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In this paper, we present our approach for the "Nuanced Arabic DialectIdentification (NADI) Shared Task 2023". We highlight our methodology forsubtask 1 which deals with country-level dialect identification. Recognizingdialects plays an instrumental role in enhancing the performance of variousdownstream NLP tasks such as speech recognition and translation. The task usesthe Twitter dataset (TWT-2023) that encompasses 18 dialects for the multi-classclassification problem. Numerous transformer-based models, pre-trained onArabic language, are employed for identifying country-level dialects. Wefine-tune these state-of-the-art models on the provided dataset. The ensemblingmethod is leveraged to yield improved performance of the system. We achieved anF1-score of 76.65 (11th rank on the leaderboard) on the test dataset.

Mavericks at NADI 2023 Shared Task: Unravelling Regional Nuances through Dialect Identification using Transformer-based Approach