Update README.md

README.md

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-<span style="font-size:larger;">**Clinical-Longformer**</span> is a clinical knowledge enriched version of Longformer that was further pre-trained using MIMIC-III clinical notes. It allows up to 4,096 tokens as the model input. Clinical-Longformer consistently out-performs ClinicalBERT across 10 baseline dataset for at least 2 percent. The dataset broadly cover clinical NER, QA and text classification tasks. For more details, please refer to: 
+<span style="font-size:larger;">**Clinical-Longformer**</span> is a clinical knowledge enriched version of Longformer that was further pre-trained using MIMIC-III clinical notes. It allows up to 4,096 tokens as the model input. Clinical-Longformer consistently out-performs ClinicalBERT across 10 baseline dataset for at least 2 percent. Those downstream experiments broadly cover named entity recognition (NER), question answering (QA), natural language inference (NLI) and text classification tasks. For more details, please refer to [our paper](https://arxiv.org/pdf/2201.11838.pdf).
+
 
 ### Pre-training
 We initialized Clinical-Longformer from the pre-trained weights of the base version of Longformer. The pre-training process was distributed in parallel to 6 32GB Tesla V100 GPUs. FP16 precision was enabled to accelerate training. We pre-trained Clinical-Longformer for 200,000 steps with batch size of 6×3. The learning rates were 3e-5 for both models. The entire pre-training process took more than 2 weeks. 
@@ -19,7 +20,7 @@ from transformers import AutoTokenizer, AutoModelForMaskedLM
 tokenizer = AutoTokenizer.from_pretrained("yikuan8/Clinical-Longformer")
 model = AutoModelForMaskedLM.from_pretrained("yikuan8/Clinical-Longformer")
 ```
-
+### Citing
 If you find our model helps, please consider citing this :)
 ```
 @article{li2022clinicallongformer,