clean-text

Why we included this project

Anyone who has tried to run NLP on scraped or user-generated text knows the chore of fighting corrupted unicode, stray markup, and inconsistent casing before any analysis can begin. clean-text packs that work into a single function call, with flags for unicode fixing, ASCII transliteration, lowercasing, URL and emoji handling, and line-break normalization. It builds on ftfy and unidecode and adds hand-written regex rules on top, so the defaults work for most messy inputs without you assembling the pipeline yourself. It is a preprocessing utility rather than a model, best treated as the dependable first step in a larger text-processing workflow. Data engineers and NLP practitioners preparing datasets for downstream modeling will find it removes a lot of repetitive grunt work.