In this paper, we propose a system for generating Python code from Bangla prompts. Our approach fine-tunes open-source models with parameter-efficient techniques and leverages proprietary models via prompting. To enhance the reasoning of smaller models, we adopt a Chain-of-Thought (CoT) augmented fine-tuning, enabling them to learn intermediate reasoning steps before generating code. A self-refinement loop further improves performance by iteratively critiquing and correcting code based on execution feedback. We also employ few-shot prompting to guide inference more effectively. Applied to both open-source and proprietary models, this pipeline achieved its best results with Gemini 2.5 Pro, where our system ranked 4th on the competition leaderboard with a Pass@1 score of 0.85. We conclude with a detailed analysis of these findings.

This paper describes our participation in Task1 (VITD) of BLP Workshop 1 at EMNLP 2023,focused on the detection and categorizationof threats linked to violence, which could po-tentially encourage more violent actions. Ourapproach involves fine-tuning of pre-trainedtransformer models and employing techniqueslike self-training with external data, data aug-mentation through back-translation, and en-semble learning (bagging and majority voting).Notably, self-training improves performancewhen applied to data from external source butnot when applied to the test-set. Our anal-ysis highlights the effectiveness of ensemblemethods and data augmentation techniques inBangla Text Classification. Our system ini-tially scored 0.70450 and ranked 19th amongthe participants but post-competition experi-ments boosted our score to 0.72740.