AventIQ-AI/bert-spam-detection

BERT Base Uncased Quantized Model for Spam Detection

This repository hosts a quantized version of the BERT model, fine-tuned for spam detection tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.

Model Details

• Model Architecture: BERT Base Uncased
• Task: Spam Email Detection
• Dataset: Hugging Face's mail_spam_ham_dataset and 'spam-mail'
• Quantization: Float16
• Fine-tuning Framework: Hugging Face Transformers

Usage

Installation

pip install transformers torch

Loading the Model

from transformers import BertTokenizer, BertForSequenceClassification
import torch

model_name = "AventIQ-AI/bert-spam-detection"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def predict_spam_quantized(text):
    """Predicts whether a given text is spam (1) or ham (0) using the quantized BERT model."""
    
    # Tokenize input text
    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=512)

    # Move inputs to GPU (if available)
    inputs = {key: value.to(device) for key, value in inputs.items()}
    
    # Perform inference
    with torch.no_grad():
        outputs = model(**inputs)

    # Get predicted label (0 = ham, 1 = spam)
    prediction = torch.argmax(outputs.logits, dim=1).item()
    
    return "Spam" if prediction == 1 else "Ham"


# Sample test messages
print(predict_spam_quantized("WINNER!! As a valued network customer you have been selected to receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only."))
# Expected output: Spam

print(predict_spam_quantized("WINNER!! As a valued network customer you have been selected to receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only."))
# Expected output: Ham

📊 Classification Report (Quantized Model - float16)

Metric	Class 0 (Non-Spam)	Class 1 (Spam)	Macro Avg	Weighted Avg
Precision	1.00	0.98	0.99	0.99
Recall	0.99	0.99	0.99	0.99
F1-Score	0.99	0.99	0.99	0.99
Accuracy	99%	99%	99%	99%

🔍 Observations

✅ Precision: High (1.00 for non-spam, 0.98 for spam) → Few false positives
✅ Recall: High (0.99 for both classes) → Few false negatives
✅ F1-Score: Near-perfect balance between precision & recall

Fine-Tuning Details

Dataset

The Hugging Face's 'spam-mail' and 'mail_spam_ham_dataset' datasets are combined together and used, containing both spam and ham (non-spam) examples.

Training

• Number of epochs: 3
• Batch size: 8
• Evaluation strategy: epoch
• Learning rate: 2e-5

Quantization

Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.

Repository Structure

.
├── model/               # Contains the quantized model files
├── tokenizer_config/    # Tokenizer configuration and vocabulary files
├── model.safetensors/   # Fine Tuned Model
├── README.md            # Model documentation

Limitations

• The model may not generalize well to domains outside the fine-tuning dataset.
• Quantization may result in minor accuracy degradation compared to full-precision models.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.