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Web search engines focus on serving highly relevant results within hundredsof milliseconds. Pre-trained language transformer models such as BERT aretherefore hard to use in this scenario due to their high computational demands.We present our real-time approach to the document ranking problem leveraging aBERT-based siamese architecture. The model is already deployed in a commercialsearch engine and it improves production performance by more than 3%. Forfurther research and evaluation, we release DaReCzech, a unique data set of 1.6million Czech user query-document pairs with manually assigned relevancelevels. We also release Small-E-Czech, an Electra-small language modelpre-trained on a large Czech corpus. We believe this data will supportendeavours both of search relevance and multilingual-focused researchcommunities.

Siamese BERT-based Model for Web Search Relevance Ranking Evaluated on a New Czech Dataset