  Subjectivity and difference of opinion are key social phenomena, and it is
crucial to take these into account in the annotation and detection process of
derogatory textual content. In this paper, we use four datasets provided by
SemEval-2023 Task 11 and fine-tune a BERT model to capture the disagreement in
the annotation. We find individual annotator modeling and aggregation lowers
the Cross-Entropy score by an average of 0.21, compared to the direct training
on the soft labels. Our findings further demonstrate that annotator metadata
contributes to the average 0.029 reduction in the Cross-Entropy score.
