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')