Update README.md

README.md

@@ -5,3 +5,38 @@ tags:
 - 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))