  Hateful and Toxic content has become a significant concern in today's world
due to an exponential rise in social media. The increase in hate speech and
harmful content motivated researchers to dedicate substantial efforts to the
challenging direction of hateful content identification. In this task, we
propose an approach to automatically classify hate speech and offensive
content. We have used the datasets obtained from FIRE 2019 and 2020 shared
tasks. We perform experiments by taking advantage of transfer learning models.
We observed that the pre-trained BERT model and the multilingual-BERT model
gave the best results. The code is made publically available at
https://github.com/suman101112/hasoc-fire-2020.
