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# 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
```sh
pip install transformers torch
```
### Loading the Model
```python
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.
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