Papers › RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model
RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model
Milan Straka, Jakub Náplava, Jana Straková, David Samuel
We present RobeCzech, a monolingual RoBERTa language representation model trained on Czech data. RoBERTa is a robustly optimized Transformer-based pretraining approach. We show that RobeCzech considerably outperforms equally-sized multilingual and Czech-trained contextualized language representation models, surpasses current state of the art in all five evaluated NLP tasks and reaches state-of-the-art results in four of them. The RobeCzech model is released publicly at https://hdl.handle.net/11234/1-3691 and https://huggingface.co/ufal/robeczech-base.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Parsing | PTG (czech, MRP 2020) | PERIN + RobeCzech | F1 | 92.36 | #1 of 3 | Archive leaderboard | report |
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