  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.
