  The Explainable Detection of Online Sexism task presents the problem of
explainable sexism detection through fine-grained categorisation of sexist
cases with three subtasks. Our team experimented with different ways to combat
class imbalance throughout the tasks using data augmentation and loss
alteration techniques. We tackled the challenge by utilising ensembles of
Transformer models trained on different datasets, which are tested to find the
balance between performance and interpretability. This solution ranked us in
the top 40\% of teams for each of the tracks.
