# Evaluating the Performance and Efficiency of Sentence-BERT for Code Comment Classification

arXiv: https://arxiv.org/abs/2506.08581 · HTML: https://mlsanity.com/paper/2506.08581/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2025.

## Built on by (most cited first, 2 of 2)

- [SAGE: A Novelty Gate for Efficient Memory Evolution in Agentic LLMs](https://mlsanity.com/paper/2605.30711/) (2026)
- [LoRA-MME: Multi-Model Ensemble of LoRA-Tuned Encoders for Code Comment Classification](https://mlsanity.com/paper/2603.03959/) (2026)

## Abstract (arXiv)

This work evaluates Sentence-BERT for a multi-label code comment classification task seeking to maximize the classification performance while controlling efficiency constraints during inference. Using a dataset of 13,216 labeled comment sentences, Sentence-BERT models are fine-tuned and combined with different classification heads to recognize comment types. While larger models outperform smaller ones in terms of F1, the latter offer outstanding efficiency, both in runtime and GFLOPS. As result, a balance between a reasonable F1 improvement (+0.0346) and a minimal efficiency degradation (+1.4x in runtime and +2.1x in GFLOPS) is reached.