  Online hatred is a growing concern on many social media platforms. To address
this issue, different social media platforms have introduced moderation
policies for such content. They also employ moderators who can check the posts
violating moderation policies and take appropriate action. Academicians in the
abusive language research domain also perform various studies to detect such
content better. Although there is extensive research in abusive language
detection in English, there is a lacuna in abusive language detection in low
resource languages like Hindi, Urdu etc. In this FIRE 2021 shared task -
"HASOC- Abusive and Threatening language detection in Urdu" the organizers
propose an abusive language detection dataset in Urdu along with threatening
language detection. In this paper, we explored several machine learning models
such as XGboost, LGBM, m-BERT based models for abusive and threatening content
detection in Urdu based on the shared task. We observed the Transformer model
specifically trained on abusive language dataset in Arabic helps in getting the
best performance. Our model came First for both abusive and threatening content
detection with an F1scoreof 0.88 and 0.54, respectively.
