1 code implementation • RANLP 2021 • 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.
1 code implementation • 9 Jun 2023 • Michal Seják, Jakub Sido, David Žahour
Electrocardiograms (ECGs) are commonly used by cardiologists to detect heart-related pathological conditions.
Ranked #1 on ECG Patient Identification (gallery-probe) on PTB-XL (using extra training data)
ECG Patient Identification (gallery-probe) Efficient Neural Network
1 code implementation • CRAC (ACL) 2022 • Zdeněk Žabokrtský, Miloslav Konopík, Anna Nedoluzhko, Michal Novák, Maciej Ogrodniczuk, Martin Popel, Ondřej Pražák, Jakub Sido, Daniel Zeman, YIlun Zhu
The public edition of CorefUD 1. 0, which contains 13 datasets for 10 languages, was used as the source of training and evaluation data.
2 code implementations • 26 Mar 2022 • Jan Pašek, Jakub Sido, Miloslav Konopík, Ondřej Pražák
This work proposes a new pipeline for leveraging data collected on the Stack Overflow website for pre-training a multimodal model for searching duplicates on question answering websites.
no code implementations • 19 Aug 2021 • Jakub Sido, Michal Seják, Ondřej Pražák, Miloslav Konopík, Václav Moravec
We describe the process of collecting and annotating the data in detail.
no code implementations • RANLP 2021 • Ondřej Pražák, Miloslav Konopík, Jakub Sido
In addition to monolingual experiments, we combine the training data in multilingual experiments and train two joined models -- for Slavic languages and for all the languages together.
1 code implementation • 24 Mar 2021 • 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.
1 code implementation • SEMEVAL 2020 • Ondřej Pražák, Pavel Přibáň, Stephen Taylor, Jakub Sido
Our method was created for the SemEval 2020 Task 1: \textit{Unsupervised Lexical Semantic Change Detection.}