  Large language models have led to remarkable progress on many NLP tasks, and
researchers are turning to ever-larger text corpora to train them. Some of the
largest corpora available are made by scraping significant portions of the
internet, and are frequently introduced with only minimal documentation. In
this work we provide some of the first documentation for the Colossal Clean
Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set
of filters to a single snapshot of Common Crawl. We begin by investigating
where the data came from, and find a significant amount of text from unexpected
sources like patents and US military websites. Then we explore the content of
the text itself, and find machine-generated text (e.g., from machine
translation systems) and evaluation examples from other benchmark NLP datasets.
To understand the impact of the filters applied to create this dataset, we
evaluate the text that was removed, and show that blocklist filtering
disproportionately removes text from and about minority individuals. Finally,
we conclude with some recommendations for how to created and document web-scale
datasets from a scrape of the internet.
