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In this paper, we describe the PUM team's entry to the SemEval-2020 Task 12.Creating our solution involved leveraging two well-known pretrained models usedin natural language processing: BERT and XLNet, which achieve state-of-the-artresults in multiple NLP tasks. The models were fine-tuned for each subtaskseparately and features taken from their hidden layers were combined and fedinto a fully connected neural network. The model using aggregated Transformerfeatures can serve as a powerful tool for offensive language identificationproblem. Our team was ranked 7th out of 40 in Sub-task C - Offense targetidentification with 64.727% macro F1-score and 64th out of 85 in Sub-task A -Offensive language identification (89.726% F1-score).

PUM at SemEval-2020 Task 12: Aggregation of Transformer-based models' features for offensive language recognition