TAE: Text anonymization evaluator

Text anonymization is essential for privacy-compliant data sharing, yet its evaluation remains challenging. Existing evaluation metrics are fragmented, and many rely on manual annotations, which hampers applicability and introduces subjectivity. We present the Text Anonymization Evaluator (TAE), a Python-based package that consolidates both traditional and state-of-the-art metrics for assessing privacy and utility in anonymized text. By integrating these metrics into a unified and extensible toolkit, TAE enables systematic, scalable, and objective evaluation and comparison of text anonymization methods.