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Create README.md
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README.md
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## POLITICS
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POLITICS, a pretrained model on English news articles of politics, is produced via continued training on RoBERTa, based on a **P**retraining **O**bjective **L**everaging **I**nter-article **T**riplet-loss using **I**deological **C**ontent and **S**tory.
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Details of our proposed training objectives (i.e., Ideology-driven Pretraining Objectives) and experimental results of POLITICS can be found in our NAACL-2022 Findings [paper](https://arxiv.org/pdf/2205.00619.pdf) and GitHub [Repo](https://github.com/launchnlp/POLITICS).
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Together with POLITICS, we also release our curated large-scale dataset (i.e., BIGNEWS) for pretraining, consisting of more than 3.6M political news articles. This asset can be requested [here](https://docs.google.com/forms/d/e/1FAIpQLSf4hft2AHbuak8jHcltVec_2HviaBBVKXPN4OC-CuW4OFORsw/viewform).
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## Citation
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Please cite our paper if you use the **POLITICS** model:
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```
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@inproceedings{liu-etal-2022-POLITICS,
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title = "POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection",
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author = "Liu, Yujian and
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Zhang, Xinliang Frederick and
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Wegsman, David and
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Beauchamp, Nicholas and
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Wang, Lu"
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booktitle = "Findings of the Association for Computational Linguistics: NAACL 2022",
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year = "2022",
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```
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