This DistilBERT-based model achieves 97.18% accuracy in classifying news articles as real or fake, with balanced precision (97.17%) and recall (97.30%).

Training Performance

Epoch	Training Loss	Validation Loss	Accuracy	F1 Score
1	-	0.1115	96.08%	96.09%
2	0.2026	0.1077	97.25%	97.28%
3	0.0647	0.1119	97.45%	97.50%

Final Test Results

Metric	Score
Accuracy	97.18%
F1 Score	97.23%
Precision	97.17%
Recall	97.30%

Usage

python

from transformers import pipeline

classifier = pipeline("text-classification", 
                    model="KenLumod/ML-Project-DistilBERT-Fake-and-Real-Classifier")
result = classifier("Scientists confirm climate change accelerating beyond previous estimates")
# Output: {'label': 'REAL', 'score': 0.982}