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    "info": {
        "author": "Matias Grioni",
        "author_email": "matgrioni@gmail.com",
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            "Intended Audience :: Education",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3 :: Only",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering",
            "Topic :: Utilities"
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        "description": "|Build Status| |Coverage Status| |Documentation Status|\n\npyconll\n-------\n\n*Easily work with **CoNLL** files using the familiar syntax of\n**python**.*\n\nThe current version is 2.2.0. This version is fully functional, stable,\ntested, documented, and actively developed.\n\nLinks\n'''''\n\n-  `Homepage <https://pyconll.github.io>`__\n-  `Documentation <https://pyconll.readthedocs.io/>`__\n\nInstallation\n~~~~~~~~~~~~\n\nAs with most python packages, simply use ``pip`` to install from PyPi.\n\n::\n\n    pip install pyconll\n\nThis package is designed for, and only tested with python 3.4 and up and\nwill not be backported to python 2.x.\n\nMotivation\n~~~~~~~~~~\n\nThis tool is intended to be a **minimal**, **low level**, and\n**functional** library in a widely used programming language. pyconll\ncreates a thin API on top of raw CoNLL annotations that is simple and\nintuitive in a popular programming language.\n\nIn my work with the Universal Dependencies project, I saw a\ndissapointing lack of low level APIs for working with the CoNLL-U\nformat. Most tooling focuses on graph transformations and DSLs for\nterse, automated changes. Tools such as `Grew <http://grew.fr/>`__ and\n`Treex <http://ufal.mff.cuni.cz/treex>`__ are very powerful and\nproductive, but have a learning curve and are limited the scope of their\nDSLs. `CL-CoNLLU <https://github.com/own-pt/cl-conllu/>`__ is simple and\nlow level, but Common Lisp is not widely used in NLP, and difficult to\npickup for beginners. `UDAPI <http://udapi.github.io/>`__ is in python\nbut it is very large and has little guidance. pyconll attempts to fill\nthe gaps between what other projects have accomplished.\n\nOther useful tools can be found on the Universal Dependencies\n`website <https://universaldependencies.org/tools.html>`__.\n\nHopefully, individual researchers find pyconll useful, and will use it\nas a building block for their tools and projects. pyconll affords a\nstandardized and complete base for building larger projects without\nworrying about CoNLL annotation and output.\n\nCode Snippet\n~~~~~~~~~~~~\n\n.. code:: python\n\n    # This snippet finds what lemmas are marked as AUX which is a closed class POS in UD\n    import pyconll\n\n    UD_ENGLISH_TRAIN = './ud/train.conll'\n\n    train = pyconll.load_from_file(UD_ENGLISH_TRAIN)\n\n    aux_lemmas = set()\n    for sentence in train:\n        for token in sentence:\n            if token.upos == 'AUX':\n                aux_lemmas.add(token.lemma)\n\nUses and Limitations\n~~~~~~~~~~~~~~~~~~~~\n\nThis package edits CoNLL-U annotations. This does not include the\nannotated text itself. Word forms on Tokens are not editable and\nSentence Tokens cannot be reassigned or reordered. ``pyconll`` focuses\non editing CoNLL-U annotation rather than creating it or changing the\nunderlying text that is annotated. If there is interest in this\nfunctionality area, please create a github issue for more visibility.\n\nThis package also is only validated against the CoNLL-U format. The\nCoNLL and CoNLL-X format are not supported, but are very similar. I\noriginally intended to support these formats as well, but their format\nis not as well defined as CoNLL-U so they are not included. Please\ncreate an issue for visibility if this feature interests you.\n\nLastly, linguistic data can often be very large and this package\nattempts to keep that in mind. pyconll provides methods for creating in\nmemory conll objects along with an iterate only version in case a corpus\nis too large to store in memory (the size of the memory structure is\nseveral times larger than the actual corpus file). The iterate only\nversion can parse upwards of 100,000 words per second on a 16gb ram\nmachine, so for most datasets to be used on a dev machine, this package\nwill perform well. The 2.2.0 release also improves parse time and memory\nfootprint by about 25%!\n\nContributing\n~~~~~~~~~~~~\n\nContributions to this project are welcome and encouraged! If you are\nunsure how to contribute, here is a\n`guide <https://help.github.com/en/articles/creating-a-pull-request-from-a-fork>`__\nfrom Github explaining the basic workflow. 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The code formatter is\n`YAPF <https://github.com/google/yapf>`__, and using this ensures that\ncoding style stays consistent over time and between authors.\n\n.. |Build Status| image:: https://travis-ci.org/pyconll/pyconll.svg?branch=master\n   :target: https://travis-ci.org/pyconll/pyconll\n.. |Coverage Status| image:: https://coveralls.io/repos/github/pyconll/pyconll/badge.svg?branch=master\n   :target: https://coveralls.io/github/pyconll/pyconll?branch=master\n.. |Documentation Status| image:: https://readthedocs.org/projects/pyconll/badge/?version=stable\n   :target: https://pyconll.readthedocs.io/en/latest/?badge=latest\n\n\n",
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