FakeGuard — Fake News Detector
Fine-tuned DistilBERT for binary fake news classification.
CMPE 258 — Deep Learning · San José State University · Spring 2026
Model Details
| Property | Value |
|---|---|
| Base model | distilbert-base-uncased |
| Task | Binary text classification (Fake/Real) |
| Training samples | 2,749 (balanced subsample of 44K articles) |
| Epochs | 2 |
| Max sequence length | 256 |
| Dropout | 0.3 |
| Optimizer | AdamW (lr=3e-5, wd=0.01) |
| LR schedule | Cosine annealing |
Performance
| Metric | Value |
|---|---|
| Test Accuracy | 0.9933 |
| Test Macro F1 | 0.9933 |
| Test ROC AUC | 0.9988 |
| Cohen's Kappa | 0.9867 |
Dataset
Kaggle "Fake and Real News Dataset" (Emine Yetm / clmentbisaillon).
Critical fix applied: Removed (Reuters) dateline prefix from real articles before training to prevent data leakage.
Live Demo
Try it at: https://huggingface.co/spaces/gpreetam236/DL_Final
Architecture
DistilBERT Encoder (6 layers, 768-d) → [CLS] token
→ Dropout(0.3) → Linear(768→256) → LayerNorm → GELU
→ Dropout(0.3) → Linear(256→2) → Logits
W&B Experiment Dashboard
https://wandb.ai/gowripreetham23-san-jose-state-university/fakeguard
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