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102 changes: 102 additions & 0 deletions README.md
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# KF-DeBERTa
카카오뱅크 & 에프엔가이드에서 학습한 금융 도메인 특화 언어모델을 공개합니다.

- [[huggingface][kakaobank/kf-deberta-base]](https://huggingface.co/kakaobank/kf-deberta-base)

## Model description
* KF-DeBERTa는 범용 도메인 말뭉치와 금융 도메인 말뭉치를 함께 학습한 언어모델 입니다.
* 모델 아키텍쳐는 [DeBERTa-v2](https://github.com/microsoft/DeBERTa#whats-new-in-v2)를 기반으로 학습하였습니다.
* ELECTRA의 RTD를 training objective로 사용한 DeBERTa-v3는 일부 task(KLUE-RE, WoS, Retrieval)에서 상당히 낮은 성능을 확인하여 최종 아키텍쳐는 DeBERTa-v2로 결정하였습니다.
* 범용 도메인 및 금융 도메인 downstream task에서 모두 우수한 성능을 확인하였습니다.
* 금융 도메인 downstream task의 철저한 성능검증을 위해 다양한 데이터셋을 통해 검증을 수행하였습니다.
* 범용 도메인 및 금융 도메인에서 기존 언어모델보다 더 나은 성능을 보여줬으며 특히 KLUE Benchmark에서는 RoBERTa-Large보다 더 나은 성능을 확인하였습니다.

## Usage
```python3
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("kakaobank/kf-deberta-base")
tokenizer = AutoTokenizer.from_pretrained("kakaobank/kf-deberta-base")

text = "카카오뱅크와 에프엔가이드가 금융특화 언어모델을 공개합니다."
tokens = tokenizer.tokenize(text)
print(tokens)

inputs = tokenizer(text, return_tensors="pt")
model_output = model(**inputs)
print(model_output)
```

## Benchmark
* 모든 task는 아래와 같은 기본적인 hyperparameter search만 수행하였습니다.
* batch size: {16, 32}
* learning_rate: {1e-5, 3e-5, 5e-5}
* weight_decay: {0, 0.01}
* warmup_proportion: {0, 0.1}

**KLUE Benchmark**

| Model | YNAT | KLUE-ST | KLUE-NLI | KLUE-NER | KLUE-RE | KLUE-DP | KLUE-MRC | WoS | AVG |
|:--------------------:|:----------------:|:----------------------:|:------------:|:---------------------------------:|:-----------------------------:|:----------------------:|:-------------------------:|:----------------------:|:----------------:|
| | F1 | Pearsonr/F1 | ACC | F1-Entity/F1-Char | F1-micro/AUC | UAS/LAS | EM/ROUGE | JGA/F1-S | |
| mBERT (Base) | 82.64 | 82.97/75.93 | 72.90 | 75.56/88.81 | 58.39/56.41 | 88.53/86.04 | 49.96/55.57 | 35.27/88.60 | 71.26 |
| XLM-R (Base) | 84.52 | 88.88/81.20 | 78.23 | 80.48/92.14 | 57.62/57.05 | 93.12/87.23 | 26.76/53.36 | 41.54/89.81 | 72.28 |
| XLM-R (Large) | 87.30 | 93.08/87.17 | 86.40 | 82.18/93.20 | 58.75/63.53 | 92.87/87.82 | 35.23/66.55 | 42.44/89.88 | 76.17 |
| KR-BERT (Base) | 85.36 | 87.50/77.92 | 77.10 | 74.97/90.46 | 62.83/65.42 | 92.87/87.13 | 48.95/58.38 | 45.60/90.82 | 74.67 |
| KoELECTRA (Base) | 85.99 | 93.14/85.89 | 86.87 | 86.06/92.75 | 62.67/57.46 | 90.93/87.07 | 59.54/65.64 | 39.83/88.91 | 77.34 |
| KLUE-BERT (Base) | 86.95 | 91.01/83.44 | 79.87 | 83.71/91.17 | 65.58/68.11 | 93.07/87.25 | 62.42/68.15 | 46.72/91.59 | 78.50 |
| KLUE-RoBERTa (Small) | 85.95 | 91.70/85.42 | 81.00 | 83.55/91.20 | 61.26/60.89 | 93.47/87.50 | 58.25/63.56 | 46.65/91.50 | 77.28 |
| KLUE-RoBERTa (Base) | 86.19 | 92.91/86.78 | 86.30 | 83.81/91.09 | 66.73/68.11 | 93.75/87.77 | 69.56/74.64 | 47.41/91.60 | 80.48 |
| KLUE-RoBERTa (Large) | 85.88 | 93.20/86.13 | **89.50** | 84.54/91.45 | **71.06**/73.33 | 93.84/87.93 | **75.26**/**80.30** | 49.39/92.19 | 82.43 |
| KF-DeBERTa (Base) | **<u>87.51</u>** | **<u>93.24/87.73</u>** | <u>88.37</u> | **<u>89.17</u>**/**<u>93.30</u>** | <u>69.70</u>/**<u>75.07</u>** | **<u>94.05/87.97</u>** | <u>72.59</u>/<u>78.08</u> | **<u>50.21/92.59</u>** | **<u>82.83</u>** |

* 굵은글씨는 모든 모델중 가장높은 점수이며, 밑줄은 base 모델 중 가장 높은 점수입니다.

**금융도메인 벤치마크**
| Model | FN-Sentiment (v1) | FN-Sentiment (v2) | FN-Adnews | FN-NER | KorFPB | KorFiQA-SA | KorHeadline | Avg (FiQA-SA 제외) |
|:-------------------:|:-----------------:|:-----------------:|:---------:|:---------:|:---------:|:----------:|:-----------:|:-----------------:|
| | ACC | ACC | ACC | F1-micro | ACC | MSE | Mean F1 | |
| KLUE-RoBERTa (Base) | 98.26 | 91.21 | 96.34 | 90.31 | 90.97 | 0.0589 | 81.11 | 94.03 |
| KoELECTRA (Base) | 98.26 | 90.56 | 96.98 | 89.81 | 92.36 | 0.0652 | 80.69 | 93.90 |
| KF-DeBERTa (Base) | **99.36** | **92.29** | **97.63** | **91.80** | **93.47** | **0.0553** | **82.12** | **95.27** |

* **FN-Sentiment**: 금융도메인 감성분석
* **FN-Adnews**: 금융도메인 광고성기사 분류
* **FN-NER**: 금융도메인 개체명인식
* **KorFPB**: FinancialPhraseBank 번역데이터
* Cite: ```Malo, Pekka, et al. "Good debt or bad debt: Detecting semantic orientations in economic texts." Journal of the Association for Information Science and Technology 65.4 (2014): 782-796.```
* **KorFiQA-SA**: FiQA-SA 번역데이터
* Cite: ```Maia, Macedo & Handschuh, Siegfried & Freitas, Andre & Davis, Brian & McDermott, Ross & Zarrouk, Manel & Balahur, Alexandra. (2018). WWW'18 Open Challenge: Financial Opinion Mining and Question Answering. WWW '18: Companion Proceedings of the The Web Conference 2018. 1941-1942. 10.1145/3184558.3192301.```
* **KorHeadline**: Gold Commodity News and Dimensions 번역데이터
* Cite: ```Sinha, A., & Khandait, T. (2021, April). Impact of News on the Commodity Market: Dataset and Results. In
Future of Information and Communication Conference (pp. 589-601). Springer, Cham.```


**범용도메인 벤치마크**
| Model | NSMC | PAWS | KorNLI | KorSTS | KorQuAD | Avg (KorQuAD 제외) |
|:-------------------:|:---------:|:---------:|:---------:|:---------:|:---------------:|:----------------:|
| | ACC | ACC | ACC | spearman | EM/F1 | |
| KLUE-RoBERTa (Base) | 90.47 | 84.79 | 81.65 | 84.40 | 86.34/94.40 | 85.33 |
| KoELECTRA (Base) | 90.63 | 84.45 | 82.24 | 85.53 | 84.83/93.45 | 85.71 |
| KF-DeBERTa (Base) | **91.36** | **86.14** | **84.54** | **85.99** | **86.60/95.07** | **87.01** |



## License
KF-DeBERTa의 소스코드 및 모델은 MIT 라이선스 하에 공개되어 있습니다.
라이선스 전문은 [MIT 파일](LICENSE)에서 확인할 수 있습니다.
모델의 사용으로 인해 발생한 어떠한 손해에 대해서도 당사는 책임을 지지 않습니다.

## Citation
```
@proceedings{jeon-etal-2023-kfdeberta,
title = {KF-DeBERTa: Financial Domain-specific Pre-trained Language Model},
author = {Eunkwang Jeon, Jungdae Kim, Minsang Song, and Joohyun Ryu},
booktitle = {Proceedings of the 35th Annual Conference on Human and Cognitive Language Technology},
moth = {oct},
year = {2023},
publisher = {Korean Institute of Information Scientists and Engineers},
url = {http://www.hclt.kr/symp/?lnb=conference},
pages = {143--148},
}
```
12 changes: 12 additions & 0 deletions example.py
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from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("kakaobank/kf-deberta-base")
tokenizer = AutoTokenizer.from_pretrained("kakaobank/kf-deberta-base")

text = "카카오뱅크와 에프엔가이드가 금융특화 언어모델을 공개합니다."
tokens = tokenizer.tokenize(text)
print(tokens)

inputs = tokenizer(text, return_tensors="pt")
model_output = model(**inputs)
print(model_output)
2 changes: 2 additions & 0 deletions requirements.txt
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torch
transformers

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