from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "naheelkk/fake-news-bert-liar"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prediction function
def predict_fake_news(text):
    inputs = tokenizer(text, truncation=True, max_length=128, 
                      padding=True, return_tensors="pt")
    
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probs = torch.softmax(logits, dim=-1)
        
        prediction = torch.argmax(probs, dim=-1).item()
        confidence = probs.max().item()
        
        label = "REAL" if prediction == 1 else "FAKE"
        return label, confidence

# Example usage
text = "The Federal Reserve announced an interest rate increase today."
prediction, confidence = predict_fake_news(text)
print(f"Prediction: {prediction} (Confidence: {confidence:.3f})")

Training Data

The model was trained on the LIAR dataset, which contains fact-checked political statements:

True statements (true, mostly-true, half-true) → labeled as REAL
False statements (barely-true, false, pants-on-fire) → labeled as FAKE

Limitations

Primarily trained on political statements
May have bias towards certain topics or writing styles
Confidence scores below 0.55 should be treated as uncertain
Best performance on English news articles

Ethical Considerations

This model should be used as a tool to assist human fact-checkers, not replace them. Always verify important information through multiple reliable sources.

Citation

If you use this model, please consider citing the LIAR dataset:

@inproceedings{wang2017liar,
  title={LIAR: A benchmark dataset for fake news detection},
  author={Wang, William Yang},
  booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics},
  year={2017}
}