  This paper describes our participation in SemEval-2023 Task 10, whose goal is
the detection of sexism in social media. We explore some of the most popular
transformer models such as BERT, DistilBERT, RoBERTa, and XLNet. We also study
different data augmentation techniques to increase the training dataset. During
the development phase, our best results were obtained by using RoBERTa and data
augmentation for tasks B and C. However, the use of synthetic data does not
improve the results for task C. We participated in the three subtasks. Our
approach still has much room for improvement, especially in the two
fine-grained classifications. All our code is available in the repository
https://github.com/isegura/hulat_edos.
