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        "author": "Marc Puig",
        "author_email": "marc.puig@gmail.com",
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        "description": "spacy-lookup: Named Entity Recognition based on dictionaries\n************************************************************\n\n`spaCy v2.0 <https://spacy.io/usage/v2>`_ extension and pipeline component\nfor adding Named Entities metadata to ``Doc`` objects. Detects Named Entities\nusing dictionaries. The extension sets the custom ``Doc``,\n``Token`` and ``Span`` attributes ``._.is_entity``, ``._.entity_type``,\n``._.has_entities`` and ``._.entities``.\n\nNamed Entities are matched using the python module ``flashtext``, and\nlooks up in the data provided by different dictionaries.\n\nInstallation\n===============\n\n``spacy-lookup`` requires ``spacy`` v2.0.16 or higher.\n\n.. code:: bash\n\n    pip install spacy-lookup\n\nUsage\n=====\nFirst, you need to download a language model.\n\n.. code:: bash\n\n    python -m spacy download en\n\nImport the component and initialise it with the shared ``nlp`` object (i.e. an\ninstance of ``Language``), which is used to initialise ``flashtext``\nwith the shared vocab, and create the match patterns. Then add the component\nanywhere in your pipeline.\n\n.. code:: python\n\n    import spacy\n    from spacy_lookup import Entity\n\n    nlp = spacy.load('en')\n    entity = Entity(keywords_list=['python', 'product manager', 'java platform'])\n    nlp.add_pipe(entity, last=True)\n\n    doc = nlp(u\"I am a product manager for a java and python.\")\n    assert doc._.has_entities == True\n    assert doc[0]._.is_entity == False\n    assert doc[3]._.entity_desc == 'product manager'\n    assert doc[3]._.is_entity == True\n\n    print([(token.text, token._.canonical) for token in doc if token._.is_entity])\n\n\n``spacy-lookup`` only cares about the token text, so you can use it on a blank\n``Language`` instance (it should work for all\n`available languages <https://spacy.io/usage/models#languages>`_!), or in\na pipeline with a loaded model. If you're loading a model and your pipeline\nincludes a tagger, parser and entity recognizer, make sure to add  the entity\ncomponent as ``last=True``, so the spans are merged at the end of the pipeline.\n\nAvailable attributes\n--------------------\n\nThe extension sets attributes on the ``Doc``, ``Span`` and ``Token``. You can\nchange the attribute names on initialisation of the extension. For more details\non custom components and attributes, see the\n`processing pipelines documentation <https://spacy.io/usage/processing-pipelines#custom-components>`_.\n\n====================== ======= ===\n``Token._.is_entity``   bool    Whether the token is an entity.\n``Token._.entity_type`` unicode A human-readable description of the entity.\n``Doc._.has_entities``    bool    Whether the document contains entity.\n``Doc._.entities``        list    ``(entity, index, description)`` tuples of the document's entities.\n``Span._.has_entities``   bool    Whether the span contains entity.\n``Span._.entities``       list    ``(entity, index, description)`` tuples of the span's entities.\n====================== ======= ===\n\nSettings\n--------\n\nOn initialisation of ``Entity``, you can define the following settings:\n\n=============== ============ ===\n``nlp``         ``Language`` The shared ``nlp`` object. Used to initialise the matcher with the shared ``Vocab``, and create ``Doc`` match patterns.\n``attrs``       tuple        Attributes to set on the ._ property. Defaults to ``('has_entities', 'is_entity', 'entity_type', 'entity')``.\n``keywords_list``      list         Optional lookup table with the list of terms to look for.\n``keywords_dict``      dict         Optional lookup table with the list of terms to look for.\n``keywords_file``      string         Optional filename with the list of terms to look for.\n=============== ============ ===\n\n.. code:: python\n\n    entity = Entity(nlp, keywords_list=['python', 'java platform'], label='ACME')\n    nlp.add_pipe(entity)\n    doc = nlp(u\"I am a product manager for a java platform and python.\")\n    assert doc[3]._.is_entity\n\n\n",
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        "license": "MIT",
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        "name": "spacy-lookup",
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