| | ---
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| | language: en
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| | license: mit
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| | tags:
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| | - spam-detection
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| | - text-classification
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| | - sms
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| | - bert
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| | - transformers
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| | datasets:
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| | - sms-spam-collection
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| | metrics:
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| | - accuracy
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| | - precision
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| | - recall
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| | - f1
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| | widget:
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| | - text: "Congratulations! You've won a $1000 gift card. Click here to claim now!"
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| | example_title: "Spam Example"
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| | - text: "Hey, are we still meeting for lunch tomorrow at 12?"
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| | example_title: "Ham Example"
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| | - text: "URGENT! Your account has been suspended. Verify now to restore access."
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| | example_title: "Spam Example 2"
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| | - text: "Thanks for your help today. I really appreciate it!"
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| | example_title: "Ham Example 2"
|
| | ---
|
| |
|
| | # SMS Spam Detection with BERT
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| |
|
| | 🎯 A high-performance SMS spam classifier built with BERT achieving **99.16% accuracy**.
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| |
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| | ## Model Description
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| |
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| | This model is a fine-tuned BERT classifier designed to detect spam messages in SMS text. It can classify messages as either:
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| | - **HAM** (legitimate message)
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| | - **SPAM** (unwanted/spam message)
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| |
|
| | ## Performance Metrics
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| |
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| | | Metric | Score |
|
| | |--------|-------|
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| | | **Accuracy** | 99.16% |
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| | | **Precision** | 97.30% |
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| | | **Recall** | 96.43% |
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| | | **F1-Score** | 96.86% |
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| |
|
| | ## Quick Start
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| |
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| | ### Using Transformers Pipeline
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| |
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| | ```python
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| | from transformers import pipeline
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| |
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| | # Load the model
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| | classifier = pipeline("text-classification", model="niru-nny/SMS_Spam_Detection")
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| |
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| | # Classify a message
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| | result = classifier("Congratulations! You've won a $1000 gift card!")
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| | print(result)
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| | # Output: [{'label': 'SPAM', 'score': 0.9987}]
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| | ```
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| |
|
| | ### Using AutoModel and AutoTokenizer
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| |
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| | ```python
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| | from transformers import AutoTokenizer, AutoModelForSequenceClassification
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| | import torch
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| |
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| | # Load model and tokenizer
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| | model_name = "niru-nny/SMS_Spam_Detection"
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| | tokenizer = AutoTokenizer.from_pretrained(model_name)
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| | model = AutoModelForSequenceClassification.from_pretrained(model_name)
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| |
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| | # Prepare input
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| | text = "Hey, are we still meeting for lunch tomorrow?"
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| | inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
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| |
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| | # Get prediction
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| | with torch.no_grad():
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| | outputs = model(**inputs)
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| | predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
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| | predicted_class = torch.argmax(predictions, dim=-1).item()
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| |
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| | # Map to label
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| | labels = ["HAM", "SPAM"]
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| | print(f"Prediction: {labels[predicted_class]} (confidence: {predictions[0][predicted_class]:.4f})")
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| | ```
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| |
|
| | ## Training Details
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| |
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| | ### Dataset
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| | - **Source:** SMS Spam Collection Dataset
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| | - **Total Messages:** 5,574
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| | - **Ham Messages:** 4,827 (86.6%)
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| | - **Spam Messages:** 747 (13.4%)
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| |
|
| | ### Training Configuration
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| | - **Base Model:** `bert-base-uncased`
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| | - **Max Sequence Length:** 128 tokens
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| | - **Batch Size:** 16
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| | - **Learning Rate:** 2e-5
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| | - **Epochs:** 3
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| | - **Optimizer:** AdamW
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| |
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| | ### Data Split
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| | - **Training:** 80%
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| | - **Validation:** 20%
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| |
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| | ## Model Architecture
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| |
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| | ```
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| | Input Text → BERT Tokenizer → BERT Encoder (12 layers) → [CLS] Token → Classification Head → Output (HAM/SPAM)
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| | ```
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| |
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| | ## Use Cases
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| |
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| | ✅ **Spam Filtering**: Automatically filter spam messages in messaging applications
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| | ✅ **SMS Gateway Protection**: Protect users from phishing and scam attempts
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| | ✅ **Content Moderation**: Pre-screen messages in communication platforms
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| | ✅ **Fraud Detection**: Identify suspicious messages in financial apps
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| |
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| | ## Limitations
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| |
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| | - Model is trained specifically on English SMS messages
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| | - May not generalize well to other languages or message formats
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| | - Performance may vary on messages with heavy slang or abbreviations
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| | - Trained on historical data; new spam patterns may emerge
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| |
|
| | ## Ethical Considerations
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| |
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| | ⚠️ **Privacy**: Ensure compliance with data protection regulations when processing user messages
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| | ⚠️ **False Positives**: Important legitimate messages might be incorrectly flagged as spam
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| | ⚠️ **Bias**: Model may reflect biases present in training data
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| |
|
| | ## Citation
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| |
|
| | If you use this model, please cite:
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| |
|
| | ```bibtex
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| | @model{sms_spam_detection_bert_2026,
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| | title={SMS Spam Detection with BERT},
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| | author={niru-nny},
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| | year={2026},
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| | url={https://huggingface.co/niru-nny/SMS_Spam_Detection}
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| | }
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| | ```
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| |
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| | ## License
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| |
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| | MIT License
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| |
|
| | ## Contact
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| |
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| | For questions or feedback, please open an issue on the [model repository](https://huggingface.co/niru-nny/SMS_Spam_Detection/discussions).
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| |
|
| | ---
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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.
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| |
|
