## KorFinASC-XLM-RoBERTa
Pretrained XLM-RoBERTA-Large transfered to the Finance domain on Korean Language.   
See [paper](https://arxiv.org/abs/2301.03136) for more details.

## Data
KorFinASC-XLM-RoBERTa is extensively trained on multiple datasets including KorFin-ASC, [Ko-FinSA](https://github.com/ukairia777/finance_sentiment_corpus), [Ko-ABSA](http://www.drbr.or.kr/datasets/view/?seq=20) and [ModuABSA](https://rlkujwkk7.toastcdn.net/73/NIKL_ABSA_2022_COMPETITION_v1.0.pdf).

## How to use.

```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

>>> tokenizer = AutoTokenizer.from_pretrained("amphora/KorFinASC-XLM-RoBERTa")
>>> model = AutoModelForSequenceClassification.from_pretrained("amphora/KorFinASC-XLM-RoBERTa")

>>> input_str = "장 전체가 폭락한 가운데 삼성전자만 상승세를 이어갔다. </s> 삼성전자"
>>> input = tokenizer(input_str, return_tensors='pt')
>>> output = model.generate(**input, max_length=20)

```

{"language": "ko", "license": ["apache-2.0"], "tags": ["XLM-RoBERTa", "KorFin-ASC", "financial-sentiment-analysis", "sentiment-analysis"], "widget": [{"text": "\uc7a5 \uc804\uccb4\uac00 \ud3ed\ub77d\ud55c \uac00\uc6b4\ub370 \uc0bc\uc131\uc804\uc790\ub9cc \uc0c1\uc2b9\uc138\ub97c \uc774\uc5b4\uac14\ub2e4. </s> \uc0bc\uc131\uc804\uc790"}]}
transformers pytorch xlm-roberta text-classification XLM-RoBERTa KorFin-ASC financial-sentiment-analysis sentiment-analysis ko arxiv:2301.03136 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #text-classification #XLM-RoBERTa #KorFin-ASC #financial-sentiment-analysis #sentiment-analysis #ko #arxiv-2301.03136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 

## KorFinASC-XLM-RoBERTa
Pretrained XLM-RoBERTA-Large transfered to the Finance domain on Korean Language.   
See paper for more details.

## Data
KorFinASC-XLM-RoBERTa is extensively trained on multiple datasets including KorFin-ASC, Ko-FinSA, Ko-ABSA and ModuABSA.

## How to use.



## KorFinASC-XLM-RoBERTa
Pretrained XLM-RoBERTA-Large transfered to the Finance domain on Korean Language.   
See paper for more details. ## Data
KorFinASC-XLM-RoBERTa is extensively trained on multiple datasets including KorFin-ASC, Ko-FinSA, Ko-ABSA and ModuABSA. ## How to use.
TAGS
#transformers #pytorch #xlm-roberta #text-classification #XLM-RoBERTa #KorFin-ASC #financial-sentiment-analysis #sentiment-analysis #ko #arxiv-2301.03136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 ## KorFinASC-XLM-RoBERTa
Pretrained XLM-RoBERTA-Large transfered to the Finance domain on Korean Language.   
See paper for more details. ## Data
KorFinASC-XLM-RoBERTa is extensively trained on multiple datasets including KorFin-ASC, Ko-FinSA, Ko-ABSA and ModuABSA. ## How to use.
TAGS
#transformers #pytorch #xlm-roberta #text-classification #XLM-RoBERTa #KorFin-ASC #financial-sentiment-analysis #sentiment-analysis #ko #arxiv-2301.03136 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
## KorFinASC-XLM-RoBERTa
Pretrained XLM-RoBERTA-Large transfered to the Finance domain on Korean Language.   
See paper for more details.## Data
KorFinASC-XLM-RoBERTa is extensively trained on multiple datasets including KorFin-ASC, Ko-FinSA, Ko-ABSA and ModuABSA.## How to use.