P O L I T I C S

Introduction
POLITICS is a pretrained language model specifically designed for analyzing English political news articles. It builds upon the RoBERTa architecture and includes novel training objectives that leverage inter-article triplet-loss to understand ideological content and story. While the model excels in understanding political news, it requires fine-tuning for specific downstream tasks like predicting ideological leanings or detecting stances.

Architecture
The POLITICS model is based on the RoBERTa architecture. It incorporates a specialized pretraining objective that focuses on ideology-driven content, enhancing its ability to process political news articles. This objective involves comparing articles with similar stories to detect ideological nuances.

Training
POLITICS was trained using a large-scale dataset named BIGNEWS, which includes over 3.6 million political news articles. The training process emphasizes ideology-driven objectives to enhance the model's comprehension of political content. Detailed methodologies and results are available in the NAACL-2022 Findings paper and the project's GitHub repository.

Guide: Running Locally

1. Setup Environment: Install necessary libraries such as Transformers and PyTorch.
2. Download Model: Access the POLITICS model from the Hugging Face Model Hub.
3. Prepare Data: Obtain or prepare a dataset for fine-tuning the model.
4. Fine-tune: Train the model on your dataset to adapt it for specific tasks.
5. Inference: Use the model for predictions on political articles.

For enhanced performance, consider using cloud GPUs from providers like AWS, Google Cloud, or Azure.

License
POLITICS is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (cc-by-nc-sa-4.0), which allows for sharing and adaptation with attribution, under non-commercial terms.