metadata
license: cc-by-4.0
language:
- en
pipeline_tag: text-classification
tags:
- distilroberta
- topic
- news
Fine-tuned distilroberta-base for detecting news on fires
Model Description
This model is a finetuned distilroberta-base, for classifying whether news articles are about fires.
How to Use
from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-fire")
classifier("Building destroyed by fire")
Training data
The model was trained on a hand-labelled sample of data from the NEWSWIRE dataset.
| Split | Size |
|---|---|
| Train | 554 |
| Dev | 118 |
| Test | 118 |
Test set results
| Metric | Result |
|---|---|
| F1 | 0.9709 |
| Accuracy | 0.9746 |
| Precision | 0.9434 |
| Recall | 1.0000 |
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.