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This article focuses on the development and evaluation of Small-sized Czechsentence embedding models. Small models are important components for real-timeindustry applications in resource-constrained environments. Given the limitedavailability of labeled Czech data, alternative approaches, includingpre-training, knowledge distillation, and unsupervised contrastive fine-tuning,are investigated. Comprehensive intrinsic and extrinsic analyses are conducted,showcasing the competitive performance of our models compared to significantlylarger counterparts, with approximately 8 times smaller size and 5 times fasterspeed than conventional Base-sized models. To promote cooperation andreproducibility, both the models and the evaluation pipeline are made publiclyaccessible. Ultimately, this article presents practical applications of thedeveloped sentence embedding models in Seznam.cz, the Czech search engine.These models have effectively replaced previous counterparts, enhancing theoverall search experience for instance, in organic search, featured snippets,and image search. This transition has yielded improved performance.

Some Like It Small: Czech Semantic Embedding Models for Industry Applications