dell-research-harvard/topic-crime

Fine-tuned RoBERTa-large for detecting news on crime

Model Description

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

How to Use

from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-crime")
classifier("Man robs bank")

Training data

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

Split	Size
Train	463
Dev	98
Test	98

Test set results

Metric	Result
F1	0.9041
Accuracy	0.9286
Precision	0.8919
Recall	0.9167

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.