Abstract
This article examines the application of sentence embedding models for clustering skills collected from the online recruitment site hh.kz. Skill clustering can be used to create a skill community within the job vacancy knowledge graph. The main objective of the research is to improve the quality of skill clustering by fine-tuning sentence transformer models on domain-specific data. The experiments conducted showed significant improvements in the model's performance metrics in Russian, including Cosine-Similarity, Manhattan-Distance, Euclidean-Distance, and Dot-Product-Similarity. Applying agglomerative clustering to skill embeddings before and after model fine-tuning demonstrated an enhancement in cluster quality, as evidenced by an increased average silhouette coefficient and a decreased percentage of erroneous clusters. The article also discusses the limitations of the conducted research and suggests directions for future development.