No abstract

The performance of existing transformer-based language models in providing state-of-the-art results on many downstream tasks is well established. However, these models tend to be limited to high-resource languages or are multilingual in nature. The availability of models dedicated to Arabic dialects is limited, and even those that exist primarily support dialects written in Arabic script. This study presents the first BERT models for Moroccan Arabic dialect, also known as Darija, called DarijaBERT, DarijaBERT-arabizi, and DarijaBERT-mix. These models are trained on the largest Arabic monodialectal corpus, supporting both Arabic and Latin character representations of the Moroccan dialect. The models' performance is evaluated and compared to existing multidialectal and multilingual models on four distinct downstream tasks, demonstrating state-of-the-art results. The data collection methodology and pre-training process are described, and the Moroccan Topic Classification Dataset (MTCD) is introduced as the first dataset for topic classification in the Moroccan Arabic dialect. The pre-trained models and MTCD dataset are available to the scientific community.