no code implementations • 30 Jan 2024 • Alex Golts, Vadim Ratner, Yoel Shoshan, Moshe Raboh, Sagi Polaczek, Michal Ozery-Flato, Daniel Shats, Liam Hazan, Sivan Ravid, Efrat Hexter
In this paper we propose a way to standardize and represent efficiently a very large dataset curated from multiple public sources, split the data into train, validation and test sets based on different meaningful strategies, and provide a concrete evaluation protocol to accomplish a benchmark.
2 code implementations • 2 Jun 2019 • Yishai Shimoni, Ehud Karavani, Sivan Ravid, Peter Bak, Tan Hung Ng, Sharon Hensley Alford, Denise Meade, Yaara Goldschmidt
Thus, the toolkit is agnostic to the machine learning model that is used.