Upload folder using huggingface_hub

README.md

@@ -1,3 +1,158 @@
 ---
-license: bsd-3-clause
+language: en
+license: mit
+tags:
+- spam-detection
+- text-classification
+- sms
+- bert
+- transformers
+datasets:
+- sms-spam-collection
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+widget:
+- text: "Congratulations! You've won a $1000 gift card. Click here to claim now!"
+  example_title: "Spam Example"
+- text: "Hey, are we still meeting for lunch tomorrow at 12?"
+  example_title: "Ham Example"
+- text: "URGENT! Your account has been suspended. Verify now to restore access."
+  example_title: "Spam Example 2"
+- text: "Thanks for your help today. I really appreciate it!"
+  example_title: "Ham Example 2"
 ---
+
+# 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
+
+```python
+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
+
+```python
+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:
+
+```bibtex
+@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](https://huggingface.co/niru-nny/SMS_Spam_Detection/discussions).
+
+---
+
+**Model Card:** For detailed information about model development, evaluation, and responsible AI considerations, see the complete model card in the repository.

app.py

@@ -0,0 +1,104 @@
+import gradio as gr
+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)
+
+def classify_message(text):
+    """
+    Classify an SMS message as HAM or SPAM
+    
+    Args:
+        text: Input SMS message text
+        
+    Returns:
+        Dictionary with classification results
+    """
+    if not text or text.strip() == "":
+        return {"Error": "Please enter a message"}
+    
+    # Tokenize input
+    inputs = tokenizer(
+        text, 
+        return_tensors="pt", 
+        truncation=True, 
+        padding=True, 
+        max_length=128
+    )
+    
+    # Get prediction
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    
+    # Extract probabilities
+    ham_prob = predictions[0][0].item()
+    spam_prob = predictions[0][1].item()
+    
+    return {
+        "HAM (Legitimate)": ham_prob,
+        "SPAM": spam_prob
+    }
+
+# Example messages
+examples = [
+    ["Congratulations! You've won a $1000 gift card. Click here to claim now!"],
+    ["Hey, are we still meeting for lunch tomorrow at 12?"],
+    ["URGENT! Your account has been suspended. Verify now to restore access."],
+    ["Thanks for your help today. I really appreciate it!"],
+    ["FREE entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121"],
+    ["I'll call you later tonight after work."],
+    ["WINNER!! As a valued customer, you have been selected to receive £900 prize reward!"],
+    ["Can you pick up some milk on your way home?"],
+]
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=classify_message,
+    inputs=gr.Textbox(
+        lines=5,
+        placeholder="Enter SMS message here...",
+        label="SMS Message"
+    ),
+    outputs=gr.Label(num_top_classes=2, label="Classification Results"),
+    title="📱 SMS Spam Detection",
+    description="""
+    This classifier uses a fine-tuned BERT model to detect spam in SMS messages.
+    
+    **Performance:** 99.16% Accuracy | 97.30% Precision | 96.43% Recall
+    
+    Simply enter an SMS message below and the model will classify it as either legitimate (HAM) or spam (SPAM).
+    """,
+    examples=examples,
+    theme=gr.themes.Soft(),
+    article="""
+    ### About This Model
+    
+    This model is trained on the SMS Spam Collection dataset and achieves state-of-the-art performance in spam detection.
+    
+    **Model:** `niru-nny/SMS_Spam_Detection`  
+    **Base Architecture:** BERT (bert-base-uncased)  
+    **Dataset:** SMS Spam Collection (5,574 messages)
+    
+    ### Use Cases
+    - 📧 Spam filtering in messaging apps
+    - 🛡️ Protection against phishing attempts
+    - 🔍 Content moderation
+    - 💰 Fraud detection
+    
+    ### Tips for Best Results
+    - The model works best with English text messages
+    - Keep messages under 128 words for optimal performance
+    - The model is trained on SMS-style language (abbreviations, slang included)
+    
+    ---
+    
+    **License:** MIT | [Model Card](https://huggingface.co/niru-nny/SMS_Spam_Detection) | [GitHub](https://github.com/niru-nny)
+    """
+)
+
+if __name__ == "__main__":
+    demo.launch()