ginza
GiNZA, An Open Source Japanese NLP Library, based on Universal Dependencies
Decision gist · record as of 2026-08-14
Yes. GiNZA is the standard choice for Japanese NLP in Python when you need accurate tokenization, parsing, and NER. It has no known vulnerabilities, low install friction, active maintenance, and permissive licensing. Install it if you're working with Japanese text and need structured linguistic analysis; the main trade-off is that transformer models require downloading large files on first use.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >= 3.8.
- Transformer-based models (ja_ginza_electra) download large pytorch_model.bin files on first run; standard models (ja_ginza) are smaller.
- Anaconda environments may have pip install issues.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions. Upstream dependencies (spaCy, SudachiPy, transformers) have their own licenses; review their terms if bundling is planned.
last release 2024-03-30 (867 days) · last repo commit 2026-07-10 · 867 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 91,326 downloads/mo, #13,523 on PyPI
Alternatives
Verify before relying
pip install -U ginza ja_ginza
import spacy
nlp = spacy.load('ja_ginza')
doc = nlp('銀座でランチをご一緒しましょう。')
for token in doc:
print(token.text, token.pos_, token.dep_)- Whether the package works reliably with Python versions beyond 3.8 (stated as supported but no upper bound documented)
- Performance characteristics and memory footprint for production deployments with large document volumes
- Accuracy metrics for tokenization, POS tagging, and NER tasks on modern Japanese text outside training datasets
What it is and what it does
GiNZA is a Japanese natural language processing library built on spaCy and Universal Dependencies. It combines SudachiPy for high-accuracy tokenization and part-of-speech tagging with transformer-based or standard parsing models for dependency analysis and named entity recognition. The library outputs structured linguistic annotations in CoNLL-U format or spaCy's JSON representation, making it suitable for downstream NLP tasks on Japanese text.
The package is actively maintained and integrates established NLP frameworks (spaCy, SudachiPy, transformers) rather than reimplementing core algorithms. It supports both lightweight standard models and more accurate transformer-based variants (ja_ginza_electra), with optional GPU acceleration via CUDA. Command-line tools (ginza, ginzame) provide quick access to parsing and tokenization without writing code.
Use it for
- Parse Japanese sentences into dependency trees and extract grammatical relationships for linguistic analysis or information extraction
- Tokenize and tag Japanese text with parts of speech for downstream machine learning pipelines or text classification
- Identify and classify named entities (persons, locations, organizations) in Japanese documents for knowledge extraction
- Convert Japanese text to structured linguistic annotations (CoNLL-U format) for training or evaluating other NLP models
- Build Japanese search or question-answering systems that require accurate morphological and syntactic analysis
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
GiNZA is the standard choice for Japanese NLP in Python when you need accurate tokenization, parsing, and NER. It has no known vulnerabilities, low install friction, active maintenance, and permissive licensing. Install it if you're working with Japanese text and need structured linguistic analysis; the main trade-off is that transformer models require downloading large files on first use.
Install
ginza on PyPI
Before you install
Installation is straightforward with low friction; the package is actively maintained with recent commits and no known vulnerabilities. Runtime dependencies (spacy, SudachiPy, SudachiDict-core, plac) are well-established NLP libraries. Note that large transformer model files download on first use rather than at install time.
Requires Python >= 3.8. Transformer-based models (ja_ginza_electra) download large pytorch_model.bin files on first run; standard models (ja_ginza) are smaller. Anaconda environments may have pip install issues.
License in practice
MIT license permits commercial and private use with minimal restrictions. Upstream dependencies (spaCy, SudachiPy, transformers) have their own licenses; review their terms if bundling is planned.
Quickstart
pip install -U ginza ja_ginza
import spacy
nlp = spacy.load('ja_ginza')
doc = nlp('銀座でランチをご一緒しましょう。')
for token in doc:
print(token.text, token.pos_, token.dep_)Verify before relying
- Whether the package works reliably with Python versions beyond 3.8 (stated as supported but no upper bound documented)
- Performance characteristics and memory footprint for production deployments with large document volumes
- Accuracy metrics for tokenization, POS tagging, and NER tasks on modern Japanese text outside training datasets
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesspacyplacSudachiPySudachiDict-core |
| Maintenance | Actively maintained 867 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 91,326 / month, #13,523 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: ginza-5.2.0-py3-none-any.whl
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See also conllu · ja-ginza · nagisa · polyglot · stanza · SudachiDict-small · SudachiDict-core · gliner · SudachiPy · SudachiDict-full