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AventIQ-AI/
SMS-Spam-Detection-Model

Name: SMS-Spam-Detection-Model
Availability: InStock

safetensors

distilbert

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🧠 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

Field	Value
Base Model	`distilbert-base-uncased`
Dataset	SMS Spam Collection (UCI)
Framework	PyTorch with 🤗 Transformers
Epochs	3
Batch Size	16
Max Length	128 tokens
Optimizer	AdamW
Loss	CrossEntropyLoss (token-level)
Device	Trained on CUDA-enabled GPU

📊 Evaluation Metrics

Metric	Score
Accuracy	0.99
F1-Score	0.96
Precision	0.98
Recall	0.93

🚀 Usage

python

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.