WorldVue Balanced Political Bias Detector
This model detects political bias in news articles across economic and social dimensions.
Model Description
- Architecture: mDeBERTa-v3-base (278M parameters)
- Training Data: 6,000 articles judged by GPT-4o-mini
- Languages: 100+ (multilingual)
- Axes:
- Economic: LEFT (-1) ← CENTER (0) → RIGHT (+1)
- Social: LIBERTARIAN (-1) ← CENTER (0) → AUTHORITARIAN (+1)
Signals Detected
Economic (4 signals):
- Economic Role of State
- Market & Business
- Taxation & Spending
- Labor & Trade
Social (4 signals):
- State Authority vs Liberty
- Cultural Identity
- Immigration & Borders
- Collective vs Individual
Usage
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn as nn
# Load model
tokenizer = AutoTokenizer.from_pretrained("Xiameineedsgpu/worldvue-balanced-bias-detector")
# ... (see example code below)
Training Details
- Test Loss: 2.13
- Political Articles: 3,112
- Non-Political Articles: 2,888
- Training Time: ~1 hour on Google Colab (T4 GPU)
Example Outputs
| Article | Economic | Social | Label |
|---|---|---|---|
| Fox News - Immigration | +0.02 | +0.83 | CENTER / AUTHORITARIAN |
| Energy Transition | -0.24 | -0.23 | LEFT / LIBERTARIAN |
| UK PPE Scandal | +0.10 | +0.25 | RIGHT / AUTHORITARIAN |
License
Apache 2.0
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