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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)) |