The rise of social media in Bangladesh has increased abusive and hateful content, which is difficult to detect due to the informal nature of Bangla and limited resources. The BLP 2025 shared task addressed this challenge with Subtask 1A (multi-label abuse categories) and Subtask 1B (target identification). We propose a parameter-efficient model using a frozen BanglaBERT backbone with hierarchical attention to capture token level importance across hidden layers. Context vectors are aggregated for classification, combining syntactic and semantic features. On Subtask 1A, our frozen model achieved a micro-F1 of 0.7178, surpassing the baseline of 0.7100, while the unfrozen variant scored 0.7149. Our submissions ranked 15th (Subtask 1A) and 12th (Subtask 1B), showing that layer-wise attention with a frozen backbone can effectively detect abusive Bangla text.

Social networks extensively feature memes, particularly cartoon images, as a prevalent form of communication often conveying complex sentiments or harmful content. Detecting such content, particularly when it involves Bengali and English text, remains a multimodal challenge. This paper introduces ***CMBan***, a novel and culturally relevant dataset of 2,641 annotated cartoon memes. It addresses meme classification based on their sentiment across five key categories: Humor, Sarcasm, Offensiveness, Motivational Content, and Overall Sentiment, incorporating both image and text features. Our curated dataset specifically aids in detecting nuanced offensive content and navigating complexities of pure Bengali, English, or code-mixed Bengali-English languages. Through rigorous experimentation involving over 12 multimodal models, including monolingual, multilingual, and proprietary architectures, and utilizing prompting methods like Chain-Of-Thought (CoT), findings suggest this cartoon-based, code-mixed meme content poses substantial understanding challenges. Experimental results demonstrate that closed models excel over open models. While the LoRA fine-tuning strategy equalizes performance across model architectures and improves classification of challenging aspects in multilingual meme contexts, this work advances meme classification by providing effective solution for detecting harmful content in multilingual meme contexts.