  In this study, we test standard neural network architectures (CNN, LSTM,
BiLSTM) and recently appeared BERT architectures on previous Russian sentiment
evaluation datasets. We compare two variants of Russian BERT and show that for
all sentiment tasks in this study the conversational variant of Russian BERT
performs better. The best results were achieved by BERT-NLI model, which treats
sentiment classification tasks as a natural language inference task. On one of
the datasets, this model practically achieves the human level.
