  As the reach of the internet increases, pejorative terms started flooding
over social media platforms. This leads to the necessity of identifying hostile
content on social media platforms. Identification of hostile contents on
low-resource languages like Hindi poses different challenges due to its diverse
syntactic structure compared to English. In this paper, we develop a simple
ensemble based model on pre-trained mBERT and popular classification algorithms
like Artificial Neural Network (ANN) and XGBoost for hostility detection in
Hindi posts. We formulated this problem as binary classification (hostile and
non-hostile class) and multi-label multi-class classification problem (for more
fine-grained hostile classes). We received third overall rank in the
competition and weighted F1-scores of ~0.969 and ~0.61 on the binary and
multi-label multi-class classification tasks respectively.
