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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.05499 |
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| _version_ | 1866915052841336832 |
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| author | Yufan, Zhu Zeyu, Hao Siqi, Li Boqian, Niu |
| author_facet | Yufan, Zhu Zeyu, Hao Siqi, Li Boqian, Niu |
| contents | SplaXBERT, built on ALBERT-xlarge with context-splitting and mixed precision training, achieves high efficiency in question-answering tasks on lengthy texts. Tested on SQuAD v1.1, it attains an Exact Match of 85.95% and an F1 Score of 92.97%, outperforming traditional BERT-based models in both accuracy and resource efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05499 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SplaXBERT: Leveraging Mixed Precision Training and Context Splitting for Question Answering Yufan, Zhu Zeyu, Hao Siqi, Li Boqian, Niu Computation and Language Machine Learning SplaXBERT, built on ALBERT-xlarge with context-splitting and mixed precision training, achieves high efficiency in question-answering tasks on lengthy texts. Tested on SQuAD v1.1, it attains an Exact Match of 85.95% and an F1 Score of 92.97%, outperforming traditional BERT-based models in both accuracy and resource efficiency. |
| title | SplaXBERT: Leveraging Mixed Precision Training and Context Splitting for Question Answering |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2412.05499 |