
# 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}}
}
```
{"language": ["ko"]}
transformers pytorch bert fill-mask ko autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us 


# 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

## Data

The training data for this model is expanded from those of KR-BERT-MEDIUM, texts from Korean Wikipedia, general news articles, legal texts crawled from the National Law Information Center and Korean Comments dataset. 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.



# 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 ## Data

The training data for this model is expanded from those of KR-BERT-MEDIUM, texts from Korean Wikipedia, general news articles, legal texts crawled from the National Law Information Center and Korean Comments dataset. 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.
TAGS
#transformers #pytorch #bert #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us 
 # 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 ## Data

The training data for this model is expanded from those of KR-BERT-MEDIUM, texts from Korean Wikipedia, general news articles, legal texts crawled from the National Law Information Center and Korean Comments dataset. 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.
passage: TAGS
#transformers #pytorch #bert #fill-mask #ko #autotrain_compatible #endpoints_compatible #region-us 
# 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## Data

The training data for this model is expanded from those of KR-BERT-MEDIUM, texts from Korean Wikipedia, general news articles, legal texts crawled from the National Law Information Center and Korean Comments dataset. 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.