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    "info": {
        "author": "Nick Morley",
        "author_email": "nick.morley111@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "# SpaCy Pattern Builder\n\nUse training examples to build and refine patterns for use with SpaCy's DependencyMatcher.\n\n## Motivation\n\nGenerating patterns programmatically from training data is more efficient than creating them manually.\n\n## Installation\n\nWith pip:\n\n```bash\npip install spacy-pattern-builder\n```\n\n## Usage\n\n```python\n# Import a SpaCy model, parse a string to create a Doc object\nimport en_core_web_sm\n\ntext = 'We introduce efficient methods for fitting Boolean models to molecular data.'\nnlp = en_core_web_sm.load()\ndoc = nlp(text)\n\nfrom spacy_pattern_builder import build_dependency_pattern\n\n# Provide a list of tokens we want to match.\nmatch_tokens = [doc[i] for i in [0, 1, 3]]  # [We, introduce, methods]\n\n''' Note that these tokens must be fully connected. That is,\nall tokens must have a path to all other tokens in the list,\nwithout needing to traverse tokens outside of the list.\nOtherwise, spacy-pattern-builder will raise a TokensNotFullyConnectedError.\nYou can get a connected set that includes your tokens with the following: '''\nfrom spacy_pattern_builder import util\nconnected_tokens = util.smallest_connected_subgraph(match_tokens, doc)\nassert match_tokens == connected_tokens  # In this case, the tokens we provided are already fully connected\n\n# Specify the token attributes / features to use\nfeature_dict = {  # This is equal to the default feature_dict\n    'DEP': 'dep_',\n    'TAG': 'tag_'\n}\n\n# Build the pattern\npattern = build_dependency_pattern(doc, match_tokens, feature_dict=feature_dict)\n\nfrom pprint import pprint\npprint(pattern)  # In the format consumed by SpaCy's DependencyMatcher:\n'''\n[{'PATTERN': {'DEP': 'ROOT', 'TAG': 'VBP'}, 'SPEC': {'NODE_NAME': 'node1'}},\n {'PATTERN': {'DEP': 'nsubj', 'TAG': 'PRP'},\n  'SPEC': {'NBOR_NAME': 'node1', 'NBOR_RELOP': '>', 'NODE_NAME': 'node0'}},\n {'PATTERN': {'DEP': 'dobj', 'TAG': 'NNS'},\n  'SPEC': {'NBOR_NAME': 'node1', 'NBOR_RELOP': '>', 'NODE_NAME': 'node3'}}]\n'''\n\n# Create a matcher and add the newly generated pattern\nfrom spacy.matcher import DependencyMatcher\n\nmatcher = DependencyTreeMatcher(doc.vocab)\nmatcher.add('pattern', None, pattern)\n\n# And get matches\nmatches = matcher(doc)\nfor match_id, token_idxs in matches:\n    tokens = [doc[i] for i in token_idxs]\n    tokens = sorted(tokens, key=lambda w: w.i)  # Make sure tokens are in their original order\n    print(tokens)  # [We, introduce, methods]\n\n```\n\n## Acknowledgements\n\nUses:\n\n- [SpaCy](https://spacy.io)\n- [networkx](https://github.com/networkx/networkx)\n\n",
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        "keywords": "spacy-pattern-builder",
        "license": "MIT",
        "maintainer": "",
        "maintainer_email": "",
        "name": "spacy-pattern-builder",
        "package_url": "https://pypi.org/project/spacy-pattern-builder/",
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        "summary": "Reverse engineer patterns for use with the SpaCy DependencyTreeMatcher",
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