Fine-tuned distilroberta-base for detecting news on the labor movement

Model Description

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

How to Use

from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-labor_movement")
classifier("Strikes in Pittsburgh")

Training data

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

Split	Size
Train	253
Dev	54
Test	54

Test set results

Metric	Result
F1	0.9412
Accuracy	0.9630
Precision	0.9412
Recall	0.9412

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.