natasha

Why we included this project

Anyone doing serious work with Russian-language text will find most of the routine NLP steps here in one Python API: tokenization, sentence segmentation, morphology tagging, lemmatization, syntax parsing, and named-entity recognition, plus rule-based fact extraction and visualization of the markup. The models are kept small and run on CPU with NumPy inference, so the stack stays practical for teams without GPU capacity. Under the hood it wraps the individual Natasha libraries behind a single Doc object, so a handful of lines moves you from raw text to tagged, parsed documents. The honest caveat is that the models are tuned on news articles, so expect quality to drop on other domains, and the README itself points production users to the lower-level libraries rather than this top-level API.