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license: cc-by-nc-4.0 |
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## Model Specification |
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- 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. |
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- 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. |
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- In the paper, it is referred as the `Fine-tuned CommunityLM` for the Democratic Twitter community. |
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## How to use the model |
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- **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. |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("CommunityLM/republican-twitter-gpt2") |
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model = AutoModelForCausalLM.from_pretrained("CommunityLM/republican-twitter-gpt2") |
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``` |
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## References |
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If you use this repository in your research, please kindly cite [our paper](https://arxiv.org/abs/2209.07065): |
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```bibtex |
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@inproceedings{jiang-etal-2022-communitylm, |
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title = "CommunityLM: Probing Partisan Worldviews from Language Models", |
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author = {Jiang, Hang and Beeferman, Doug and Roy, Brandon and Roy, Deb}, |
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booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", |
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year = "2022", |
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publisher = "International Committee on Computational Linguistics", |
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} |
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``` |
