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

# Fine-tuned distilroberta-base for detecting news on the labor movement

# Model Description

This model is a finetuned distilroberta-base, for classifying whether news articles are about the labor movement. 

# How to Use

```python
from transformers import pipeline
classifier = pipeline("text-classification", model="dell-research-harvard/topic-labor_movement")
classifier("Strikes in Pittsburgh")
```

# 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|253
Dev|54
Test|54

# Test set results

Metric|Result
-|-
F1|0.9412
Accuracy|0.9630
Precision|0.9412
Recall|0.9412


# 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).