esCorpius-m: A Massive Multilingual Crawling Corpus with a Focus on Spanish

Author:

Gutiérrez-Fandiño Asier1ORCID,Pérez-Fernández David2ORCID,Armengol-Estapé Jordi3ORCID,Griol David4ORCID,Kharitonova Ksenia4ORCID,Callejas Zoraida45ORCID

Affiliation:

1. LHF Labs, 48007 Bilbao, Spain

2. Department of Mathematics, Universidad Autónoma de Madrid, 28049 Madrid, Spain

3. School of Informatics, University of Edinburgh, Edinburgh EH8 9YL, UK

4. Department of Software Engineering, University of Granada, 18071 Granada, Spain

5. Research Centre for Information and Communications Technologies (CITIC-UGR), 18071 Granada, Spain

Abstract

In recent years, transformer-based models have played a significant role in advancing language modeling for natural language processing. However, they require substantial amounts of data and there is a shortage of high-quality non-English corpora. Some recent initiatives have introduced multilingual datasets obtained through web crawling. However, there are notable limitations in the results for some languages, including Spanish. These datasets are either smaller compared to other languages or suffer from lower quality due to insufficient cleaning and deduplication. In this paper, we present esCorpius-m, a multilingual corpus extracted from around 1 petabyte of Common Crawl data. It is the most extensive corpus for some languages with such a level of high-quality content extraction, cleanliness, and deduplication. Our data curation process involves an efficient cleaning pipeline and various deduplication methods that maintain the integrity of document and paragraph boundaries. We also ensure compliance with EU regulations by retaining both the source web page URL and the WARC shared origin URL.

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference49 articles.

1. Gutiérrez-Fandiño, A., Pérez-Fernández, D., Armengol-Estapé, J., Griol, D., and Callejas, Z. (2022, January 14–16). esCorpius: A Massive Spanish Crawling Corpus. Proceedings of the IberSPEECH 2022 Conference, Granada, Spain.

2. Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., and Brunskill, E. (2022). On the Opportunities and Risks of Foundation Models. arXiv.

3. Exploring the frontiers of deep learning and natural language processing: A comprehensive overview of key challenges and emerging trends;Khan;Nat. Lang. Process. J.,2023

4. OECD (2023). AI Language Models: Technological, Socio-Economic and Policy Considerations, OECD Publishing.

5. CTRAN: CNN-Transformer-based network for natural language understanding;Rafiepour;Eng. Appl. Artif. Intell.,2023

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