  Hostile content on social platforms is ever increasing. This has led to the
need for proper detection of hostile posts so that appropriate action can be
taken to tackle them. Though a lot of work has been done recently in the
English Language to solve the problem of hostile content online, similar works
in Indian Languages are quite hard to find. This paper presents a transfer
learning based approach to classify social media (i.e Twitter, Facebook, etc.)
posts in Hindi Devanagari script as Hostile or Non-Hostile. Hostile posts are
further analyzed to determine if they are Hateful, Fake, Defamation, and
Offensive. This paper harnesses attention based pre-trained models fine-tuned
on Hindi data with Hostile-Non hostile task as Auxiliary and fusing its
features for further sub-tasks classification. Through this approach, we
establish a robust and consistent model without any ensembling or complex
pre-processing. We have presented the results from our approach in
CONSTRAINT-2021 Shared Task on hostile post detection where our model performs
extremely well with 3rd runner up in terms of Weighted Fine-Grained F1 Score.
