Topic Govt Regulation

🚀 Fine-tuned RoBERTa-large for detecting news on government regulation

A fine-tuned RoBERTa-large model designed to classify whether news articles are related to government regulation, offering high accuracy in text classification tasks.

🚀 Quick Start

This model is a fine-tuned RoBERTa-large, specifically crafted for classifying whether news articles pertain to government regulation.

💻 Usage Examples

Basic Usage

from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-govt_regulation")
classifier("Senate passes gun control bill")

📚 Documentation

Training data

The model was trained on a hand-labelled sample of data from the NEWSWIRE dataset.

Test set results

Applications

We applied this model to a century of historical news articles. You can see all the classifications in the NEWSWIRE dataset.

📄 License

This model is licensed under CC BY 4.0.

📚 Citation Information

You can cite this dataset using

@misc{silcock2024newswirelargescalestructureddatabase,
      title={Newswire: A Large-Scale Structured Database of a Century of Historical News}, 
      author={Emily Silcock and Abhishek Arora and Luca D'Amico-Wong and Melissa Dell},
      year={2024},
      eprint={2406.09490},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2406.09490}, 
}