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.

本研究通过开发系统解决孟加拉暴力煽动文本检测的共享任务，探讨了我们所采用的传统和最新方法，该系统有助于分类给定文本是否包含威胁。实验研究了在有限数据集情况下数据增强的影响，定量结果表明，与其他基于变压器结构的模型相比，微调多语言 - e5 基础模型在任务中表现最佳。 在测试集中，宏 F1 得分达到了 68.11％，并且在该共享任务的排行榜中排名第 23 位。