Update README.md

README.md

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+
+## 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}
+}
+```