File size: 3,526 Bytes
c4a28d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
# Roberta Base Quantized Model for Spam Detection
This repository hosts a quantized version of the **roberta-base** 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:** Roberta Base
- **Task:** Spam Detection
- **Dataset:** Hugging Face's `sms_spam`, `spam_mail`, and `mail_spam_ham_dataset`
- **Quantization:** Float16
- **Fine-tuning Framework:** Hugging Face Transformers
## Usage
### Installation
```sh
pip install transformers torch
```
### Loading the Model
```python
from transformers import RobertaTokenizer, RobertaForSequenceClassification
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "AventIQ-AI/roberta-spam-detection"
model = RobertaForSequenceClassification.from_pretrained(model_name).to(device)
tokenizer = RobertaTokenizer.from_pretrained(model_name)
def predict(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
# Move input tensors to the same device as the model
inputs = {key: value.to(device) for key, value in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class = torch.argmax(logits).item()
return "Spam" if predicted_class == 1 else "Ham"
# Sample test messages
input_text = "Congratulations! You have won a free iPhone. Click here to claim your prize."
print(f"Prediction: {predict(input_text)}") # Expected output: Spam
```
## 📊 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`, `spam_mail`, and `mail_spam_ham_dataset` dataset was used, containing both spam and ham (non-spam) examples.
### Training
- Number of epochs: 3
- Batch size: 8
- Evaluation strategy: epoch
- Learning rate: 3e-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.
|