

# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)

## Overview

- Language model: xlm-roberta-large
- Fine-tune: [deepset/xlm-roberta-large-squad2](https://huggingface.co/deepset/xlm-roberta-large-squad2)
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: [mailong25/bert-vietnamese-question-answering](https://github.com/mailong25/bert-vietnamese-question-answering/tree/master/dataset)
- Training data: train-v2.0.json (SQuAD 2.0 format)
- Eval data: dev-v2.0.json (SQuAD 2.0 format)
- Infrastructure: 1x Tesla P100 (Google Colab)

## Performance

Evaluated on dev-v2.0.json
```
  exact: 136 / 141
  f1: 0.9692671394799054
```

Evaluated on Vietnamese XQuAD: [xquad.vi.json](https://github.com/deepmind/xquad/blob/master/xquad.vi.json)
```
  exact: 604 / 1190
  f1: 0.7224454217571596
 ```

## Author

An Pham (ancs21.ps [at] gmail.com)

## License

MIT
{"language": "vi", "license": "mit", "tags": ["vi", "xlm-roberta"], "metrics": ["f1", "em"], "widget": [{"text": "To\u00e0 nh\u00e0 n\u00e0o cao nh\u1ea5t Vi\u1ec7t Nam?", "context": "Landmark 81 l\u00e0 m\u1ed9t to\u00e0 nh\u00e0 ch\u1ecdc tr\u1eddi trong t\u1ed5 h\u1ee3p d\u1ef1 \u00e1n Vinhomes T\u00e2n C\u1ea3ng, m\u1ed9t d\u1ef1 \u00e1n c\u00f3 t\u1ed5ng m\u1ee9c \u0111\u1ea7u t\u01b0 40.000 t\u1ef7 \u0111\u1ed3ng, do C\u00f4ng ty C\u1ed5 ph\u1ea7n \u0110\u1ea7u t\u01b0 x\u00e2y d\u1ef1ng T\u00e2n Li\u00ean Ph\u00e1t thu\u1ed9c Vingroup l\u00e0m ch\u1ee7 \u0111\u1ea7u t\u01b0. To\u00e0 th\u00e1p cao 81 t\u1ea7ng, hi\u1ec7n t\u1ea1i l\u00e0 to\u00e0 nh\u00e0 cao nh\u1ea5t Vi\u1ec7t Nam v\u00e0 l\u00e0 to\u00e0 nh\u00e0 cao nh\u1ea5t \u0110\u00f4ng Nam \u00c1 t\u1eeb th\u00e1ng 3 n\u0103m 2018."}]}
transformers pytorch xlm-roberta question-answering vi license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #xlm-roberta #question-answering #vi #license-mit #endpoints_compatible #region-us 



# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)

## Overview

- Language model: xlm-roberta-large
- Fine-tune: deepset/xlm-roberta-large-squad2
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: mailong25/bert-vietnamese-question-answering
- Training data: train-v2.0.json (SQuAD 2.0 format)
- Eval data: dev-v2.0.json (SQuAD 2.0 format)
- Infrastructure: 1x Tesla P100 (Google Colab)

## Performance

Evaluated on dev-v2.0.json


Evaluated on Vietnamese XQuAD: URL


## Author

An Pham (URL [at] URL)

## License

MIT
# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages) ## Overview

- Language model: xlm-roberta-large
- Fine-tune: deepset/xlm-roberta-large-squad2
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: mailong25/bert-vietnamese-question-answering
- Training data: train-v2.0.json (SQuAD 2.0 format)
- Eval data: dev-v2.0.json (SQuAD 2.0 format)
- Infrastructure: 1x Tesla P100 (Google Colab) ## Performance

Evaluated on dev-v2.0.json


Evaluated on Vietnamese XQuAD: URL ## Author

An Pham (URL [at] URL) ## License

MIT
TAGS
#transformers #pytorch #xlm-roberta #question-answering #vi #license-mit #endpoints_compatible #region-us 
 # XLM-RoBERTa large for QA on Vietnamese languages (also support various languages) ## Overview

- Language model: xlm-roberta-large
- Fine-tune: deepset/xlm-roberta-large-squad2
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: mailong25/bert-vietnamese-question-answering
- Training data: train-v2.0.json (SQuAD 2.0 format)
- Eval data: dev-v2.0.json (SQuAD 2.0 format)
- Infrastructure: 1x Tesla P100 (Google Colab) ## Performance

Evaluated on dev-v2.0.json


Evaluated on Vietnamese XQuAD: URL ## Author

An Pham (URL [at] URL) ## License

MIT
passage: TAGS
#transformers #pytorch #xlm-roberta #question-answering #vi #license-mit #endpoints_compatible #region-us 
# XLM-RoBERTa large for QA on Vietnamese languages (also support various languages)## Overview

- Language model: xlm-roberta-large
- Fine-tune: deepset/xlm-roberta-large-squad2
- Language: Vietnamese
- Downstream-task: Extractive QA
- Dataset: mailong25/bert-vietnamese-question-answering
- Training data: train-v2.0.json (SQuAD 2.0 format)
- Eval data: dev-v2.0.json (SQuAD 2.0 format)
- Infrastructure: 1x Tesla P100 (Google Colab)## Performance

Evaluated on dev-v2.0.json


Evaluated on Vietnamese XQuAD: URL## Author

An Pham (URL [at] URL)## License

MIT