NER-PMR-large
NER-PMR-large is initialized with PMR-large and further fine-tuned with 4 NER training data, namely CoNLL, WNUT17, ACE2004, and ACE2005.
The model performance on the test sets are:
| CoNLL | WNUT17 | ACE2004 | ACE2005 | |
|---|---|---|---|---|
| RoBERTa-large (single-task model) | 92.8 | 57.1 | 86.3 | 87.0 |
| PMR-large (single-task model) | 93.6 | 60.8 | 87.5 | 87.4 |
| NER-PMR-large (multi-task model) | 92.9 | 54.7 | 87.8 | 88.4 |
Note that the performance of RoBERTa-large and PMR-large are single-task fine-tuning, while NER-PMR-large is a multi-task fine-tuned model. As it is fine-tuned on multiple datasets, we believe that NER-PMR-large has a better generalization capability to other NER tasks than PMR-large and RoBERTa-large.
How to use
You can try the codes from this repo for both training and inference.
BibTeX entry and citation info
@article{xu2022clozing,
title={From Clozing to Comprehending: Retrofitting Pre-trained Language Model to Pre-trained Machine Reader},
author={Xu, Weiwen and Li, Xin and Zhang, Wenxuan and Zhou, Meng and Bing, Lidong and Lam, Wai and Si, Luo},
journal={arXiv preprint arXiv:2212.04755},
year={2022}
}
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