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# DistilBERT Base Uncased Quantized Model for Spam Detection
This repository hosts a quantized version of the DistilBERT 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:** DistilBERT Base Uncased
- **Task:** Spam Detection
- **Dataset:** Hugging Face's `sms_spam`
- **Quantization:** BrainFloat16
- **Fine-tuning Framework:** Hugging Face Transformers
## Usage
### Installation
```sh
pip install transformers torch
```
### Loading the Model
```python
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
import torch
model_name = "AventIQ-AI/distilbert-spam-detection"
tokenizer = DistilBertTokenizer.from_pretrained(model_name)
model = DistilBertForSequenceClassification.from_pretrained(model_name)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def predict_spam(text, model, tokenizer, device):
model.eval() # Set to evaluation mode
inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to(device)
with torch.no_grad():
outputs = model(**inputs)
probs = torch.softmax(outputs.logits, dim=-1)
pred_class = torch.argmax(probs).item()
return "Spam" if pred_class == 1 else "Not Spam"
# Sample test messages
test_messages = [
"Congratulations! You have won a lottery of $1,000,000. Claim now!", # Spam
"Hey, are we still meeting for dinner tonight?", # Not Spam
"URGENT: Your bank account is at risk! Click this link to secure it now.", # Spam
"Let's catch up this weekend. It’s been a while!", # Not Spam
"Exclusive offer! Get 50% off on your next purchase. Limited time only!", # Spam
]
# Run inference on test messages
for i, msg in enumerate(test_messages):
prediction = predict_spam(msg, model, tokenizer, device)
print(f"Sample {i+1}: {msg} -> Prediction: {prediction}")
```
## 📊 Classification Report (Quantized Model - bfloat16)
| 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 `sms_spam` dataset was used, containing both spam and ham (non-spam) examples.
### Training
- Number of epochs: 7
- Batch size: 16
- Evaluation strategy: epoch
- Learning rate: 5e-6
### 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
├── pytorch_model.bin/ # 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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