🦅 Pretrained BigBird Model for Korean (up to 4096 tokens)

Last update: Dec 14, 2022

Overview

Pretrained BigBird Model for Korean

What is BigBird • How to Use • Pretraining • Evaluation Result • Docs • Citation

한국어 | English

What is BigBird?

BigBird: Transformers for Longer Sequences에서 소개된 sparse-attention 기반의 모델로, 일반적인 BERT보다 더 긴 sequence를 다룰 수 있습니다.

🦅 Longer Sequence - 최대 512개의 token을 다룰 수 있는 BERT의 8배인 최대 4096개의 token을 다룸

⏱️ Computational Efficiency - Full attention이 아닌 Sparse Attention을 이용하여 O(n²)에서 O(n)으로 개선

How to Use

🤗 Huggingface Hub에 업로드된 모델을 곧바로 사용할 수 있습니다:)
일부 이슈가 해결된 transformers>=4.11.0 사용을 권장합니다. (MRC 이슈 관련 PR)
BigBirdTokenizer 대신에 BertTokenizer 를 사용해야 합니다. (AutoTokenizer 사용시 BertTokenizer가 로드됩니다.)
자세한 사용법은 BigBird Tranformers documentation을 참고해주세요.

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("monologg/kobigbird-bert-base")  # BigBirdModel
tokenizer = AutoTokenizer.from_pretrained("monologg/kobigbird-bert-base")  # BertTokenizer

Pretraining

자세한 내용은 [Pretraining BigBird] 참고

	Hardware	Max len	LR	Batch	Train Step	Warmup Step
KoBigBird-BERT-Base	TPU v3-8	4096	1e-4	32	2M	20k

모두의 말뭉치, 한국어 위키, Common Crawl, 뉴스 데이터 등 다양한 데이터로 학습
ITC (Internal Transformer Construction) 모델로 학습 (ITC vs ETC)

Evaluation Result

1. Short Sequence (<=512)

자세한 내용은 [Finetune on Short Sequence Dataset] 참고

	NSMC (acc)	KLUE-NLI (acc)	KLUE-STS (pearsonr)	Korquad 1.0 (em/f1)	KLUE MRC (em/rouge-w)
KoELECTRA-Base-v3	91.13	86.87	93.14	85.66 / 93.94	59.54 / 65.64
KLUE-RoBERTa-Base	91.16	86.30	92.91	85.35 / 94.53	69.56 / 74.64
KoBigBird-BERT-Base	91.18	87.17	92.61	87.08 / 94.71	70.33 / 75.34

2. Long Sequence (>=1024)

자세한 내용은 [Finetune on Long Sequence Dataset] 참고

	TyDi QA (em/f1)	Korquad 2.1 (em/f1)	Fake News (f1)	Modu Sentiment (f1-macro)
KLUE-RoBERTa-Base	76.80 / 78.58	55.44 / 73.02	95.20	42.61
KoBigBird-BERT-Base	79.13 / 81.30	67.77 / 82.03	98.85	45.42

Docs

Citation

KoBigBird를 사용하신다면 아래와 같이 인용해주세요.

@software{jangwon_park_2021_5654154,
  author       = {Jangwon Park and Donggyu Kim},
  title        = {KoBigBird: Pretrained BigBird Model for Korean},
  month        = nov,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.5654154},
  url          = {https://doi.org/10.5281/zenodo.5654154}
}

Contributors

Jangwon Park and Donggyu Kim

Acknowledgements

KoBigBird는 Tensorflow Research Cloud (TFRC) 프로그램의 Cloud TPU 지원으로 제작되었습니다.

또한 멋진 로고를 제공해주신 Seyun Ahn님께 감사를 전합니다.

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Comments

Pretraining Epoch 질문
Checklist

[x] I've searched the project's issues

❓ Question

안녕하세요 저는 현재 친구들과 함께 4096 토큰을 입력받아 요약 태스크를 수행할 수 있는 모델을 만들고 있습니다. 처음엔 빅버드 + 버트 조합으로 해보려고 했는데, 이미 monologg 님께서 만들어주셨더라구요 ㅎㅎ 그래서 롱포머 + 바트 + 페가수스 조합으로 학습을 진행하려 하고 있습니다. pretrained된 KoBart를 기반으로 어텐션을 롱포머로 바꾼 후, 페가수스 task를 수행하는 구조로 되어 있습니다.

현재 13GB의 데이터를 모아서 전처리와 데이터로더 작성, 모델 코드까지는 완료한 상태입니다. 이번 주 내로 학습을 진행하려 하고 있습니다.

저희가 가진 GPU로는 대략 이틀이면 1 에포크를 돌 수 있을 것 같은데, monologg님께서는 KoBirBird 모델 개발 시 에포크를 얼마나 도셨는지 여쭤보고 싶습니다.

아무래도 pretrained 된 모델을 가져다 쓰다보니 에포크를 많이 돌 필요는 없을 것 같은데, 기준점으로 삼고 싶어서요!

말이 길어졌는데 요약하자면, KoBirBird 학습 시 에포크를 얼마나 주셨는지 궁금합니다. 또한, 그 기준은 무엇으로 삼으셨는지도 궁금합니다.
question
opened by KimJaehee0725 2
Specific information about this model.
Checklist

[ x ] I've searched the project's issues

❓ Question

You mentioned "모두의 말뭉치, 한국어 위키, Common Crawl, 뉴스 데이터 등 다양한 데이터로 학습" and I want to know the size of total corpus for pre-training.

Also I want to know the vocab size of this model.

📎 Additional context
question
opened by midannii 2
Fix some minors

Description

코드와 주석 등을 읽다가 보인 작은 오타 등을 수정했습니다

다양한 노하우를 아낌없이 공유해주신 @monologg , @donggyukimc 에게 감사의 말씀드립니다.

이후에는 GPU 환경에서 finetuning을 테스트해 볼 예정입니다 고맙습니다.

Related Issue
chore

opened by sackoh 0

Releases(v1.0.0)

v1.0.0(Nov 8, 2021)

Initial release for KoBigBird - Pretrained BigBird Model for Korean
Source code(tar.gz)
Source code(zip)

Owner

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🦅 Pretrained BigBird Model for Korean (up to 4096 tokens)

Related tags

Overview

Pretrained BigBird Model for Korean

What is BigBird?

How to Use

Pretraining

Evaluation Result

1. Short Sequence (<=512)

2. Long Sequence (>=1024)

Docs

Citation

Contributors

Acknowledgements

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