Stanza
Stanford NLPThe Stanford NLP Group's Python library for linguistic analysis in many languages. Its neural pipeline does tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological tagging, dependency and constituency parsing, named-entity recognition, sentiment and coreference, with pretrained models for about 80 languages trained on Universal Dependencies treebanks. Every module can be retrained on the user's data, and it also gives Python access to the Java CoreNLP toolkit.
Openness
5 high confidence- license
- Apache-2.0(OSI)
- source
- public(github.com/stanfordnlp/stanza)
- core features withheld
- no — published by Stanford University's NLP group
Apache-2.0, built from the public repository and published by Stanford University's NLP group, with its pretrained models also Apache-2.0 on the Hub. Nothing is sold.
- https://huggingface.co/api/models?author=stanfordnlp&sort=downloads&limit=1000&expand[]=downloads&expand[]=cardData&expand[]=gated&search=stanza recorded 2026-09-27
108 stanfordnlp/stanza-* model repositories, none gated, 107 tagged apache-2.0.
- https://raw.githubusercontent.com/stanfordnlp/stanza/main/LICENSE recorded 2026-09-27
LICENSE opens "Copyright 2019 The Board of Trustees of The Leland Stanford Junior University" and "Licensed under the Apache License, Version 2.0".
- https://raw.githubusercontent.com/stanfordnlp/stanza/main/README.md recorded 2026-09-27
README: "The Stanford NLP Group's official Python NLP library."; "Stanza is released under the Apache License, Version 2.0."
- https://ungh.cc/repos/stanfordnlp/stanza/files/main recorded 2026-09-27
Repository tree of 659 paths with no ee, enterprise, commercial or pro directory.
Adoption
3 high confidenceMeasured on monthly PyPI downloads of the stanza package. The package fetches its language models from the Hub, where they are counted separately and far lower.
- https://pypistats.org/api/packages/stanza/recent recorded 2026-09-27
last_month 723,139 downloads of stanza
Capability
3 high confidenceStanza ships trained pipelines that analyze text in many languages straight away, as spaCy does, and like spaCy it needs a training run to recognize entity types or labels beyond the ones its models were trained on. Its breadth of languages is the main difference.
- https://raw.githubusercontent.com/stanfordnlp/stanza/main/README.md recorded 2026-09-27
README: "All neural modules in this library can be trained with your own data."
- https://stanfordnlp.github.io/stanza/available_models.html recorded 2026-09-27
"Stanza provides pretrained NLP models for a total of 80 human languages"; models divided into Universal Dependencies models and NER models by training dataset.
Verified 2026-09-27