| Name | fugashi JSON |
| Version |
1.5.2
JSON |
| download |
| home_page | None |
| Summary | Cython MeCab wrapper for fast, pythonic Japanese tokenization. |
| upload_time | 2025-10-24 07:24:27 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | None |
| keywords |
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://fugashi.streamlit.app)
[](https://pypi.org/project/fugashi/)
[](https://pypi.org/project/fugashi/)
# fugashi
<img src="https://github.com/polm/fugashi/raw/main/fugashi.png" width=125 height=125 alt="fugashi by Irasutoya" />
fugashi is a Cython wrapper for [MeCab](https://taku910.github.io/mecab/), a
Japanese tokenizer and morphological analysis tool. Wheels are provided for
Linux, OSX (Intel), and Win64, and UniDic is [easy to install](#installing-a-dictionary).
**issueを英語で書く必要はありません。**
Check out the [interactive demo][], see the [blog post](https://www.dampfkraft.com/nlp/fugashi.html) for background
on why fugashi exists and some of the design decisions, or see [this
guide][guide] for a basic introduction to Japanese tokenization.
[guide]: https://www.dampfkraft.com/nlp/how-to-tokenize-japanese.html
[interactive demo]: https://fugashi.streamlit.app
If you are on a platform for which wheels are not provided, you'll need to
install MeCab first. It's recommended you install [from
source](https://github.com/taku910/mecab). If you need to build from source on
Windows, [@chezou's fork](https://github.com/chezou/mecab) is recommended; see
[issue #44](https://github.com/polm/fugashi/issues/44#issuecomment-954426115)
for an explanation of the problems with the official repo.
Known platforms without wheels:
- musl-based distros like alpine [#77](https://github.com/polm/fugashi/issues/77)
- PowerPC
- Windows 32bit
## Usage
```python
from fugashi import Tagger
tagger = Tagger('-Owakati')
text = "麩菓子は、麩を主材料とした日本の菓子。"
tagger.parse(text)
# => '麩 菓子 は 、 麩 を 主材 料 と し た 日本 の 菓子 。'
for word in tagger(text):
print(word, word.feature.lemma, word.pos, sep='\t')
# "feature" is the Unidic feature data as a named tuple
```
## Installing a Dictionary
fugashi requires a dictionary. [UniDic](https://unidic.ninjal.ac.jp/) is
recommended, and two easy-to-install versions are provided.
- [unidic-lite](https://github.com/polm/unidic-lite), a slightly modified version 2.1.2 of Unidic (from 2013) that's relatively small
- [unidic](https://github.com/polm/unidic-py), the latest UniDic 3.1.0, which is 770MB on disk and requires a separate download step
If you just want to make sure things work you can start with `unidic-lite`, but
for more serious processing `unidic` is recommended. For production use you'll
generally want to generate your own dictionary too; for details see the [MeCab
documentation](https://taku910.github.io/mecab/learn.html).
To get either of these dictionaries, you can install them directly using `pip`
or do the below:
```sh
pip install 'fugashi[unidic-lite]'
# The full version of UniDic requires a separate download step
pip install 'fugashi[unidic]'
python -m unidic download
```
For more information on the different MeCab dictionaries available, see [this article](https://www.dampfkraft.com/nlp/japanese-tokenizer-dictionaries.html).
## Dictionary Use
fugashi is written with the assumption you'll use Unidic to process Japanese,
but it supports arbitrary dictionaries.
If you're using a dictionary besides Unidic you can use the GenericTagger like this:
```python
from fugashi import GenericTagger
tagger = GenericTagger()
# parse can be used as normal
tagger.parse('something')
# features from the dictionary can be accessed by field numbers
for word in tagger(text):
print(word.surface, word.feature[0])
```
You can also create a dictionary wrapper to get feature information as a named tuple.
```python
from fugashi import GenericTagger, create_feature_wrapper
CustomFeatures = create_feature_wrapper('CustomFeatures', 'alpha beta gamma')
tagger = GenericTagger(wrapper=CustomFeatures)
for word in tagger.parseToNodeList(text):
print(word.surface, word.feature.alpha)
```
## Citation
If you use fugashi in research, it would be appreciated if you cite this paper. You can read it at [the ACL Anthology](https://www.aclweb.org/anthology/2020.nlposs-1.7/) or [on Arxiv](https://arxiv.org/abs/2010.06858).
@inproceedings{mccann-2020-fugashi,
title = "fugashi, a Tool for Tokenizing {J}apanese in Python",
author = "McCann, Paul",
booktitle = "Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.nlposs-1.7",
pages = "44--51",
abstract = "Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. This paper introduces fugashi, a MeCab wrapper for Python, and gives an introduction to tokenizing Japanese.",
}
## Alternatives
If you have a problem with fugashi feel free to open an issue. However, there
are some cases where it might be better to use a different library.
- If you don't want to deal with installing MeCab at all, try [SudachiPy](https://github.com/WorksApplications/sudachi.rs).
- If you need to work with Korean, try [pymecab-ko](https://github.com/NoUnique/pymecab-ko) or [KoNLPy](https://konlpy.org/en/latest/).
## License and Copyright Notice
fugashi is released under the terms of the [MIT license](./LICENSE). Please
copy it far and wide.
fugashi is a wrapper for MeCab, and fugashi wheels include MeCab binaries.
MeCab is copyrighted free software by Taku Kudo `<taku@chasen.org>` and Nippon
Telegraph and Telephone Corporation, and is redistributed under the [BSD
License](./LICENSE.mecab).
Raw data
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"description": "[](https://fugashi.streamlit.app)\r\n[](https://pypi.org/project/fugashi/)\r\n\r\n[](https://pypi.org/project/fugashi/)\r\n\r\n\r\n# fugashi\r\n\r\n<img src=\"https://github.com/polm/fugashi/raw/main/fugashi.png\" width=125 height=125 alt=\"fugashi by Irasutoya\" />\r\n\r\nfugashi is a Cython wrapper for [MeCab](https://taku910.github.io/mecab/), a\r\nJapanese tokenizer and morphological analysis tool. Wheels are provided for\r\nLinux, OSX (Intel), and Win64, and UniDic is [easy to install](#installing-a-dictionary).\r\n\r\n**issue\u3092\u82f1\u8a9e\u3067\u66f8\u304f\u5fc5\u8981\u306f\u3042\u308a\u307e\u305b\u3093\u3002**\r\n\r\nCheck out the [interactive demo][], see the [blog post](https://www.dampfkraft.com/nlp/fugashi.html) for background\r\non why fugashi exists and some of the design decisions, or see [this\r\nguide][guide] for a basic introduction to Japanese tokenization.\r\n\r\n[guide]: https://www.dampfkraft.com/nlp/how-to-tokenize-japanese.html\r\n[interactive demo]: https://fugashi.streamlit.app\r\n\r\nIf you are on a platform for which wheels are not provided, you'll need to\r\ninstall MeCab first. It's recommended you install [from\r\nsource](https://github.com/taku910/mecab). If you need to build from source on\r\nWindows, [@chezou's fork](https://github.com/chezou/mecab) is recommended; see\r\n[issue #44](https://github.com/polm/fugashi/issues/44#issuecomment-954426115)\r\nfor an explanation of the problems with the official repo.\r\n\r\nKnown platforms without wheels:\r\n\r\n- musl-based distros like alpine [#77](https://github.com/polm/fugashi/issues/77)\r\n- PowerPC\r\n- Windows 32bit\r\n\r\n## Usage\r\n\r\n```python\r\nfrom fugashi import Tagger\r\n\r\ntagger = Tagger('-Owakati')\r\ntext = \"\u9ea9\u83d3\u5b50\u306f\u3001\u9ea9\u3092\u4e3b\u6750\u6599\u3068\u3057\u305f\u65e5\u672c\u306e\u83d3\u5b50\u3002\"\r\ntagger.parse(text)\r\n# => '\u9ea9 \u83d3\u5b50 \u306f \u3001 \u9ea9 \u3092 \u4e3b\u6750 \u6599 \u3068 \u3057 \u305f \u65e5\u672c \u306e \u83d3\u5b50 \u3002'\r\nfor word in tagger(text):\r\n print(word, word.feature.lemma, word.pos, sep='\\t')\r\n # \"feature\" is the Unidic feature data as a named tuple\r\n```\r\n\r\n## Installing a Dictionary\r\n\r\nfugashi requires a dictionary. [UniDic](https://unidic.ninjal.ac.jp/) is\r\nrecommended, and two easy-to-install versions are provided.\r\n\r\n - [unidic-lite](https://github.com/polm/unidic-lite), a slightly modified version 2.1.2 of Unidic (from 2013) that's relatively small\r\n - [unidic](https://github.com/polm/unidic-py), the latest UniDic 3.1.0, which is 770MB on disk and requires a separate download step\r\n\r\nIf you just want to make sure things work you can start with `unidic-lite`, but\r\nfor more serious processing `unidic` is recommended. For production use you'll\r\ngenerally want to generate your own dictionary too; for details see the [MeCab\r\ndocumentation](https://taku910.github.io/mecab/learn.html).\r\n\r\nTo get either of these dictionaries, you can install them directly using `pip`\r\nor do the below:\r\n\r\n```sh\r\npip install 'fugashi[unidic-lite]'\r\n\r\n# The full version of UniDic requires a separate download step\r\npip install 'fugashi[unidic]'\r\npython -m unidic download\r\n```\r\n\r\nFor more information on the different MeCab dictionaries available, see [this article](https://www.dampfkraft.com/nlp/japanese-tokenizer-dictionaries.html).\r\n\r\n## Dictionary Use\r\n\r\nfugashi is written with the assumption you'll use Unidic to process Japanese,\r\nbut it supports arbitrary dictionaries. \r\n\r\nIf you're using a dictionary besides Unidic you can use the GenericTagger like this:\r\n\r\n```python\r\nfrom fugashi import GenericTagger\r\ntagger = GenericTagger()\r\n\r\n# parse can be used as normal\r\ntagger.parse('something')\r\n# features from the dictionary can be accessed by field numbers\r\nfor word in tagger(text):\r\n print(word.surface, word.feature[0])\r\n```\r\n\r\nYou can also create a dictionary wrapper to get feature information as a named tuple. \r\n\r\n```python\r\nfrom fugashi import GenericTagger, create_feature_wrapper\r\nCustomFeatures = create_feature_wrapper('CustomFeatures', 'alpha beta gamma')\r\ntagger = GenericTagger(wrapper=CustomFeatures)\r\nfor word in tagger.parseToNodeList(text):\r\n print(word.surface, word.feature.alpha)\r\n```\r\n\r\n## Citation\r\n\r\nIf you use fugashi in research, it would be appreciated if you cite this paper. You can read it at [the ACL Anthology](https://www.aclweb.org/anthology/2020.nlposs-1.7/) or [on Arxiv](https://arxiv.org/abs/2010.06858).\r\n\r\n @inproceedings{mccann-2020-fugashi,\r\n title = \"fugashi, a Tool for Tokenizing {J}apanese in Python\",\r\n author = \"McCann, Paul\",\r\n booktitle = \"Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS)\",\r\n month = nov,\r\n year = \"2020\",\r\n address = \"Online\",\r\n publisher = \"Association for Computational Linguistics\",\r\n url = \"https://www.aclweb.org/anthology/2020.nlposs-1.7\",\r\n pages = \"44--51\",\r\n abstract = \"Recent years have seen an increase in the number of large-scale multilingual NLP projects. However, even in such projects, languages with special processing requirements are often excluded. One such language is Japanese. Japanese is written without spaces, tokenization is non-trivial, and while high quality open source tokenizers exist they can be hard to use and lack English documentation. 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