ArabicMedicalBERT-QA-82 at AbjadMed: Fighting Class Imbalance in Arabic Medical Text Classification

AbstractWe present a supervised system for Arabic medical question-answer classification developed for the AbjadMed shared task. The task involves assigning one of 82 highly imbalanced medical categories and is evaluated using macro-averaged F1. Our approach builds on an AraBERT model further pretrained on a related Arabic medical classification dataset. Under a unified fine-tuning setup, this domain-adapted model consistently outperforms general-purpose Arabic backbones, with the best results obtained using a low backbone learning rate, indicating that only limited adaptation is required. The final system achieves a macro F1 score of 0.51 on the private test split. For comparison, we evaluate several cost-efficient large language models under constrained prompting and observe substantially lower performance.