  Recently the NLP community has started showing interest towards the
challenging task of Hostile Post Detection. This paper present our system for
Shared Task at Constraint2021 on "Hostile Post Detection in Hindi". The data
for this shared task is provided in Hindi Devanagari script which was collected
from Twitter and Facebook. It is a multi-label multi-class classification
problem where each data instance is annotated into one or more of the five
classes: fake, hate, offensive, defamation, and non-hostile. We propose a two
level architecture which is made up of BERT based classifiers and statistical
classifiers to solve this problem. Our team 'Albatross', scored 0.9709 Coarse
grained hostility F1 score measure on Hostile Post Detection in Hindi subtask
and secured 2nd rank out of 45 teams for the task. Our submission is ranked 2nd
and 3rd out of a total of 156 submissions with Coarse grained hostility F1
score of 0.9709 and 0.9703 respectively. Our fine grained scores are also very
encouraging and can be improved with further finetuning. The code is publicly
available.
