Tables are extensively utilized to represent and store data, however, they often lack explicit semantics necessary for machine interpretation of their contents. Semantic table interpretation is essential for integrating structured data with knowledge graphs, yet existing methods face challenges with Russian-language tables due to limited labeled data and linguistic peculiarities. This paper introduces a contrastive learning approach to minimize reliance on manual labeling and enhance the accuracy of column annotation for rare semantic types. The proposed method adapts contrastive learning for tabular data through augmentations and employs a distilled multilingual BERT model trained on the unlabeled RWT corpus (comprising 7.4 million columns). The resulting table representations are incorporated into the RuTaBERT pipeline, reducing computational overhead. Experimental results demonstrate a micro-F1 score of 97% and a macro-F1 score of 92%, surpassing several baseline approaches. These findings emphasize the efficiency of the proposed method in addressing data sparsity and handling unique features of the Russian language. The results further confirm that contrastive learning effectively captures semantic similarities among columns without explicit supervision, which is particularly vital for rare data types.