  Recent advances in Natural Language Processing have demonstrated the
effectiveness of pretrained language models like BERT for a variety of
downstream tasks. We present GiusBERTo, the first BERT-based model specialized
for anonymizing personal data in Italian legal documents. GiusBERTo is trained
on a large dataset of Court of Auditors decisions to recognize entities to
anonymize, including names, dates, locations, while retaining contextual
relevance. We evaluate GiusBERTo on a held-out test set and achieve 97%
token-level accuracy. GiusBERTo provides the Italian legal community with an
accurate and tailored BERT model for de-identification, balancing privacy and
data protection.
