Spam-Bert-Uncased
| Property | Value |
|---|---|
| Model Type | BERT Sequence Classification |
| Base Architecture | bert-base-uncased |
| Task | Binary Classification (Spam/Ham) |
| Dataset Size | 5.57k entries |
| Model Performance | 99.37% Validation Accuracy |
| Author | prithivMLmods |
| Model URL | huggingface.co/prithivMLmods/Spam-Bert-Uncased |
What is Spam-Bert-Uncased?
Spam-Bert-Uncased is a specialized BERT-based model fine-tuned for detecting spam messages with exceptional accuracy. Built on the bert-base-uncased architecture, this model has been optimized for binary classification of text messages as either spam or legitimate (ham) communications.
Implementation Details
The model leverages the BERT architecture with specific optimizations for spam detection. It was trained using carefully selected hyperparameters: learning rate of 2e-5, batch size of 16, and 3 training epochs. The implementation includes comprehensive experiment tracking through Weights & Biases (wandb) for performance monitoring.
- Pre-trained on bert-base-uncased backbone
- Fine-tuned on Spam-Text-Detect-Analysis dataset
- Implements cross-entropy loss function
- Includes built-in Gradio interface for easy testing
Core Capabilities
- Binary classification of text messages (Spam/Ham)
- Achieves 99.37% validation accuracy
- Precision: 99.31%
- Recall: 95.97%
- F1 Score: 97.61%
- Real-time inference through Gradio UI
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful BERT architecture with specialized fine-tuning for spam detection, achieving remarkably high accuracy while maintaining good precision-recall balance. The inclusion of a Gradio interface makes it particularly accessible for practical applications.
Q: What are the recommended use cases?
The model is ideal for email filtering systems, comment moderation on websites, SMS filtering, and any application requiring automated detection of spam messages. Its high precision makes it suitable for production environments where false positives must be minimized.
