---
license: cc-by-4.0
language:
- en
pipeline_tag: text-classification
tags:
- RoBERTa-large
- topic
- news
---

# Fine-tuned RoBERTa-large for detecting news on government regulation

# Model Description

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

# How to Use

```python
from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-govt_regulation")
classifier("Senate passes gun control bill")
```

# 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|612
Dev|131
Test|131

# Test set results

Metric|Result
-|-
F1|0.8750
Accuracy|0.9237
Precision|0.7955
Recall|0.9722


# 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).