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---
license: cc-by-4.0
language:
- en
pipeline_tag: text-classification
tags:
- roberta-large
- topic
- news
widget:
- text: "Diplomatic efforts to deal with the world’s two wars — the civil war in Spain and the undeclared Chinese - Japanese conflict — received sharp setbacks today."
- text: "WASHINGTON. AP. A decisive development appeared in the offing in the tug-of-war between the federal government and the states over the financing of relief."
- text: "A frantic bride called the Rochester Gas and Electric corporation to complain that her new refrigerator “freezes ice cubes too fast.”"
---
# 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
```python
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](https://huggingface.co./datasets/dell-research-harvard/newswire).
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](https://huggingface.co./datasets/dell-research-harvard/newswire).
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