Metadata-Version: 2.1
Name: fugashi
Version: 0.2.1
Summary: A Cython wrapper for MeCab
Home-page: https://github.com/polm/fugashi
Author: Paul O'Leary McCann
Author-email: polm@dampfkraft.com
License: MIT
Description: [![Current PyPI packages](https://badge.fury.io/py/fugashi.svg)](https://pypi.org/project/fugashi/)
        
        # fugashi
        
        <img src="https://github.com/polm/fugashi/raw/master/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, and Win64, and UniDic is easy to install (see docs below).
        
        See the [blog post](https://www.dampfkraft.com/nlp/fugashi.html) for background
        on why Fugashi exists and some of the design decisions.
        
        If you are on an unsupported platform (like PowerPC), you'll need to install
        MeCab first. It's recommended you install [from
        source](https://github.com/taku910/mecab).
        
        ## Usage
        
            from fugashi import Tagger
        
            tagger = Tagger('-Owakati')
            text = "麩菓子（ふがし）は、麩を主材料とした日本の菓子。"
            tagger.parse(text)
            # => '麩 菓子 （ ふ が し ） 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 2013 version of Unidic that's relatively small
          - [unidic](https://github.com/polm/unidic-py), the latest UniDic 2.3.0, which is 1GB 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:
        
            pip install fugashi[unidic-lite]
        
            # The full version of UniDic requires a separate download step
            pip install fugashi[unidic]
            python -m unidic download
        
        ## 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:
        
            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. 
        
            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)
        
        ## 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 want to use MeCab on a platform we don't have wheels for, but don't have a C compiler, use [natto-py](https://github.com/buruzaemon/natto-py).
        - If you don't want to deal with installing MeCab at all, try [SudachiPy](https://github.com/WorksApplications/SudachiPy).
        - If you need to work with Korean, try [KoNLPy](https://konlpy.org/en/latest/).
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: Japanese
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Provides-Extra: unidic
Provides-Extra: unidic-lite
