Update README.md

README.md

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 [![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)]()
 
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-    <a><img src="https://raw.githubusercontent.com/UBC-NLP/infodcl/blob/master/images/infodcl_vis.png" alt="Title" style="width: 95%; min-width: 300px; display: block; margin: auto;"></a>
+    <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>
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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.
 
 ## Checkpoints of Models Pre-Trained with InfoDCL 
 * InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-emoji 
-* InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-hashtag
+* InfoDCL-RoBERTa trained with TweetHashtag-EN: https://huggingface.co/UBC-NLP/InfoDCL-hashtag
 
 ## Model Performance
 
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-    <a><img src="https://raw.githubusercontent.com/UBC-NLP/infodcl/blob/master/images/main_table.png" alt="main table" style="width: 95%; min-width: 300px; display: block; margin: auto;"></a>
+    <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>
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 Fine-tuning results on our 24 Socio-pragmatic Meaning datasets (average macro-F1 over five runs).