dell-research-harvard/topic-protests

Fine-tuned distilroberta-base for detecting news on protests

Model Description

This model is a finetuned distilroberta-base, for classifying whether news articles are about protests.

How to Use

from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-protests")
classifier("March on Washington")

Training data

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

Split	Size
Train	351
Dev	75
Test	75

Test set results

Metric	Result
F1	0.9057
Accuracy	0.9333
Precision	0.8889
Recall	0.9231

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.