| # Roberta Base Quantized Model for Spam Detection |
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| 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. |
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| ## Model Details |
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| - **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 |
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| ## Usage |
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| ### Installation |
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| ```sh |
| pip install transformers torch |
| ``` |
|
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| ### Loading the Model |
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|
| ```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 |
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| ## Fine-Tuning Details |
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| ### Dataset |
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| The Hugging Face's `sms_spam`, `spam_mail`, and `mail_spam_ham_dataset` dataset was used, containing both spam and ham (non-spam) examples. |
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| ### Training |
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| - Number of epochs: 3 |
| - Batch size: 8 |
| - Evaluation strategy: epoch |
| - Learning rate: 3e-5 |
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| ### Quantization |
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| Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency. |
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| ## Repository Structure |
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| ``` |
| . |
| ├── model/ # Contains the quantized model files |
| ├── tokenizer_config/ # Tokenizer configuration and vocabulary files |
| ├── model.safetensors/ # Fine Tuned Model |
| ├── README.md # Model documentation |
| ``` |
|
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| ## Limitations |
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| - 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. |
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| ## Contributing |
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| Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements. |
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