Fake News Detection with DistilBERT
Model Description
Fine-tuned DistilBERT model for classifying news articles as Real or Fake.
Training Data
- Total: 18,281 samples
- Train: 12,796 (70%)
- Validation: 1,829 (10%)
- Test: 3,656 (20%)
Performance
- Test Accuracy: 96.36%
- Test Loss: 0.194
Classification Report
| | Precision | Recall | F1-Score | Support | |----------|-----------|--------|----------|---------|| | Real | 0.97 | 0.96 | 0.97 | 2,072 | | Fake | 0.95 | 0.96 | 0.96 | 1,584 |
Usage
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained('Aakash22134/fake_news_DistilBert')
model = AutoModelForSequenceClassification.from_pretrained('Aakash22134/fake_news_DistilBert')
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