xlm-r-bertic

XLM-R-BERTić

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

Average macro-F1 scores from three runs 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:

@inproceedings{ljubesic-etal-2024-language,
    title = "Language Models on a Diet: Cost-Efficient Development of Encoders for Closely-Related Languages via Additional Pretraining",
    author = "Ljube{\v{s}}i{\'c}, Nikola  and
      Suchomel, V{\'\i}t  and
      Rupnik, Peter  and
      Kuzman, Taja  and
      van Noord, Rik",
    editor = "Melero, Maite  and
      Sakti, Sakriani  and
      Soria, Claudia",
    booktitle = "Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.sigul-1.23",
    pages = "189--203",
}