  Arabic dialect identification is a complex problem for a number of inherent
properties of the language itself. In this paper, we present the experiments
conducted, and the models developed by our competing team, Mawdoo3 AI, along
the way to achieving our winning solution to subtask 1 of the Nuanced Arabic
Dialect Identification (NADI) shared task. The dialect identification subtask
provides 21,000 country-level labeled tweets covering all 21 Arab countries. An
unlabeled corpus of 10M tweets from the same domain is also presented by the
competition organizers for optional use. Our winning solution itself came in
the form of an ensemble of different training iterations of our pre-trained
BERT model, which achieved a micro-averaged F1-score of 26.78% on the subtask
at hand. We publicly release the pre-trained language model component of our
winning solution under the name of Multi-dialect-Arabic-BERT model, for any
interested researcher out there.
