  This paper presents our contribution to PolEval 2019 Task 6: Hate speech and
bullying detection. We describe three parallel approaches that we followed:
fine-tuning a pre-trained ULMFiT model to our classification task, fine-tuning
a pre-trained BERT model to our classification task, and using the TPOT library
to find the optimal pipeline. We present results achieved by these three tools
and review their advantages and disadvantages in terms of user experience. Our
team placed second in subtask 2 with a shallow model found by TPOT: a~logistic
regression classifier with non-trivial feature engineering.
