{
    "info": {
        "author": "Peng-Hsuan Li",
        "author_email": "jacobvsdanniel@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "# CkipTagger\nAlso: [\u4e2d\u6587 README](https://github.com/ckiplab/ckiptagger/wiki/Chinese-README)\n\n#### GitHub\n\nhttps://github.com/ckiplab/ckiptagger\n\n[![GitHub release](https://img.shields.io/github/v/release/ckiplab/ckiptagger.svg?maxAge=3600)](https://github.com/ckiplab/ckiptagger/releases)\n[![GitHub release date](https://img.shields.io/github/release-date/ckiplab/ckiptagger.svg?maxAge=3600)](https://github.com/ckiplab/ckiptagger/releases)\n[![GitHub issues](https://img.shields.io/github/issues/ckiplab/ckiptagger.svg?maxAge=3600)](https://github.com/ckiplab/ckiptagger/issues)\n\n#### PyPI\n\nhttps://pypi.org/project/ckiptagger\n\n[![PyPI version](https://img.shields.io/pypi/v/ckiptagger.svg?maxAge=3600)](https://pypi.org/project/ckiptagger)\n[![PyPI license](https://img.shields.io/pypi/l/ckiptagger.svg?maxAge=3600)](https://github.com/ckiplab/ckiptagger/blob/master/LICENSE)\n[![PyPI python](https://img.shields.io/pypi/pyversions/ckiptagger.svg?maxAge=3600)](https://pypi.org/project/ckiptagger)\n\n#### Documentation\n\nhttps://github.com/ckiplab/ckiptagger/wiki\n\n#### Author / Contacts\n\nPeng-Hsuan Li <<https://jacobvsdanniel.github.io>><br />\nPrimary Contact <<ckiptagger@iis.sinica.edu.tw>><br />\n\n## Introduction\n\nThis open-source library implements neural CKIP-style Chinese NLP tools.\n* (WS) word segmentation\n* (POS) part-of-speech tagging\n* (NER) named entity recognition\n\nRelated demo sites\n- [CkipTagger](http://ckip.iis.sinica.edu.tw/service/ckiptagger)\n- [CKIP CoreNLP](http://ckip.iis.sinica.edu.tw/service/corenlp)\n- [CKIPWS (classic)](http://ckipsvr.iis.sinica.edu.tw)\n\nFeatures\n- Performance improvements\n- Do not auto delete/change/add characters\n- Support indefinitely long sentences\n- Support user-defined recommended-word list and must-word list\n\nASBC 4.0 Test Split (50,000 sentences)\n\n| Tool | (WS) prec | (WS) rec | (WS) f1 | (POS) acc |\n|:-:|:-:|:-:|:-:|:-:|\n| CkipTagger       | 97.49% | 97.17% | 97.33% | 94.59% |\n| CKIPWS (classic) | 95.85% | 95.96% | 95.91% | 90.62% |\n| Jieba-zh_TW      | 90.51% | 89.10% | 89.80% | -- |\n\n## Installation\n\ntl;dr.\n```\npip install -U ckiptagger[tf,gdown]\n```\n\nCkipTagger is a Python library hosted on PyPI. Requirements:\n- python>=3.6\n- tensorflow / tensorflow-gpu (one of them)\n- gdown (optional, for downloading model files from google drive)\n\n(Minimum installation) If you have set up tensorflow, and would like to download model files by yourself.\n```\npip install -U ckiptagger\n```\n\n(Complete installation) If you have just set up a clean virtual environment, and want everything, including GPU support.\n```\npip install -U ckiptagger[tfgpu,gdown]\n```\n\n## Usage\n\nComplete demo script: demo.py. The following sections assume:\n```python\nfrom ckiptagger import data_utils, construct_dictionary, WS, POS, NER\n```\n\n### 1. Download model files\n\nThe model files are available on several mirror sites.\n- [iis-ckip](http://ckip.iis.sinica.edu.tw/data/ckiptagger/data.zip)\n- [gdrive-ckip](https://drive.google.com/drive/folders/105IKCb88evUyLKlLondvDBoh7Dy_I1tm)\n- [gdrive-jacobvsdanniel](https://drive.google.com/drive/folders/15BDjL2IaX3eYdFVzT422VwCb743Hrbi3)\n\nYou can download and extract to the desired path by one of the included API.\n```python\n# Downloads to ./data.zip (2GB) and extracts to ./data/\n# data_utils.download_data_url(\"./\") # iis-ckip\ndata_utils.download_data_gdown(\"./\") # gdrive-ckip\n```\n- ./data/model_ner/pos_list.txt -> POS tag list, see [Wiki](https://github.com/ckiplab/ckiptagger/wiki/POS-Tags) / [Technical Report no. 93-05](http://ckip.iis.sinica.edu.tw/CKIP/tr/9305_2013%20revision.pdf)\n- ./data/model_ner/label_list.txt -> Entity type list, see [Wiki](https://github.com/ckiplab/ckiptagger/wiki/Entity-Types) / [OntoNotes Release 5.0](https://catalog.ldc.upenn.edu/docs/LDC2013T19/OntoNotes-Release-5.0.pdf)\n- ./data/embedding_* -> character/word embeddings\n\n### 2. Load model\n```python\nws = WS(\"./data\")\npos = POS(\"./data\")\nner = NER(\"./data\")\n```\n\n### 3. (Optional) Create dictionary\n\nYou can supply words for WS speicial consideration, including their relative weights.\n```python\nword_to_weight = {\n    \"\u571f\u5730\u516c\": 1,\n    \"\u571f\u5730\u5a46\": 1,\n    \"\u516c\u6709\": 2,\n    \"\": 1,\n    \"\u4f86\u4e82\u7684\": \"\u5566\",\n    \"\u7def\u4f86\u9ad4\u80b2\u53f0\": 1,\n}\ndictionary = construct_dictionary(word_to_weight)\nprint(dictionary)\n```\n```\n[(2, {'\u516c\u6709': 2.0}), (3, {'\u571f\u5730\u516c': 1.0, '\u571f\u5730\u5a46': 1.0}), (5, {'\u7def\u4f86\u9ad4\u80b2\u53f0': 1.0})]\n```\n\n### 4. Run the WS-POS-NER pipeline\n```python\nsentence_list = [\n    \"\u5085\u9054\u4ec1\u4eca\u5c07\u57f7\u884c\u5b89\u6a02\u6b7b\uff0c\u537b\u7a81\u7136\u7206\u51fa\u81ea\u5df120\u5e74\u524d\u906d\u7def\u4f86\u9ad4\u80b2\u53f0\u5c01\u6bba\uff0c\u4ed6\u4e0d\u61c2\u81ea\u5df1\u54ea\u88e1\u5f97\u7f6a\u5230\u96fb\u8996\u53f0\u3002\",\n    \"\u7f8e\u570b\u53c3\u8b70\u9662\u91dd\u5c0d\u4eca\u5929\u7e3d\u7d71\u5e03\u4ec0\u6240\u63d0\u540d\u7684\u52de\u5de5\u90e8\u9577\u8d99\u5c0f\u862d\u5c55\u958b\u8a8d\u53ef\u807d\u8b49\u6703\uff0c\u9810\u6599\u5979\u5c07\u6703\u5f88\u9806\u5229\u901a\u904e\u53c3\u8b70\u9662\u652f\u6301\uff0c\u6210\u70ba\u8a72\u570b\u6709\u53f2\u4ee5\u4f86\u7b2c\u4e00\u4f4d\u7684\u83ef\u88d4\u5973\u6027\u5167\u95a3\u6210\u54e1\u3002\",\n    \"\",\n    \"\u571f\u5730\u516c\u6709\u653f\u7b56?\uff1f\u9084\u662f\u571f\u5730\u5a46\u6709\u653f\u7b56\u3002.\",\n    \"\u2026 \u4f60\u78ba\u5b9a\u55ce\u2026 \u4e0d\u8981\u518d\u9a19\u4e86\u2026\u2026\",\n    \"\u6700\u591a\u5bb9\u7d0d59,000\u500b\u4eba,\u62165.9\u842c\u4eba,\u518d\u591a\u5c31\u4e0d\u884c\u4e86.\u9019\u662f\u74b0\u8a55\u7684\u7d50\u8ad6.\",\n    \"\u79d1\u9577\u8aaa:1,\u576a\u6578\u5c0d\u4eba\u6578\u70ba1:3\u30022,\u53ef\u4ee5\u518d\u589e\u52a0\u3002\",\n]\n\nword_sentence_list = ws(\n    sentence_list,\n    # sentence_segmentation=True, # To consider delimiters\n    # segment_delimiter_set = {\",\", \"\u3002\", \":\", \"?\", \"!\", \";\"}), # This is the defualt set of delimiters\n    # recommend_dictionary = dictionary1, # words in this dictionary are encouraged\n    # coerce_dictionary = dictionary2, # words in this dictionary are forced\n)\n\npos_sentence_list = pos(word_sentence_list)\n\nentity_sentence_list = ner(word_sentence_list, pos_sentence_list)\n```\n\n### 5. (Optional) Release memory\n```python\ndel ws\ndel pos\ndel ner\n```\n\n### 6. Show Results\n```python\ndef print_word_pos_sentence(word_sentence, pos_sentence):\n    assert len(word_sentence) == len(pos_sentence)\n    for word, pos in zip(word_sentence, pos_sentence):\n        print(f\"{word}({pos})\", end=\"\\u3000\")\n    print()\n    return\n\nfor i, sentence in enumerate(sentence_list):\n    print()\n    print(f\"'{sentence}'\")\n    print_word_pos_sentence(word_sentence_list[i],  pos_sentence_list[i])\n    for entity in sorted(entity_sentence_list[i]):\n        print(entity)\n```\n```\n\n'\u5085\u9054\u4ec1\u4eca\u5c07\u57f7\u884c\u5b89\u6a02\u6b7b\uff0c\u537b\u7a81\u7136\u7206\u51fa\u81ea\u5df120\u5e74\u524d\u906d\u7def\u4f86\u9ad4\u80b2\u53f0\u5c01\u6bba\uff0c\u4ed6\u4e0d\u61c2\u81ea\u5df1\u54ea\u88e1\u5f97\u7f6a\u5230\u96fb\u8996\u53f0\u3002'\n\u5085\u9054\u4ec1(Nb)\u3000\u4eca(Nd)\u3000\u5c07(D)\u3000\u57f7\u884c(VC)\u3000\u5b89\u6a02\u6b7b(Na)\u3000\uff0c(COMMACATEGORY)\u3000\u537b(D)\u3000\u7a81\u7136(D)\u3000\u7206\u51fa(VJ)\u3000\u81ea\u5df1(Nh)\u300020(Neu)\u3000\u5e74(Nf)\u3000\u524d(Ng)\u3000\u906d(P)\u3000\u7def\u4f86(Nb)\u3000\u9ad4\u80b2\u53f0(Na)\u3000\u5c01\u6bba(VC)\u3000\uff0c(COMMACATEGORY)\u3000\u4ed6(Nh)\u3000\u4e0d(D)\u3000\u61c2(VK)\u3000\u81ea\u5df1(Nh)\u3000\u54ea\u88e1(Ncd)\u3000\u5f97\u7f6a\u5230(VJ)\u3000\u96fb\u8996\u53f0(Nc)\u3000\u3002(PERIODCATEGORY)\u3000\n(0, 3, 'PERSON', '\u5085\u9054\u4ec1')\n(18, 22, 'DATE', '20\u5e74\u524d')\n(23, 28, 'ORG', '\u7def\u4f86\u9ad4\u80b2\u53f0')\n\n'\u7f8e\u570b\u53c3\u8b70\u9662\u91dd\u5c0d\u4eca\u5929\u7e3d\u7d71\u5e03\u4ec0\u6240\u63d0\u540d\u7684\u52de\u5de5\u90e8\u9577\u8d99\u5c0f\u862d\u5c55\u958b\u8a8d\u53ef\u807d\u8b49\u6703\uff0c\u9810\u6599\u5979\u5c07\u6703\u5f88\u9806\u5229\u901a\u904e\u53c3\u8b70\u9662\u652f\u6301\uff0c\u6210\u70ba\u8a72\u570b\u6709\u53f2\u4ee5\u4f86\u7b2c\u4e00\u4f4d\u7684\u83ef\u88d4\u5973\u6027\u5167\u95a3\u6210\u54e1\u3002'\n\u7f8e\u570b(Nc)\u3000\u53c3\u8b70\u9662(Nc)\u3000\u91dd\u5c0d(P)\u3000\u4eca\u5929(Nd)\u3000\u7e3d\u7d71(Na)\u3000\u5e03\u4ec0(Nb)\u3000\u6240(D)\u3000\u63d0\u540d(VC)\u3000\u7684(DE)\u3000\u52de\u5de5\u90e8\u9577(Na)\u3000\u8d99\u5c0f\u862d(Nb)\u3000\u5c55\u958b(VC)\u3000\u8a8d\u53ef(VC)\u3000\u807d\u8b49\u6703(Na)\u3000\uff0c(COMMACATEGORY)\u3000\u9810\u6599(VE)\u3000\u5979(Nh)\u3000\u5c07(D)\u3000\u6703(D)\u3000\u5f88(Dfa)\u3000\u9806\u5229(VH)\u3000\u901a\u904e(VC)\u3000\u53c3\u8b70\u9662(Nc)\u3000\u652f\u6301(VC)\u3000\uff0c(COMMACATEGORY)\u3000\u6210\u70ba(VG)\u3000\u8a72(Nes)\u3000\u570b(Nc)\u3000\u6709\u53f2\u4ee5\u4f86(D)\u3000\u7b2c\u4e00(Neu)\u3000\u4f4d(Nf)\u3000\u7684(DE)\u3000\u83ef\u88d4(Na)\u3000\u5973\u6027(Na)\u3000\u5167\u95a3(Na)\u3000\u6210\u54e1(Na)\u3000\u3002(PERIODCATEGORY)\u3000\n(0, 2, 'GPE', '\u7f8e\u570b')\n(2, 5, 'ORG', '\u53c3\u8b70\u9662')\n(7, 9, 'DATE', '\u4eca\u5929')\n(11, 13, 'PERSON', '\u5e03\u4ec0')\n(17, 21, 'ORG', '\u52de\u5de5\u90e8\u9577')\n(21, 24, 'PERSON', '\u8d99\u5c0f\u862d')\n(42, 45, 'ORG', '\u53c3\u8b70\u9662')\n(56, 58, 'ORDINAL', '\u7b2c\u4e00')\n(60, 62, 'NORP', '\u83ef\u88d4')\n\n''\n\n\n'\u571f\u5730\u516c\u6709\u653f\u7b56?\uff1f\u9084\u662f\u571f\u5730\u5a46\u6709\u653f\u7b56\u3002.'\n\u571f\u5730\u516c(Nb)\u3000\u6709(V_2)\u3000\u653f\u7b56(Na)\u3000?(QUESTIONCATEGORY)\u3000\uff1f(QUESTIONCATEGORY)\u3000\u9084\u662f(Caa)\u3000\u571f\u5730(Na)\u3000\u5a46(Na)\u3000\u6709(V_2)\u3000\u653f\u7b56(Na)\u3000\u3002(PERIODCATEGORY)\u3000.(PERIODCATEGORY)\u3000\n(0, 3, 'PERSON', '\u571f\u5730\u516c')\n\n'\u2026 \u4f60\u78ba\u5b9a\u55ce\u2026 \u4e0d\u8981\u518d\u9a19\u4e86\u2026\u2026'\n\u2026(ETCCATEGORY)\u3000 (WHITESPACE)\u3000\u4f60(Nh)\u3000\u78ba\u5b9a(VK)\u3000\u55ce(T)\u3000\u2026(ETCCATEGORY)\u3000 (WHITESPACE)\u3000\u4e0d\u8981(D)\u3000\u518d(D)\u3000\u9a19(VC)\u3000\u4e86(Di)\u3000\u2026(ETCCATEGORY)\u3000\u2026(ETCCATEGORY)\u3000\n\n'\u6700\u591a\u5bb9\u7d0d59,000\u500b\u4eba,\u62165.9\u842c\u4eba,\u518d\u591a\u5c31\u4e0d\u884c\u4e86.\u9019\u662f\u74b0\u8a55\u7684\u7d50\u8ad6.'\n\u6700\u591a(VH)\u3000\u5bb9\u7d0d(VJ)\u300059,000(Neu)\u3000\u500b(Nf)\u3000\u4eba(Na)\u3000,(COMMACATEGORY)\u3000\u6216(Caa)\u30005.9\u842c(Neu)\u3000\u4eba(Na)\u3000,(COMMACATEGORY)\u3000\u518d(D)\u3000\u591a(D)\u3000\u5c31(D)\u3000\u4e0d\u884c(VH)\u3000\u4e86(T)\u3000.(PERIODCATEGORY)\u3000\u9019(Nep)\u3000\u662f(SHI)\u3000\u74b0\u8a55(Na)\u3000\u7684(DE)\u3000\u7d50\u8ad6(Na)\u3000.(PERIODCATEGORY)\u3000\n(4, 10, 'CARDINAL', '59,000')\n(14, 18, 'CARDINAL', '5.9\u842c')\n\n'\u79d1\u9577\u8aaa:1,\u576a\u6578\u5c0d\u4eba\u6578\u70ba1:3\u30022,\u53ef\u4ee5\u518d\u589e\u52a0\u3002'\n\u79d1\u9577(Na)\u3000\u8aaa(VE)\u3000:1,(Neu)\u3000\u576a\u6578(Na)\u3000\u5c0d(P)\u3000\u4eba\u6578(Na)\u3000\u70ba(VG)\u30001:3(Neu)\u3000\u3002(PERIODCATEGORY)\u30002(Neu)\u3000,(COMMACATEGORY)\u3000\u53ef\u4ee5(D)\u3000\u518d(D)\u3000\u589e\u52a0(VHC)\u3000\u3002(PERIODCATEGORY)\u3000\n(4, 6, 'CARDINAL', '1,')\n(12, 13, 'CARDINAL', '1')\n(14, 15, 'CARDINAL', '3')\n(16, 17, 'CARDINAL', '2')\n\n```\n\n## Model Details\n\nPlease see:\n\nPeng-Hsuan Li, Tsu-Jui Fu, and Wei-Yun Ma. 2019. [Remedying BiLSTM-CNN Deficiency in Modeling Cross-Context for NER](https://arxiv.org/abs/1908.11046). arXiv preprint arXiv:1908.11046.\n\n## LICENSE\n\nCopyright (c) 2019 [CKIP Lab](https://ckip.iis.sinica.edu.tw/).\n\nThis Work is licensed under the GNU General Public License v3.0 without any warranties. The license text in full can be getting access at the file named COPYING-GPL-3.0. Any person obtaining a copy of this Work and associated documentation files is granted the rights to use, copy, modify, merge, publish, and distribute the Work for any purpose. However if any work is based upon this Work and hence constitutes a Derivative Work, the GPL-3.0 license requires distributions of the Work and the Derivative Work to remain under the same license or a similar license with the Source Code provision obligation.\n\nFor commercial license without the Source Code conveying liability, please contact <<ckiptagger_cm@iis.sinica.edu.tw>>\n\n\n",
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