  In this paper, we explore the effects of language variants, data sizes, and
fine-tuning task types in Arabic pre-trained language models. To do so, we
build three pre-trained language models across three variants of Arabic: Modern
Standard Arabic (MSA), dialectal Arabic, and classical Arabic, in addition to a
fourth language model which is pre-trained on a mix of the three. We also
examine the importance of pre-training data size by building additional models
that are pre-trained on a scaled-down set of the MSA variant. We compare our
different models to each other, as well as to eight publicly available models
by fine-tuning them on five NLP tasks spanning 12 datasets. Our results suggest
that the variant proximity of pre-training data to fine-tuning data is more
important than the pre-training data size. We exploit this insight in defining
an optimized system selection model for the studied tasks.
