AventIQ-AI / SMS-Spam-Detection-Model

🧠 SMSDetection-DistilBERT-SMS

A DistilBERT-based binary classifier fine-tuned on the SMS Spam Collection dataset. It classifies messages as either spam or ham (not spam). This model is suitable for real-world applications like mobile SMS spam filters, automated customer message triage, and telecom fraud detection.

✨ Model Highlights

• 📌 Based on distilbert-base-uncased
• 🔍 Fine-tuned on the SMS Spam Collection dataset
• ⚡ Supports binary classification: Spam vs Not Spam
• 💾 Lightweight and optimized for both CPU and GPU environments

🧠 Intended Uses

• ✅ Mobile SMS spam filtering
• ✅ Telecom customer service automation
• ✅ Fraudulent message detection
• ✅ User inbox categorization
• ✅ Regulatory compliance monitoring

• 🚫 Limitations

• ❌ Trained on English SMS messages only

• ❌ May underperform on emails, social media texts, or non-English content

• ❌ Not designed for multilingual datasets

• ❌ Slight performance dip expected for long messages (>128 tokens)

🏋️‍♂️ Training Details

📊 Evaluation Metrics

🚀 Usage

from transformers import BertTokenizerFast, BertForTokenClassification
from transformers import pipeline
import torch

model_name = "AventIQ-AI/SMS-Spam-Detection-Model"
tokenizer = BertTokenizerFast.from_pretrained(model_name)
model = BertForTokenClassification.from_pretrained(model_name)
model.eval()


# Inference
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

def predict_sms(text):
    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=128)
    inputs = {k: v.to(device) for k, v in inputs.items()}
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        predicted = torch.argmax(logits, dim=1).item()
    return "spam" if predicted == 1 else "ham"

# Test example
print(predict_sms("You've won $1,000,000! Call now to claim your prize!"))

• 🧩 Quantization
• Post-training static quantization applied using PyTorch to reduce model size and accelerate inference on edge devices.

🗂 Repository Structure

.
├── model/               # Quantized model files
├── tokenizer_config/    # Tokenizer and vocab files
├── model.safensors/     # Fine-tuned model in safetensors format
├── README.md            # Model card

🤝 Contributing

Open to improvements and feedback! Feel free to submit a pull request or open an issue if you find any bugs or want to enhance the model.