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        "author": "Ekate Kuznetsova",
        "author_email": "char-classify@technekate.com",
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            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Topic :: Software Development :: Libraries",
            "Topic :: Text Processing"
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        "description": "Char Classifier\n---------\n\nGiven a short phrase, train a neural network to classify it based on the characters that it contains.\n\nThe trainer takes in a file of phrases (separated by new line) and a file of target values (separated by new line).\n\nTo train on the sample data (parts of speech classification):\n\n```\nimport chClassifier\np = chClassifier.Trainer(\"sample/words\", \"sample/parts-of-speech\")\np.train([100, 100], 20, 100)\np.save(\"sample\", \"example-run\")\n```\n\nThis will save a trained model with the tag 'example-run' in the sample directory. To use that model, run:\n\n```\nimport chClassifier\nk = chClassifier.Classifier(\"sample\", \"example-run\")\nprint k.classify(\"dog\")\n```\n\nThis will return an array of tuples of original label + likelyhood that the label is correct, like so:\n\n```\n>> print k.classify(\"dog\")\n[(u'VB', 0.050349433), (u'NN', 3.8027303)]\n```\n\nThe higher the number, the more sure we are of the classification (`dog` is definitely a noun, for example, and probably not a verb).",
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        "keywords": "deep-learning machine-learning string classifier",
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