nlp_roberta_machine-reading-comprehension_for-ner

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:

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}
}