Abstract Text embedding in vertical industrial domains provides a critical foundation for professional information retrieval, semantic matching, and intelligent decision-making. However, general-purpose embedding models often fail to meet the demands of complex industrial applications because they lack effective modeling of domain knowledge and fine-grained semantic distinctions. To address this limitation, we propose a two-stage efficient fine-tuning framework for vertical-domain text embedding.
