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 https://huggingface.co/faisalq/SaudiBERT.

本文介绍了 SaudiBERT，这是一个在沙特方言文本上进行预训练的单一方言阿拉伯语言模型。通过与其他六个多方言阿拉伯语言模型在 11 个评估数据集上的比较，分为情感分析和文本分类两组，SaudiBERT 在这两组中分别达到了 86.15% 和 87.86% 的平均 F1 得分，显著优于其他比较模型。此外，我们还介绍了两个新的沙特方言语料库：沙特推特巨型语料库（STMC），其中包含超过 1.41 亿条沙特方言推文，以及沙特论坛语料库（SFC），其中包含从五个沙特在线论坛收集的 15.2 GB 文本。这两个语料库被用于预训练所提出的模型，它们是文献中报道的最大的沙特方言语料库。结果确认了 SaudiBERT 在理解和分析使用沙特方言表达的阿拉伯文本方面的有效性，在大多数任务中实现了最先进的结果，并超越了研究中包含的其他语言模型。SaudiBERT 模型可以在 https://huggingface.co/faisalq/SaudiBERT 公开获取。