stanza

What it is and what it does

Stanza is Stanford NLP Group's official Python library for multilingual natural language processing. It provides a neural pipeline for core NLP tasks—tokenization, lemmatization, part-of-speech tagging, dependency parsing, and named entity recognition—across 60+ languages. The library also wraps Java Stanford CoreNLP for users who need additional capabilities. Biomedical and clinical English models are available for domain-specific work in medical text and literature.

The package depends on PyTorch for its neural components, along with supporting libraries like numpy, networkx, requests, and huggingface-hub for model management. Installation is straightforward via pip, and models auto-download on first pipeline use. It's designed for researchers, developers, and production systems that need accurate, language-agnostic NLP without building models from scratch.

Use it for:

• Extract grammatical structure and named entities from documents in multiple languages for information extraction pipelines
• Analyze syntactic dependencies in biomedical literature or clinical notes for domain-specific NLP tasks
• Build multilingual chatbots or question-answering systems that require tokenization and semantic understanding
• Process non-English text in research projects where language support across 60+ languages is needed
• Integrate with Java CoreNLP workflows from Python for advanced linguistic analysis or custom rule-based processing

Worth the install?

AI-flagged interpretation of the facts on this page — verify before relying

Stanza is a Python NLP library that runs accurate natural language processing tools on 60+ languages, including tokenization, part-of-speech tagging, dependency parsing, and named entity recognition, with optional access to Java Stanford CoreNLP.

Yes. Stanza is actively maintained, has no known vulnerabilities, installs with low friction, and is permissively licensed. It's the standard choice for researchers and developers needing accurate, out-of-the-box multilingual NLP. Install it if you need dependency parsing, NER, or POS tagging across many languages or in biomedical domains; skip it only if you need real-time performance on resource-constrained systems or prefer a lighter-weight alternative.

Install

stanza on PyPI

pip install stanza

uv add stanza

poetry add stanza

Installing stanza

Before you install

Low friction installation via pip with wheel distribution. Active maintenance—last release 30 days ago, repository updated 2026-08-14, 7861 stars. Requires PyTorch 1.3.0 or above as a core dependency alongside 10 other runtime packages.

License in practice

Apache License 2.0 (permissive) allows commercial and private use with minimal restrictions, making it suitable for most production and research applications.

Quickstart

pip install stanza

import stanza
stanza.download('en')
nlp = stanza.Pipeline('en')
doc = nlp("Barack Obama was born in Hawaii.")
print(doc.sentences[0].print_dependencies())

Requires Python 3.9 or later. PyTorch 1.3.0+ must be installed (typically resolved by pip). Models are downloaded on first use or via explicit stanza.download() call.

Verify before relying

• Performance characteristics (speed, memory usage) for large-scale document processing across different languages
• Exact model download size and storage requirements for the 60+ supported languages
• Compatibility and integration details with the Java Stanford CoreNLP backend beyond basic setup

Package facts

Evidence: stanza-1.14.0-py3-none-any.whl

Keywords: natural-language-processing, nlp, natural-language-understanding, stanford-nlp, deep-learning

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