PolarOps: DistilBERT for Political Polarity Classification

This is a fine-tuned DistilBERT 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

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)