Rifat, Rakib HossainShruti, Abanti ChakrabortyKamal, MarufaSadeque, Farig2026-09-062026-09-062023-01-01Rifat, R. H., Shruti, A., Kamal, M., & Sadeque, F. (2023). Acsmkrhr at semeval-2023 task 10: Explainable online sexism detection(Edos). Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), 724–732. https://doi.org/10.18653/v1/2023.semeval-1.9997819594299992-s2.0-85175398914https://hdl.handle.net/10361/29779People are expressing their opinions online for a lot of years now. Although these opinions and comments provide people an opportunity of expressing their views, there is a lot of hate speech that can be found online. More specifically, sexist comments are very popular affecting and creating a negative impact on a lot of women and girls online. This paper describes the approaches of the SemEval-2023 Task 10 competition for Explainable Online Sexism Detection (EDOS). The task has been divided into 3 subtasks, introducing different classes of sexist comments. We have approached these tasks using the bert-cased and uncased models which are trained on the annotated dataset that has been provided in the competition. Task A provided the best F1 score of 80% on the test set, and tasks B and C provided 58% and 40% respectively. © 2023 Association for Computational Linguistics.724 - 732en-USAnnotated datasetsDifferent classF1 scoresSubtaskTest setsTest tasksSexism in mass media.Natural language processing (Computer science).Computational linguistics.Women--Crimes against.Conference Paper10.18653/v1/2023.semeval-1.99