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README.md

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 license: cc-by-nc-4.0
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+
+## Model Specification
+- This is the **Democratic** community GPT-2 language model, fine-tuned on 4.7M (~100M tokens) tweets of Democratic Twitter users between 2019-01-01 and 2020-04-10. 
+- For more details about the `CommunityLM` project, please refer to this [our paper](https://arxiv.org/abs/2209.07065) and [github](https://github.com/hjian42/communitylm) page. 
+- In the paper, it is referred as the `Fine-tuned CommunityLM` for the Democratic Twitter community. 
+
+## How to use the model
+
+- **PRE-PROCESSING**: when you apply the model on tweets, please make sure that tweets are preprocessed by the [TweetTokenizer](https://github.com/VinAIResearch/BERTweet/blob/master/TweetNormalizer.py) to get the best performance.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("CommunityLM/republican-twitter-gpt2")
+
+model = AutoModelForCausalLM.from_pretrained("CommunityLM/republican-twitter-gpt2")
+```
+
+## References
+
+If you use this repository in your research, please kindly cite [our paper](https://arxiv.org/abs/2209.07065): 
+
+```bibtex
+@inproceedings{jiang-etal-2022-communitylm,
+    title = "CommunityLM: Probing Partisan Worldviews from Language Models",
+     author = {Jiang, Hang and Beeferman, Doug and Roy, Brandon and Roy, Deb},
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    year = "2022",
+    publisher = "International Committee on Computational Linguistics",
+}
+```