Welcome to 𝜏 Tau-Eval’s documentation!

Tau-Eval is a user-friendly, modular, and customizable Python library designed to benchmark and evaluate text anonymization algorithms. It enables granular analysis of anonymization impacts from both privacy and utility perspectives. Tau-Eval seamlessly integrates with LiteLLM and 🤗 Hugging Face to support a wide range of datasets, models, and evaluation metrics.

Contents:

• Installation
  • From PyPI
  • From source
• Quickstart
  • 1. Define Your Anonymization Model
  • 2. Configure Evaluation Metrics
  • 3. Instantiate Tasks
  • 4. Configure and Run Your Experiment
  • 5. Visualize Results
• Tutorials
• Citation
• tau_eval
  • Tau-Eval package