Abstract: Extracting of NERs from low-resource languages and recognizing their types is one of the important tasks in the entity extraction domain. Recently many studies have been conducted in this area of research. In our study, we introduce a system for identifying complex entities and recognizing their types from low-resource language Bangla, which was published in SemEval Task 2 MulitCoNER II 2023. For this sequence labeling task, we use a pre-trained language model built on a natural language processing framework. Our team name in this competition is \textbackslash{}textbf\{MLlab4CS\}. Our model \textbackslash{}textbf\{\textbackslash{}Muril\} produces a macro average F-score of 76.27\textbackslash{}\%, which is a comparable result for this competition.