maximuspowers/nmbp-bert-full-articles-balanced

BERT NMB+ (Disinformation Sequence Classification):

Classifies 512 chunks of a news article as "Likely" or "Unlikely" biased/disinformation.

Fine-tuned BERT (bert-base-uncased) on the headline, aritcle_text and text_label fields in the News Media Bias Plus Dataset.

This model was trained with weighted sampling so that each batch contains 50% 'Likely' examples and 50% 'Unlikely' examples. The same model trained without weighted sampling is here, and got slightly better taining eval metrics. However, this model preformed better when predictions were evaluated by gpt-4o as a judge.

Metics

Evaluated on a 0.1 random sample of the NMB+ dataset, unseen during training

• Accuracy: 0.7597
• Precision: 0.9223
• Recall: 0.7407
• F1 Score: 0.8216

How to Use:

Keep in mind, this model was trained on full 512 token chunks (tends to over-predict Unlikely for standalone sentences). If you're planning on processing stand alone sentences, you may find better results with this NMB+ model, which was trained on biased headlines.

from transformers import pipeline

classifier = pipeline("text-classification", model="maximuspowers/nmbp-bert-full-articles-balanced")
result = classifier("He was a terrible politician.", top_k=2)

Example Response:

[
  {
    'label': 'Likely',
    'score': 0.9967995882034302
  },
  {
    'label': 'Unlikely',
    'score': 0.003200419945642352
  }
]