SaudiBERT: A Large Language Model Pretrained on Saudi Dialect Corpora
This work addresses the need for better natural language processing tools for Saudi dialect Arabic, which is an incremental improvement over existing multidialect models.
The authors tackled the problem of understanding Saudi dialect Arabic by pretraining SaudiBERT exclusively on Saudi dialect corpora, achieving average F1-scores of 86.15% and 87.86% in sentiment analysis and text classification tasks, significantly outperforming six other multidialect Arabic models.
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}.