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    "info": {
        "author": "Tyler Barrus",
        "author_email": "barrust@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries",
            "Topic :: Utilities"
        ],
        "description": "pyspellchecker\n===============================================================================\n\n.. image:: https://badge.fury.io/py/pyspellchecker.svg\n    :target: https://badge.fury.io/py/pyspellchecker\n.. image:: https://travis-ci.org/barrust/pyspellchecker.svg?branch=master\n    :target: https://travis-ci.org/barrust/pyspellchecker\n.. image:: https://coveralls.io/repos/github/barrust/pyspellchecker/badge.svg?branch=master\n    :target: https://coveralls.io/github/barrust/pyspellchecker?branch=master\n.. image:: http://pepy.tech/badge/pyspellchecker\n    :target: http://pepy.tech/count/pyspellchecker\n    :alt: Downloads\n\n\nPure Python Spell Checking based on `Peter\nNorvig's <https://norvig.com/spell-correct.html>`__ blog post on setting\nup a simple spell checking algorithm.\n\nIt uses a `Levenshtein Distance <https://en.wikipedia.org/wiki/Levenshtein_distance>`__\nalgorithm to find permutations within an edit distance of 2 from the\noriginal word. It then compares all permutations (insertions, deletions,\nreplacements, and transpositions) to known words in a word frequency\nlist. Those words that are found more often in the frequency list are\n**more likely** the correct results.\n\n``pyspellchecker`` supports multiple languages including English, Spanish,\nGerman, French, and Portuguese. Dictionaries were generated using\nthe `WordFrequency project <https://github.com/hermitdave/FrequencyWords>`__ on GitHub.\n\n``pyspellchecker`` supports **Python 3** and Python 2.7 but, as always, Python 3\nis the preferred version!\n\n``pyspellchecker`` allows for the setting of the Levenshtein Distance to check.\nFor longer words, it is highly recommended to use a distance of 1 and not the\ndefault 2. See the quickstart to find how one can change the distance parameter.\n\n\nInstallation\n-------------------------------------------------------------------------------\n\nThe easiest method to install is using pip:\n\n.. code:: bash\n\n    pip install pyspellchecker\n\nTo install from source:\n\n.. code:: bash\n\n    git clone https://github.com/barrust/pyspellchecker.git\n    cd pyspellchecker\n    python setup.py install\n\nAs always, I highly recommend using the\n`Pipenv <https://github.com/pypa/pipenv>`__ package to help manage\ndependencies!\n\n\nQuickstart\n-------------------------------------------------------------------------------\n\nAfter installation, using ``pyspellchecker`` should be fairly straight\nforward:\n\n.. code:: python\n\n    from spellchecker import SpellChecker\n\n    spell = SpellChecker()\n\n    # find those words that may be misspelled\n    misspelled = spell.unknown(['something', 'is', 'hapenning', 'here'])\n\n    for word in misspelled:\n        # Get the one `most likely` answer\n        print(spell.correction(word))\n\n        # Get a list of `likely` options\n        print(spell.candidates(word))\n\n\n\nIf the Word Frequency list is not to your liking, you can add additional\ntext to generate a more appropriate list for your use case.\n\n.. code:: python\n\n    from spellchecker import SpellChecker\n\n    spell = SpellChecker()  # loads default word frequency list\n    spell.word_frequency.load_text_file('./my_free_text_doc.txt')\n\n    # if I just want to make sure some words are not flagged as misspelled\n    spell.word_frequency.load_words(['microsoft', 'apple', 'google'])\n    spell.known(['microsoft', 'google'])  # will return both now!\n\n\nIf the words that you wish to check are long, it is recommended to reduce the\n`distance` to 1. This can be accomplished either when initializing the spell\ncheck class or after the fact.\n\n.. code:: python\n\n    from spellchecker import SpellChecker\n\n    spell = SpellChecker(distance=1)  # set at initialization\n\n    # do some work on longer words\n\n    spell.distance = 2  # set the distance parameter back to the default\n\n\n\nAdditional Methods\n-------------------------------------------------------------------------------\n\n`On-line documentation <http://pyspellchecker.readthedocs.io/en/latest/>`__ is available; below contains the cliff-notes version of some of the available functions:\n\n\n``correction(word)``: Returns the most probable result for the\nmisspelled word\n\n``candidates(word)``: Returns a set of possible candidates for the\nmisspelled word\n\n``known([words])``: Returns those words that are in the word frequency\nlist\n\n``unknown([words])``: Returns those words that are not in the frequency\nlist\n\n``word_probability(word)``: The frequency of the given word out of all\nwords in the frequency list\n\nThe following are less likely to be needed by the user but are available:\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n``edit_distance_1(word)``: Returns a set of all strings at a Levenshtein\nDistance of one based on the alphabet of the selected language\n\n``edit_distance_2(word)``: Returns a set of all strings at a Levenshtein\nDistance of two based on the alphabet of the selected language\n\n\nCredits\n-------------------------------------------------------------------------------\n\n* `Peter Norvig <https://norvig.com/spell-correct.html>`__ blog post on setting up a simple spell checking algorithm\n\n* `hermetdave's WordFrequency project <https://github.com/hermitdave/FrequencyWords>`__ for providing the basis for Non-English dictionaries\n\n\n",
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        "license": "MIT",
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