This paper describes our system used in theBLP-2025 Task 1: Hate Speech Detection.We participated in Subtask 1A and Subtask1B, addressing hate speech classification inBangla text. Our approach employs a unified architecture that integrates BanglaBERTembeddings with multiple parallel processingbranches based on GRUs and CNNs, followedby attention and dense layers for final classification. The model is designed to capture bothcontextual semantics and local linguistic cues,enabling robust performance across subtasks.The proposed system demonstrated high competitiveness, obtaining 0.7345 micro F1-Score(2nd place) in Subtask 1A and 0.7317 microF1-Score (5th place) in Subtask 1B.

Emotions influence human behavior, speech, and expression, making their detection crucial in Natural Language Processing (NLP). While most prior research has focused on single-label emotion classification, real-world emotions are often multi-faceted. This paper describes our participation in SemEval-2025 Task 11, Track A (Multi-label Emotion Detection) and Track B (Emotion Intensity). We employed BERT as a feature extractor with stacked GRUs, which resulted in better stability and convergence. Our system was evaluated across 19 languages for Track A and 9 languages for Track B.