Create README.md

README.md

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+---
+license: cc
+language:
+- en
+library_name: transformers
+tags:
+- social media
+- contrastive learning
+---
+# Contrastive Learning of Sociopragmatic Meaning in Social Media
+
+<p align="center"> <a href="https://chiyuzhang94.github.io/" target="_blank">Chiyu Zhang</a>,  <a href="https://mageed.arts.ubc.ca/" target="_blank">Muhammad Abdul-Mageed</a>, <a href="https://ganeshjawahar.github.io/" target="_blank">Ganesh Jarwaha</a></p>
+<p align="center" float="left">
+  
+<p align="center">Publish at Findings of ACL 2023</p>
+
+[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)]()
+[![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)]()
+
+
+<p align="center" width="100%">
+    <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/infodcl_vis.png?raw=true" alt="Title" style="width: 90%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+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.
+
+## 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
+
+## Model Performance
+
+<p align="center" width="100%">
+    <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/main_table.png?raw=true" alt="main table" style="width: 95%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+Fine-tuning results on our 24 Socio-pragmatic Meaning datasets (average macro-F1 over five runs).