Update README.md

README.md

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 license: mit
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-# From Clozing to Comprehending: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader
-Pre-trained Machine Reader (PMR) is  pre-trained with 18 million Machine Reading Comprehension (MRC) examples constructed with Wikipedia Hyperlinks.
-It was introduced in the paper From Clozing to Comprehending: Retrofitting Pre-trained Masked Language Model to Pre-trained Machine Reader by 
-Weiwen Xu, Xin Li, Wenxuan Zhang, Meng Zhou, Wai Lam, Luo Si, Lidong Bing
-and first released in [this repository](https://github.com/DAMO-NLP-SG/PMR).
 
-
-
-## Model description
-This model is initialized with [PMR-large](https://huggingface.co/DAMO-NLP-SG/PMR-large) and further fine-tuned with 4 NER training data, namely [CoNLL](https://huggingface.co/datasets/conll2003), [WNUT17](https://huggingface.co/datasets/wnut_17), [ACE2004](https://paperswithcode.com/sota/nested-named-entity-recognition-on-ace-2004), and [ACE2005](https://paperswithcode.com/sota/nested-named-entity-recognition-on-ace-2005).
+## NER-PMR-large
+NER-PMR-large is initialized with [PMR-large](https://huggingface.co/DAMO-NLP-SG/PMR-large) and further fine-tuned with 4 NER training data, namely [CoNLL](https://huggingface.co/datasets/conll2003), [WNUT17](https://huggingface.co/datasets/wnut_17), [ACE2004](https://paperswithcode.com/sota/nested-named-entity-recognition-on-ace-2004), and [ACE2005](https://paperswithcode.com/sota/nested-named-entity-recognition-on-ace-2005).
 
 The model performance on the test sets are: 
 
@@ -19,11 +12,11 @@ The model performance on the test sets are:
 |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. 
 
 ### How to use
-You can try the codes from [this repo](https://github.com/DAMO-NLP-SG/PMR/NER).
-
+You can try the codes from [this repo](https://github.com/DAMO-NLP-SG/PMR/NER) for both training and inference.
 
 
 ### BibTeX entry and citation info