README.md

metadata

title: PII-Guard – Deep Learning Model for PII Classification
emoji: 🔒
colorFrom: red
colorTo: purple
sdk: streamlit
sdk_version: 1.40.0
app_file: app.py
pinned: false
license: mit

PIIDetector 🔒

Detecting Personally Identifiable Information (PII) using BiLSTM-CRF model

🚀 Demo

Watch Demo Video

✨ Features

• PII Detection: Identify various types of Personally Identifiable Information in text
• BiLSTM-CRF Model: Utilizes a powerful deep learning model for sequence labeling
• Streamlit Web Interface: User-friendly interface for easy interaction
• Multiple PII Types: Detects various PII entities including names, addresses, financial information, and more

📦 Installation

1. Clone the repository

   git clone https://github.com/yourusername/PIIDetector.git
   cd PIIDetector
   

2. Create and activate a virtual environment

   # Create a virtual environment
   python -m venv .venv
   
   # Activate it
   # On Linux/Mac:
   source .venv/bin/activate
   # On Windows:
   .venv\Scripts\activate
   

3. Install dependencies

   pip install -r requirements.txt
   

🚀 Usage

1. Run the Streamlit app

   streamlit run app.py
   

2. Enter text in the text area and click "Analyze" to detect PII entities

3. View results in the table showing tokens and their predicted PII labels

🛠 Configuration

The application uses a pre-trained BiLSTM-CRF model located in the models/ directory. The model supports the following PII entity types:

• Personal Information (names, age, gender, etc.)
• Contact Information (emails, phone numbers, addresses)
• Financial Information (credit cards, account numbers, IBAN, etc.)
• Identification Numbers (SSN, passport numbers, etc.)
• And many more...

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

• Hugging Face Transformers
• PyTorch
• Streamlit