Metadata-Version: 2.1
Name: fugashi
Version: 0.1.9
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/).
        
        See the [blog post](https://www.dampfkraft.com/nlp/fugashi.html) for background
        on why Fugashi exists and some of the design decisions.
        
        Any reasonable version of MeCab should work, but 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.parseToNodeList(text):
                print(word, word.feature.lemma, word.pos, sep='\t')
                # "feature" is the Unidic feature data as a named tuple
        
        ## 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.parseToNodeList(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 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).
        
        Note that these are both slower than Fugashi according to a [benchmark I
        wrote](https://github.com/polm/ja-tokenizer-benchmark). 
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: Japanese
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: unidic
