OSCAR-2201
OSCAR or Open Super-large Crawled Aggregated coRpus is a huge multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the ungoliant architecture. Data is distributed by language in both original and deduplicated form.
nan
Towards a Cleaner Document-Oriented Multilingual Crawled Corpus
text generation, language modeling
nan
Julien Abadji, Pedro Ortiz Suarez, Laurent Romary, Benoıt Sagot

The need for raw large raw corpora has dramatically increased in recent years with the introduction of transfer learning and
semi-supervised learning methods to Natural Language Processing. And while there have been some recent attempts to
manually curate the amount of data necessary to train large language models, the main way to obtain this data is still through
automatic web crawling. In this paper we take the existing multilingual web corpus OSCAR and its pipeline Ungoliant that
extracts and classifies data from Common Crawl at the line level, and propose a set of improvements and automatic annotations
in order to produce a new document-oriented version of OSCAR that could prove more suitable to pre-train large generative
language models as well as hopefully other applications in Natural Language Processing and Digital Humanities.
