  The detection of hate speech online has become an important task, as
offensive language such as hurtful, obscene and insulting content can harm
marginalized people or groups. This paper presents TU Berlin team experiments
and results on the task 1A and 1B of the shared task on hate speech and
offensive content identification in Indo-European languages 2021. The success
of different Natural Language Processing models is evaluated for the respective
subtasks throughout the competition. We tested different models based on
recurrent neural networks in word and character levels and transfer learning
approaches based on Bert on the provided dataset by the competition. Among the
tested models that have been used for the experiments, the transfer
learning-based models achieved the best results in both subtasks.
