dell-research-harvard/topic-antitrust

Fine-tuned RoBERTa-large for detecting news on antitrust

Model Description

This model is a finetuned RoBERTa-large, for classifying whether news articles are about antitrust.

How to Use

from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-antitrust")
classifier("Merger is approved")

Training data

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

Split	Size
Train	329
Dev	70
Test	70

Test set results

Metric	Result
F1	0.9375
Accuracy	0.9429
Precision	0.9091
Recall	0.9677

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}, 
}

Applications

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