This paper presents our system developed for the AbjadNLP Shared Task 4 on Medical Text Classification in Arabic, which aims to assign Arabic medical question-answer pairs to a predefined set of medical categories. The task poses significant challenges due to severe class imbalance across 82 categories and the linguistic complexity of domain-specific Arabic medical text. To address these challenges, we propose an imbalance-aware training framework that combines targeted data augmentation for minority classes with class-weighted focal loss during fine-tuning. We evaluate multiple Arabic pretrained transformer models under a unified training configuration and further improve robustness through a majority-voting ensemble of the best-performing models. Our approach achieves competitive performance, ranking 15th on the private leaderboard with a macro F1 score of 0.4052, demonstrating the effectiveness of combining different data augmentation techniques, imbalance-aware training objectives, and ensemble learning for large-scale, highly imbalanced Arabic medical text classification. The code is available on GitHub.

In this paper, we present our submission to Task 2 of the BLP-2025 shared task on code generation from Bangla instructions. Our approach focused on enhancing instruction quality through translation and improving model performance with a two-stage ensemble strategy. We evaluated two proprietary and several open-source models under three instruction settings: original Bangla instructions, Bangla instructions translated into English using Facebook NLLB, and instructions rewritten in English with GPT-4.1. Experimental results showed that GPT-4.1-rewritten instructions consistently achieved the highest accuracy across models. For final predictions, we used a two-stage ensemble, achieving a pass@1 score of 80.0% on the hidden test set and securing 12th place on the official leaderboard. Additionally, we conducted a qualitative analysis of selected translations to illustrate how variations in instruction phrasing influenced model outputs.