Update README.md

README.md

@@ -3,7 +3,7 @@
 We present and release [PHS-BERT](https://arxiv.org/abs/2204.04521), a transformer-based pretrained language model (PLM), to identify tasks related to public health surveillance (PHS) on social media. Compared with existing PLMs that are mainly evaluated on limited tasks, PHS-BERT achieved state-of-the-art performance on 25 tested datasets, showing that our PLM is robust and generalizable in common PHS tasks.
 
 ## Usage
-Load the model via [Huggingface's Transformers library](https://github.com/huggingface/transformers])
+Load the model via [Huggingface's Transformers library](https://github.com/huggingface/transformers]):
 ```
 from transformers import AutoTokenizer, AutoModel
 tokenizer = AutoTokenizer.from_pretrained("publichealthsurveillance/PHS-BERT")
@@ -26,6 +26,8 @@ We used the embedding of the special token [CLS] of the last hidden layer as the
 We train and release a PLM to accelerate the automatic identification of tasks related to PHS on social media. Our work aims to develop a new computational method for screening users in need of early intervention and is not intended to use in clinical settings or as a diagnostic tool.
 
 ## BibTex entry and citation info
+For more details, refer to the paper [Benchmarking for Public Health Surveillance tasks on Social Media with a Domain-Specific Pretrained Language Model](https://arxiv.org/abs/2204.04521).
+
 ```
 @misc{https://doi.org/10.48550/arxiv.2204.04521,
   doi = {10.48550/ARXIV.2204.04521},