This paper presents one of the top winning solution systems for task 7 at SemEval2021, "Ha-Hackathon: Detecting and Rating Humor and Offense". The shared task 7 consists of two parts, task-1 with three sub-tasks 1a,1b, and 1c, and task-2. The goal of task-1 is to predict if the text would be considered humorous or not, then if it is yes, predict how humorous it is and whether the humor rating would be perceived as controversial. The goal of task-2 is to predict how the text is considered offensive for users in general. The proposed solution, Sar-casmDet, has been developed using RoBERTa pre-trained model with ensemble techniques. The paper describes the submitted system's architecture with the experiments and the hyperparameter fine-tuning that led to this robust system. Our model ranked third and fourth places out of 50 teams in tasks 1c and 1a with F1-Score of 0.6270 and 0.9675, respectively. At the same time, the model ranked one of the top 10 models in task 1b and task 2 with RMSE scores of 0.5446 and 0.4469, respectively.

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