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README.md

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 # Zero-shot text classification (base-sized model) trained with self-supervised tuning
 
 Zero-shot text classification model trained with self-supervised tuning (SSTuning). 
-It was introduced in the paper Zero-Shot Text Classification via Self-Supervised Tuning by 
+It was introduced in the paper [Zero-Shot Text Classification via Self-Supervised Tuning](https://arxiv.org/abs/2305.11442) by 
 Chaoqun Liu, Wenxuan Zhang, Guizhen Chen, Xiaobao Wu, Anh Tuan Luu, Chip Hong Chang, Lidong Bing
 and first released in [this repository](https://github.com/DAMO-NLP-SG/SSTuning).
 
 The model backbone is RoBERTa-base.
 
 ## Model description
+
 The model is tuned with unlabeled data using a learning objective called first sentence prediction (FSP). 
 The FSP task is designed by considering both the nature of the unlabeled corpus and the input/output format of classification tasks. 
 The training and validation sets are constructed from the unlabeled corpus using FSP.