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dell-research-harvard/wire-classifier
wire-classifier is a text classification model from dell-research-harvard. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as cc-by-4.0.
This model is a finetuned distilroberta-base, for classifying whether news articles are from a wire service.
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From the Hugging Face model README
This model is a finetuned distilroberta-base, for classifying whether news articles are from a wire service.
from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/wire-classifier")
classifier("Washington (AP) Two men died in a car crash")
The model was trained on a hand-labelled sample of data from the NEWSWIRE dataset.
| Split | Size |
|---|---|
| Train | 1,459 |
| Dev | 336 |
| Test | 448 |
| Metric | Result |
|---|---|
| F1 | 0.96 |
| Accuracy | |
| Precision | |
| Recall |
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},
}
We applied this model to a century of historical news articles. You can see all the classifications in the NEWSWIRE dataset.