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nguyenvulebinh
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vi-mrc-large

Question Answering
Transformers
PyTorch
Vietnamese
vn
English
roberta
Inference Endpoints
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2
nguyenvulebinh commited on
Commit
e89f4c3
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1 Parent(s): 4f637e4

add checkpoint top1 vlsp mrc 2021

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  1. README.md +5 -2
README.md CHANGED
@@ -35,9 +35,12 @@ This model is intended to be used for QA in the Vietnamese language so the valid
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  | Model | EM | F1 |
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  | ------------- | ------------- | ------------- |
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- | [large](https://huggingface.co/nguyenvulebinh/vi-mrc-large) public_test_set | 85.847 | 83.826 |
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- | [large](https://huggingface.co/nguyenvulebinh/vi-mrc-large) private_test_set | 82.072 | 78.071 |
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  [MRCQuestionAnswering](https://github.com/nguyenvulebinh/extractive-qa-mrc) using [XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html) as a pre-trained language model. By default, XLM-RoBERTa will split word in to sub-words. But in my implementation, I re-combine sub-words representation (after encoded by BERT layer) into word representation using sum strategy.
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  | Model | EM | F1 |
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  | ------------- | ------------- | ------------- |
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+ | [large](https://huggingface.co/nguyenvulebinh/vi-mrc-large) public_test_set | 85.847 | 83.826 |
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+ | [large](https://huggingface.co/nguyenvulebinh/vi-mrc-large) private_test_set | 82.072 | 78.071 |
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+ Public leaderboard | Private leaderboard
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+ :-------------------------:|:-------------------------:
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+ ![](https://i.ibb.co/tJX6V6T/public-leaderboard.jpg) | ![](https://i.ibb.co/nmsX2pG/private-leaderboard.jpg)
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  [MRCQuestionAnswering](https://github.com/nguyenvulebinh/extractive-qa-mrc) using [XLM-RoBERTa](https://huggingface.co/transformers/model_doc/xlmroberta.html) as a pre-trained language model. By default, XLM-RoBERTa will split word in to sub-words. But in my implementation, I re-combine sub-words representation (after encoded by BERT layer) into word representation using sum strategy.
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