semvec/pypi-packages huggingface dataset:
phishing-detection-py 0.1.6
A Python library for phishing detection using machine learning models.
License: Apache-2.0


# Phishing Detection Framework

## Overview
The Phishing Detection Framework provides an easy-to-use Python library for detecting phishing attempts in URLs and email messages. It leverages state-of-the-art machine learning models from Hugging Face to ensure high accuracy and reliability.

### Key Features
- Supports both URL and email phishing detection.
- Uses pre-trained models for high performance:
  - [`bert-finetuned-phishing`](https://huggingface.co/ealvaradob/bert-finetuned-phishing)
  - [`phishing-email-detection-distilbert_v2.4.1`](https://huggingface.co/cybersectony/phishing-email-detection-distilbert_v2.4.1)
- Batch processing for multiple inputs.
- Flexible API for customization and integration.
- Open-source and built for developers.

## Installation
Follow the steps outlined in the [Installation Documentation](docs/installation.md) to install the library and its dependencies.

## Usage
Refer to the [Usage Documentation](docs/usage.md) for examples and instructions on how to:
- Detect phishing in single URLs or emails.
- Process batches of URLs or emails.
- Customize the framework for your use case.

### Quick Start Example
```python
from phishing_detection_py import PhishingDetector

detector = PhishingDetector(model_type="url")
result = detector.predict("http://example-phishing-site.com")
print(result)
```

## Documentation
Full documentation is available in the `docs/` directory:
- [API Reference](docs/api_reference.md)
- [Installation Guide](docs/installation.md)
- [Usage Examples](docs/usage.md)

## License
This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for details.

## Contributing
We welcome contributions! Please read the [Contributing Guide](CONTRIBUTING.md) for guidelines.

## Acknowledgments
- Hugging Face for providing pre-trained models and tools.
- Inspiration from the `cybersectony` and `ealvaradob` models.

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