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        "description": "# Terms Tree Library\n\nLibrary to build *terms tree* from indented text file and to search terms in that tree.\n\nMay be used for text labeling/classification tasks.\n\nSee also: [termstree library](https://pypi.org/project/termsquery/)\n\n## Example\n\nDemo Script: \n\n```python\nimport termstree\n\nTERMS_TREE_SRC = \"\"\"\n# comment\n\nAsia\n    Japan\n        Tokyo [url=\"https://en.wikipedia.org/wiki/Tokyo\"]\n        Osaka\n    China\n        Beijing\n        Shanghai\nEurope\n    England\n        London\n\n    Germany [url=\"https://en.wikipedia.org/wiki/Germany\"]\n        Berlin\n        Munich\n\"\"\"\n\nterms_tree = termstree.build(TERMS_TREE_SRC, terms_normalizer=None)\n\ntext = 'During the 16th century, Munich was a centre of the German counter reformation. Europe ...'\n\nfor hit in terms_tree.search_in(text):\n    print(hit)\n```\n\nResult (list of 'hits' - terms found in the text):\n```\nHit(node=Node('Munich'), dhits=1, ihits=0)\nHit(node=Node('Europe'), dhits=1, ihits=1)\nHit(node=Node('Germany', {'url': 'https://en.wikipedia.org/wiki/Germany'}), dhits=0, ihits=1)\n```\n\nEvery hit corresponds to a term from terms tree and has next attributes:\n\n- node - found term\n- dhits (direct hits) - number of direct term occurrences in the text\n- ihits (indirect hits) - number of term's children occurrences",
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