the content without code blocks, tables, and redundant new lines, you can use the command
sed '/```/,/```/d' file.md | sed '/^|.*|$/d' | sed '/^$/N;/^\n$/D'. In case you are using a mobile device, you can check the link https://sed.js.org/ for using sed online.To initiate the translation process, you need to provide your sentences as input to ChatGPT. Your first prompt should look like this:
After submitting the first prompt, you can use the following prefix for the next ten prompts:
Note that after ten prompts, you must remind ChatGPT of the task if you are not using LangChain.
By following these guidelines, you can create a first draft of your translation in a shorter time frame. However, it is crucial to emphasize that the quality of the final output depends on the accuracy of the input and the proofreading process.
PS: Please note that we do not have a Korean LLM that can automate the proofreading process at the moment. However, in July, Naver plans to launch their HyperCLOVA Korean LLM model, which might automate the entire process. We are optimistic that our government proposal will be accepted, allowing us to increase our talent pool and work towards achieving a more automated translation process with them.
wonhyeongseo commentedon Apr 24, 2023
Dear @LysandreJik ,
I hope you are doing well. I wanted to inform you that I have sent an email with the subject line "[i18n-KO] Request for Collaboration: Hugging Face Mentorship Program." Whenever you have a moment, please take a look and provide a response. Thank you so much for your interest to this collaboration. If you have any questions, please don't hesitate to contact me.
Best regards,
Wonhyeong Seo
wonhyeongseo commentedon Apr 28, 2023
@gabrielwithappy @sim-so @jungnerd @HanNayeoniee @0525hhgus @kihoon71
From this merge of
model_sharing.mdx#22991 , I learned that we don't have togit rebase -ias other open source libraries mandate. Therefore, I propose we commit in 4 steps like this:docs: ko: <file-name>- As we always do for the first commit. Copy the initial English file underkoand edit TOC: both external and (soon-to-be-automated) internal.feat: [nmt|manual] draft- Machine-translate the entire file with: dedicated translators, prompts, or any kind of automation. You may choose to translate manually, and that is ok as long as you specify it in the commit message.fix: manual edits- Proofread the draft thoroughly.fix: resolve suggestions- Get reviews and resolve suggestions.With this, it will be easier for collaborators to see the original English and your changes side by side. Not to mention, we can use diffs as pre-training data for the in-house rlhf translation model.
@ArthurZucker @sgugger , when merging a PR, how is the main commit message decided if there are multiple commits? Do you have to manually write it, or is the first commit message of the PR selected? Thank you for your insights and continued support. Much love from Korea 🇰🇷💖💕🙏
15 remaining items
wo0jun commentedon Jul 24, 2025
Hi, @taemincode . Like steven said,
(번역중)actually means we are planning to translate.You can check the docs translation status at this link, and please refer to the translation contribution guide we created(link)!
If you have any trouble with the translation, feel free to reach out!
taemincode commentedon Jul 24, 2025
@jungnerd, thanks for sharing these helpful links with me! The contribution guide makes the translation much easier 😊
wo0jun commentedon Aug 23, 2026
Hi @stevhliu , it’s been a while!
The Transformers documentation has changed significantly since this issue was opened, so @junejae , @harheem , @sim-so, @Jwaminju , @4N3MONE and I are planning to organize a sprint to update the Korean translations.
Since this thread has grown quite long and its checklist may no longer fully reflect the current documentation structure, would it be okay to close this issue and open a new one with an updated checklist to coordinate the sprint and track our progress?
If so, I’d be happy to create the new issue and link back to this one for context and history.
stevhliu commentedon Aug 25, 2026
Hey @jungnerd !
I'm experimenting with a translation feature in huggingface/doc-builder#813 that automatically translates the docs and keeps them in sync which is increasingly becoming a challenge for us. The Korean docs have the best coverage, but for many other languages without such a strong and organized community of contributors, this is quite difficult! This should make it a lot easier to maintain for everyone.
I'd love to have your feedback on this, and I appreciate all the effort and time the Korean team has put in to translating the docs ❤️
wo0jun commentedon Aug 31, 2026
Hi @stevhliu, thanks for sharing this and for the kind words about the Korean team! 🤗
I looked through huggingface/doc-builder#813. It seems the initial test focuses on Japanese, while
validate.pynotes that no one on the team reads Japanese. Since the Korean docs have strong human-translated coverage and native contributors, could we also test the pipeline with Korean?We’d be happy to review the actual translation quality and contribute Korean support, glossary guidance, and language-specific fixes. Please let us know if this would be helpful and how we could contribute!
LeonxLJX commentedon Sep 1, 2026
I'd like to take this one (
🌐 [i18n-KO] Translating docs to Korean). I'll dig into the root cause and follow up with a PR shortly. (claiming via @LeonxLJX)LeonxLJX commentedon Sep 1, 2026
I'd like to take this one (
🌐 [i18n-KO] Translating docs to Korean). I'll look into the root cause and follow up with a PR. (claiming via @LeonxLJX)LeonxLJX commentedon Sep 1, 2026
Hi! I'd like to translate docs to Korean (i18n-KO). Plan: translate the marked pages, keep formatting consistent, link from the i18n index. May I be assigned?
stevhliu commentedon Sep 1, 2026
That would be amazing @wo0jun, thanks so much for the offer!
I'll give you a ping when we add Korean and have something ready for you to review :)
wo0jun commentedon Sep 1, 2026
That sounds great, @stevhliu ! One reason I suggested Korean is that our native Korean contributors can check not only whether the pipeline runs successfully, but also whether it actually produces high-quality translations.
We could apply the pipeline to the Korean docs, evaluate the output directly, and identify concrete translation-quality or language-specific issues.
Would it be okay for us to try this and, if we find areas where the pipeline could be improved, contribute those improvements back to doc-builder?
stevhliu commentedon Sep 1, 2026
Yes feel free to try it, especially if you maybe think a different model would be better! I'm currently using
google/gemma-4-26B-A4B-itas a general model, but if you know of a smaller fine-tuned Korean language model that would also be quite nice I thinkLeonxLJX commentedon Sep 2, 2026
Thanks @stevhliu, I'll take this one.
On your model question — for translating technical docs I'd argue fluency isn't the bottleneck, terminology consistency is. In this repo's docs, terms like pipeline, checkpoint, attention mask, and tokenizer are proper nouns: a general-purpose model will happily render them several different ways across pages, and that churn is far more costly to review than an awkward sentence from a smaller model.
So my suggestion, rather than swapping in a smaller tuned model outright:
{{ }}placeholders are byte-identical to the source, since those must never be translated.A smaller tuned model (e.g. an NLLB-sized multilingual checkpoint) is a reasonable fallback if throughput becomes the constraint, but I'd only reach for it if review bandwidth, not translation quality, is the limiting factor.
Before I start: are there existing i18n guidelines or a glossary for the KO translation I should follow? And would you like me to scope this to a specific set of pages first so we can calibrate on one round?
qgallouedec commentedon Oct 21, 2026
Hey @wo0jun, sorry for the slow answer on your close question. Agreed, let's close this one.
The checklist still lists pages that no longer exist in the English docs (TF/TFLite export, agents, benchmarks, BERTology), while the Korean toctree now tracks the real gaps as
in_translationentries.Huge thanks to @wonhyeongseo and the whole Pseudo Lab team, Korean has the best coverage of any translation. Please go ahead and open the fresh sprint tracker you proposed and link back here. The translation pipeline discussion can continue in huggingface/doc-builder#813.
mjkim0219 commentedon Oct 23, 2026
안녕하세요! generation_features.md 번역을 맡겠습니다.