stanfordnlp/stanza

Health v2 · maintenance only

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Adoption not part of the score

7867 stars · 956 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Stanza is the Stanford NLP Group's official Python library for linguistic analysis of human language text. It provides a neural pipeline built on PyTorch for tokenization, sentence segmentation, lemmatization, POS tagging, dependency parsing, and named entity recognition across 70+ languages, plus a Python interface to Java CoreNLP.

Use cases

• tokenize and segment sentences in many languages
• run named entity recognition on text
• get dependency parses in Universal Dependencies format
• tag parts of speech and morphological features
• extract entities from biomedical literature and clinical notes
• access CoreNLP features like coreference resolution from Python
• train custom NLP models on annotated data

When to choose

• you need accurate multilingual NLP with pretrained models for 70+ languages
• you want a native Python pipeline without a Java dependency
• you need Universal Dependencies-compliant tokenization and parsing
• you need biomedical/clinical NER models

When to avoid

• you need fast CPU-only inference for high-throughput production (GPU recommended)
• you want a lightweight rule-based toolkit rather than neural models
• you need tasks like question answering or text generation

Facets

library · maturity stable

nlp machine-learning deep-learning parser machine-learning artificial-intelligence python cross-platform pytorch named-entity-recognition universal-dependencies tokenization dependency-parsing corenlp biomedical-nlp multilingual natural-language-processing gpu

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