  This paper presents the system that we have developed while solving this
shared task on violence inciting text detection in Bangla. We explain both the
traditional and the recent approaches that we have used to make our models
learn. Our proposed system helps to classify if the given text contains any
threat. We studied the impact of data augmentation when there is a limited
dataset available. Our quantitative results show that finetuning a
multilingual-e5-base model performed the best in our task compared to other
transformer-based architectures. We obtained a macro F1 of 68.11\% in the test
set and our performance in this shared task is ranked at 23 in the leaderboard.
