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    "info": {
        "author": "Damir Cavar, Oren Baldinger, Maanvitha Gongalla, Anurag Kumar, Murali Kammili, Boli Fang",
        "author_email": "damir@cavar.me",
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        "classifiers": [
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# Flair JSON-NLP Wrapper\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\n[Flair] v4.1 wrapper for [JSON-NLP]. [Flair] provides state-of-the-art embeddings, and tagging capabilities,\nin particular, POS-tagging, NER, shallow syntax chunking, and semantic frame detection.\n\n## FlairPipeline\n\nWe provide a `FlairPipeline` class, with the following parameters for ease of use:\n\n- `lang`: defaults to `en`. Different languages support different models, see the [Flair Docs] for details.\n- `use_ontonotes`: defaults to `False`. Whether or not to use 4-class (True) or 12-class (False) NER tagging.\n- `fast`: defaults to `True`. Whether or not to use the smaller, faster, but slightly less accurate versions of the models.\n- `use_embeddings`: defaults to ''. Passing `default` will map to `glove,multi-forward,multi-backward`, the recommended stacked-embedding configuration.\n- `char_embeddings`: defaults to `False`. Whether or not to include character-level embeddings.\n- `bpe_size`: defaults to 0. If you want to include [Byte-Pair Encodings](https://nlp.h-its.org/bpemb/), set this value to 50, 100, 200, or 300. See more at [Flair Embeddings Docs](https://github.com/zalandoresearch/flair/blob/master/resources/docs/TUTORIAL_3_WORD_EMBEDDING.md).\n- `pos`: defaults to `True`. Whether or not to include language-specific part-of-speech tags.\n- `sentinment`: defaults to `True`. Whether or not to include sentiment analysis, if it is available for the given language.\n\nTagging and Embedding models are downloaded automatically the first time they are called.\nThis may take a while depending on your internet connection.\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 flairjsonnlp/server.py\n\nSince `server.py` extends the [Flask] app, a WSGI file would contain:\n\n    from flairjsonnlp.server import app as application\n\nThe microservice exposes the following URIs:\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/expressions?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 additional [Flair] parameters can be passed as parameters as well.\n\nHere is an example `GET` call:\n\n    http://localhost:5000?lang=de&constituents=0&text=Ich bin ein Berliner.\n\n\n\n[Damir Cavar]: http://damir.cavar.me/ \"Damir Cavar\"\n[Oren Baldinger]: https://oren.baldinger.me/ \"Oren Baldinger\"\n[Maanvitha Gongalla]: https://maanvithag.github.io/MaanvithaGongalla/ \"Maanvitha Gongalla\"\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[Flair Docs]: https://github.com/zalandoresearch/flair/tree/master/resources/docs \"Flair Docs\"\n\n\n",
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