niru-nny/SMS_Spam_Detection

SMS Spam Detection with BERT

🎯 A high-performance SMS spam classifier built with BERT achieving 99.16% accuracy.

Model Description

This model is a fine-tuned BERT classifier designed to detect spam messages in SMS text. It can classify messages as either:

• HAM (legitimate message)
• SPAM (unwanted/spam message)

Performance Metrics

Metric	Score
Accuracy	99.16%
Precision	97.30%
Recall	96.43%
F1-Score	96.86%

Quick Start

Using Transformers Pipeline

from transformers import pipeline

# Load the model
classifier = pipeline("text-classification", model="niru-nny/SMS_Spam_Detection")

# Classify a message
result = classifier("Congratulations! You've won a $1000 gift card!")
print(result)
# Output: [{'label': 'SPAM', 'score': 0.9987}]

Using AutoModel and AutoTokenizer

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

# Load model and tokenizer
model_name = "niru-nny/SMS_Spam_Detection"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Prepare input
text = "Hey, are we still meeting for lunch tomorrow?"
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)

# Get prediction
with torch.no_grad():
    outputs = model(**inputs)
    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(predictions, dim=-1).item()

# Map to label
labels = ["HAM", "SPAM"]
print(f"Prediction: {labels[predicted_class]} (confidence: {predictions[0][predicted_class]:.4f})")

Training Details

Dataset

• Source: SMS Spam Collection Dataset
• Total Messages: 5,574
• Ham Messages: 4,827 (86.6%)
• Spam Messages: 747 (13.4%)

Training Configuration

• Base Model: bert-base-uncased
• Max Sequence Length: 128 tokens
• Batch Size: 16
• Learning Rate: 2e-5
• Epochs: 3
• Optimizer: AdamW

Data Split

• Training: 80%
• Validation: 20%

Model Architecture

Input Text → BERT Tokenizer → BERT Encoder (12 layers) → [CLS] Token → Classification Head → Output (HAM/SPAM)

Use Cases

✅ Spam Filtering: Automatically filter spam messages in messaging applications
✅ SMS Gateway Protection: Protect users from phishing and scam attempts
✅ Content Moderation: Pre-screen messages in communication platforms
✅ Fraud Detection: Identify suspicious messages in financial apps

Limitations

• Model is trained specifically on English SMS messages
• May not generalize well to other languages or message formats
• Performance may vary on messages with heavy slang or abbreviations
• Trained on historical data; new spam patterns may emerge

Ethical Considerations

⚠️ Privacy: Ensure compliance with data protection regulations when processing user messages
⚠️ False Positives: Important legitimate messages might be incorrectly flagged as spam
⚠️ Bias: Model may reflect biases present in training data

Citation

If you use this model, please cite:

@model{sms_spam_detection_bert_2026,
  title={SMS Spam Detection with BERT},
  author={niru-nny},
  year={2026},
  url={https://huggingface.co/niru-nny/SMS_Spam_Detection}
}

License

MIT License

Contact

For questions or feedback, please open an issue on the model repository.

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Model Card: For detailed information about model development, evaluation, and responsible AI considerations, see the complete model card in the repository.