Abstract: As social media platforms grow, so too does the volume of hate speech and negative sentiment expressed towards particular social groups. In this paper, we describe our approach to SemEval-2023 Task 10, involving the detection and classification of online sexism (abuse directed towards women), with fine-grained categorisations intended to facilitate the development of a more nuanced understanding of the ideologies and processes through which online sexism is expressed. We experiment with several approaches involving language model finetuning, class-specific adapters, and pseudo-labelling. Our best-performing models involve the training of adapters specific to each subtask category (combined via fusion layers) using a weighted loss function, in addition to performing naive pseudo-labelling on a large quantity of unlabelled data. We successfully outperform the baseline models on all 3 subtasks, placing 56th (of 84) on Task A, 43rd (of 69) on Task B,and 37th (of 63) on Task C.