  Due to the exponentially increasing reach of social media, it is essential to
focus on its negative aspects as it can potentially divide society and incite
people into violence. In this paper, we present our system description of work
on the shared task ComMA@ICON, where we have to classify how aggressive the
sentence is and if the sentence is gender-biased or communal biased. These
three could be the primary reasons to cause significant problems in society. As
team Hypers we have proposed an approach that utilizes different pretrained
models with Attention and mean pooling methods. We were able to get Rank 3 with
0.223 Instance F1 score on Bengali, Rank 2 with 0.322 Instance F1 score on
Multi-lingual set, Rank 4 with 0.129 Instance F1 score on Meitei and Rank 5
with 0.336 Instance F1 score on Hindi. The source code and the pretrained
models of this work can be found here.
