The EstBERT model is trained both on 128 and 512 sequence length of data. For training the EstBERT we used the Estonian National Corpus 2017, which was the largest Estonian language corpus available at the time. It consists of four sub-corpora: Estonian Reference Corpus 1990-2008, Estonian Web Corpus 2013, Estonian Web Corpus 2017 and Estonian Wikipedia Corpus 2017.
Overall EstBERT performs better in parts of speech (POS), name entity recognition (NER), rubric, and sentiment classification tasks compared to mBERT and XLM-RoBERTa. The comparative results can be found below;
Model
UPOS
XPOS
Morph
bf UPOS
bf XPOS
Morph
EstBERT
97.89
98.40
96.93
97.84
98.43
96.80
mBERT
97.42
98.06
96.24
97.43
98.13
96.13
XLM-RoBERTa
97.78
98.36
96.53
97.80
98.40
96.69
Model
Rubric128
Sentiment128
Rubric128
Sentiment512
EstBERT
81.70
74.36
80.96
74.50
mBERT
75.67
70.23
74.94
69.52
XLM-RoBERTa
80.34
74.50
78.62
76.07
Model
Precicion128
Recall128
F1-Score128
Precision512
Recall512
F1-Score512
EstBERT
88.42
90.38
89.39
88.35
89.74
89.04
mBERT
85.88
87.09
86.51
88.47
88.28
88.37
XLM-RoBERTa
87.55
91.19
89.34
87.50
90.76
89.10
BibTeX entry and citation info
@inproceedings{tanvir-etal-2021-estbert,
title = {{EstBERT: A Pretrained Language-Specific BERT for Estonian}},
author = "Tanvir, Hasan and
Kittask, Claudia and
Eiche, Sandra and
Sirts, Kairit",
editor = "Dobnik, Simon and
{\O}vrelid, Lilja",
booktitle = "Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)",
year = "2021",
address = "Reykjavik, Iceland (Online)",
publisher = {Link{\"o}ping University Electronic Press, Sweden},
url = "https://aclanthology.org/2021.nodalida-main.2/",
pages = "11--19"
}
@misc{tanvir2020estbert,
title={{EstBERT: A Pretrained Language-Specific BERT for Estonian}},
author={Hasan Tanvir and Claudia Kittask and Kairit Sirts},
year={2020},
eprint={2011.04784},
archivePrefix={arXiv},
primaryClass={cs.CL}
}