README.md

---
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).