README.md

---
license: apache-2.0
language:
- en
pipeline_tag: text-classification

model-index:
- name: DistilBERT Political Bias Detector
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      type: custom
      name: Political Bias Dataset
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.85
      verified: false
    - name: AUC (Left)
      type: roc_auc
      value: 0.95
      verified: false
    - name: AUC (Center)
      type: roc_auc
      value: 0.96
      verified: false
    - name: AUC (Right)
      type: roc_auc
      value: 0.97
      verified: false
---
# DistilBERT Political Bias Detector

This is a fine-tuned **DistilBERT** model designed to classify English text (such as news articles, opinion pieces, or social media posts) into three categories of political leaning: **Left**, **Center**, or **Right**.

The model was trained with class weights and label smoothing to ensure highly calibrated, reliable probability scores across all three political spectrums.

##  How to Use

You can easily use this model in your own applications using the Hugging Face `pipeline` for text classification.

### Installation
First, ensure you have the `transformers` library installed:
```bash
pip install transformers torch
```
```
from transformers import pipeline

# Initialize the bias classifier
# Note: Replace 'your_username/your_model_name' with your actual repository path
bias_classifier = pipeline(
    "text-classification", 
    model="your_username/your_model_name", 
    return_all_scores=True
)

# Test it with a sample sentence
text = "The new tax policy heavily favors corporate interests while ignoring the working class."
predictions = bias_classifier(text)

print(predictions)
```

**This model is highly suitable for:**

Media Analysis: Automatically auditing news sources or aggregators to analyze the spread of left/right-leaning content.

Content Moderation/Tagging: Tagging articles or forum posts in political discussion boards for balanced reading feeds.

Academic Research: Tracking political polarization trends over time in digital media.

Browser Extensions: Building tools that notify readers of potential bias in the article they are currently reading.

**Limitations & Bias:**

Subjectivity of "Center": The line between "Center" and slight "Left/Right" is highly subjective. The model may occasionally struggle with nuanced or purely factual reporting, misclassifying it if it contains politically charged keywords.

Context Length: The model uses DistilBERT, which truncates text after 512 tokens (roughly 350-400 words). For very long articles, it only analyzes the text passed to it (we recommend passing the first 200 and last 200 words for long-form articles).

Language: The model is trained exclusively on English text and US-centric political datasets. It may not accurately reflect political spectrums in other countries.