XLM-R-SloBertić

This model was produced by pre-training XLM-Roberta-large 48k steps on South Slavic languages using XLM-R-BERTić dataset

Benchmarking

Three tasks were chosen for model evaluation:

• Named Entity Recognition (NER)
• Sentiment regression
• COPA (Choice of plausible alternatives)

In all cases, this model was finetuned for specific downstream tasks.

NER

Mean F1 scores were used to evaluate performance. Datasets used: hr500k, ReLDI-sr, ReLDI-hr, and SETimes.SR.

Sentiment regression

ParlaSent dataset was used to evaluate sentiment regression for Bosnian, Croatian, and Serbian languages. The procedure is explained in greater detail in the dedicated benchmarking repository.

COPA

Citation

Please cite the following paper:

 @article{Ljubešić_Suchomel_Rupnik_Kuzman_van Noord_2024,
title={Language Models on a Diet: Cost-Efficient Development of Encoders for Closely-Related Languages via Additional Pretraining},
url={http://arxiv.org/abs/2404.05428},
DOI={10.48550/arXiv.2404.05428},
abstractNote={The world of language models is going through turbulent times, better and ever larger models are coming out at an unprecedented speed. However, we argue that, especially for the scientific community, encoder models of up to 1 billion parameters are still very much needed, their primary usage being in enriching large collections of data with metadata necessary for downstream research. We investigate the best way to ensure the existence of such encoder models on the set of very closely related languages - Croatian, Serbian, Bosnian and Montenegrin, by setting up a diverse benchmark for these languages, and comparing the trained-from-scratch models with the new models constructed via additional pretraining of existing multilingual models. We show that comparable performance to dedicated from-scratch models can be obtained by additionally pretraining available multilingual models even with a limited amount of computation. We also show that neighboring languages, in our case Slovenian, can be included in the additional pretraining with little to no loss in the performance of the final model.},
note={arXiv:2404.05428 [cs]},
 number={arXiv:2404.05428},
publisher={arXiv},
author={Ljubešić, Nikola and Suchomel, Vít and Rupnik, Peter and Kuzman, Taja and van Noord, Rik},
year={2024},
month=apr
}