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Roberta-eus Euscrawl large 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:

 title={Does corpus quality really matter for low-resource languages?},
 author={Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri,
         Olatz Perez-de-Viñaspre, Aitor Soroa},
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