ja-ginza
Japanese multi-task CNN trained on UD-Japanese BCCWJ r2.8 + GSK2014-A(2019). Assigns word2vec token vectors. Components: tok2vec, parser, ner, morphologizer, atteribute_ruler, compound_splitter, bunsetu_recognizer.
Decision gist · record as of 2026-08-14
Yes, if you need Japanese NLP and are comfortable with spaCy 3.2.x. The model is actively maintained, permissively licensed, and provides a complete pipeline with strong parsing and tagging performance. Be aware that NER accuracy is moderate (55.40 F-score), so validate it on your data before relying on entity extraction in production. Install friction is low and no known vulnerabilities exist.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires spaCy >=3.2.0,<3.3.0 and its runtime dependencies (spacy, sudachipy, sudachidict-core, ginza); model file download on first load.
- Low install friction; distributed as a wheel.
- Actively maintained with recent commits.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions, making it straightforward to integrate into most projects without licensing concerns.
last release 2024-03-30 (867 days) · last repo commit 2026-07-10 · 867 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 83,718 downloads/mo, #14,053 on PyPI
Alternatives
Verify before relying
import spacy
nlp = spacy.load('ja_ginza')
doc = nlp('すもももももももものうち')
for token in doc:
print(token.text, token.pos_, token.dep_)- Whether the model's NER performance (55.40 F-score) is sufficient for your use case, as it is notably lower than parsing accuracy
- Whether spaCy version constraints (>=3.2.0,<3.3.0) align with your environment
- Memory footprint and inference speed characteristics for production deployment
- Exact dimensions and count of word2vec token vectors included in the model
What it is and what it does
ja_ginza is a pre-trained spaCy model specialized for Japanese text analysis. It bundles a CNN trained on UD-Japanese BCCWJ r2.8 and GSK2014-A corpora, providing a complete NLP pipeline with seven components: tok2vec, parser, attribute_ruler, ner, morphologizer, compound_splitter, and bunsetu_recognizer. The model includes word2vec token vectors with 300 dimensions.
You install it as a Python package and load it through spaCy's standard interface. It works by processing Japanese text through its pipeline components, assigning linguistic annotations to each token and entity. The model achieves high accuracy on dependency parsing (90.95 UAS) and POS tagging (97.44), moderate accuracy on sentence segmentation (83.03 F), and lower accuracy on named entity recognition (55.40 F). It is maintained by Megagon Labs and actively used in production, though releases are infrequent.
Use it for
- Extract named entities (people, places, organizations, dates) from Japanese documents for information extraction pipelines
- Parse Japanese sentence structure to understand grammatical relationships for machine translation or semantic analysis
- Tokenize and tag Japanese text with parts of speech for downstream NLP tasks like text classification or sentiment analysis
- Identify compound words and phrase boundaries (bunsetu) to improve Japanese text segmentation for search or indexing
- Build Japanese chatbots or question-answering systems that need to understand sentence structure and entity types
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need Japanese NLP and are comfortable with spaCy 3.2.x.
The model is actively maintained, permissively licensed, and provides a complete pipeline with strong parsing and tagging performance. Be aware that NER accuracy is moderate (55.40 F-score), so validate it on your data before relying on entity extraction in production. Install friction is low and no known vulnerabilities exist.
Install
ja-ginza on PyPI
Before you install
Low install friction; distributed as a wheel. Actively maintained with recent commits. Last release was 867 days ago, indicating the project is established but not under continuous rapid development.
Requires spaCy >=3.2.0,<3.3.0 and its runtime dependencies (spacy, sudachipy, sudachidict-core, ginza); model file download on first load.
License in practice
MIT License permits commercial and private use with minimal restrictions, making it straightforward to integrate into most projects without licensing concerns.
Quickstart
import spacy
nlp = spacy.load('ja_ginza')
doc = nlp('すもももももももものうち')
for token in doc:
print(token.text, token.pos_, token.dep_)Verify before relying
- Whether the model's NER performance (55.40 F-score) is sufficient for your use case, as it is notably lower than parsing accuracy
- Whether spaCy version constraints (>=3.2.0,<3.3.0) align with your environment
- Memory footprint and inference speed characteristics for production deployment
- Exact dimensions and count of word2vec token vectors included in the model
Package facts
| License | MIT License permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesspacysudachipysudachidict-coreginza |
| Maintenance | Actively maintained 867 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 83,718 / month, #14,053 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: ja_ginza-5.2.0-py3-none-any.whl
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See also ginza · nagisa · Janome · spacy · keyphrase-vectorizers · SudachiDict-core · SudachiDict-full · SudachiDict-small · jieba3k · gensim