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This paper presents the system that we have developed while solving thisshared task on violence inciting text detection in Bangla. We explain both thetraditional and the recent approaches that we have used to make our modelslearn. Our proposed system helps to classify if the given text contains anythreat. We studied the impact of data augmentation when there is a limiteddataset available. Our quantitative results show that finetuning amultilingual-e5-base model performed the best in our task compared to othertransformer-based architectures. We obtained a macro F1 of 68.11\% in the testset and our performance in this shared task is ranked at 23 in the leaderboard.

BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bengali