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        "author": "Ryuya Ikeda",
        "author_email": "rikeda71@gmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Text Processing"
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        "description": "# miNER\n\nA python library for NER (Named Entity Recognition) evaluation\n\nWe can evaluate the performance of NER by distinguishing between known entities and unknown entities using this library.\n\n\n## Support\n\n- Tagging Scheme\n    - IOB2\n    - BIOES\n    - BIOUL\n- metrics\n    - precision\n    - recall\n    - f1\n\n\n## Requirements\n\n- python3\n- cython\n\n\n## Installation\n\n```shell\npip install mi-ner\n```\n\n\n## Usage\n\n#### Sample\n\n```python\n>>> from miner import Miner\n>>> answers = [\n    'B-PSN O O B-LOC O O O O'.split(' '),\n    'B-PSN I-PSN O O B-LOC I-LOC O O O O'.split(' '),\n    'S-PSN O O S-PSN O O B-LOC I-LOC E-LOC O O O O'.split(' ')\n]\n>>> predicts = [\n    'B-PSN O O B-LOC O O O O'.split(' '),\n    'B-PSN B-PSN O O B-LOC I-LOC O O O O'.split(' '),\n    'S-PSN O O O O O B-LOC I-LOC E-LOC O O O O'.split(' ')\n]\n>>> sentences = [\n    '\u82b1\u5b50 \u3055\u3093 \u306f \u6771\u4eac \u306b \u884c\u304d \u307e\u3057 \u305f'.split(' '),\n    '\u5c71\u7530 \u592a\u90ce \u541b \u306f \u6771\u4eac \u99c5 \u306b \u5411\u304b\u3044 \u307e\u3057 \u305f'.split(' '),\n    '\u82b1\u5b50 \u3055\u3093 \u3068 \u30dc\u30d6 \u304f\u3093 \u306f \u6771\u4eac \u30b9\u30ab\u30a4 \u30c4\u30ea\u30fc \u306b \u884c\u304d \u307e\u3057 \u305f'.split(' '),\n]\n>>> knowns = {'PSN': ['\u82b1\u5b50'], 'LOC': ['\u6771\u4eac']} # known words (words included in training data)\n>>> m = Miner(answers, predicts, sentences, knowns)\n>>> m.default_report(True)\n\tprecision    recall    f1_score   num\nPSN\t 0.500        0.500     0.500      4\nLOC\t 1.000        1.000     1.000      3\n{'PSN': {'precision': 0.5, 'recall': 0.5, 'f1_score': 0.5, 'num': 4}, 'LOC': {'precision': 1.0, 'recall': 1.0, 'f1_score': 1.0, 'num': 3}}\n>>> m.return_predict_named_entities()\n{'known': {'PSN': ['\u82b1\u5b50'], 'LOC': ['\u6771\u4eac']}, 'unknown': {'PSN': ['\u592a\u90ce', '\u5c71\u7530'], 'LOC': ['\u6771\u4eac\u99c5', '\u6771\u4eac\u30b9\u30ab\u30a4\u30c4\u30ea\u30fc']}}\n```\n\n#### Methods\n\n|  method  |  description  |\n| ---- | ---- |\n|  default\\_report(print\\_)  |  return result of named entity recognition. if print\\_=True, showing result   |\n|  known\\_only\\_report(print\\_)  |  return result of known named entity recognition.  |\n|  unknown\\_only\\_report(print\\_)  |  return result of unknown named entity recognition.  |\n|  return\\_predict\\_named\\_entities()  |  return named entities along predicted label(predicts). |\n|  return\\_answer\\_named\\_entities()  |  return named entities along answer label(answer). |\n|  return\\_miss\\_labelings() | return miss labeling sentences. |\n|  segmentation\\_score(mode) | show parcentages of matching answer and predict labels.  if `known` or` unknown` for `mode`, return labeling accuracy for known or unknown NE. |\n\n\n## License\n\nMIT\n\n\n",
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