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    "info": {
        "author": "Simon D. Hernandez",
        "author_email": "py.kleis@totum.one",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Software Development :: Build Tools"
        ],
        "description": "# Kleis: Python package for keyphrase extraction\n\nKleis is a python package to label keyphrases in scientific text. It is named after the ancient greek word [\u03ba\u03bb\u03b5\u03af\u03c2](https://en.wiktionary.org/w/index.php?title=%CE%BA%CE%BB%CE%B5%CE%AF%CF%82).\n## Install \n\n### Pip (Easy and quick)\n\n```\n$ pip install kleis-keyphrase-extraction\n```\n\n### Make your own wheel\n\n```\n$ git clone https://github.com/sdhdez/kleis-keyphrase-extraction.git\n$ cd kleis-keyphrase-extraction/\n$ python setup.py sdist bdist_wheel\n$ pip install dist/kleis_keyphrase_extraction-0.1.X.devX-py3-none-any.whl\n```\nReplace X with the corresponding values.\n\nNote: This method doesn't include pre-trained models, you should download the corpus so it can train.\n\n## Usage \n\nExample [here](https://github.com/sdhdez/kleis-keyphrase-extraction/blob/r0.1.2/notebooks/minimal-example.ipynb)\n\n\n## Datasets\n\nThepackage already includes some pre-trained models but if you want to test by your own you should download the datasets. \n\nDownload from [SemEval 2017 Task 10](https://scienceie.github.io/resources.html) and decompress in \"~/kleis_data/corpus/semeval2017-task10\" or \"./kleis_data/corpus/semeval2017-task10\"\n\n```\n$ ls ~/kleis_data/corpus/semeval2017-task10\n\nbrat_config  eval.py       __MACOSX            README_data.md  scienceie2017_test_unlabelled  train2   xml_utils.py\ndev          eval_py27.py  README_data_dev.md  README.md       semeval_articles_test          util.py  zips\n```\n\n## Test\n\nYou can test your installation with [keyphrase-extraction-example.py](https://github.com/sdhdez/kleis-keyphrase-extraction/blob/master/keyphrase-extraction-example.py)\n\n```\n$ python keyphrase-extraction-example.py\n```\n\nAlso, see [here](https://github.com/sdhdez/kleis-keyphrase-extraction/blob/r0.1.2/notebooks/Keyphrase_extraction.ipynb) for another example.\n\n\n## Requirements \n\n - Python 3 (Tested: 3.6.5)\n - nltk (with corpus) (Tested: 3.2.5)\n - python-crfsuite (Tested: 0.9.5)\n\n## Optional\n\n### Notebooks\n\nTo run the noteooks in this repository install JupyterLab.\n\n```\n$ pip install jupyterlab\n```\n\nThen run the following command. \n\n```\njupyter lab\n```\n\n## Further information\n\nThis method uses a CRFs model (Conditional Random Fields) to label keyphrases in text, the model is trained with keyphrase candidates filtered with Part-of-Spech tag sequences. It is based on the method described [here](https://aclanthology.coli.uni-saarland.de/papers/S17-2174/s17-2174), but with a better performance. Please, feel free to send us comments or questions.\n\nIn this version we use [python-crfsuite](https://github.com/scrapinghub/python-crfsuite). \n\n\n",
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        "keywords": "keyphrase-extraction keyphrase-labeling keyword-extraction crf pos-tag-sequences",
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