Papers › Czert – Czech BERT-like Model for Language Representation
Czert – Czech BERT-like Model for Language Representation
Jakub Sido, Ondřej Pražák, Pavel Přibáň, Jan Pašek, Michal Seják, Miloslav Konopík
This paper describes the training process of the first Czech monolingual language representation models based on BERT and ALBERT architectures. We pre-train our models on more than 340K of sentences, which is 50 times more than multilingual models that include Czech data. We outperform the multilingual models on 9 out of 11 datasets. In addition, we establish the new state-of-the-art results on nine datasets. At the end, we discuss properties of monolingual and multilingual models based upon our results. We publish all the pre-trained and fine-tuned models freely for the research community.
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