skLEP: A Slovak General Language Understanding Benchmark

In this work, we introduce skLEP, the first comprehensive benchmark specifically designed for evaluating Slovak natural language understanding (NLU) models. We have compiled skLEP to encompass nine diverse tasks that span tokenlevel, sentence-pair, and document-level challenges, thereby offering a thorough assessment of model capabilities. To create this benchmark, we curated new, original datasets tailored for Slovak and meticulously translated established English NLU resources. Within this paper, we also present the first systematic and extensive evaluation of a wide array of Slovak-specific, multilingual, and English pre-trained language models using the skLEP tasks. Finally, we also release the complete benchmark data, an opensource toolkit facilitating both fine-tuning and evaluation of models, and a public leaderboard at https://github.com/slovak-nlp/sklep in the hopes of fostering reproducibility and drive future research in Slovak NLU. 1 The skLEP code, data and models are available at https: //github.com/slovak-nlp/sklep Tasks The skLEP benchmark comprises three task types: token classification, sentence-pair tasks, and document classification. Below, we describe the datasets, their tasks, creation processes, and any major modifications made for inclusion in skLEP.