adityaG04/fake-news-bert-finetuned

language: en license: mit tags:

• —fake-news-detection
• —roberta
• —text-classification
• —transfer-learning datasets:
• —GonzaloA/fake_news metrics:
• —accuracy
• —f1 pipeline_tag: text-classification model-index:
• —name: fake-news-bert-finetuned results:
• —task: type: text-classification name: Fake News Detection dataset: name: GonzaloA/fakenews type: GonzaloA/fakenews metrics:
• —type: accuracy value: 0.98
• —type: f1 value: 0.98 ---

🔬 Fake News Detector (Fine-Tuned RoBERTa)

A RoBERTa-based fake news classifier fine-tuned using transfer learning.

Model Details

Training Details

• —Method: Transfer learning with layer freezing
• —Frozen Layers: Layers 0-5 (embeddings + first 6 encoder layers)
• —Trainable Layers: Layers 6-11 + Classification head
• —Learning Rate: 2e-5 with cosine scheduler
• —Batch Size: 32 (effective)
• —Epochs: 5 with early stopping (patience=2)
• —Optimizer: AdamW with weight decay 0.01

Usage

from transformers import pipeline

classifier = pipeline("text-classification", model="adityaG04/fake-news-bert-finetuned")

# Test with a real news article
result = classifier("The Federal Reserve raised interest rates by 0.25 percentage points on Wednesday.")
print(result)
# [{'label': 'LABEL_1', 'score': 0.99}]  → REAL ✅

# Test with a fake article
result = classifier("BREAKING: Scientists confirm drinking bleach cures all diseases!")
print(result)
# [{'label': 'LABEL_0', 'score': 0.98}]  → FAKE 🔴