  In this paper, we introduce SaudiBERT, a monodialect Arabic language model
pretrained exclusively on Saudi dialectal text. To demonstrate the model's
effectiveness, we compared SaudiBERT with six different multidialect Arabic
language models across 11 evaluation datasets, which are divided into two
groups: sentiment analysis and text classification. SaudiBERT achieved average
F1-scores of 86.15\% and 87.86\% in these groups respectively, significantly
outperforming all other comparative models. Additionally, we present two novel
Saudi dialectal corpora: the Saudi Tweets Mega Corpus (STMC), which contains
over 141 million tweets in Saudi dialect, and the Saudi Forums Corpus (SFC),
which includes 15.2 GB of text collected from five Saudi online forums. Both
corpora are used in pretraining the proposed model, and they are the largest
Saudi dialectal corpora ever reported in the literature. The results confirm
the effectiveness of SaudiBERT in understanding and analyzing Arabic text
expressed in Saudi dialect, achieving state-of-the-art results in most tasks
and surpassing other language models included in the study. SaudiBERT model is
publicly available on \url{https://huggingface.co/faisalq/SaudiBERT}.
