This paper summarizes the solution of the Nuanced Arabic Dialect Identification (NADI) 2022 shared task. It consists of two subtasks: a country-level Arabic Dialect Identification (ADID) and an Arabic Sentiment Analysis (ASA). Our work shows the importance of using domain-adapted models and language-specific pre-processing in NLP task solutions. We implement a simple but strong baseline technique to increase the stability of fine-tuning settings to obtain a good generalization of models. Our best model for the Dialect Identification subtask achieves a Macro F-1 score of 25.54% as an average of both Test-A (33.89%) and Test-B (19.19%) F-1 scores. We also obtained a Macro F-1 score of 74.29% of positive and negative sentiments only, in the Sentiment Analysis task.