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.
- Downloads last month
- 20
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.