BERT NMB+ (Disinformation Sequence Classification):
Classifies sentences as "Likely" or "Unlikely" biased/disinformation (max token len 128).
Fine-tuned BERT (bert-base-uncased) on the headline
and text_label
fields in the News Media Bias Plus Dataset.
This model was trained without weighted sampling, and the dataset contains 81.9% 'Likely' and 18.1% 'Unlikely' examples. The same model trained with weighted sampling preformed better when evaluated by gpt-4o-mini as a judge and is available here.
Metics
Evaluated on a 0.1 random sample of the NMB+ dataset, unseen during training
- Accuracy: 0.7990
- Precision: 0.8096
- Recall: 0.9556
- F1 Score: 0.8766
How to Use:
from transformers import pipeline
classifier = pipeline("text-classification", model="maximuspowers/nmbp-bert-headlines")
result = classifier("He was a terrible politician.", top_k=2)
Example Response:
[
{
'label': 'Likely',
'score': 0.9967995882034302
},
{
'label': 'Unlikely',
'score': 0.003200419945642352
}
]
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Base model
google-bert/bert-base-uncased