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The effectiveness of the BERT model on multiple linguistic tasks has beenwell documented. On the other hand, its potentials for narrow and specificdomains such as Legal, have not been fully explored. In this paper, we examinehow BERT can be used in the Arabic legal domain and try customizing thislanguage model for several downstream tasks using several differentdomain-relevant training and testing datasets to train BERT from scratch. Weintroduce the AraLegal-BERT, a bidirectional encoder Transformer-based modelthat have been thoroughly tested and carefully optimized with the goal toamplify the impact of NLP-driven solution concerning jurisprudence, legaldocuments, and legal practice. We fine-tuned AraLegal-BERT and evaluated itagainst three BERT variations for Arabic language in three natural languagesunderstanding (NLU) tasks. The results show that the base version ofAraLegal-BERT achieve better accuracy than the general and original BERT overthe Legal text.

AraLegal-BERT: A pretrained language model for Arabic Legal text