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

python

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