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README.md

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 <p align="center">Publish at Findings of ACL 2023</p>
+<p align="center"> <a href="https://arxiv.org/abs/2203.07648" target="_blank">Paper</a></p> 
 
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 Illustration of our proposed InfoDCL framework. We exploit distant/surrogate labels (i.e., emojis) to supervise two contrastive losses, corpus-aware contrastive loss (CCL) and Light label-aware contrastive loss (LCL-LiT). Sequence representations from our model should keep the cluster of each class distinguishable and preserve semantic relationships between classes.
 
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 ## Checkpoints of Models Pre-Trained with InfoDCL 
 * InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-emoji 
 * InfoDCL-RoBERTa trained with TweetHashtag-EN: https://huggingface.co/UBC-NLP/InfoDCL-hashtag