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