
# Kconvo-roberta: Korean conversation RoBERTa ([github](https://github.com/HeoTaksung/Domain-Robust-Retraining-of-Pretrained-Language-Model))
- There are many PLMs (Pretrained Language Models) for Korean, but most of them are trained with written language.
- Here, we introduce a retrained PLM for prediction of Korean conversation data where we use verbal data for training.

## Usage
```python
# Kconvo-roberta
from transformers import RobertaTokenizerFast, RobertaModel

tokenizer_roberta = RobertaTokenizerFast.from_pretrained("yeongjoon/Kconvo-roberta")
model_roberta = RobertaModel.from_pretrained("yeongjoon/Kconvo-roberta")
```

-----------------
## Domain Robust Retraining of Pretrained Language Model

- Kconvo-roberta uses [klue/roberta-base](https://huggingface.co/klue/roberta-base) as the base model and retrained additionaly with the conversation dataset.
- The retrained dataset was collected through the [National Institute of the Korean Language](https://corpus.korean.go.kr/request/corpusRegist.do) and [AI-Hub](https://www.aihub.or.kr/aihubdata/data/list.do?pageIndex=1&currMenu=115&topMenu=100&dataSetSn=&srchdataClCode=DATACL001&srchOrder=&SrchdataClCode=DATACL002&searchKeyword=&srchDataRealmCode=REALM002&srchDataTy=DATA003), and the collected dataset is as follows.

```
- National Institute of the Korean Language
   * 온라인 대화 말뭉치 2021
   * 일상 대화 말뭉치 2020
   * 구어 말뭉치
   * 메신저 말뭉치

- AI-Hub
   * 온라인 구어체 말뭉치 데이터
   * 상담 음성
   * 한국어 음성
   * 자유대화 음성(일반남여)
   * 일상생활 및 구어체 한-영 번역 병렬 말뭉치 데이터
   * 한국인 대화음성
   * 감성 대화 말뭉치
   * 주제별 텍스트 일상 대화 데이터
   * 용도별 목적대화 데이터
   * 한국어 SNS
```

{"language": ["ko"], "license": "mit"}
transformers pytorch roberta fill-mask ko license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #ko #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# Kconvo-roberta: Korean conversation RoBERTa (github)
- There are many PLMs (Pretrained Language Models) for Korean, but most of them are trained with written language.
- Here, we introduce a retrained PLM for prediction of Korean conversation data where we use verbal data for training.

## Usage


-----------------
## Domain Robust Retraining of Pretrained Language Model

- Kconvo-roberta uses klue/roberta-base as the base model and retrained additionaly with the conversation dataset.
- The retrained dataset was collected through the National Institute of the Korean Language and AI-Hub, and the collected dataset is as follows.



# Kconvo-roberta: Korean conversation RoBERTa (github)
- There are many PLMs (Pretrained Language Models) for Korean, but most of them are trained with written language.
- Here, we introduce a retrained PLM for prediction of Korean conversation data where we use verbal data for training. ## Usage


----------------- ## Domain Robust Retraining of Pretrained Language Model

- Kconvo-roberta uses klue/roberta-base as the base model and retrained additionaly with the conversation dataset.
- The retrained dataset was collected through the National Institute of the Korean Language and AI-Hub, and the collected dataset is as follows.
TAGS
#transformers #pytorch #roberta #fill-mask #ko #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # Kconvo-roberta: Korean conversation RoBERTa (github)
- There are many PLMs (Pretrained Language Models) for Korean, but most of them are trained with written language.
- Here, we introduce a retrained PLM for prediction of Korean conversation data where we use verbal data for training. ## Usage


----------------- ## Domain Robust Retraining of Pretrained Language Model

- Kconvo-roberta uses klue/roberta-base as the base model and retrained additionaly with the conversation dataset.
- The retrained dataset was collected through the National Institute of the Korean Language and AI-Hub, and the collected dataset is as follows.
TAGS
#transformers #pytorch #roberta #fill-mask #ko #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# Kconvo-roberta: Korean conversation RoBERTa (github)
- There are many PLMs (Pretrained Language Models) for Korean, but most of them are trained with written language.
- Here, we introduce a retrained PLM for prediction of Korean conversation data where we use verbal data for training.## Usage


-----------------## Domain Robust Retraining of Pretrained Language Model

- Kconvo-roberta uses klue/roberta-base as the base model and retrained additionaly with the conversation dataset.
- The retrained dataset was collected through the National Institute of the Korean Language and AI-Hub, and the collected dataset is as follows.