Name: CC-100
Description: monolingual datasets from Common Crawl for a variety of languages
Provider: Facebook
Paper Title: CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data
Tasks: text generation, language modeling
Venue Name: Language Resources and Evaluation Conference
Authors: Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary,
  Francisco Guzmán, Armand Joulin, Edouard Grave
Abstract: Pre-training text representations have led to significant improvements in many areas of natural language processing. The
  quality of these models benefits greatly from the size of the pretraining corpora as long as its quality is preserved. In this
  paper, we describe an automatic pipeline to extract massive high-quality monolingual datasets from Common Crawl for
  a variety of languages. Our pipeline follows the data processing introduced in fastText (Mikolov et al., 2017; Grave et al.,
  2018), that deduplicates documents and identifies their language. We augment this pipeline with a filtering step to select
  documents that are close to high quality corpora like Wikipedia