Code comment classification is a critical task for automated software documentation and analysis. In the context of the NLBSE'26 Tool Competition, we present LoRA-MME, a Multi-Model Ensemble architecture utilizing Parameter-Efficient Fine-Tuning (PEFT). Our approach addresses the multi-label classification challenge across Java, Python, and Pharo by combining the strengths of four distinct transformer encoders: UniXcoder, CodeBERT, GraphCodeBERT, and CodeBERTa. By independently fine-tuning these models using Low-Rank Adaptation(LoRA) and aggregating their predictions via a learned weighted ensemble strategy, we maximize classification performance without the memory overhead of full model fine-tuning. Our tool achieved an F1 Weighted score of 0.7906 and a Macro F1 of 0.6867 on the test set. However, the computational cost of the ensemble resulted in a final submission score of 41.20%, highlighting the trade-off between semantic accuracy and inference efficiency.
Figures & tables
Figure 1. Learned ensemble weights per category. Darker cells indicate higher contribution. The model dynamically assigns higher importance to specific encoders based on the category (e.g., UniXcoder for Pharo/Example). A heatmap showing the weight distribution of the 4 models across 17 categories.
Table 4. Optimized Classification Thresholds Ablation Study (Illustrative Samples)
Figure 2. Average Model Contribution Summary. The chart visualizes the aggregated importance of UniXcoder, CodeBERT, GraphCodeBERT, and CodeBERTa across all categories. A chart showing the relative contribution of each model to the ensemble.
Faculty of Mathematics and Informatics, Sofia University "St. Kliment Ohridski", Bulgaria · Mohamed bin Zayed University of Artificial Intelligence, UAE