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        "author": "Adam Leskis",
        "author_email": "leskis@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6"
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        "description": "# Awlify\n\n[![made-with-python](https://img.shields.io/badge/Made%20with-Python3.6-1f425f.svg)](https://www.python.org/)\n[![GitHub license](https://img.shields.io/github/license/Naereen/StrapDown.js.svg)](https://github.com/lpmi-13/awlify-python/blob/master/LICENSE)\n\n\nA very basic tool that takes in a sentence of text and outputs\nthe same text, annotated with information about whether any of\nits words are in the [Academic Word List](https://www.victoria.ac.nz/lals/resources/academicwordlist/information).\n\n## installing\n`pip install awlify`\n\nand if you haven't used spacy on your system before, you'll need\nto install the model we're using here with the command below:\n\n`python -m spacy download en_core_web_sm`\n\n## tests\n`python -m unittest`\n\n## usage inside a file\n```\nfrom awlify import awlify\n\nresult = awlify('please inform me of the academic words in this sentence')\n\nprint(result)\n{\"data\": {\"sentence\": \"please inform me of the academic words in this sentence\", \"awl_words\": [{\"index\": 5, \"word\": \"academic\", \"meta\": {\"head\": \"academy\", \"sublist\": 5}}]}}\n```\n\n## usage from the command line\n`python -m awlify 'this is a sentence to check'`\n\n`{\"data\": {\"sentence\": \"this is a sentence to check\", \"awl_words\": []}}`\n\n## expected input / output\nformat for output:\n```\n{\n  \"data\": {\n    \"sentence\": \"THIS IS THE ORIGINAL SENTENCE\",\n    \"awl_words\": [\n      {\n        \"index\": INDEX_OF_AWL_WORD_FOUND,\n        \"word\": \"AWL_WORD_FOUND\",\n        \"meta\": {\n          \"head\": \"THE_HEADWORD_FROM_THE_AWL\",\n          \"sublist\": THE_AWL_SUBLIST_OF_THE_WORD\n        }\n      }\n    ]\n  }\n}\n```\n\nexample input for a simple sentence (no AWL words):\n```\nsimple_sentence = awlify('this is a sentence')\n```\n\n\nexample output for a simple sentence (no AWL words):\n```\n{\n  \"data\": {\n    \"sentence\": \"this is a sentence\",\n    \"awl_words\": []\n  }\n}\n```\n\nexample input for a complex sentence (a few AWL words):\n```\ncomplex_sentence = awlify('the economic recovery is ongoing and potentially problematic')\n```\n\nexample output for a complex sentence (a few AWL words):\n```\n{\n  \"data\": {\n    \"sentence\": \"the economic recovery is ongoing and potentially problematic\",\n    \"awl_words\": [\n      {\n        \"index\": 1,\n        \"word\": \"economic\",\n        \"meta\": {\n          \"head\": \"economy\",\n          \"sublist\": 1\n        }\n      },\n      {\n        \"index\": 2,\n        \"word\": \"recovery\",\n        \"meta\": {\n          \"head\": \"recover\",\n          \"sublist\": 6\n        }\n      },\n      {\n        \"index\": 6,\n        \"word\": \"potentially\",\n        \"meta\": {\n          \"head\": \"potential\",\n          \"sublist\": 2\n        }\n      }\n    ]\n  }\n}\n```\n\n## NOTES\n\nThe current implementation of the sentence tokenization uses spacy,\nand so it's a bit heavier than absolutely necessary, since we're\nnot taking advantage of any of the more advanced characteristics\nof the package.\n\nIn theory, it could probably perform 98% as well with just a simple\nregex, so I might add the option to do that in the future if there\naren't any real use cases for needing the full weight of spacy.\n\n## REFERENCES\nCoxhead, Averil (2000) A New Academic Word List. TESOL Quarterly, 34(2): 213-238.\n\n\n",
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