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    "info": {
        "author": "Damir Cavar, Oren Baldinger, Maanvitha Gongalla, Anurag Kumar, Murali Kammili",
        "author_email": "damir@cavar.me",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
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        "description": "# Python JSON-NLP Module\n\n(C) 2019 by [Damir Cavar], [Oren Baldinger], [Maanvitha Gongalla], [Anurag Kumar], Murali Kammili\n\nBrought to you by the [NLP-Lab.org]!\n\n\n## Introduction\n\nThere is a growing number of Natural Language Processing (NLP) tools, modules, pipelines. There does not seem to be any standard for the output format. Here we are focusing on a standard for the output format syntax. Some future version of [JSON-NLP] might address the output semantics as well.\n\n[JSON-NLP] is a standard for the most important outputs NLP pipelines and components can generate. The relevant documentation can be found in the [JSON-NLP] GitHub repo and on its website at the [NLP-Lab].\n\n\nThe Python [JSON-NLP] module contains general mapping functions for [JSON-NLP] to [CoNLL-U], a validator for the generated output, an NLP pipeline interface (for [Flair], [spaCy], [NLTK], [Polyglot], [Xrenner], etc.), and various utility functions.\n\nThere is a [Java JSON-NLP](https://github.com/dcavar/J-JSON-NLP) Maven module as well, and there are wrappers for numerous popular NLP pipelines and tools linked from the [NLP-Lab.org] website.\n\n\n## Installation\n\nFor more details, see [JSON-NLP].\n\nThis module is a wrapper for outputs from different NLP pipelines and modules into a standardized [JSON-NLP] format.\n\nTo install this package, run the following command:\n\n    pip install pyjsonnlp\n\nYou might have to use *pip3* on some systems.\n\n## Validation\n\n[JSON-NLP] is based on a schema, built by [NLP-Lab.org], to comprehensively and concisely represent linguistic annotations. \nWe provide a validator to help ensure that generated JSON validates against the schema:\n\n    result = MyPipeline().proces(text=\"I am a sentence\")\n    assert pyjsonnlp.validation.is_valid(result)\n\n## Conversion\n\nTo enable interoperability with other annotation formats, we support conversions between them.\nNote that conversion could be lossy, if the relative depths of annotation are not the same.\nCurrently we have a [CoNLL-U] to [JSON-NLP] converter, that covers most annotations:\n\n    pyjsonnlp.conversion.parse_conllu(conllu_text)\n\nTo convert the other direction:\n\n    pyjsonnlp.conversion.to_conllu(jsonnlp)\n\n## Pipeline\n\n[JSON-NLP] provides a simple `Pipeline` interface that should be implemented for embedding into a microservice:\n\n    from collections import OrderedDict\n\n    class MockPipeline(pyjsonnlp.pipeline.Pipeline):\n        @staticmethod\n        def process(text='', coreferences=False, constituents=False, dependencies=False, expressions=False,\n                    **kwargs) -> OrderedDict: \n            return OrderedDict()\n\nThe provided keyword arguments should be used to toggle on or off processing components within the method.        \n\nIf you have deployed a `Pipeline` as a microservice (see below), we provide a local endpoint for a remotely \ndeployed `Pipeline` via the `RemotePipeline` class:\n\n    pipeline = pyjsonnlp.pipeline.RemotePipeline('localhost', port=9000)\n    print(pipeline.process(text='I am a sentence', dependencies=True, something='else'), spacing=2)\n\n## Microservice\n\nThe next step is the [JSON-NLP] a Microservice class, with a pre-built implementation of [Flask]. \n\n    from pyjsonnlp.microservices.flask_server import FlaskMicroservice\n\n    app = FlaskMicroservice(__name__, MyPipeline(), base_route='/')\n\nWe recommend creating a `server.py` with the `FlaskMicroservice` class, which extends the [Flask] app. A corresponding WSGI file would contain:\n\n    from mypipeline.server import app as application\n\nTo disable a pipeline component (such as phrase structure parsing), add\n\n    application.constituents = False\n\nThe full list of properties available that can be disabled or enabled are\n- constituents\n- dependencies\n- coreference\n- expressions\n\nThe microservice exposes the following URIs:\n- /constituents\n- /dependencies\n- /coreference\n- /expressions\n- /token_list\n\nThese URIs are shortcuts to disable the other components of the parse. In all cases, `tokenList` will be included in the `JSON-NLP` output. An example url is:\n\n    http://localhost:5000/dependencies?text=I am a sentence\n\nText is provided to the microservice with the `text` parameter, via either `GET` or `POST`. If you pass `url` as a parameter, the microservice will scrape that url and process the text of the website.\n\nOther parameters specific to your pipeline implementation can be passed as well:\n\n    http://localhost:5000?lang=en&constituents=0&text=I am a sentence.\n\n\n\n[Damir Cavar]: http://damir.cavar.me/ \"Damir Cavar\"\n[Oren Baldinger]: https://oren.baldinger.me/ \"Oren Baldinger\"\n[Anurag Kumar]: https://github.com/anuragkumar95/ \"Anurag Kumar\"\n[Maanvitha Gongalla]: https://maanvithag.github.io/MaanvithaGongalla/\n[NLP-Lab.org]: http://nlp-lab.org/ \"NLP-Lab.org\"\n[JSON-NLP]: https://github.com/dcavar/JSON-NLP \"JSON-NLP\"\n[Flair]: https://github.com/zalandoresearch/flair \"Flair\"\n[spaCy]: https://spacy.io/ \"spaCy\"\n[NLTK]: http://nltk.org/ \"Natural Language Processing Toolkit\"\n[Polyglot]: https://github.com/aboSamoor/polyglot \"Polyglot\" \n[Xrenner]: https://github.com/amir-zeldes/xrenner \"Xrenner\"\n[CoNLL-U]: https://universaldependencies.org/format.html \"CoNNL-U\"\n\n\n",
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