---
license: mit
---

## EQA-PMR-large
EQA-PMR-large is initialized with [PMR-large](https://huggingface.co/DAMO-NLP-SG/PMR-large) and further fine-tuned on 6 Extractive Question Answering (EQA) training data from training split of [MRQA](https://aclanthology.org/D19-5801).

The model performance on the in-dev sets are:  

||  SQuAD | NewsQA | HotpotQA | NaturalQuestions | TriviaQA | SearchQA |
|--|-------|-------|-------|------------|--------|--------|
|RoBERTa-large (single-task model)|  94.2 | 73.8 | 81.6|83.3| 85.1 | 85.7 |
|PMR-large  (single-task model)|   94.5 | 74.0 | 83.6 | 83.8 | 85.1 | 88.3 | 
|EQA-PMR-large  (multi-task model)|   94.2 | 73.7 | 66.9 | 82.3 | 85.4 | 88.7 |

Note that the performance of RoBERTa-large and PMR-large are single-task fine-tuning, while EQA-PMR-large is a multi-task fine-tuned model. 
As it is fine-tuned on multiple datasets, we believe that EQA-PMR-large has a better generalization capability to other EQA tasks than PMR-large and RoBERTa-large.

### How to use
You can try the codes from [this repo](https://github.com/DAMO-NLP-SG/PMR/QA) for both training and inference.


### BibTeX entry and citation info
```bibtxt
@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}
}
```