| # 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. | |