  This study presents EgyBERT, an Arabic language model pretrained on 10.4 GB
of Egyptian dialectal texts. We evaluated EgyBERT's performance by comparing it
with five other multidialect Arabic language models across 10 evaluation
datasets. EgyBERT achieved the highest average F1-score of 84.25% and an
accuracy of 87.33%, significantly outperforming all other comparative models,
with MARBERTv2 as the second best model achieving an F1-score 83.68% and an
accuracy 87.19%. Additionally, we introduce two novel Egyptian dialectal
corpora: the Egyptian Tweets Corpus (ETC), containing over 34.33 million tweets
(24.89 million sentences) amounting to 2.5 GB of text, and the Egyptian Forums
Corpus (EFC), comprising over 44.42 million sentences (7.9 GB of text)
collected from various Egyptian online forums. Both corpora are used in
pretraining the new model, and they are the largest Egyptian dialectal corpora
to date reported in the literature. Furthermore, this is the first study to
evaluate the performance of various language models on Egyptian dialect
datasets, revealing significant differences in performance that highlight the
need for more dialect-specific models. The results confirm the effectiveness of
EgyBERT model in processing and analyzing Arabic text expressed in Egyptian
dialect, surpassing other language models included in the study. EgyBERT model
is publicly available on \url{https://huggingface.co/faisalq/EgyBERT}.
