metadata
language:
- en
metrics:
- accuracy
- f1
- precision
- recall
base_model:
- distilbert/distilbert-base-uncased-finetuned-sst-2-english
pipeline_tag: text-classification
tags:
- fake
- real
- news
library_name: transformers
DistilBERT Fake News Classifier
Model Description
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
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}