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AventIQ-AI/
roberta-spam-detection

Name: roberta-spam-detection
Rating: 2.5 (8 reviews)

safetensors

roberta

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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

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.