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    "info": {
        "author": "Yukino Ikegami",
        "author_email": "yknikgm@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Information Technology",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: Chinese (Simplified)",
            "Natural Language :: Chinese (Traditional)",
            "Natural Language :: Japanese",
            "Programming Language :: Python :: 2.6",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Text Processing :: Linguistic"
        ],
        "description": "Rakuten MA Python\n===================\n\n|travis| |coveralls| |pyversion| |version|  |landscape|  |license|\n\nRakuten MA Python (morphological analyzer) is a Python version of Rakuten MA (word segmentor + PoS Tagger) for Chinese and Japanese.\n\nFor details about Rakuten MA, See https://github.com/rakuten-nlp/rakutenma\n\nSee also http://qiita.com/yukinoi/items/925bc238185aa2fad8a7 (In Japanese)\n\nContributions are welcome!\n\n\nInstallation\n==============\n\n::\n\n pip install rakutenma\n\nExample\n===========\n\n.. code:: python\n\n from rakutenma import RakutenMA\n\n # Initialize a RakutenMA instance with an empty model\n # the default ja feature set is set already\n rma = RakutenMA()\n\n # Let's analyze a sample sentence (from http://tatoeba.org/jpn/sentences/show/103809)\n # With a disastrous result, since the model is empty!\n print(rma.tokenize(\"\u5f7c\u306f\u65b0\u3057\u3044\u4ed5\u4e8b\u3067\u304d\u3063\u3068\u6210\u529f\u3059\u308b\u3060\u308d\u3046\u3002\"))\n\n # Feed the model with ten sample sentences from tatoeba.com\n # \"tatoeba.json\" is available at https://github.com/rakuten-nlp/rakutenma\n import json\n tatoeba = json.load(open(\"tatoeba.json\"))\n for i in tatoeba:\n     rma.train_one(i)\n\n # Now what does the result look like?\n print(rma.tokenize(\"\u5f7c\u306f\u65b0\u3057\u3044\u4ed5\u4e8b\u3067\u304d\u3063\u3068\u6210\u529f\u3059\u308b\u3060\u308d\u3046\u3002\"))\n\n # Initialize a RakutenMA instance with a pre-trained model\n rma = RakutenMA(phi=1024, c=0.007812)  # Specify hyperparameter for SCW (for demonstration purpose)\n rma.load(\"model_ja.json\")\n\n # Set the feature hash function (15bit)\n rma.hash_func = rma.create_hash_func(15)\n\n # Tokenize one sample sentence\n print(rma.tokenize(\"\u3046\u3089\u306b\u308f\u306b\u306f\u306b\u308f\u306b\u308f\u3068\u308a\u304c\u3044\u308b\"));\n\n # Re-train the model feeding the right answer (pairs of [token, PoS tag])\n res = rma.train_one(\n        [[\"\u3046\u3089\u306b\u308f\",\"N-nc\"],\n         [\"\u306b\",\"P-k\"],\n         [\"\u306f\",\"P-rj\"],\n         [\"\u306b\u308f\",\"N-n\"],\n         [\"\u306b\u308f\u3068\u308a\",\"N-nc\"],\n         [\"\u304c\",\"P-k\"],\n         [\"\u3044\u308b\",\"V-c\"]])\n # The result of train_one contains:\n #   sys: the system output (using the current model)\n #   ans: answer fed by the user\n #   update: whether the model was updated\n print(res)\n\n # Now what does the result look like?\n print(rma.tokenize(\"\u3046\u3089\u306b\u308f\u306b\u306f\u306b\u308f\u306b\u308f\u3068\u308a\u304c\u3044\u308b\"))\n\n\nNOTE\n===========\n\nAdded API\n--------------\nAs compared to original RakutenMA, following methods are added:\n\n- RakutenMA::load(model_path)\n  - Load model from JSON file\n\n- RakutenMA::save(model_path)\n  - Save model to path\n\nmisc\n--------------\nAs initial setting, following values are set:\n\n- rma.featset = CTYPE_JA_PATTERNS  # RakutenMA.default_featset_ja\n- rma.hash_func = rma.create_hash_func(15)\n- rma.tag_scheme = \"SBIEO\"  # if using Chinese, set \"IOB2\"\n\nLICENSE\n=========\n\nApache License version 2.0\n\n\nCopyright\n=============\n\nRakuten MA Python\n(c) 2015- Yukino Ikegami. All Rights Reserved.\n\nRakuten MA (original)\n(c) 2014 Rakuten NLP Project. All Rights Reserved.\n\n.. |travis| image:: https://travis-ci.org/ikegami-yukino/rakutenma-python.svg?branch=master\n    :target: https://travis-ci.org/ikegami-yukino/rakutenma-python\n    :alt: travis-ci.org\n.. |coveralls| image:: https://coveralls.io/repos/ikegami-yukino/rakutenma-python/badge.png\n    :target: https://coveralls.io/r/ikegami-yukino/rakutenma-python\n    :alt: coveralls.io\n\n.. |pyversion| image:: https://img.shields.io/pypi/pyversions/rakutenma.svg\n\n.. |version| image:: https://img.shields.io/pypi/v/rakutenma.svg\n    :target: http://pypi.python.org/pypi/rakutenma/\n    :alt: latest version\n\n.. |landscape| image:: https://landscape.io/github/ikegami-yukino/rakutenma-python/master/landscape.svg?style=flat\n   :target: https://landscape.io/github/ikegami-yukino/rakutenma-python/master\n   :alt: Code Health\n\n.. |license| image:: https://img.shields.io/pypi/l/rakutenma.svg\n    :target: http://pypi.python.org/pypi/rakutenma/\n    :alt: license\n\n\nCHANGES\n=======\n\n0.3.3 (2017-05-22)\n-------------------------\n\n- Bug fix about training\n\n0.3.2 (2017-02-01)\n-------------------------\n\n- Use ujson when possible\n- Enable POS to MeCab style\n- Support Python 3.5 and 3.6\n\n0.3 (2016-04-10)\n-------------------------\n\n- Add CUI ($ rakutenma)\n\n0.2.2 (2016-04-09)\n-------------------------\n\n- Bundle model files (model_ja.json, model_ja_min.json)\n- Support Windows\n\n0.2 (2015-01-10)\n-------------------------\n\n- Support Python 2.6 and 2.7\n\n0.1.1 (2015-01-08)\n-------------------------\n\n- Slightly improve performance\n\n0.1 (2015-01-01)\n-------------------------\n\n- First release.\n",
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