Language models have proven to be very useful when adapted to specific domains. Nonetheless, little research has been done on the adaptation of domain-specific BERT models in the French language. In this paper, we focus on creating a language model adapted to French legal text with the goal of helping law professionals. We conclude that some specific tasks do not benefit from generic language models pre-trained on large amounts of data. We explore the use of smaller architectures in domain-specific sub-languages and their benefits for French legal text. We prove that domain-specific pre-trained models can perform better than their equivalent generalised ones in the legal domain. Finally, we release JuriBERT, a new set of BERT models adapted to the French legal domain.
@article{arxiv.2110.01485,
title = {JuriBERT: A Masked-Language Model Adaptation for French Legal Text},
author = {Stella Douka and Hadi Abdine and Michalis Vazirgiannis and Rajaa El Hamdani and David Restrepo Amariles},
journal= {arXiv preprint arXiv:2110.01485},
year = {2022}
}