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In this paper, we have worked on interpretability, trust, and understandingof the decisions made by models in the form of classification tasks. The taskis divided into 3 subtasks. The first task consists of determining BinarySexism Detection. The second task describes the Category of Sexism. The thirdtask describes a more Fine-grained Category of Sexism. Our work exploressolving these tasks as a classification problem by fine-tuningtransformer-based architecture. We have performed several experiments with ourarchitecture, including combining multiple transformers, using domain adaptivepretraining on the unlabelled dataset provided by Reddit and Gab, Jointlearning, and taking different layers of transformers as input to aclassification head. Our system (with team name Attention) was able to achievea macro F1 score of 0.839 for task A, 0.5835 macro F1 score for task B and0.3356 macro F1 score for task C at the Codalab SemEval Competition. Later weimproved the accuracy of Task B to 0.6228 and Task C to 0.3693 in the test set.

Attention at SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS)