ginza

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

GiNZA is a Japanese NLP library that performs tokenization, part-of-speech tagging, dependency parsing, and named entity recognition on Japanese text using Universal Dependencies standards.

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

pip install ginza

uv add ginza

poetry add ginza

Installing ginza

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.

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_)

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.

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

Evidence: ginza-5.2.0-py3-none-any.whl

Tags

More Linguistic packages