dell-research-harvard/topic-obits

Fine-tuned RoBERTa-large for detecting news on obituaries

Model Description

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

How to Use

from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-obits")
classifier("John Smith died after a long illness")

Training data

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

Split	Size
Train	272
Dev	57
Test	57

Test set results

Metric	Result
F1	1.000
Accuracy	1.000
Precision	1.000
Recall	1.000

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.