This paper presents contribution to PolEval 20191 automatic cyberbullying detection task. The goal of the task is to classify tweets as harmful or normal. Firstly, the data is preprocessed. Then two classifiers adjusted to the problem are tested: Flair and fastText. Flair utilizes

character-based language models, which are evaluated using perplexity. Both classifiers obtained similar scores on test data