kotan 1.0.0

Korean Translation and Augmentation with fine-tuned NLLB

KoTAN: Korean Translation and Augmentation with fine-tuned NLLB

A KoTAN package can exercise korean data augmentation task and en->ko, ko->en translation task. In case of translation model, we are fine-tuning facebook NLLB model. About data augmentation task, we processe backtranslation task. In addition, we also provide speech-style conversion options.

Package install

• torch=2.0.0 (cuda 12.0) and python>=3.8 are avaliable.
• You can install the package with below command.

pip3 install kotan

Usage

• You can use KoTAN with below command.
• Import package.

>>> from kotan import KoTAN

• Avaliable tasks

>>> KoTAN.available_tasks()

• Avaliable languages

>>> KoTAN.available_lang()

• Data augmentation options

>>> KoTAN.available_level()

• origin: Before fine-tuning nllb model.
• fine: After fine-tuning nllb model.

• Speech-style conversion options

>>> KoTAN.available_style()

• formal: 문어체
• informal: 구어체
• android: 안드로이드
• azae: 아재
• chat: 채팅
• choding: 초등학생
• emoticon: 이모티콘
• enfp: enfp
• gentle: 신사
• halbae: 할아버지
• halmae: 할머니
• joongding: 중학생
• king: 왕
• naruto: 나루토
• seonbi: 선비
• sosim: 소심한
• translator: 번역기

Translation

>>> from kotan import KoTAN
>>> mt = KoTAN(task="translation", tgt="en")
>>> inputs = ['나는 온 세상 사람들이 행복해지길 바라', '나는 선한 영향력을 펼치는 사람이 되고 싶어']
>>> mt.predict(inputs)

Data Augmentation

Origin nllb model (before fine-tuning)

>>> from kotan import KoTAN
>>> aug = KoTAN(task="augmentation", level="origin")
>>> inputs = ['나는 온 세상 사람들이 행복해지길 바라', '나는 선한 영향력을 펼치는 사람이 되고 싶어']
>>> aug.predict(inputs)

Fine-tuned nllb model with Aihub datasets.

>>> from kotan import KoTAN
>>> aug = KoTAN(task="augmentation", level="fine")
>>> inputs=['나는 온 세상 사람들이 행복해지길 바라', '나는 선한 영향력을 펼치는 사람이 되고 싶어']
>>> aug.predict(inputs)

Apply style-convert option.

>>> from kotan import KoTAN
>>> aug = KoTAN(task="augmentation", style="chat")
>>> inputs=['나는 온 세상 사람들이 행복해지길 바라', '나는 선한 영향력을 펼치는 사람이 되고 싶어']
>>> aug.predict(inputs)

Speech-style conversion

>>> from kotan import KoTAN
>>> style = KoTAN(task="augmentation", style="king")
>>> inputs=['나는 온 세상 사람들이 행복해지길 바라', '나는 선한 영향력을 펼치는 사람이 되고 싶어']
>>> style.predict(inputs)

Demo

Huggingface KoTAN space

Citation

@misc{KoTAN,
  author       = {Juhwan Lee and Jisu Kim},
  title        = {KoTAN: Korean Translation and Augmentation with fine-tuned NLLB},
  howpublished = {\url{https://github.com/KoJLabs/KoTAN}},
  year         = {2023},
}

Contributors

Jisu, Kim, Juhwan, Lee

License

KoTAN project follow Apache License 2.0 lisence