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This paper describes our system for SemEval-2021 Task 5 on Toxic SpansDetection. We developed ensemble models using BERT-based neural architecturesand post-processing to combine tokens into spans. We evaluated severalpre-trained language models using various ensemble techniques for toxic spanidentification and achieved sizable improvements over our baseline fine-tunedBERT models. Finally, our system obtained a F1-score of 67.55% on test data.

MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with Pre-trained Language Models for Toxic Spans Detection