nagisa
A Japanese tokenizer based on recurrent neural networks
Decision gist · record as of 2026-08-14
Yes, if you need Japanese NLP. Nagisa is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and is in the top 5000 PyPI packages by download volume. The medium install friction (neural network dependencies) is a trade-off for having pre-trained models; if you work with Japanese text regularly, the convenience outweighs the setup cost. Not suitable if you need only English or other languages.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires numpy and DyNet38/DyNet (compiled neural network libraries); installation may take time on first setup.
- Medium install friction due to dependencies on numpy and DyNet38/DyNet (neural network libraries).
- Wheels are provided for Python 3.10–3.13 across Linux, macOS, and Windows.
License · maintenance · safety
MIT License (permissive) — MIT License permits commercial and private use with minimal restrictions, requiring only license and copyright notice retention.
last release 2026-07-06 (39 days) · last repo commit 2026-07-06 · 419 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 823,562 downloads/mo, #4,967 on PyPI
Alternatives
Verify before relying
pip install nagisa
import nagisa
text = 'Pythonで簡単に使えるツールです'
words = nagisa.tagging(text)
print(words.words)
print(words.postags)- Whether pre-trained models are bundled or require separate download on first use
- Memory and runtime performance characteristics for typical Japanese text volumes
- Compatibility and stability of DyNet38 vs DyNet dependency resolution
What it is and what it does
Nagisa is a Python module that tokenizes and tags Japanese text using neural networks trained on character- and word-level features. It splits Japanese sentences into words and assigns part-of-speech tags (noun, verb, particle, etc.) in a single pass, with output normalized to Unicode NFKC form.
The package is designed for ease of use: import, call tagging() on a string, and receive a result object with .words and .postags attributes. It supports filtering/extracting words by POS tag, adding custom dictionaries, and includes a built-in Japanese stopwords list. Advanced users can train custom models on annotated datasets using the fit() method and load them with a custom Tagger instance.
Use it for
- Extract nouns, verbs, or other POS categories from Japanese documents for downstream NLP tasks
- Tokenize Japanese text for search indexing, removing particles and auxiliary words via stopword filtering
- Train a domain-specific word segmentation and tagging model on annotated Japanese corpora
- Normalize and parse Japanese user input in chatbots or form processing pipelines
- Analyze Japanese social media or news text to identify named entities or key terms by POS tag
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need Japanese NLP.
Nagisa is actively maintained, has no known vulnerabilities, carries a permissive MIT license, and is in the top 5000 PyPI packages by download volume. The medium install friction (neural network dependencies) is a trade-off for having pre-trained models; if you work with Japanese text regularly, the convenience outweighs the setup cost. Not suitable if you need only English or other languages.
Install
nagisa on PyPI
Before you install
Medium install friction due to dependencies on numpy and DyNet38/DyNet (neural network libraries). Wheels are provided for Python 3.10–3.13 across Linux, macOS, and Windows. Maintenance is active with a recent release (39 days old) and ongoing repository updates.
Requires numpy and DyNet38/DyNet (compiled neural network libraries); installation may take time on first setup.
License in practice
MIT License permits commercial and private use with minimal restrictions, requiring only license and copyright notice retention.
Quickstart
pip install nagisa
import nagisa
text = 'Pythonで簡単に使えるツールです'
words = nagisa.tagging(text)
print(words.words)
print(words.postags)Verify before relying
- Whether pre-trained models are bundled or require separate download on first use
- Memory and runtime performance characteristics for typical Japanese text volumes
- Compatibility and stability of DyNet38 vs DyNet dependency resolution
Package facts
| License | MIT License permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 4 packagessixnumpyDyNet38DyNet |
| Maintenance | Actively maintained 39 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 823,562 / month, #4,967 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: MIT LicenseNatural Language :: JapaneseOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: UnixProgramming Language :: Python :: 2.7Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.5Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Software Development :: Libraries :: Python ModulesTopic :: Text Processing :: Linguistic |
Evidence: nagisa-0.3.0-cp310-cp310-macosx_11_0_arm64.whl; nagisa-0.3.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; nagisa-0.3.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; nagisa-0.3.0-cp310-cp310-musllinux_1_2_aarch64.whl; nagisa-0.3.0-cp310-cp310-musllinux_1_2_x86_64.whl; nagisa-0.3.0-cp310-cp310-win_amd64.whl; nagisa-0.3.0-cp311-cp311-macosx_11_0_arm64.whl; nagisa-0.3.0-cp311-cp311-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; nagisa-0.3.0-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; nagisa-0.3.0-cp311-cp311-musllinux_1_2_aarch64.whl; nagisa-0.3.0-cp311-cp311-musllinux_1_2_x86_64.whl; nagisa-0.3.0-cp311-cp311-win_amd64.whl; nagisa-0.3.0-cp312-cp312-macosx_11_0_arm64.whl; nagisa-0.3.0-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl; nagisa-0.3.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl; nagisa-0.3.0-cp312-cp312-musllinux_1_2_aarch64.whl; nagisa-0.3.0-cp312-cp312-musllinux_1_2_x86_64.whl; nagisa-0.3.0-cp312-cp312-win_amd64.whl; nagisa-0.3.0-cp313-cp313-macosx_11_0_arm64.whl; nagisa-0.3.0-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl
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