classla/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.

system	dataset	F1 score
XLM-R-BERTić	hr500k	0.927
BERTić	hr500k	0.925
XLM-R-SloBERTić	hr500k	0.923
XLM-Roberta-Large	hr500k	0.919
crosloengual-bert	hr500k	0.918
XLM-Roberta-Base	hr500k	0.903

system	dataset	F1 score
XLM-R-SloBERTić	ReLDI-hr	0.812
XLM-R-BERTić	ReLDI-hr	0.809
crosloengual-bert	ReLDI-hr	0.794
BERTić	ReLDI-hr	0.792
XLM-Roberta-Large	ReLDI-hr	0.791
XLM-Roberta-Base	ReLDI-hr	0.763

system	dataset	F1 score
XLM-R-SloBERTić	SETimes.SR	0.949
XLM-R-BERTić	SETimes.SR	0.940
BERTić	SETimes.SR	0.936
XLM-Roberta-Large	SETimes.SR	0.933
crosloengual-bert	SETimes.SR	0.922
XLM-Roberta-Base	SETimes.SR	0.914

system	dataset	F1 score
XLM-R-BERTić	ReLDI-sr	0.841
XLM-R-SloBERTić	ReLDI-sr	0.824
BERTić	ReLDI-sr	0.798
XLM-Roberta-Large	ReLDI-sr	0.774
crosloengual-bert	ReLDI-sr	0.751
XLM-Roberta-Base	ReLDI-sr	0.734

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.

system	train	test	r^2
xlm-r-parlasent	ParlaSent_BCS.jsonl	ParlaSent_BCS_test.jsonl	0.615
BERTić	ParlaSent_BCS.jsonl	ParlaSent_BCS_test.jsonl	0.612
XLM-R-SloBERTić	ParlaSent_BCS.jsonl	ParlaSent_BCS_test.jsonl	0.607
XLM-Roberta-Large	ParlaSent_BCS.jsonl	ParlaSent_BCS_test.jsonl	0.605
XLM-R-BERTić	ParlaSent_BCS.jsonl	ParlaSent_BCS_test.jsonl	0.601
crosloengual-bert	ParlaSent_BCS.jsonl	ParlaSent_BCS_test.jsonl	0.537
XLM-Roberta-Base	ParlaSent_BCS.jsonl	ParlaSent_BCS_test.jsonl	0.500
dummy (mean)	ParlaSent_BCS.jsonl	ParlaSent_BCS_test.jsonl	-0.12

COPA

system	dataset	Accuracy score
BERTić	Copa-SR	0.689
XLM-R-SloBERTić	Copa-SR	0.665
XLM-R-BERTić	Copa-SR	0.637
crosloengual-bert	Copa-SR	0.607
XLM-Roberta-Base	Copa-SR	0.573
XLM-Roberta-Large	Copa-SR	0.570

system	dataset	Accuracy score
BERTić	Copa-HR	0.669
XLM-R-SloBERTić	Copa-HR	0.628
XLM-R-BERTić	Copa-HR	0.635
crosloengual-bert	Copa-HR	0.669
XLM-Roberta-Base	Copa-HR	0.585
XLM-Roberta-Large	Copa-HR	0.571

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",
}