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This paper describes our system on SemEval-2023 Task 10: ExplainableDetection of Online Sexism (EDOS). This work aims to design an automatic systemfor detecting and classifying sexist content in online spaces. We propose a setof transformer-based pre-trained models with task-adaptive pretraining andensemble learning. The main contributions of our system include analyzing theperformance of different transformer-based pre-trained models and combiningthese models, as well as providing an efficient method using large amounts ofunlabeled data for model adaptive pretraining. We have also explored severalother strategies. On the test dataset, our system achieves F1-scores of 83%,64%, and 47% on subtasks A, B, and C, respectively.

IUST_NLP at SemEval-2023 Task 10: Explainable Detecting Sexism with Transformers and Task-adaptive Pretraining