README.md

---
language:
- ko

---

# KR-FinBert & KR-FinBert-SC

Much progress has been made in the NLP (Natural Language Processing) field, with numerous studies showing that domain adaptation using small-scale corpus and fine-tuning with labeled data is effective for overall performance improvement. 
we proposed KR-FinBert for the financial domain by further pre-training it on a financial corpus and fine-tuning it for sentiment analysis. As many studies have shown, the performance improvement through adaptation and conducting the downstream task was also clear in this experiment. 

![KR-FinBert](https://huggingface.co/snunlp/KR-FinBert/resolve/main/images/KR-FinBert.png)

## Data

The training data for this model is expanded from those of **[KR-BERT-MEDIUM](https://huggingface.co/snunlp/KR-Medium)**, texts from Korean Wikipedia, general news articles, legal texts crawled from the National Law Information Center and [Korean Comments dataset](https://www.kaggle.com/junbumlee/kcbert-pretraining-corpus-korean-news-comments). For the transfer learning, **corporate related economic news articles from 72 media sources** such as the Financial Times, The Korean Economy Daily, etc and **analyst reports from 16 securities companies** such as Kiwoom Securities, Samsung Securities, etc are added. Included in the dataset is 440,067 news titles with their content and 11,237 analyst reports. **The total data size is about 13.22GB.** For mlm training, we split the data line by line and **the total no. of lines is 6,379,315.**
KR-FinBert is trained for 5.5M steps with the maxlen of 512, training batch size of 32, and learning rate of 5e-5, taking 67.48 hours to train the model using NVIDIA TITAN XP.


## Citation
```
@misc{kr-FinBert,
  author = {Kim, Eunhee and Hyopil Shin},
  title = {KR-FinBert: KR-BERT-Medium Adapted With Financial Domain Data},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://huggingface.co/snunlp/KR-FinBert}}
}
```