Roberta-eus mc4 base cased

This is a RoBERTa model for Basque model presented in Does corpus quality really matter for low-resource languages?. There are several models for Basque using the RoBERTa architecture, using different corpora:

roberta-eus-euscrawl-base-cased: Basque RoBERTa model trained on Euscrawl, a corpus created using tailored crawling from Basque sites. EusCrawl contains 12,528k documents and 423M tokens.
roberta-eus-euscrawl-large-cased: RoBERTa large trained on EusCrawl.
roberta-eus-mC4-base-cased: Basque RoBERTa model trained on the Basque portion of mc4 dataset.
roberta-eus-CC100-base-cased: Basque RoBERTa model trained on Basque portion of cc100 dataset.

The models have been tested on five different downstream tasks for Basque: Topic classification, Sentiment analysis, Stance detection, Named Entity Recognition (NER), and Question Answering (refer to the paper for more details). See summary of results below:

Model	Topic class.	Sentiment	Stance det.	NER	QA	Average
roberta-eus-euscrawl-base-cased	76.2	77.7	57.4	86.8	34.6	66.5
roberta-eus-euscrawl-large-cased	77.6	78.8	62.9	87.2	38.3	69.0
roberta-eus-mC4-base-cased	75.3	80.4	59.1	86.0	35.2	67.2
roberta-eus-CC100-base-cased	76.2	78.8	63.4	85.2	35.8	67.9

If you use any of these models, please cite the following paper:

@misc{artetxe2022euscrawl,
 title={Does corpus quality really matter for low-resource languages?},
 author={Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri,
         Olatz Perez-de-Viñaspre, Aitor Soroa},
 year={2022},
 eprint={2203.08111},
 archivePrefix={arXiv},
 primaryClass={cs.CL}
}