---
pipeline_tag: text-classification
tags:
- transformers
- text-classification
- polarops
---

# PolarOps: DistilBERT for Political Polarity Classification

This is a fine-tuned [DistilBERT](https://huggingface.co/distilbert-base-uncased) model for binary text classification on political polarization data. It predicts whether a given sentence is *polarized* or *healthy* based on training data from the PolarOps project.

## Example Usage

```python
from transformers import pipeline
classifier = pipeline("text-classification", model="divilian/polarops")
classifier("The government should be overthrown.")
```

## Labels

- `healthy` — Civil, constructive language  
- `polarized` — Toxic or partisan rhetoric

## Training Details

Trained on X samples using `Trainer()` for Y epochs with learning rate Z.

## Intended Use

Designed for research and experimentation in political discourse classification. Not suitable for deployment in high-stakes settings.

## Limitations

- Binary labels only  
- English language only  
- May reflect training data biases

## Author

Stephen Davies ([@divilian](https://huggingface.co/divilian))