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        "author": "Damir Cavar, Oren Baldinger, Maanvitha Gongalla, Anurag Kumar, Murali Kammili",
        "author_email": "damir@cavar.me",
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            "Programming Language :: Python :: 3.6",
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        "description": "# spaCy to JSON-NLP\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\nThis only works with spaCy 2.1.0!\n\nI had the same issue. I downgraded Spacy from 2.1.4 to 2.1.0 and numpy to 1.14.5 to resolve.\n\n\n## Introduction\n\nCurrently this module requires Python 3.6+.\n\nThis module provides a [spaCy] v2.1 wrapper for [JSON-NLP]. It takes the [spaCy] output and generates a [JSON-NLP] output. It also provides a Microservice wrapper that allows you to launch the [spaCy] module as a persistent RESTful service using [Flask] or other WSGI-based server.\n\nSince this microservice is built on [spaCy], you will need to have its models download, for example:\n\n    python -m spacy download en\n    python -m spacy download en_core_web_md\n\n## Additional Pipeline Modules\n\n[spaCy] allows for the addition of additional models as pipeline modules. We provide such integrations for coreference and phrase structure trees.\n\n### Anaphora and Coreference Resolution\n\nWe provide [HuggingFace] coreference resolution, a fast system tightly integrated into [spaCy]. Note that the first time the parser is run, it will download the coreference models if they are not already present. These models only work for English.\n\n### Phrase Structure Trees (Constituency Parse)\n\nWe provide the CPU version of the [benepar] parser, a highly accurate phrase structure parser. Bear in mind it is a Tensorflow module, as such it has a notable start-up time, and relatively high memory requirements (4GB+).\n\nIf you have a GPU available, you can install the GPU version of the module with:\n\n    pip install --upgrade benepar[gpu] \n\n## Microservice\n\nThe [JSON-NLP] repository provides a Microservice class, with a pre-built implementation of [Flask]. To run it, execute:\n\n    python spacyjsonnlp/server.py\n\nSince `server.py` extends the [Flask] app, a WSGI file would contain:\n\n    from spacyjsonnlp.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 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\nThe [spaCy] language model to use for parsing can be selected with the `spacy_model` parameter.\n\nHere is an example `GET` call:\n\n    http://localhost:5000?spacy_model=en&constituents=0&text=I am a sentence.\n\n[Damir Cavar]: http://damir.cavar.me/ \"Damir Cavar\"\n[Oren Baldinger]: https://oren.baldinger.me/ \"Oren Baldinger\"\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 \"CONLL-U\"\n[Flask]: http://flask.pocoo.org/ \"Flask\"\n[HuggingFace]: https://github.com/huggingface/neuralcoref/ \"Hugging Face\"\n[benepar]: https://github.com/nikitakit/self-attentive-parser \"Berkeley Neural Parser\"\n\n\n",
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