{
    "info": {
        "author": "lightsmile",
        "author_email": "iamlightsmile@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "# lightNLP, lightsmile\u4e2a\u4eba\u7684\u81ea\u7136\u8bed\u8a00\u5904\u7406\u6846\u67b6\n\n## \u524d\u8a00\n\n\u4f9d\u636e\u81ea\u7136\u8bed\u8a00\u5904\u7406\u56db\u5927\u4efb\u52a1\u7b49\uff0c\u6846\u67b6\u4e3b\u8981\u8bbe\u8ba1\u4e3a\u6709\u4ee5\u4e0b\u4e94\u5927\u529f\u80fd\uff1a\n\n- \u5e8f\u5217\u6807\u6ce8\uff0c Sequence Labeling\n- \u6587\u672c\u5206\u7c7b\uff0c Text Classification\n- \u53e5\u5b50\u5173\u7cfb\uff0c Sentence Relation\n- \u6587\u672c\u751f\u6210\uff0c Text Generation\n- \u7ed3\u6784\u5206\u6790\uff0c Structure Parsing\n\n\u56e0\u6b64\u5c06\u6709\u4e94\u4e2a\u4e3b\u8981\u7684\u529f\u80fd\u6a21\u5757\uff1asl\uff08\u5e8f\u5217\u6807\u6ce8\uff09\u3001tc\uff08\u6587\u672c\u5206\u7c7b\uff09\u3001sr\uff08\u53e5\u5b50\u5173\u7cfb\uff09\u3001tg\uff08\u6587\u672c\u751f\u6210\uff09\u3001sp\uff08\u7ed3\u6784\u5206\u6790\uff09\u548c\u5176\u4ed6\u529f\u80fd\u6a21\u5757\u5982we\uff08\u8bcd\u5411\u91cf\uff09\u3002\n\n## \u5f53\u524d\u5df2\u5b9e\u73b0\u7684\u529f\u80fd\n\n### \u5e8f\u5217\u6807\u6ce8\uff0csl\n- \u4e2d\u6587\u5206\u8bcd\uff0ccws\n- \u547d\u540d\u5b9e\u4f53\u8bc6\u522b\uff0cner\n- \u8bcd\u6027\u6807\u6ce8\uff0cpos\n- \u8bed\u4e49\u89d2\u8272\u6807\u6ce8\uff0c srl\n\n### \u7ed3\u6784\u5206\u6790\uff0csp\n- \u57fa\u4e8e\u56fe\u7684\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\uff0cgdp\n- \u57fa\u4e8e\u8f6c\u79fb\u7684\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\uff0c tdp\n\n### \u53e5\u5b50\u5173\u7cfb\uff0csr\n- \u8bed\u53e5\u76f8\u4f3c\u5ea6\uff0css\n- \u6587\u672c\u8574\u542b\uff0cte\n\n### \u6587\u672c\u5206\u7c7b\uff0ctc\n- \u5173\u7cfb\u62bd\u53d6\uff0cre\n- \u60c5\u611f\u6781\u6027\u5206\u6790\uff0csa\n\n### \u6587\u672c\u751f\u6210\uff0ctg\n- \u8bed\u8a00\u6a21\u578b\uff0clm\n- \u804a\u5929\u673a\u5668\u4eba\uff0ccb\n- \u673a\u5668\u7ffb\u8bd1\uff0cmt\n- \u6587\u672c\u6458\u8981\uff0cts\n\n### \u8bcd\u5411\u91cf\uff0cwe\n- \u8bcd\u888b\u6a21\u578b\uff0ccbow\n    - base\n    - hierarchical_softmax\n    - negative_sampling\n- \u8df3\u5b57\u6a21\u578b\uff0cskip_gram\n    - base\n    - hierarchical_softmax\n    - negative_sampling\n\n### \n\n## \u5b89\u88c5\n\n\u672c\u9879\u76ee\u57fa\u4e8ePytorch1.0\n\n```bash\npip install lightNLP\n```\n\n\u5efa\u8bae\u4f7f\u7528\u56fd\u5185\u6e90\u6765\u5b89\u88c5\uff0c\u5982\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\uff1a\n```bash\npip install -i https://pypi.douban.com/simple/ lightNLP\n```\n\n### \u5b89\u88c5\u4f9d\u8d56\n\n\u7531\u4e8e\u6709\u4e9b\u5e93\u5982pytorch\u3001torchtext\u5e76\u4e0d\u5728pypi\u6e90\u4e2d\u6216\u8005\u91cc\u9762\u53ea\u6709\u6bd4\u8f83\u8001\u65e7\u7684\u7248\u672c\uff0c\u6211\u4eec\u9700\u8981\u5355\u72ec\u5b89\u88c5\u4e00\u4e9b\u5e93\u3002\n#### \u5b89\u88c5pytorch\n\n\u5177\u4f53\u5b89\u88c5\u53c2\u89c1[pytorch\u5b98\u7f51](https://pytorch.org/get-started/locally/)\u6765\u6839\u636e\u5e73\u53f0\u3001\u5b89\u88c5\u65b9\u5f0f\u3001Python\u7248\u672c\u3001CUDA\u7248\u672c\u6765\u9009\u62e9\u9002\u5408\u81ea\u5df1\u7684\u7248\u672c\u3002\n\n#### \u5b89\u88c5torchtext\n\n\u4f7f\u7528\u4ee5\u4e0b\u547d\u4ee4\u5b89\u88c5\u6700\u65b0\u7248\u672ctorchtext\uff1a\n```bash\npip install https://github.com/pytorch/text/archive/master.zip\n```\n\n## \u6a21\u578b\n\n- ner: BiLstm-Crf\n- cws: BiLstm-Crf\n- pos: BiLstm-Crf\n- srl:BiLstm-Crf\n- sa: TextCnn\n- re: TextCnn,\u5f53\u524d\u8fd9\u91cc\u53ea\u662f\u6709\u76d1\u7763\u5173\u7cfb\u62bd\u53d6\n- lm: Lstm,\u57fa\u7840\u7684LSTM\uff0c\u6ca1\u6709\u4f7f\u7528Seq2Seq\u6a21\u578b\n- ss: \u5171\u4eabLSTM + \u66fc\u54c8\u987f\u8ddd\u79bb\n- te:\u5171\u4eabLSTM + \u5168\u8fde\u63a5\n- tdp: lstm + mlp + shift-reduce(\u79fb\u5165\u89c4\u7ea6)\n- gdp: lstm + mlp + biaffine\uff08\u53cc\u4eff\u5c04\uff09\n- cbow: base\u3001hierarchical_softmax\u3001negative_sampling\n- skip_gram: base\u3001hierarchical_softmax\u3001negative_sampling\n- cb: Seq2Seq+Attention\n- mt: Seq2Seq+Attention\n- ts: Seq2Seq+Attention\n\n## \u8bad\u7ec3\u6570\u636e\u8bf4\u660e\n\n\u6211\u8fd9\u91cc\u4ec5\u662f\u9488\u5bf9\u5f53\u524d\u5404\u4efb\u52a1\u4ece\u7f51\u4e0a\u83b7\u53d6\u5230\u7684\u8bad\u7ec3\u6570\u636e\u7ed3\u6784\u7c7b\u578b\uff0c\u6709\u7684\u5f62\u5f0f\u53ef\u80fd\u5e76\u4e0d\u89c4\u8303\u6216\u7edf\u4e00\u3002\n\n#### ner\n\nBIO\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n\n```bash\n\u6e05 B_Time\n\u660e I_Time\n\u662f O\n\u4eba B_Person\n\u4eec I_Person\n\u796d O\n\u626b O\n\u5148 B_Person\n\u4eba I_Person\n\uff0c O\n\u6000 O\n\u5ff5 O\n\u8ffd O\n\u601d O\n\u7684 O\n\u65e5 B_Time\n\u5b50 I_Time\n\u3002 O\n\n\u6b63 O\n\u5982 O\n\u5b8b B_Time\n\u4ee3 I_Time\n\u8bd7 B_Person\n\u4eba I_Person\n```\n\n#### cws\n\nBIS\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n\n```bash\n4 S\n\u65e5 S\n\u6e05 B\n\u6668 I\n\uff0c S\n\u540c B\n\u6837 I\n\u5728 S\n\u5b89 B\n\u65b0 I\n\u53bf I\n\u4eba B\n\u6c11 I\n\u653f I\n\u5e9c I\n\u95e8 B\n\u524d I\n\uff0c S\n\u4e0d B\n\u65f6 I\n\u6709 S\n\u6c11 B\n\u4f17 I\n\u4e13 B\n\u7a0b I\n\u6765 I\n\u6b64 S\n\u62cd B\n\u7167 I\n\u7559 B\n\u5ff5 I\n\uff0c S\n\u6709 S\n\u7684 S\n\u751a B\n\u81f3 I\n\u7a7f B\n\u7740 I\n\u7edf B\n\u4e00 I\n\u7684 S\n\u670d B\n\u9970 I\n\u62cd B\n\u8d77 I\n\u4e86 S\n\u96c6 B\n\u4f53 I\n\u7167 I\n\u3002 S\n```\n\n#### pos\n\nBIS\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n\n```bash\n\u53ea B-c\n\u8981 I-c\n\u6211 B-r\n\u4eec I-r\n\u8fdb B-d\n\u4e00 I-d\n\u6b65 I-d\n\u89e3 B-i\n\u653e I-i\n\u601d I-i\n\u60f3 I-i\n\uff0c S-w\n\u5b9e B-i\n\u4e8b I-i\n\u6c42 I-i\n\u662f I-i\n\uff0c S-w\n\u6293 B-v\n\u4f4f I-v\n\u673a B-n\n\u9047 I-n\n\uff0c S-w\n\u5f00 B-l\n\u62d3 I-l\n\u8fdb I-l\n\u53d6 I-l\n\uff0c S-w\n\u5efa B-v\n\u8bbe I-v\n\u6709 S-v\n\u4e2d B-ns\n\u56fd I-ns\n\u7279 B-n\n\u8272 I-n\n\u793e B-n\n\u4f1a I-n\n\u4e3b I-n\n\u4e49 I-n\n\u7684 S-u\n\u9053 B-n\n\u8def I-n\n\u5c31 S-c\n\u4f1a S-v\n\u8d8a S-d\n\u8d70 S-v\n\u8d8a S-d\n\u5bbd B-a\n\u5e7f I-a\n\u3002 S-w\n```\n\n#### srl\n\nCONLL\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff0c\u5176\u4e2d\u5404\u5217\u5206\u522b\u4e3a`\u8bcd`\u3001`\u8bcd\u6027`\u3001`\u662f\u5426\u8bed\u4e49\u8c13\u8bcd`\u3001`\u89d2\u8272`\uff0c\u6bcf\u53e5\u4ec5\u6709\u4e00\u4e2a\u8c13\u8bed\u52a8\u8bcd\u4e3a\u8bed\u4e49\u8c13\u8bcd\uff0c\u5373\u6bcf\u53e5\u4e2d\u7b2c\u4e09\u5217\u4ec5\u6709\u4e00\u884c\u53d6\u503c\u4e3a1\uff0c\u5176\u4f59\u90fd\u4e3a0.\n\n```bash\n\u5b8b\u6d69\u4eac  NR      0       O\n\u8f6c\u8fbe    VV      0       O\n\u4e86      AS      0       O\n\u671d\u9c9c    NR      0       O\n\u9886\u5bfc\u4eba  NN      0       O\n\u5bf9      P       0       O\n\u4e2d\u56fd    NR      0       O\n\u9886\u5bfc\u4eba  NN      0       O\n\u7684      DEG     0       O\n\u4eb2\u5207    JJ      0       O\n\u95ee\u5019    NN      0       O\n\uff0c      PU      0       O\n\u4ee3\u8868    VV      0       O\n\u671d\u65b9    NN      0       O\n\u5bf9      P       0       O\n\u4e2d\u56fd    NR      0       B-ARG0\n\u515a\u653f    NN      0       I-ARG0\n\u9886\u5bfc\u4eba  NN      0       I-ARG0\n\u548c      CC      0       I-ARG0\n\u4eba\u6c11    NN      0       E-ARG0\n\u54c0\u60bc    VV      1       rel\n\u91d1\u65e5\u6210  NR      0       B-ARG1\n\u4e3b\u5e2d    NN      0       I-ARG1\n\u901d\u4e16    VV      0       E-ARG1\n\u8868\u793a    VV      0       O\n\u6df1\u5207    JJ      0       O\n\u8c22\u610f    NN      0       O\n\u3002      PU      0       O\n```\n\n#### sa\n\ntsv\u6587\u4ef6\u683c\u5f0f\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n\n```bash\n        label   text\n0       0       \u5907\u80ce\u662f\u786c\u4f24\uff01\n1       0       \u8981\u8bf4\u4e0d\u6ee1\u610f\u7684\u8bdd\uff0c\u90a3\u5c31\u662f\u52a8\u529b\u4e86\uff0c1.5\u81ea\u7136\u5438\u6c14\u53d1\u52a8\u673a\u5bf9\u8fd9\u6b3e\u8f66\u6709\u79cd\u5c0f\u9a6c\u62c9\u5927\u8f66\u7684\u611f\u89c9\u3002\u5982\u4eca\u5929\u6c14\u8fd9\u4e48\u70ed\uff0c\u4e0a\u8def\u80af\u5b9a\u5f97\u5f00\u7a7a\u8c03\uff0c\u5f00\u4e86\u540e\u52a8\u529b\u660e\u663e\u611f\u89c9\u6709\u4e9b\u4e0d\u7ed9\u529b\u4e0d\u8fc7\u7a7a\u8c03\u5236\u51b7\u6548\u679c\u8fd8\u662f\u4e0d\u9519\u7684\u3002\n2       0       \u6cb9\u8017\u663e\u793a13\u5347\u8fd8\u591a\u4e00\u70b9\uff0c\u5e0c\u671b\u6162\u6162\u4e0b\u964d\u3002\u6ca1\u6709\u5012\u8f66\u96f7\u8fbe\u771f\u53ef\u6068\n3       0       \u7a7a\u8c03\u4e0d\u592a\u51c9\uff0c\u5e94\u8be5\u662f\u5c0f\u95ee\u9898\u3002\n4       0       1\u3001\u540e\u6392\u5ea7\u6905\u4e0d\u80fd\u5e73\u653e\uff1b2\u3001\u79d1\u6280\u611f\u4e0d\u5f3a\uff0c\u8fd8\u4e0d\u5982\u767e\u4e07\u5e1d\u8c6a\uff0c\u6700\u5e0c\u671b\u589e\u52a0\u8f66\u8054\u7f51\u7684\u8f66\u673a\u3002\u50cf\u4f60\u597d\u535a\u8d8a\u4e00\u6837\u30023\u3001\u5168\u666f\u6444\u50cf\u5934\u4e0d\u6e05\u695a\uff0c\u665a\u4e0a\u57fa\u672c\u4e0a\u7528\u5904\u4e0d\u5927\n5       1       \u8f66\u5b50\u5916\u89c2\u597d\u770b\uff0c\u8f66\u5185\u7a7a\u95f4\u5927\u3002\n6       1       \u6700\u6ee1\u610f\u7684\u771f\u7684\u4e0d\u53ea\u4e00\u70b9\uff0c\u6982\u62ec\u4e00\u4e0b\u6700\u6ee1\u610f\u7684\u5c31\u662f\u6027\u4ef7\u6bd4\u4e86\u3002ps:\u867d\u7136\u6ca1\u6709s7\u6027\u4ef7\u6bd4\u9ad8(\u539f\u5382\u8bb0\u5f55\u4eea,\u7eff\u51c0)\n7       0       \u5e95\u76d8\u8c03\u6559\u7684\u5f88\u4f4e\uff0c\u5750\u7684\u611f\u89c9\u6709\u4e9b\u522b\u626d\uff0c\u89c6\u89d2\u4e0d\u662f\u5f88\u597d\u3002\n8       0       \u5f00\u7a7a\u8c03\u65f6\uff0c\u4e00\u6863\u8d77\u6b65\u52a8\u529b\u4e0d\u8db3\u3002\u8f66\u5b50\u505a\u5de5\u6709\u70b9\u9a6c\u864e\u3002\n```\n\n#### re\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff0c\u5176\u4e2d\u5404\u5217\u5206\u522b\u4e3a`\u5b9e\u4f531`\u3001`\u5b9e\u4f532`\u3001`\u5173\u7cfb`\u3001`\u53e5\u5b50`\n\n```bash\n\u94b1\u949f\u4e66\t\u8f9b\u7b1b\t\u540c\u95e8\t\u4e0e\u8f9b\u7b1b\u4eac\u6caa\u5531\u548c\u807d\u94b1\u949f\u4e66\u4e0e\u94b1\u949f\u4e66\u662f\u6e05\u534e\u6821\u53cb\uff0c\u94b1\u949f\u4e66\u9ad8\u8f9b\u7b1b\u4e24\u73ed\u3002\n\u5143\u6b66\t\u5143\u534e\tunknown\t\u4e8e\u5e08\u5085\u5728\u4e00\u6b21\u4eac\u5267\u8868\u6f14\u4e2d\uff0c\u9009\u4e86\u5143\u9f99\uff08\u6d2a\u91d1\u5b9d\uff09\u3001\u5143\u697c\uff08\u5143\u594e\uff09\u3001\u5143\u5f6a\u3001\u6210\u9f99\u3001\u5143\u534e\u3001\u5143\u6b66\u3001\u5143\u6cf07\u4eba\u62c5\u4efb\u4e03\u5c0f\u798f\u7684\u4e3b\u89d2\u3002\n```\n\n#### lm\n\u5c31\u666e\u901a\u7684\u6587\u672c\u683c\u5f0f\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n```bash\n\u7b2c\u4e00\u7ae0 \u9668\u843d\u7684\u5929\u624d\n\n    \u201c\u6597\u4e4b\u529b\uff0c\u4e09\u6bb5\uff01\u201d\n    \u671b\u7740\u6d4b\u9a8c\u9b54\u77f3\u7891\u4e0a\u9762\u95ea\u4eae\u5f97\u751a\u81f3\u6709\u4e9b\u523a\u773c\u7684\u4e94\u4e2a\u5927\u5b57\uff0c\u5c11\u5e74\u9762\u65e0\u8868\u60c5\uff0c\u5507\u89d2\u6709\u7740\u4e00\u62b9\u81ea\u5632\uff0c\u7d27\u63e1\u7684\u624b\u638c\uff0c\u56e0\u4e3a\u5927\u529b\uff0c\u800c\u5bfc\u81f4\u7565\u5fae\u5c16\u9510\u7684\u6307\u7532\u6df1\u6df1\u7684\u523a\u8fdb\u4e86\u638c\u5fc3\u4e4b\u4e2d\uff0c\u5e26\u6765\u4e00\u9635\u9635\u94bb\u5fc3\u7684\u75bc\u75db\u2026\u2026\n    \u201c\u8427\u708e\uff0c\u6597\u4e4b\u529b\uff0c\u4e09\u6bb5\uff01\u7ea7\u522b\uff1a\u4f4e\u7ea7\uff01\u201d\u6d4b\u9a8c\u9b54\u77f3\u7891\u4e4b\u65c1\uff0c\u4e00\u4f4d\u4e2d\u5e74\u7537\u5b50\uff0c\u770b\u4e86\u4e00\u773c\u7891\u4e0a\u6240\u663e\u793a\u51fa\u6765\u7684\u4fe1\u606f\uff0c\u8bed\u6c14\u6f20\u7136\u7684\u5c06\u4e4b\u516c\u5e03\u4e86\u51fa\u6765\u2026\u2026\n    \u4e2d\u5e74\u7537\u5b50\u8bdd\u521a\u521a\u8131\u53e3\uff0c\u4fbf\u662f\u4e0d\u51fa\u610f\u5916\u7684\u5728\u4eba\u5934\u6c79\u6d8c\u7684\u5e7f\u573a\u4e0a\u5e26\u8d77\u4e86\u4e00\u9635\u5632\u8bbd\u7684\u9a9a\u52a8\u3002\n    \u201c\u4e09\u6bb5\uff1f\u563f\u563f\uff0c\u679c\u7136\u4e0d\u51fa\u6211\u6240\u6599\uff0c\u8fd9\u4e2a\u201c\u5929\u624d\u201d\u8fd9\u4e00\u5e74\u53c8\u662f\u5728\u539f\u5730\u8e0f\u6b65\uff01\u201d\n    \u201c\u54ce\uff0c\u8fd9\u5e9f\u7269\u771f\u662f\u628a\u5bb6\u65cf\u7684\u8138\u90fd\u7ed9\u4e22\u5149\u4e86\u3002\u201d\n    \u201c\u8981\u4e0d\u662f\u65cf\u957f\u662f\u4ed6\u7684\u7236\u4eb2\uff0c\u8fd9\u79cd\u5e9f\u7269\uff0c\u65e9\u5c31\u88ab\u9a71\u8d76\u51fa\u5bb6\u65cf\uff0c\u4efb\u5176\u81ea\u751f\u81ea\u706d\u4e86\uff0c\u54ea\u8fd8\u6709\u673a\u4f1a\u5f85\u5728\u5bb6\u65cf\u4e2d\u767d\u5403\u767d\u559d\u3002\u201d\n    \u201c\u5509\uff0c\u6614\u5e74\u90a3\u540d\u95fb\u4e4c\u5766\u57ce\u7684\u5929\u624d\u5c11\u5e74\uff0c\u5982\u4eca\u600e\u4e48\u843d\u9b44\u6210\u8fd9\u822c\u6a21\u6837\u4e86\u554a\uff1f\u201d\n\n```\n\n#### ss\ntsv\u6587\u4ef6\u7c7b\u578b\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff0c\u5176\u4e2d\u5404\u5217\u5206\u522b\u4e3a`\u8bed\u53e5a`\uff0c`\u8bed\u53e5b`\uff0c`\u76f8\u4f3c\u5173\u7cfb`\uff0c\u5305\u62ec`0\uff0c\u4e0d\u76f8\u4f3c`\uff0c`1\uff0c\u76f8\u4f3c`\uff1a\n```bash\n1       \u600e\u4e48\u66f4\u6539\u82b1\u5457\u624b\u673a\u53f7\u7801    \u6211\u7684\u82b1\u5457\u662f\u4ee5\u524d\u7684\u624b\u673a\u53f7\u7801\uff0c\u600e\u4e48\u66f4\u6539\u6210\u73b0\u5728\u7684\u652f\u4ed8\u5b9d\u7684\u53f7\u7801\u624b\u673a\u53f7    1\n2       \u4e5f\u5f00\u4e0d\u4e86\u82b1\u5457\uff0c\u5c31\u8fd9\u6837\u4e86\uff1f\u5b8c\u4e8b\u4e86  \u771f\u7684\u561b\uff1f\u5c31\u662f\u82b1\u5457\u4ed8\u6b3e    0\n3       \u82b1\u5457\u51bb\u7ed3\u4ee5\u540e\u8fd8\u80fd\u5f00\u901a\u5417  \u6211\u7684\u6761\u4ef6\u53ef\u4ee5\u5f00\u901a\u82b1\u5457\u501f\u6b3e\u5417      0\n4       \u5982\u4f55\u5f97\u77e5\u5173\u95ed\u501f\u5457        \u60f3\u6c38\u4e45\u5173\u95ed\u501f\u5457  0\n5       \u82b1\u5457\u626b\u7801\u4ed8\u94b1    \u4e8c\u7ef4\u7801\u626b\u63cf\u53ef\u4ee5\u7528\u82b1\u5457\u5417  0\n6       \u82b1\u5457\u903e\u671f\u540e\u4e0d\u80fd\u5206\u671f\u5417    \u6211\u8fd9\u4e2a \u903e\u671f\u540e\u8fd8\u5b8c\u4e86 \u6700\u4f4e\u8fd8\u6b3e \u540e \u80fd\u5206\u671f\u5417        0\n7       \u82b1\u5457\u5206\u671f\u6e05\u7a7a    \u82b1\u5457\u5206\u671f\u67e5\u8be2    0\n8       \u501f\u5457\u903e\u671f\u77ed\u4fe1\u901a\u77e5        \u5982\u4f55\u8d2d\u4e70\u82b1\u5457\u77ed\u4fe1\u901a\u77e5    0\n9       \u501f\u5457\u5373\u5c06\u5230\u671f\u8981\u8fd8\u7684\u8d26\u5355\u8fd8\u80fd\u5206\u671f\u5417        \u501f\u5457\u8981\u5206\u671f\u8fd8\uff0c\u662f\u5417      0\n10      \u82b1\u5457\u4e3a\u4ec0\u4e48\u4e0d\u80fd\u652f\u4ed8\u624b\u673a\u4ea4\u6613      \u82b1\u5457\u900f\u652f\u4e86\u4e3a\u4ec0\u4e48\u4e0d\u53ef\u4ee5\u7ee7\u7eed\u7528\u4e86  0\n```\n\n#### te\ntsv\u6587\u4ef6\u7c7b\u578b\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff0c\u5176\u4e2d\u5404\u5217\u5206\u522b\u4e3a`\u524d\u63d0`\u3001`\u5047\u8bbe`\u3001`\u5173\u7cfb`\uff0c\u5176\u4e2d\u5173\u7cfb\u5305\u62ec`entailment\uff0c\u8574\u542b`\u3001`neutral\uff0c\u4e2d\u7acb`\u3001`contradiction\uff0c\u77db\u76fe`\n\n```bash\n\u662f\u7684\uff0c\u6211\u60f3\u4e00\u4e2a\u6d1e\u7a74\u4e5f\u4f1a\u6709\u8fd9\u6837\u7684\u95ee\u9898      \u6211\u8ba4\u4e3a\u6d1e\u7a74\u53ef\u80fd\u4f1a\u6709\u66f4\u4e25\u91cd\u7684\u95ee\u9898\u3002        neutral\n\u51e0\u5468\u524d\u6211\u5e26\u4ed6\u548c\u4e00\u4e2a\u670b\u53cb\u53bb\u770b\u5e7c\u513f\u56ed\u8b66\u5bdf    \u6211\u8fd8\u6ca1\u770b\u8fc7\u5e7c\u513f\u56ed\u8b66\u5bdf\uff0c\u4f46\u4ed6\u770b\u4e86\u3002        contradiction\n\u822a\u7a7a\u65c5\u884c\u7684\u6269\u5f20\u5f00\u59cb\u4e86\u5927\u4f17\u65c5\u6e38\u7684\u65f6\u4ee3\uff0c\u5e0c\u814a\u548c\u7231\u7434\u6d77\u7fa4\u5c9b\u6210\u4e3a\u5317\u6b27\u4eba\u9003\u79bb\u6f6e\u6e7f\u51c9\u723d\u7684\u590f\u5929\u7684\u4ee4\u4eba\u5174\u594b\u7684\u76ee\u7684\u5730\u3002    \u822a\u7a7a\u65c5\u884c\u7684\u6269\u5927\u5f00\u59cb\u4e86\u8bb8\u591a\u65c5\u6e38\u4e1a\u7684\u53d1\u5c55\u3002  entailment\n\u5f53\u4e24\u540d\u5de5\u4eba\u5f85\u547d\u65f6\uff0c\u4e00\u6761\u5927\u7684\u767d\u8272\u7ba1\u5b50\u6b63\u88ab\u653e\u5728\u62d6\u8f66\u4e0a\u3002      \u8fd9\u4e9b\u4eba\u6b63\u5728\u76d1\u7763\u7ba1\u9053\u7684\u88c5\u8f7d\u3002      neutral\n\u7537\u4eba\u4fe9\u4e92\u76f8\u4ea4\u6362\u4e00\u4e2a\u5f88\u5927\u7684\u91d1\u5c5e\u73af\uff0c\u9a91\u7740\u706b\u8f66\u5411\u76f8\u53cd\u7684\u65b9\u5411\u884c\u9a76\u3002      \u5a5a\u793c\u6b63\u5728\u6559\u5802\u4e3e\u884c\u3002      contradiction\n\u4e00\u4e2a\u5c0f\u7537\u5b69\u5728\u79cb\u5343\u4e0a\u73a9\u3002  \u5c0f\u7537\u5b69\u73a9\u79cb\u5343    entailment\n\n```\n\n#### tdp\n\u683c\u5f0f\u5927\u81f4\u5982\u4e0b, \u5176\u4e2d\u6bcf\u884c\u4ee3\u8868\u4e00\u4e2a`sentence`\u548c\u5bf9\u5e94\u7684`Actions`\uff0c\u4e24\u8005\u7528` ||| `\u5206\u9694\uff0c\u5176\u4e2dActions\u5305\u62ec\u4e09\u79cd\uff1a`Shift`\u3001`REDUCE_R`\u548c`REDUCE_L`\uff0c\u5206\u522b\u4ee3\u8868`\u79fb\u5165`\u3001`\u53f3\u89c4\u7ea6`\u3001`\u5de6\u89c4\u7ea6`\uff0c\u5176\u4e2dsentence\u548cActions\u4e4b\u95f4\u7684\u5e8f\u5217\u957f\u5ea6\u5bf9\u5e94\u5173\u7cfb\u4e3a```len(Actions) = 2 * len(sentence) - 1``` \uff1a\n\n```bash\nBell , based in Los Angeles , makes and distributes electronic , computer and building products . ||| SHIFT SHIFT REDUCE_R SHIFT SHIFT SHIFT SHIFT REDUCE_L REDUCE_R REDUCE_R REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_L SHIFT REDUCE_R SHIFT REDUCE_R SHIFT SHIFT REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_L REDUCE_R SHIFT REDUCE_R\n`` Apparently the commission did not really believe in this ideal . '' ||| SHIFT SHIFT SHIFT SHIFT REDUCE_L SHIFT SHIFT SHIFT SHIFT REDUCE_L REDUCE_L REDUCE_L REDUCE_L REDUCE_L REDUCE_L SHIFT SHIFT SHIFT REDUCE_L REDUCE_R REDUCE_R SHIFT REDUCE_R SHIFT REDUCE_R\n```\n\n#### gdp\n\nCONLL\u683c\u5f0f\uff0c\u5176\u4e2d\u5404\u5217\u542b\u4e49\u5982\u4e0b\uff1a\n\n```bash\n1\tID\t\u5f53\u524d\u8bcd\u5728\u53e5\u5b50\u4e2d\u7684\u5e8f\u53f7\uff0c\uff11\u5f00\u59cb.\n2\tFORM\t\u5f53\u524d\u8bcd\u8bed\u6216\u6807\u70b9  \n3\tLEMMA\t\u5f53\u524d\u8bcd\u8bed\uff08\u6216\u6807\u70b9\uff09\u7684\u539f\u578b\u6216\u8bcd\u5e72\uff0c\u5728\u4e2d\u6587\u4e2d\uff0c\u6b64\u5217\u4e0eFORM\u76f8\u540c\n4\tCPOSTAG\t\u5f53\u524d\u8bcd\u8bed\u7684\u8bcd\u6027\uff08\u7c97\u7c92\u5ea6\uff09\n5\tPOSTAG\t\u5f53\u524d\u8bcd\u8bed\u7684\u8bcd\u6027\uff08\u7ec6\u7c92\u5ea6\uff09\n6\tFEATS\t\u53e5\u6cd5\u7279\u5f81\uff0c\u5728\u672c\u6b21\u8bc4\u6d4b\u4e2d\uff0c\u6b64\u5217\u672a\u88ab\u4f7f\u7528\uff0c\u5168\u90e8\u4ee5\u4e0b\u5212\u7ebf\u4ee3\u66ff\u3002\n7\tHEAD\t\u5f53\u524d\u8bcd\u8bed\u7684\u4e2d\u5fc3\u8bcd\n8\tDEPREL\t\u5f53\u524d\u8bcd\u8bed\u4e0e\u4e2d\u5fc3\u8bcd\u7684\u4f9d\u5b58\u5173\u7cfb\n```\n \u5728CONLL\u683c\u5f0f\u4e2d\uff0c\u6bcf\u4e2a\u8bcd\u8bed\u5360\u4e00\u884c\uff0c\u65e0\u503c\u5217\u7528\u4e0b\u5212\u7ebf'_'\u4ee3\u66ff\uff0c\u5217\u7684\u5206\u9694\u7b26\u4e3a\u5236\u8868\u7b26'\\t'\uff0c\u884c\u7684\u5206\u9694\u7b26\u4e3a\u6362\u884c\u7b26'\\n'\uff1b\u53e5\u5b50\u4e0e\u53e5\u5b50\u4e4b\u95f4\u7528\u7a7a\u884c\u5206\u9694\u3002\n\n \u793a\u4f8b\u5982\uff1a\n\n ```bash\n1       \u575a\u51b3    \u575a\u51b3    a       ad      _       2       \u65b9\u5f0f\n2       \u60e9\u6cbb    \u60e9\u6cbb    v       v       _       0       \u6838\u5fc3\u6210\u5206\n3       \u8d2a\u6c61    \u8d2a\u6c61    v       v       _       7       \u9650\u5b9a\n4       \u8d3f\u8d42    \u8d3f\u8d42    n       n       _       3       \u8fde\u63a5\u4f9d\u5b58\n5       \u7b49      \u7b49      u       udeng   _       3       \u8fde\u63a5\u4f9d\u5b58\n6       \u7ecf\u6d4e    \u7ecf\u6d4e    n       n       _       7       \u9650\u5b9a\n7       \u72af\u7f6a    \u72af\u7f6a    v       vn      _       2       \u53d7\u4e8b\n\n1       \u6700\u9ad8    \u6700\u9ad8    n       nt      _       3       \u9650\u5b9a\n2       \u4eba\u6c11    \u4eba\u6c11    n       nt      _       3       \u9650\u5b9a\n3       \u68c0\u5bdf\u9662  \u68c0\u5bdf\u9662  n       nt      _       4       \u9650\u5b9a\n4       \u68c0\u5bdf\u957f  \u68c0\u5bdf\u957f  n       n       _       0       \u6838\u5fc3\u6210\u5206\n5       \u5f20\u601d\u537f  \u5f20\u601d\u537f  n       nr      _       4       \u540c\u4f4d\u8bed\n ``` \n\n#### cbow\n\u5c31\u666e\u901a\u7684\u6587\u672c\u683c\u5f0f\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n```bash\n\u7b2c\u4e00\u7ae0 \u9668\u843d\u7684\u5929\u624d\n\n    \u201c\u6597\u4e4b\u529b\uff0c\u4e09\u6bb5\uff01\u201d\n    \u671b\u7740\u6d4b\u9a8c\u9b54\u77f3\u7891\u4e0a\u9762\u95ea\u4eae\u5f97\u751a\u81f3\u6709\u4e9b\u523a\u773c\u7684\u4e94\u4e2a\u5927\u5b57\uff0c\u5c11\u5e74\u9762\u65e0\u8868\u60c5\uff0c\u5507\u89d2\u6709\u7740\u4e00\u62b9\u81ea\u5632\uff0c\u7d27\u63e1\u7684\u624b\u638c\uff0c\u56e0\u4e3a\u5927\u529b\uff0c\u800c\u5bfc\u81f4\u7565\u5fae\u5c16\u9510\u7684\u6307\u7532\u6df1\u6df1\u7684\u523a\u8fdb\u4e86\u638c\u5fc3\u4e4b\u4e2d\uff0c\u5e26\u6765\u4e00\u9635\u9635\u94bb\u5fc3\u7684\u75bc\u75db\u2026\u2026\n    \u201c\u8427\u708e\uff0c\u6597\u4e4b\u529b\uff0c\u4e09\u6bb5\uff01\u7ea7\u522b\uff1a\u4f4e\u7ea7\uff01\u201d\u6d4b\u9a8c\u9b54\u77f3\u7891\u4e4b\u65c1\uff0c\u4e00\u4f4d\u4e2d\u5e74\u7537\u5b50\uff0c\u770b\u4e86\u4e00\u773c\u7891\u4e0a\u6240\u663e\u793a\u51fa\u6765\u7684\u4fe1\u606f\uff0c\u8bed\u6c14\u6f20\u7136\u7684\u5c06\u4e4b\u516c\u5e03\u4e86\u51fa\u6765\u2026\u2026\n    \u4e2d\u5e74\u7537\u5b50\u8bdd\u521a\u521a\u8131\u53e3\uff0c\u4fbf\u662f\u4e0d\u51fa\u610f\u5916\u7684\u5728\u4eba\u5934\u6c79\u6d8c\u7684\u5e7f\u573a\u4e0a\u5e26\u8d77\u4e86\u4e00\u9635\u5632\u8bbd\u7684\u9a9a\u52a8\u3002\n    \u201c\u4e09\u6bb5\uff1f\u563f\u563f\uff0c\u679c\u7136\u4e0d\u51fa\u6211\u6240\u6599\uff0c\u8fd9\u4e2a\u201c\u5929\u624d\u201d\u8fd9\u4e00\u5e74\u53c8\u662f\u5728\u539f\u5730\u8e0f\u6b65\uff01\u201d\n    \u201c\u54ce\uff0c\u8fd9\u5e9f\u7269\u771f\u662f\u628a\u5bb6\u65cf\u7684\u8138\u90fd\u7ed9\u4e22\u5149\u4e86\u3002\u201d\n    \u201c\u8981\u4e0d\u662f\u65cf\u957f\u662f\u4ed6\u7684\u7236\u4eb2\uff0c\u8fd9\u79cd\u5e9f\u7269\uff0c\u65e9\u5c31\u88ab\u9a71\u8d76\u51fa\u5bb6\u65cf\uff0c\u4efb\u5176\u81ea\u751f\u81ea\u706d\u4e86\uff0c\u54ea\u8fd8\u6709\u673a\u4f1a\u5f85\u5728\u5bb6\u65cf\u4e2d\u767d\u5403\u767d\u559d\u3002\u201d\n    \u201c\u5509\uff0c\u6614\u5e74\u90a3\u540d\u95fb\u4e4c\u5766\u57ce\u7684\u5929\u624d\u5c11\u5e74\uff0c\u5982\u4eca\u600e\u4e48\u843d\u9b44\u6210\u8fd9\u822c\u6a21\u6837\u4e86\u554a\uff1f\u201d\n\n```\n\n#### skip_gram\n\u5c31\u666e\u901a\u7684\u6587\u672c\u683c\u5f0f\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n```bash\n\u7b2c\u4e00\u7ae0 \u9668\u843d\u7684\u5929\u624d\n\n    \u201c\u6597\u4e4b\u529b\uff0c\u4e09\u6bb5\uff01\u201d\n    \u671b\u7740\u6d4b\u9a8c\u9b54\u77f3\u7891\u4e0a\u9762\u95ea\u4eae\u5f97\u751a\u81f3\u6709\u4e9b\u523a\u773c\u7684\u4e94\u4e2a\u5927\u5b57\uff0c\u5c11\u5e74\u9762\u65e0\u8868\u60c5\uff0c\u5507\u89d2\u6709\u7740\u4e00\u62b9\u81ea\u5632\uff0c\u7d27\u63e1\u7684\u624b\u638c\uff0c\u56e0\u4e3a\u5927\u529b\uff0c\u800c\u5bfc\u81f4\u7565\u5fae\u5c16\u9510\u7684\u6307\u7532\u6df1\u6df1\u7684\u523a\u8fdb\u4e86\u638c\u5fc3\u4e4b\u4e2d\uff0c\u5e26\u6765\u4e00\u9635\u9635\u94bb\u5fc3\u7684\u75bc\u75db\u2026\u2026\n    \u201c\u8427\u708e\uff0c\u6597\u4e4b\u529b\uff0c\u4e09\u6bb5\uff01\u7ea7\u522b\uff1a\u4f4e\u7ea7\uff01\u201d\u6d4b\u9a8c\u9b54\u77f3\u7891\u4e4b\u65c1\uff0c\u4e00\u4f4d\u4e2d\u5e74\u7537\u5b50\uff0c\u770b\u4e86\u4e00\u773c\u7891\u4e0a\u6240\u663e\u793a\u51fa\u6765\u7684\u4fe1\u606f\uff0c\u8bed\u6c14\u6f20\u7136\u7684\u5c06\u4e4b\u516c\u5e03\u4e86\u51fa\u6765\u2026\u2026\n    \u4e2d\u5e74\u7537\u5b50\u8bdd\u521a\u521a\u8131\u53e3\uff0c\u4fbf\u662f\u4e0d\u51fa\u610f\u5916\u7684\u5728\u4eba\u5934\u6c79\u6d8c\u7684\u5e7f\u573a\u4e0a\u5e26\u8d77\u4e86\u4e00\u9635\u5632\u8bbd\u7684\u9a9a\u52a8\u3002\n    \u201c\u4e09\u6bb5\uff1f\u563f\u563f\uff0c\u679c\u7136\u4e0d\u51fa\u6211\u6240\u6599\uff0c\u8fd9\u4e2a\u201c\u5929\u624d\u201d\u8fd9\u4e00\u5e74\u53c8\u662f\u5728\u539f\u5730\u8e0f\u6b65\uff01\u201d\n    \u201c\u54ce\uff0c\u8fd9\u5e9f\u7269\u771f\u662f\u628a\u5bb6\u65cf\u7684\u8138\u90fd\u7ed9\u4e22\u5149\u4e86\u3002\u201d\n    \u201c\u8981\u4e0d\u662f\u65cf\u957f\u662f\u4ed6\u7684\u7236\u4eb2\uff0c\u8fd9\u79cd\u5e9f\u7269\uff0c\u65e9\u5c31\u88ab\u9a71\u8d76\u51fa\u5bb6\u65cf\uff0c\u4efb\u5176\u81ea\u751f\u81ea\u706d\u4e86\uff0c\u54ea\u8fd8\u6709\u673a\u4f1a\u5f85\u5728\u5bb6\u65cf\u4e2d\u767d\u5403\u767d\u559d\u3002\u201d\n    \u201c\u5509\uff0c\u6614\u5e74\u90a3\u540d\u95fb\u4e4c\u5766\u57ce\u7684\u5929\u624d\u5c11\u5e74\uff0c\u5982\u4eca\u600e\u4e48\u843d\u9b44\u6210\u8fd9\u822c\u6a21\u6837\u4e86\u554a\uff1f\u201d\n\n```\n\n#### cb\n\ntsv\u6587\u4ef6\u683c\u5f0f\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n\n```bash\n\u5475\u5475    \u662f\u738b\u82e5\u732b\u7684\u3002\n\u4e0d\u662f    \u90a3\u662f\u4ec0\u4e48\uff1f\n\u600e\u4e48\u4e86  \u6211\u5f88\u96be\u8fc7\uff0c\u5b89\u6170\u6211~\n\u5f00\u5fc3\u70b9\u54c8,\u4e00\u5207\u90fd\u4f1a\u597d\u8d77\u6765 \u55ef \u4f1a\u7684\n\u6211\u8fd8\u559c\u6b22\u5979,\u600e\u4e48\u529e       \u6211\u5e2e\u4f60\u544a\u8bc9\u5979\uff1f\u53d1\u77ed\u4fe1\u8fd8\u662f\u6253\u7535\u8bdd\uff1f\n\u77ed\u4fe1    \u55ef\u55ef\u3002\u6211\u4e5f\u76f8\u4fe1\n\u4f60\u77e5\u9053\u8c01\u4e48      \u80af\u5b9a\u4e0d\u662f\u6211\uff0c\u662f\u962e\u5fb7\u57f9\n\u8bb8\u5175\u662f\u8c01        \u5434\u9662\u56db\u73ed\u5c0f\u5e05\u54e5\n```\n\n#### mt\n\ntsv\u6587\u4ef6\u683c\u5f0f\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n\n```bash\nHi.     \u55e8\u3002\nHi.     \u4f60\u597d\u3002\nRun.    \u4f60\u7528\u8dd1\u7684\u3002\nWait!   \u7b49\u7b49\uff01\nHello!  \u4f60\u597d\u3002\nI try.  \u8ba9\u6211\u6765\u3002\nI won!  \u6211\u8d62\u4e86\u3002\nOh no!  \u4e0d\u4f1a\u5427\u3002\nCheers! \u5e72\u676f!\nHe ran. \u4ed6\u8dd1\u4e86\u3002\n```\n\n#### ts\n\ntsv\u6587\u4ef6\u683c\u5f0f\n\n\u8bad\u7ec3\u6570\u636e\u793a\u4f8b\u5982\u4e0b\uff1a\n\n```bash\n\u5f90\u5dde18\u5c81\u519c\u5bb6\u5973\u5b69\u5b8b\u723d\uff0c\u4eca\u5e74\u8003\u5165\u6e05\u534e\u5927\u5b66\u3002\u9664\u4e86\u81ea\u5df1\u4e00\u8def\u95ef\u5173\uff0c\u5e74\u5e74\u62ff\u5956\uff0c\u8fd8\u5e2e\u59b9\u59b9\u3001\u5f1f\u5f1f\u5236\u5b9a\u5b66\u4e60\u8ba1\u5212\uff0c\u59d0\u5f1f\u4ee8\u9f50\u5934\u5e76\u8fdb\uff0c\u59b9\u59b9\u4e5f\u8003\u4e0a\u533a\u91cc\u6700\u597d\u7684\u4e2d\u5b66\u3002\u8fd9\u4e2a\u5bb6\u91cc\u7684\u6536\u5165\uff0c\u5168\u9760\u7236\u4eb2\u52a1\u519c\u548c\u6253\u96f6\u5de5\uff0c\u4f46\u5b8b\u723d\u61c2\u4e8b\u5f97\u8ba9\u4eba\u5fc3\u75bc\uff0c\u66fe\u9700\u8981200\u5143\u5965\u6570\u7ade\u8d5b\u7684\u6559\u6750\u8d39\uff0c\u5979\u7f9e\u4e8e\u5f00\u53e3\uff0c\u6123\u662f\u6025\u54ed\u4e86... \u6233\u817e\u8baf\u516c\u76ca\u5e2e\u5e2e\u5979\u4eec\uff01#\u52a9\u5b66\u5706\u68a6# \u6c5f\u82cf\u65b0\u95fb\u7684\u79d2\u62cd\u89c6\u9891   \u5f90\u5dde\u519c\u5bb6\u5973\u5b69\u8003\u4e0a\u6e05\u534e\uff0c\u5979\u7684\u61c2\u4e8b\u8ba9\u4eba\u5fc3\u9178\u2026\n\u76d6\u88ab\u5b50\uff0c\u6447\u6447\u7bee\uff0c\u6c6a\u661f\u4eba\u7b80\u76f4\u8981\u628a\u840c\u5a03\u5ba0\u4e0a\u5929\uff5e\u7ec6\u81f4\u5468\u5230\u6709\u8010\u5fc3\uff0c\u813e\u6c14\u8fd8\u597d\uff0c\u6c6a\u661f\u4eba\u4e0d\u6127\u662f\u4e00\u5c4a\u5e26\u5a03\u597d\u624b[\u7b11\u800c\u4e0d\u8bed]\u5076\u4e70\u5676\u89c6\u9891\u7684\u79d2\u62cd\u89c6\u9891      \u5e26\u5a03\u597d\u624b\u6c6a\u661f\u4eba\uff01\u628a\u5b9d\u5b9d\u4eec\u5ba0\u4e0a\u5929[\u61a7\u61ac]\n\u4eba\u4eec\u901a\u5e38\u88ab\u793e\u4f1a\u8d4b\u4e88\u7684\"\u6210\u529f\"\u6240\u5b9a\u4e49\uff0c\u201c\u505a\u4ec0\u4e48\u5de5\u4f5c\u201d\u201c\u8d5a\u591a\u5c11\u94b1\u201d\u90fd\u7528\u6765\u8bc4\u5224\u4e00\u4e2a\u4eba\u7684\u5168\u90e8\u4ef7\u503c\uff0c\u5f88\u591a\u4eba\u51fa\u73b0\u8eab\u4efd\u7126\u8651\u3002\u8eab\u4efd\u7126\u8651\u4e0d\u4ec5\u5f71\u54cd\u5e78\u798f\u611f\uff0c\u8fd8\u4f1a\u5bfc\u81f4\u7cbe\u795e\u538b\u529b\uff0c\u751a\u81f3\u81ea\u6740\u3002\u5982\u679c\u4f60\u4e5f\u6709\u8eab\u4efd\u7126\u8651\uff0c\u8fd9\u4e2a\u77ed\u7247\u6216\u8bb8\u4f1a\u6709\u5e2e\u52a9\u3002\u79d2\u62cd\u89c6\u9891  \u611f\u5230\u538b\u529b\u5927\u7684\u540c\u5b66\u770b\u8fc7\u6765\uff01\u5982\u4f55\u7f13\u89e3\u8eab\u4efd\u7126\u8651\uff1f[\u5e76\u4e0d\u7b80\u5355]\n\u7f51\u53cb@\u661f\u84ddseiran \u6559\u5927\u5bb6\u81ea\u5236\u7684\u6355\u6349\u5668\u6559\u7a0b\uff0c\u7b80\u5355\u65b9\u4fbf\uff0c\u91cc\u9762\u7684\u6d17\u6d01\u7cbe\u6362\u6210\u80a5\u7682\u6c34\u6216\u6d17\u8863\u7c89\u6c34\u90fd\u53ef\u4ee5\uff08\u7528\u4e8e\u6eb6\u89e3\u87d1\u8782\u8179\u90e8\u6cb9\u8102\u9632\u6b62\u722c\u51fa\uff09\uff0c\u767d\u7cd6\u7a0d\u5fae\u591a\u653e\u70b9\u3002\u6015\u87d1\u8782\u7684\u7ae5\u978b\uff0c\u53ef\u4ee5\u6362\u6210\u4e0d\u900f\u660e\u7684\u74f6\u5b50\u3002\u8f6c\u9700~     \u8fd9\u4e2a\u5389\u5bb3\u4e86\uff01[good]\n```\n\n## \u4f7f\u7528\n\n### ner\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.sl import NER\n\n# \u521b\u5efaNER\u5bf9\u8c61\nner_model = NER()\n\ntrain_path = '/home/lightsmile/NLP/corpus/ner/train.sample.txt'\ndev_path = '/home/lightsmile/NLP/corpus/ner/test.sample.txt'\nvec_path = '/home/lightsmile/NLP/embedding/char/token_vec_300.bin'\n\n# \u53ea\u9700\u6307\u5b9a\u8bad\u7ec3\u6570\u636e\u8def\u5f84\uff0c\u9884\u8bad\u7ec3\u5b57\u5411\u91cf\u53ef\u9009\uff0c\u5f00\u53d1\u96c6\u8def\u5f84\u53ef\u9009\uff0c\u6a21\u578b\u4fdd\u5b58\u8def\u5f84\u53ef\u9009\u3002\nner_model.train(train_path, vectors_path=vec_path, dev_path=dev_path, save_path='./ner_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\n# \u52a0\u8f7d\u6a21\u578b\uff0c\u9ed8\u8ba4\u5f53\u524d\u76ee\u5f55\u4e0b\u7684`saves`\u76ee\u5f55\nner_model.load('./ner_saves')\n# \u5bf9train_path\u4e0b\u7684\u6d4b\u8bd5\u96c6\u8fdb\u884c\u8bfb\u53d6\u6d4b\u8bd5\nner_model.test(train_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nfrom pprint import pprint\n\npprint(ner_model.predict('\u53e6\u4e00\u4e2a\u5f88\u9177\u7684\u4e8b\u60c5\u662f\uff0c\u901a\u8fc7\u6846\u67b6\u6211\u4eec\u53ef\u4ee5\u505c\u6b62\u5e76\u5728\u7a0d\u540e\u6062\u590d\u8bad\u7ec3\u3002'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\uff1a\n\n```bash\n[{'end': 15, 'entity': '\u6211\u4eec', 'start': 14, 'type': 'Person'}]\n```\n\n### cws\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.sl import CWS\n\ncws_model = CWS()\n\ntrain_path = '/home/lightsmile/NLP/corpus/cws/train.sample.txt'\ndev_path = '/home/lightsmile/NLP/corpus/cws/test.sample.txt'\nvec_path = '/home/lightsmile/NLP/embedding/char/token_vec_300.bin'\n\ncws_model.train(train_path, vectors_path=vec_path, dev_path=dev_path, save_path='./cws_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\ncws_model.load('./cws_saves')\n\ncws_model.test(dev_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nprint(cws_model.predict('\u6297\u65e5\u6218\u4e89\u65f6\u671f\uff0c\u80e1\u8001\u5728\u4e0e\u4fb5\u534e\u65e5\u519b\u4ea4\u6218\u4e2d\u56db\u6b21\u8d1f\u4f24\uff0c\u662f\u4e00\u4f4d\u4e0d\u6298\u4e0d\u6263\u7684\u6297\u6218\u8001\u82f1\u96c4'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\uff1a\n\n```bash\n['\u6297\u65e5\u6218\u4e89', '\u65f6\u671f', '\uff0c', '\u80e1\u8001', '\u5728', '\u4e0e', '\u4fb5\u534e\u65e5\u519b', '\u4ea4\u6218', '\u4e2d', '\u56db\u6b21', '\u8d1f\u4f24', '\uff0c', '\u662f', '\u4e00\u4f4d', '\u4e0d\u6298\u4e0d\u6263', '\u7684', '\u6297\u6218', '\u8001', '\u82f1\u96c4']\n```\n\n### pos\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.sl import POS\n\npos_model = POS()\n\ntrain_path = '/home/lightsmile/NLP/corpus/pos/train.sample.txt'\ndev_path = '/home/lightsmile/NLP/corpus/pos/test.sample.txt'\nvec_path = '/home/lightsmile/NLP/embedding/char/token_vec_300.bin'\n\npos_model.train(train_path, vectors_path=vec_path, dev_path=dev_path, save_path='./pos_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\npos_model.load('./pos_saves')\n\npos_model.test(dev_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nprint(pos_model.predict('\u5411\u5168\u56fd\u5404\u65cf\u4eba\u6c11\u81f4\u4ee5\u8bda\u631a\u7684\u95ee\u5019\uff01'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\uff1a\n\n```bash\n[('\u5411', 'p'), ('\u5168\u56fd', 'n'), ('\u5404\u65cf', 'r'), ('\u4eba\u6c11', 'n'), ('\u81f4\u4ee5', 'v'), ('\u8bda\u631a', 'a'), ('\u7684', 'u'), ('\u95ee\u5019', 'vn'), ('\uff01', 'w')]\n```\n\n### srl\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.sl import SRL\n\nsrl_model = SRL()\n\ntrain_path = '/home/lightsmile/NLP/corpus/srl/train.sample.tsv'\ndev_path = '/home/lightsmile/NLP/corpus/srl/test.sample.tsv'\nvec_path = '/home/lightsmile/NLP/embedding/word/sgns.zhihu.bigram-char'\n\n\nsrl_model.train(train_path, vectors_path=vec_path, dev_path=dev_path, save_path='./srl_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\nsrl_model.load('./srl_saves')\n\nsrl_model.test(dev_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nword_list = ['\u4ee3\u8868', '\u671d\u65b9', '\u5bf9', '\u4e2d\u56fd', '\u515a\u653f', '\u9886\u5bfc\u4eba', '\u548c', '\u4eba\u6c11', '\u54c0\u60bc', '\u91d1\u65e5\u6210', '\u4e3b\u5e2d', '\u901d\u4e16', '\u8868\u793a', '\u6df1\u5207', '\u8c22\u610f', '\u3002']\npos_list = ['VV', 'NN', 'P', 'NR', 'NN', 'NN', 'CC', 'NN', 'VV', 'NR', 'NN', 'VV', 'VV', 'JJ', 'NN', 'PU']\nrel_list = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]\n\nprint(srl_model.predict(word_list, pos_list, rel_list))\n```\n\n\u9884\u6d4b\u7ed3\u679c\uff1a\n\n```bash\n{'ARG0': '\u4e2d\u56fd\u515a\u653f\u9886\u5bfc\u4eba\u548c\u4eba\u6c11', 'rel': '\u54c0\u60bc', 'ARG1': '\u91d1\u65e5\u6210\u4e3b\u5e2d\u901d\u4e16'}\n```\n\n### sa\n\n#### \u8bad\u7ec3\n\n```python\n\nfrom lightnlp.tc import SA\n\n# \u521b\u5efaSA\u5bf9\u8c61\nsa_model = SA()\n\ntrain_path = '/home/lightsmile/Projects/NLP/chinese_text_cnn/data/train.sample.tsv'\ndev_path = '/home/lightsmile/Projects/NLP/chinese_text_cnn/data/dev.sample.tsv'\nvec_path = '/home/lightsmile/Downloads/1410356697_\u6d45\u7b11\u54e5fight/\u81ea\u7136\u8bed\u8a00\u5904\u7406/\u8bcd\u5411\u91cf/sgns.zhihu.bigram-char'\n\n# \u53ea\u9700\u6307\u5b9a\u8bad\u7ec3\u6570\u636e\u8def\u5f84\uff0c\u9884\u8bad\u7ec3\u5b57\u5411\u91cf\u53ef\u9009\uff0c\u5f00\u53d1\u96c6\u8def\u5f84\u53ef\u9009\uff0c\u6a21\u578b\u4fdd\u5b58\u8def\u5f84\u53ef\u9009\u3002\nsa_model.train(train_path, vectors_path=vec_path, dev_path=dev_path, save_path='./sa_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\n\n# \u52a0\u8f7d\u6a21\u578b\uff0c\u9ed8\u8ba4\u5f53\u524d\u76ee\u5f55\u4e0b\u7684`saves`\u76ee\u5f55\nsa_model.load('./sa_saves')\n\n# \u5bf9train_path\u4e0b\u7684\u6d4b\u8bd5\u96c6\u8fdb\u884c\u8bfb\u53d6\u6d4b\u8bd5\nsa_model.test(train_path)\n```\n\n#### \u9884\u6d4b\n\n```python\n\nsa_model.load('./sa_saves')\n\nfrom pprint import pprint\n\npprint(sa_model.predict('\u5916\u89c2\u6f02\u4eae\uff0c\u5b89\u5168\u6027\u4f73\uff0c\u52a8\u529b\u591f\u5f3a\uff0c\u6cb9\u8017\u591f\u4f4e'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\uff1a\n\n```python\n(1.0, '1') # return\u683c\u5f0f\u4e3a\uff08\u9884\u6d4b\u6982\u7387\uff0c\u9884\u6d4b\u6807\u7b7e\uff09\n```\n\n### re\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.tc import RE\n\nre = RE()\n\ntrain_path = '/home/lightsmile/Projects/NLP/ChineseNRE/data/people-relation/train.sample.txt'\ndev_path = '/home/lightsmile/Projects/NLP/ChineseNRE/data/people-relation/test.sample.txt'\nvec_path = '/home/lightsmile/NLP/embedding/word/sgns.zhihu.bigram-char'\n\nre.train(train_path, dev_path=dev_path, vectors_path=vec_path, save_path='./re_saves')\n\n```\n\n#### \u6d4b\u8bd5\n\n```python\nre.load('./re_saves')\nre.test(dev_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nprint(re.predict('\u94b1\u949f\u4e66', '\u8f9b\u7b1b', '\u4e0e\u8f9b\u7b1b\u4eac\u6caa\u5531\u548c\u807d\u94b1\u949f\u4e66\u4e0e\u94b1\u949f\u4e66\u662f\u6e05\u534e\u6821\u53cb\uff0c\u94b1\u949f\u4e66\u9ad8\u8f9b\u7b1b\u4e24\u73ed\u3002'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\uff1a\n\n```python\n(0.7306928038597107, '\u540c\u95e8') # return\u683c\u5f0f\u4e3a\uff08\u9884\u6d4b\u6982\u7387\uff0c\u9884\u6d4b\u6807\u7b7e\uff09\n```\n\n### lm\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.tg import LM\n\nlm_model = LM()\n\ntrain_path = '/home/lightsmile/NLP/corpus/lm_test.txt'\ndev_path = '/home/lightsmile/NLP/corpus/lm_test.txt'\nvec_path = '/home/lightsmile/NLP/embedding/char/token_vec_300.bin'\n\nlm_model.train(train_path, vectors_path=vec_path, dev_path=train_path, save_path='./lm_saves')\n```\n#### \u6d4b\u8bd5\n```python\nlm_model.load('./lm_saves')\n\nlm_model.test(dev_path)\n```\n#### \u9884\u6d4b\n\n##### \u6587\u672c\u751f\u6210\n\u9ed8\u8ba4\u751f\u621030\u4e2a\n\n```python\nprint(lm_model.generate_sentence('\u5c11\u5e74\u9762\u65e0\u8868\u60c5\uff0c\u5507\u89d2\u6709\u7740\u4e00\u62b9\u81ea\u5632'))\n```\n\u7ed3\u679c\uff1a\n\n```bash\n\u5c11\u5e74\u9762\u65e0\u8868\u60c5\uff0c\u5507\u89d2\u6709\u7740\u4e00\u62b9\u81ea\u5632\uff0c\u7d27\u63e1\u7684\u624b\u638c\uff0c\u56e0\uff0c\u65e0\u6240\u8c13\u7684\u9762\u4e0a\uff0c\u90a3\u62b9\u8ba5\u8bbd\u6240\u83ab\u4e0b\u4e86\u811a\u6b65\uff0c\u5f53\u65f6\u7684\n```\n\n#### \u5f97\u5230\u7ed9\u5b9a\u4e0a\u6587\u4e0b\uff0c\u4e0b\u4e00\u4e2a\u5b57\u7684topK\u5019\u9009\u96c6\u53ca\u5176\u6982\u7387\n\u9ed8\u8ba4\u8f93\u51fatop5\u4e2a\n\n```python\nprint(lm_model.next_word_topk('\u5c11\u5e74\u9762\u65e0\u8868\u60c5\uff0c\u5507\u89d2'))\n```\n\n\u7ed3\u679c\uff1a\n\n```bash\n[('\u6709', 0.9791949987411499), ('\u4e00', 0.006628755945712328), ('\u4e0d', 0.004853296559303999), ('\u51fa', 0.0026260288432240486), ('\u72e0', 0.0017451468156650662)]\n```\n\n#### \u8bc4\u4f30\u8bed\u53e5\u5206\u6570\n\u7ed3\u679c\u4e3a\u4ee510\u4e3a\u5e95\u7684\u5bf9\u6570\uff0c\u5373`log10(x)`\n\n```python\nprint(lm_model.sentence_score('\u5c11\u5e74\u9762\u65e0\u8868\u60c5\uff0c\u5507\u89d2\u6709\u7740\u4e00\u62b9\u81ea\u5632'))\n```\n\u7ed3\u679c\uff1a\n\n```bash\n-11.04862759023672\n```\n\n#### \u8bc4\u4f30\u5f53\u524d\u4e0a\u6587\u4e0b\uff0c\u67d0\u4e00\u4e2a\u5b57\u4f5c\u4e3a\u4e0b\u4e00\u4e2a\u5b57\u7684\u53ef\u80fd\u6027\n\n```python\nprint(lm_model.next_word('\u8981\u4e0d\u662f', '\u8427'))\n```\n\u7ed3\u679c\uff1a\n\n```bash\n0.006356663070619106\n```\n\n### ss\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.sr import SS\n\nss_model = SS()\n\ntrain_path = '/home/lightsmile/Projects/NLP/sentence-similarity/input/atec/ss_train.tsv'\ndev_path = '/home/lightsmile/Projects/NLP/sentence-similarity/input/atec/ss_dev.tsv'\nvec_path = '/home/lightsmile/NLP/embedding/char/token_vec_300.bin'\n\nss_model.train(train_path, vectors_path=vec_path, dev_path=train_path, save_path='./ss_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\nss_model.load('./ss_saves')\nss_model.test(dev_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nprint(float(ss_model.predict('\u82b1\u5457\u66f4\u6539\u7ed1\u5b9a\u94f6\u884c\u5361', '\u5982\u4f55\u66f4\u6362\u82b1\u5457\u7ed1\u5b9a\u94f6\u884c\u5361')))\n```\n\n\u9884\u6d4b\u7ed3\u679c\uff1a\n\n```bash\n0.9970847964286804\n```\n\n### te\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.sr import TE\n\nte_model = TE()\n\ntrain_path = '/home/lightsmile/Projects/liuhuaiyong/ChineseTextualInference/data/te_train.tsv'\ndev_path = '/home/lightsmile/Projects/liuhuaiyong/ChineseTextualInference/data/te_dev.tsv'\nvec_path = '/home/lightsmile/NLP/embedding/char/token_vec_300.bin'\n\nte_model.train(train_path, vectors_path=vec_path, dev_path=train_path, save_path='./te_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\nte_model.load('./te_saves')\nte_model.test(dev_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nprint(te_model.predict('\u4e00\u4e2a\u5c0f\u7537\u5b69\u5728\u79cb\u5343\u4e0a\u73a9\u3002', '\u5c0f\u7537\u5b69\u73a9\u79cb\u5343'))\nprint(te_model.predict('\u4e24\u4e2a\u5e74\u8f7b\u4eba\u7528\u6ce1\u6cab\u5851\u6599\u676f\u5b50\u559d\u9152\u65f6\u505a\u9b3c\u8138\u3002', '\u4e24\u4e2a\u4eba\u5728\u8df3\u5343\u65a4\u9876\u3002'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\u4e3a\uff1a\n\n```bash\n(0.4755808413028717, 'entailment')\n(0.5721057653427124, 'contradiction')\n```\n\n### tdp\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.sp import TDP\n\ntdp_model = TDP()\n\ntrain_path = '/home/lightsmile/Projects/NLP/DeepDependencyParsingProblemSet/data/train.sample.txt'\ndev_path = '/home/lightsmile/Projects/NLP/DeepDependencyParsingProblemSet/data/dev.txt'\nvec_path = '/home/lightsmile/NLP/embedding/english/glove.6B.100d.txt'\n\ntdp_model.train(train_path, dev_path=dev_path, vectors_path=vec_path,save_path='./tdp_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\ntdp_model.load('./tdp_saves')\ntdp_model.test(dev_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nfrom pprint import pprint\npprint(tdp_model.predict('Investors who want to change the required timing should write their representatives '\n                         'in Congress , he added . '))\n```\n\n\u9884\u6d4b\u7ed3\u679c\u5982\u4e0b\uff1a\n```bash\n{DepGraphEdge(head=(',', 14), modifier=('he', 15)),\n DepGraphEdge(head=('<ROOT>', -1), modifier=('Investors', 0)),\n DepGraphEdge(head=('Congress', 13), modifier=(',', 14)),\n DepGraphEdge(head=('Investors', 0), modifier=('who', 1)),\n DepGraphEdge(head=('he', 15), modifier=('added', 16)),\n DepGraphEdge(head=('in', 12), modifier=('Congress', 13)),\n DepGraphEdge(head=('representatives', 11), modifier=('in', 12)),\n DepGraphEdge(head=('required', 6), modifier=('timing', 7)),\n DepGraphEdge(head=('should', 8), modifier=('their', 10)),\n DepGraphEdge(head=('the', 5), modifier=('change', 4)),\n DepGraphEdge(head=('the', 5), modifier=('required', 6)),\n DepGraphEdge(head=('their', 10), modifier=('representatives', 11)),\n DepGraphEdge(head=('their', 10), modifier=('write', 9)),\n DepGraphEdge(head=('timing', 7), modifier=('should', 8)),\n DepGraphEdge(head=('to', 3), modifier=('the', 5)),\n DepGraphEdge(head=('want', 2), modifier=('to', 3)),\n DepGraphEdge(head=('who', 1), modifier=('want', 2))}\n```\n\n\u8fd4\u56de\u7684\u683c\u5f0f\u7c7b\u578b\u4e3a`set`\uff0c\u5176\u4e2d`DepGraphEdge`\u4e3a\u547d\u540d\u5143\u7ec4\uff0c\u5305\u542b`head`\u548c`modifier`\u4e24\u5143\u7d20\uff0c\u8fd9\u4e24\u5143\u7d20\u90fd\u4e3a`(word, position)`\u5143\u7ec4\n\n### gdp\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.sp import GDP\n\ngdp_model = GDP()\n\ntrain_path = '/home/lightsmile/NLP/corpus/dependency_parse/THU/train.sample.conll'\nvec_path = '/home/lightsmile/NLP/embedding/word/sgns.zhihu.bigram-char'\n\n\ngdp_model.train(train_path, dev_path=train_path, vectors_path=vec_path, save_path='./gdp_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\ngdp_model.load('./gdp_saves')\ngdp_model.test(train_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nword_list = ['\u6700\u9ad8', '\u4eba\u6c11', '\u68c0\u5bdf\u9662', '\u68c0\u5bdf\u957f', '\u5f20\u601d\u537f']\npos_list = ['nt', 'nt', 'nt', 'n', 'nr']\nheads, rels = gdp_model.predict(word_list, pos_list)\nprint(heads)\nprint(rels)\n```\n\n\u9884\u6d4b\u7ed3\u679c\u5982\u4e0b\uff0c\u5176\u4e2d\u7a0b\u5e8f\u4f1a\u81ea\u52a8\u5728\u8bed\u53e5\u548c\u8bcd\u6027\u5e8f\u5217\u9996\u90e8\u586b\u5145`<ROOT>`\uff0c\u56e0\u6b64\u8fd4\u56de\u7684\u7ed3\u679c\u957f\u5ea6\u4e3a`len(word_list) + 1`\uff1a\n```bash\n[0, 3, 3, 4, 0, 4]\n['<ROOT>', '\u9650\u5b9a', '\u9650\u5b9a', '\u9650\u5b9a', '\u6838\u5fc3\u6210\u5206', '\u540c\u4f4d\u8bed']\n```\n\n### cbow\n\nCBOW\u5171\u5b9e\u73b0\u4e86\u4e09\u79cd\u6a21\u578b\uff0c\u5206\u522b\u4e3a\u57fa\u7840softmax\u6a21\u578b(CBOWBaseModule)\u3001\u57fa\u4e8e\u8d1f\u91c7\u6837\u7684\u4f18\u5316\u6a21\u578b(CBOWNegativeSamplingModule)\u3001\u57fa\u4e8e\u5c42\u6b21softmax\u7684\u4f18\u5316\u6a21\u578b(CBOWHierarchicalSoftmaxModule).\n\n\u4e09\u79cd\u6a21\u578b\u63d0\u4f9b\u7684\u63a5\u53e3\u4e00\u81f4\uff0c\u5982\u4e0b\u6240\u793a\uff1a\n\n#### \u5bfc\u5165\u63a5\u53e3\n\n```python\nfrom lightnlp.we import CBOWHierarchicalSoftmaxModule, CBOWNegativeSamplingModule, CBOWBaseModule # \u5206\u522b\u5bfc\u5165CBOW\u7684\u4e0d\u540c\u6a21\u578b\n```\n\n#### \u8bad\u7ec3\n\n```python\n# cbow_model = CBOWHierarchicalSoftmaxModule()\n# cbow_model = CBOWBaseModule()\ncbow_model = CBOWNegativeSamplingModule()\n\ntrain_path = '/home/lightsmile/NLP/corpus/novel/test.txt'\ndev_path = '/home/lightsmile/NLP/corpus/novel/test.txt'\n\ncbow_model.train(train_path, dev_path=dev_path, save_path='./cbow_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\ncbow_model.load('./cbow_saves')\ncbow_model.test(dev_path)\n```\n\n#### \u9884\u6d4b\n\n```python\ntest_context = ['\u65cf\u957f', '\u662f', '\u7684', '\u7236\u4eb2']\nprint(cbow_model.evaluate(test_context, '\u4ed6'))\nprint(cbow_model.evaluate(test_context, '\u63d0\u9632'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\uff1a\n\n```python\n0.9992720484733582\n2.4813576079191313e-30\n```\n\n#### \u4fdd\u5b58\u8bcd\u5411\u91cf\n\n```python\ncbow_model.save_embeddings('./cbow_saves/cbow_ns.bin')\n```\n\n`./cbow_saves/cbow_ns.bin`\u6587\u4ef6\u5185\u5bb9\uff1a\n\n```bash\n623 300\n<unk> -0.69455165 -1.3275498 -1.1975913 -0.3417502 0.13073823 1.3608844 0.15316872 -2.295731 0.45459792 0.09420798 -0.73944765 0.11755463 -1.6275359 0.6623806 0.8247673 1.7149547 -0.49345177 -0.5932094 -1.3025115 0.40126365 1.8675354 0.46296182 0.81418717 -0.51671696 -1.328723 -0.27371547 -1.5537426 1.0326972 0.11647574 0.1607528 0.5110576 -1.2010366 -0.81535685 0.5231469 2.212553 0.43934354 -0.8626878 1.5049676 -0.8135034 -0.8322859 0.068298176 0.7376674 0.6459309 0.07635216 -0.77374196 0.29933965 1.6596211 0.46682465 -0.8282705 -0.22142725 1.7853647 1.4777366 -0.63895816 2.1443112 -2.2435715 0.85962945 1.6643075 1.082537 -0.6922347 -2.2418396 -0.20210272 -1.2102528 -0.48685002 0.65887684 -0.2534356 -1.0342008 -1.1101105 0.94670665 0.21063486 -0.2467249 0.16507177 0.61120677 0.27850544 -1.0511587 -0.9382702 -0.105773546 -1.2759126 0.77076215 1.6730801 0.7634321 0.22365877 -1.7401465 -1.6434158 0.94023687 -1.3609751 -2.153141 0.3826534 0.32158422 -2.4204254 -2.1351569 -0.7265906 1.2896249 -1.6444998 0.62701744 3.9122646e-05 -1.348553 1.6431069 0.4589956 -1.8367497 0.81131816 0.13370599 0.9231004 -0.2677846 0.22468318 0.10889411 -1.0416583 0.016111592 -0.36729148 0.24761267 -1.143464 -0.6162608 -0.6412186 0.79434645 -0.11785016 1.8588868 -0.06067674 -1.1092484 -0.039183926 -0.5137064 -0.15945728 -1.4222018 0.31517547 -0.81327593 0.0048671463 -0.18886662 0.28870773 1.0241542 0.24846096 0.15484594 0.83580816 -0.59276813 0.12078259 -0.2424585 -0.1992609 -1.7673252 -0.45719153 0.3185026 0.052791957 0.072982006 0.27393457 0.24782388 -1.073425 0.2915962 -0.52252334 -0.0066470583 -0.4599936 0.34365907 0.7187273 -0.7599531 -0.5792492 1.1238049 0.8469614 -0.078110866 0.20481071 -0.015566204 0.39713895 0.27844605 -0.37874687 -0.32269904 0.18351592 -1.2942557 1.0065168 2.6649168 -0.09024592 -0.115473986 -0.29874867 0.5950803 -0.6491804 0.9974534 -1.0031152 -2.4024782 -0.11324192 0.3452371 -0.68466026 -0.7123374 -0.61712 -2.0060632 0.49333447 0.4248587 -0.05601518 0.099164896 1.8789287 -0.2811404 0.91072047 2.713236 1.3424015 -0.007254917 -1.2505476 -0.7478102 0.7299547 -0.089441456 -0.43519676 0.45425606 0.49322376 -1.0130681 -0.56024987 -0.74189216 0.5030309 -1.023638 -1.7686493 0.638495 0.612898 0.5948498 2.5866709 0.1675552 -0.059030745 -0.3356758 0.66674125 1.1920244 0.24162059 1.3198696 0.28690717 -2.68874 -0.48055518 -1.5761619 0.14664873 0.83967185 -0.7924626 0.7860132 -0.7246394 1.0014578 0.14658897 -0.64450735 0.86360186 2.015226 -0.06311106 0.54246426 -2.120671 0.60732156 -0.9577766 -0.962489 -0.13819228 -1.9003396 1.477142 0.13473822 -1.3756094 0.21764572 0.71171355 0.03748321 -0.393383 0.011907921 0.5097328 -0.710836 0.8421267 -0.89845014 -0.31148115 -0.12334009 -0.58898896 0.35046947 0.26125875 1.1667713 -0.77842957 -0.5580311 0.7409664 -1.3743105 -0.8576632 0.8552787 -0.70344007 -0.86729395 0.8507328 0.081006676 -0.36887273 0.93737006 -0.8049869 -1.1607035 -1.4482615 -0.4097167 0.45684943 -0.71613914 0.41646683 2.408504 -0.29688725 -0.45588523 -2.1563365 0.6449749 0.06401941 -0.5306914 1.9065568 -0.8465525 2.175783 0.6279667 -0.18118665 -0.7002306 0.08241815 -1.2743592 0.86315835 0.2589759 -0.11746242 -2.0128748 0.85062236 1.7910615 -0.23783809 0.22933501 0.8359954 -0.16953708 0.711695 -0.13198276 1.3160635 0.48212075 -0.83564043\n<pad> 0.8598764 -0.8392776 0.21543029 1.0473262 -0.35116714 0.92687714 0.19446017 0.43463743 -0.50851333 -1.5483292 0.4361628 -0.05452338 -0.26497063 0.66488725 -0.55493516 -0.2797728 1.2510214 -0.65309256 1.1241713 0.41626474 1.9894124 -0.51694274 1.5471387 1.0384578 0.2893607 0.8567941 -0.2927318 0.24968228 0.7357801 0.01763151 -0.46739513 -1.3317417 -0.36859253 -0.9243944 -0.35533777 -1.6850173 -0.23949681 1.8554561 0.68137765 0.7045612 1.2475091 -1.6330634 -0.052583996 -1.7476727 -0.692077 0.7417215 0.12882428 -1.0369412 -0.84594417 -0.2566721 0.34262887 -1.07697 -0.61600417 -0.15071104 -0.44881743 -0.7726476 1.7515095 0.20912598 0.70576566 -0.36712894 -0.31342962 0.47315833 -1.1460096 -0.70875674 -0.4837299 1.4506056 -0.9727428 0.39702946 0.07864575 0.3648432 0.49154198 0.020293105 -0.7249207 0.97864133 1.4640467 0.5678606 -2.860407 -0.39765677 -0.4860878 0.8766392 0.84922194 0.41535607 0.87215734 0.28720066 -0.7825528 0.5715837 0.15444374 0.76095456 -1.0340949 1.3190961 0.34591895 1.2966202 -0.8545642 0.9938145 0.1409012 0.99152505 0.8077086 0.93903935 -0.6754034 -0.91347355 -1.8044235 -0.7238192 0.2459109 0.15390426 0.1533081 -1.2125725 -0.854381 0.49695554 -1.7440581 -0.64858806 -1.2289644 0.5474777 0.9272567 0.22399819 -0.034679767 2.3584945 0.07103437 0.81011516 0.0698216 0.3754226 -0.65767145 0.3823659 0.40215418 -1.707603 0.114939004 0.8273572 0.29516712 -0.6673007 -1.2765539 0.99865556 -1.2278188 0.03912367 -0.45458874 -1.0813018 -2.2441347 1.9152719 0.47215146 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-0.1494729 0.3972236 -0.88379115 0.20430249 -1.1207113 -0.9375089 -0.12876953 1.4187068 1.8777137 -1.999467 -1.9011496 0.4638691 -0.15722306 -1.509574 0.051803187 0.6853142 -1.0125363 -0.99807036 -0.86616534 -0.32387426 0.97010213 1.0255684 1.4593514 -0.36234704 -0.21524686 -1.7589426 0.66719395 0.70087874 0.95069945 0.6235363 0.14841044 0.27994245 0.13287897 -0.44436157 0.7895685 1.2041568 -0.47667173 -1.4123715 1.0322057 -1.709688 -1.225889 0.08815727 -0.6686178 -0.7308128 0.7389635 0.17666328 1.5924493 1.3784972 0.6649754 1.31653 0.9976657 -1.3411351 -0.05105546 -0.887594 0.67946136 1.041635 0.43628508 0.048369333 0.19013812 0.8495835 -0.08113135 -0.32964498 0.59289676 -0.11091884 1.1329387 1.3676411 1.5922078 0.09468127 1.1554819 1.0879983 -0.939253 0.72018343\n\uff0c 0.8955515 0.17006782 -1.0863748 2.0142775 0.14233534 1.0502641 -1.9146186 1.5254054 0.41852686 -1.0021765 0.78738636 -1.1434265 -1.15919 1.3279808 -1.2685264 1.046601 1.8198309 -0.37393337 0.5671053 -1.6003635 1.3942565 -0.37112692 -0.83049476 0.7837918 -0.82138366 1.5960232 -0.5573124 -1.2436191 -1.428412 -1.8232468 0.6043092 -0.20802903 1.5128951 0.05398989 -0.7654913 -0.012385335 -0.48144546 1.1542314 -0.37977073 0.5381807 -0.25640526 -1.974048 1.2697856 -0.117085345 1.1256135 -1.0347183 1.5650568 0.2384594 -0.56699204 1.3157853 -1.0845431 1.0153542 0.59760785 -0.111005 -0.28848082 1.481634 -1.4323399 1.9391705 0.71281475 -0.14659926 -0.31929898 0.25538835 -0.5943959 1.8931442 1.4746904 -1.3227429 -0.93419975 0.7907077 1.2796596 0.9307215 -0.9653225 1.6776038 -0.96885055 -0.43495205 -0.83466965 0.1481599 0.19585872 1.8247943 -0.65230006 -0.647656 2.3732457 1.7634729 -0.6315052 -0.98673785 0.22707199 0.34494942 -0.06548499 1.1624743 0.47225925 0.6032354 0.83202213 0.3773793 -3.0592716 -0.8640957 0.39665133 -0.2816198 0.70281863 0.03667511 -1.1006662 -0.26202416 0.18258236 0.10605982 1.4086753 -0.70381814 -2.1561215 -1.2411748 -0.43822768 -0.51837033 0.6421206 -1.0362594 -2.428365 -0.16523075 1.1456362 -0.08391047 -2.687007 -0.6657906 1.4064697 -0.06454672 0.5299312 0.20851675 0.15787014 -0.5516159 0.57306266 1.0307944 0.37152547 0.62519145 0.21139014 -1.4073379 -1.3968574 1.8451492 0.11915406 0.57241035 -1.1742092 -0.48484102 -1.2159579 0.09127683 0.7116044 -0.06038856 -2.3160555 0.41553587 1.1015201 -0.40176693 0.3578966 0.52032125 -1.8040376 -1.5734198 -0.74014616 0.11765343 0.0928774 -1.784013 -0.63376683 -1.4449115 -1.0861475 -0.4310936 -1.4024754 1.5356311 0.07252996 1.5902004 1.0634187 0.015993338 0.21429028 0.8970561 -0.12790991 -1.9200468 0.6151161 -0.47694612 -0.41159615 1.0849681 0.5325725 -1.4720529 0.5552602 -0.53370255 0.5525359 0.62440306 -0.7017466 1.1594017 0.8523005 0.38567367 1.6300334 0.6926544 -0.69930124 -1.3093007 0.05683967 -1.094428 0.28537703 -0.78053284 0.6161773 1.2817806 -0.28649428 2.1111324 0.45189494 0.39454496 0.4957133 0.91635454 -0.004030827 -0.5518505 -0.9888321 0.3439788 0.9749812 -0.7467686 0.5536774 0.114550285 -1.4094499 -0.74071133 0.19150798 -1.6008753 -0.42580312 -0.5062191 -1.0444416 0.7498658 -1.3065071 -2.2079031 -0.7719429 2.131896 -1.5503948 0.05682873 0.81364197 0.6815463 1.0333269 0.48120993 0.40403336 0.786213 -0.5750243 -0.1394561 -0.20901637 0.515619 -0.079941645 -0.8154894 -0.4348516 2.139911 -0.26203522 -0.12534955 -1.080352 0.40559825 -0.43517712 0.19666079 -0.99644816 -1.9872378 -0.11382233 -0.082110204 0.16832533 0.27074367 -0.42697617 0.50094104 0.9432737 -0.8051666 -0.24928531 -1.5930034 -1.1854583 -0.7315353 1.0935879 0.5686678 0.6817074 -0.497519 -1.7803068 1.0525339 -1.1816463 0.4849164 -0.5876447 -1.0767654 -0.90534335 0.7111435 0.6387782 -0.6795654 -0.17411323 -0.11259085 0.07922964 -1.5371228 1.1217103 0.46036267 1.0601455 -0.16958186 0.057950106 -1.0218472 0.4218457 0.76899123 -1.3247061 -0.58687806 1.5984517 -0.90742105 -0.17568123 0.26020217 1.0052223 0.669329 1.8048744 -0.057761785 0.6754414 0.41463077 -0.485256 0.7811767 0.44659016 0.48198953 1.0696205 1.6955587 -1.3530792 0.7582639 -0.93256533 0.30515102 1.6443563 1.0251727\n\u7684 -0.019410107 -0.24678797 -0.5141552 2.7299752 0.6342168 -0.110809356 0.2703856 0.41705674 -0.76466995 -2.4204311 -0.59976536 -0.7159314 -0.8618017 1.0497526 0.54623944 -0.7981596 -0.67481875 1.0958283 -0.46740645 1.0951735 0.61883473 -1.0565901 -0.32493624 -0.31894302 -1.8763341 -0.94696546 -0.56408083 0.7680552 -0.37237883 1.875175 1.5623778 0.16714819 1.5595838 0.0839203 -0.8165728 -1.2181876 -1.4141134 -2.221717 1.0910231 0.39918897 -1.4147882 -1.9443827 2.6638284 -2.5849214 -0.3483093 -1.2768111 1.2041935 0.41885737 -0.6264915 -1.2598635 -0.17101997 -0.09451551 0.5562106 1.8215355 -1.3849229 -0.16678634 -1.3049109 1.3956747 -0.425332 -0.58320785 -0.62582475 -0.16236432 0.8221694 0.20428674 -0.27942896 0.121347904 0.3831149 0.19451053 0.3466418 -1.2984078 0.36676487 0.75776196 1.5233855 1.6458269 1.73043 -0.5802344 -0.48261273 -0.6443515 -1.0062621 0.8157141 0.0649764 0.13610162 -0.33701542 -0.42747515 -0.0011477228 -0.9921381 0.558996 0.48417446 0.42329437 0.54720676 0.57775104 -1.2895788 0.64017355 0.9923972 0.64543486 -2.407712 0.40264577 0.738344 1.1438419 0.6721332 0.18367681 -0.5367812 1.710209 0.22282977 -0.37812966 2.1818678 0.61612314 1.6069653 1.6151379 -1.0042768 0.8307863 0.085298695 -0.5351512 0.77987534 -1.1209589 -1.2757269 0.19029789 0.09809208 0.30246544 -0.14954329 -0.66100293 1.0569872 0.28426272 0.9857154 0.75427866 1.4701519 -0.12504229 -0.87289083 -0.43871146 0.20166902 0.2271485 -0.05514332 -0.720507 -0.4757063 0.8947587 0.36385572 1.272678 -0.35486463 1.2087017 -0.4758017 -0.18907958 0.24432425 -1.2633739 -0.37864834 -1.0377893 -1.0432142 0.60313225 -0.4432806 0.597437 -0.5591857 0.28537536 0.039966587 -1.1142912 -0.7018597 -0.2819324 1.0536848 -0.040540628 0.16402985 0.70751774 -1.624833 -1.2773706 0.05926119 0.4667645 0.6903434 1.0204479 -1.7858443 -0.26309192 1.6994039 -1.0891271 -0.71158147 0.24580163 -0.07374777 -1.4286835 1.8534608 0.12186845 -1.1296402 -0.7697011 1.6788592 2.6152475 0.606213 0.3166484 0.30229023 -1.2840998 0.012669044 0.87669975 0.32712832 -0.4437163 0.53256166 -0.54276496 0.32467005 -0.9636277 -0.58549994 -0.1298496 0.67720413 -2.3554142 1.3474101 -0.81879246 2.5617309 1.878895 0.49217474 -1.3570213 1.1938144 0.3645778 -0.29008883 0.50031495 -1.5553544 -1.2081774 0.87830913 -1.1718067 1.7222011 -0.13035145 -1.9812089 -1.8173308 -0.41010964 -0.26526994 -0.4790508 0.45257586 0.80826676 2.0087717 -1.0434382 -2.4669588 0.54181504 0.054128893 -0.33712658 -2.437975 1.0693933 0.13688947 -0.60142255 -0.10989515 -1.1721189 1.1690396 0.98004854 1.7259405 -0.63115627 0.17960648 0.1349787 1.8558581 0.2962184 -0.47908902 -0.13066223 -0.49583495 -0.80173033 1.1078131 -0.21119505 -0.8546662 0.6391783 -0.5089646 -0.96097887 0.038478117 -0.67008615 -0.54741406 -0.9072827 -0.06801312 1.3966236 -0.547623 0.16072778 -1.3989493 -2.599672 0.2585235 0.25142732 0.1333462 -1.0716463 -1.0153651 -0.6559947 0.51636326 -1.7126486 -0.073620744 -0.6133027 -0.74761003 0.09934151 1.0121211 -0.95096993 1.5341284 -1.079764 0.113598 0.29572484 -0.2686275 0.64157134 2.4731357 -1.695656 0.55485827 -0.47317806 0.26248395 0.28782308 -0.53618616 -0.8938534 -0.5614469 -0.16780692 -0.86070776 0.7112449 0.95629495 -0.4078699 0.73303235 0.22123657 0.44072202 1.5468754 0.09615625 2.2312448 1.7467606 1.3082488\n\u4e86 0.124426864 1.8280954 0.9831009 0.14293717 -1.4974583 3.1034458 -0.7097836 0.20220008 1.4538946 -1.8817077 -0.22880717 -1.027875 -0.53895986 0.80745065 -1.0450182 -0.08144022 1.3482633 0.2743296 -0.39580986 -1.505056 0.51076716 -0.28799066 -0.9882684 0.44040823 -0.2843285 1.0525922 -0.40245408 -1.1113168 0.58638555 -0.86827195 0.4367374 -0.59662205 0.7141082 -0.8070898 -0.96410495 0.35778406 -0.2732946 0.43445915 1.7109047 -0.41755947 0.810394 -1.0918777 1.1574733 -1.2285464 0.2751894 0.10051493 0.9152668 0.19070739 0.48134676 0.086716995 0.9004895 0.5559789 -0.050192833 0.112029955 -1.439684 0.75009805 -1.5054841 -1.3146921 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-2.3347435 -0.31022587 -0.8826532 -0.42418194 -0.7870713 1.9317689 -0.5187978 1.2357754 0.072576575 -0.15375821 -0.57340276 -0.15085204 0.47972527 0.14387 -0.85539544 0.7481106 0.59370905 0.37782627 -0.9562182 -0.14203326 -0.6214096 -1.2952368 -1.9361837 0.66784674 -0.8764587 1.3920652 1.3384788 1.1676358 0.5798174 0.27975932 -1.9524069 -0.0073854607 -0.26425046 -1.0647621 -0.14070114 -0.48506567 1.7909943 -1.2614187 0.3135924 0.8464774 0.6025425 0.865754 -0.6702711 1.0650029 0.5283241 0.38650712 -0.9644218 1.1394185 -1.9817309 -0.55233175 -0.13839766 -0.4280309 0.1417486 -0.79457724 0.58854914 -0.34508002 0.8903802 1.9166594 -0.22798921 0.8145917 1.0230062 0.049085077 -1.4656824 0.4805433 -0.9354194 0.15922448 -0.050655097 0.32922944 0.28885496 0.21598572 -0.7406716 1.0585318 -0.8170561 -0.031450592 0.6143301 1.1952467 0.0184183 0.16429812 1.6212403 -0.6389256 0.74482936 -0.23121776 0.60105395 0.7080985 1.2886081 -0.1550115 -1.2381295 0.4256766 -0.24611914 -0.25242683 -1.4610463 0.7941693 -0.99647474 1.0309753 -1.1659817 0.37439004 -0.029903825 0.7499461 0.0016405185 0.4898123 0.34486088 0.16148868 0.93313223 2.2235749 -0.71705014 0.77442616 0.7843878 1.1499043 -1.9716254 0.7126426 -0.1423409 -1.7253298 0.03773442 1.9197751 0.69600886 0.36871806 -0.048697434 -0.26592514 -1.3058069 -0.19177404 -0.22102174 -0.32699153 0.84755427 0.2087623 -0.47857174 0.9743888 -0.97826356 -1.8312483 1.7447314 -0.11683806 -0.32776853 -1.9126707 0.36183694 -0.18245338 0.037486456 1.1031898 -0.6431696 -0.66300964 -1.121779 1.6951121 1.9903591 -0.63814366 0.85539633 1.642792 0.31545052 0.7557653 -0.8640382 -1.1982353 2.0471108 -1.367175\n\u2026 1.7539198 -0.07875835 -0.51359785 0.5462624 1.0336319 0.33710518 0.7153517 -0.14696723 -0.4674709 0.585131 -0.09571628 -0.044367265 -0.43465808 -1.075802 -0.29818213 -0.7845866 1.1654521 -1.3100251 1.8042226 0.2514134 1.4274467 -1.0617328 -0.3200904 1.2856162 0.3420093 1.7161297 1.8614627 -0.20988376 -0.42488077 -0.7149864 0.41926503 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-1.7237631 0.51812005 -0.71558076 1.7924819 0.14843622 -0.29164916 1.126084 -0.20472099 1.6225713 -0.60215634 -0.23482214 -1.5326608 0.6890701 -1.2694215 -0.20689794 -1.0027355 0.7053792 -0.8321893 1.176607 1.0103234 -1.3610929 0.16453268 -2.3285384 1.4695607 -0.022401335 -1.6919589 -0.61018145 -1.6643481 0.65750724 -0.15422283 -0.33395147 0.77055055 -0.2663506 -0.640906 -1.2953341 -1.2691419 -0.9496096 1.4021212 -0.29681277 -1.2956185 -0.81685257 -0.93699765 -0.10026271 0.4026852 0.17704841 0.14466256 -1.3512911 -0.9849602 1.561256 1.6520786 0.2695429 -0.3704157 -0.66111404 1.3731217 -1.2292235 0.35934207 -1.1112843 1.3329659 -0.4493885 -0.693006 -1.4414659 0.21878286 -2.2706199 1.1016893 -0.16959193 -0.13103354 -0.051698178 -0.8295336 0.46076056 -0.3791775 0.5837915 0.3287772 -0.1266879 0.29440388 3.2369833 0.22973283 0.39704415 -0.99494326 0.69763094 -0.075644396 -0.031685505 0.6717069 0.6972548 -0.8750802 0.25193936 0.91673565 0.44680834 0.36706924 1.1802963\n\u5c11\u5973 0.5887249 -0.78131676 -0.9086393 -1.1748865 -0.7446431 -0.33194453 -0.018740159 -0.6819682 1.1373322 -0.2449827 0.38390064 -0.4037972 -0.42380548 1.8774717 -0.056615744 1.1482375 1.0340028 -0.57691437 -0.10536296 0.602655 0.7542164 -0.5638564 -0.71151686 -0.08572001 0.29281658 0.52927524 1.5935234 0.09691928 -1.0369319 0.18286628 1.6077064 -0.6484846 1.2906547 0.82070255 0.42539054 -0.46507382 0.32321668 -1.6392659 -0.264856 1.2421234 -0.20365983 -0.020171288 0.86471444 0.7232603 -0.9572046 1.6881616 -0.5733427 0.34953114 -0.7623181 -0.1049821 -0.23901421 1.7843546 1.4431484 -1.0618613 0.88080454 -0.42794758 -1.6699258 0.3234611 -0.35222912 -1.1160336 0.057735726 -0.7693502 0.1561758 0.50093096 1.9453335 -0.812546 -2.8262587 -0.009005266 -0.09875295 -1.333687 -0.14573775 0.46749806 0.755247 1.1295704 0.895495 -1.5277107 -1.5787225 0.124769524 -1.6838331 1.7976208 0.86056334 1.5805879 0.4043093 0.86494225 1.6273291 0.40853548 -1.7177533 1.3041753 -0.40075505 -1.908944 -0.35136628 -1.6667027 -0.3832609 1.4697397 -1.7034197 -0.7213212 -0.34379014 1.3429763 0.12348689 -1.4705572 -1.4270422 0.24953331 1.3322998 0.02141577 -0.04586138 -0.08307748 -0.9784215 0.04490414 1.383406 -0.57164764 0.18689618 -0.46882167 -0.05742165 -0.90621465 -1.7430568 0.64610285 0.22093566 0.71984667 0.23604086 -2.0309274 0.18095501 0.79003716 0.7923131 2.2337909 0.50145984 -0.20433225 0.24310149 1.6265295 -2.0527804 0.076875634 -0.19025083 -0.51757085 0.22870481 0.027272848 1.1691102 0.4587316 0.43038988 -1.4018912 0.31812528 -1.0155283 -0.6313369 -0.6585674 0.22004573 -0.6052359 1.5660753 0.4774539 2.1519923 0.11055413 0.32297432 0.3056909 1.5830464 -0.14859697 0.49388915 1.1956668 -2.5543363 0.22358978 1.3447273 1.3092629 -0.14362293 0.7085022 -1.7020465 0.09408313 -1.417123 0.7645757 0.060660124 -0.36149168 0.7115275 1.7099825 -0.15572844 0.27442068 0.048999123 -0.19752415 -0.8670349 -0.26930657 -0.27720222 -0.17450356 1.3144078 -0.2786439 1.4584504 0.5331807 -2.408406 -1.1464162 -0.7464278 -0.88895607 -0.5660856 -0.14826216 -0.8454592 -0.41659743 0.73387223 1.8717443 1.2645547 0.5606523 -0.78016657 0.95922476 2.5326197 1.6011894 0.6156151 -0.4252702 0.3975298 -1.6362991 1.4911361 0.28891438 0.87486833 0.7208409 0.5737307 -1.0389473 -1.3981676 -0.4815167 0.03707392 1.7858388 0.59070474 -0.5626557 0.3910045 0.035984877 2.1952462 -0.9893836 0.62462777 -0.3701214 -1.3561703 0.7157114 -1.0020103 1.1730001 -0.48587084 0.57544714 -0.7790919 0.52735734 -0.3946973 -0.58449775 1.0182343 0.85085005 0.2953459 -1.9785928 -0.3930518 -0.72646505 0.9768115 0.17771009 -0.44179973 0.78593755 0.8447062 -0.005129957 0.5753596 0.6570053 0.70418715 -0.6634827 0.5337006 0.3853094 -0.28450736 -1.0903058 -0.14038745 1.3840564 0.7502709 -0.043994833 -1.3120382 1.4737962 -0.09856514 -0.053444806 1.3115609 -0.9847638 2.2367926 -0.30558985 1.4043404 0.18040906 -0.36622265 -0.8305084 -1.085571 -0.012008861 -0.89203405 -0.18426119 1.6373096 -1.3801707 0.3139381 -1.0484347 0.44056708 -0.14707406 0.5474443 0.2298568 -1.53983 2.0013795 -1.0588335 -0.009949998 1.066051 -2.4138741 0.5206372 0.023850137 -0.62356704 0.34778613 -0.6537413 0.42022324 -0.12714641 -0.28691298 0.60363704 -0.3824652 0.60583377 0.24133673 -0.85732937 -0.27193385 -0.535049 -2.1983075 2.1011653 -0.15304893\n```\n\n### skip_gram\n\nskip_gram\u5171\u5b9e\u73b0\u4e86\u4e09\u79cd\u6a21\u578b\uff0c\u5206\u522b\u4e3a\u57fa\u7840softmax\u6a21\u578b(SkipGramBaseModule)\u3001\u57fa\u4e8e\u8d1f\u91c7\u6837\u7684\u4f18\u5316\u6a21\u578b(SkipGramNegativeSamplingModule)\u3001\u57fa\u4e8e\u5c42\u6b21softmax\u7684\u4f18\u5316\u6a21\u578b(SkipGramHierarchicalSoftmaxModule).\n\n\u4e09\u79cd\u6a21\u578b\u63d0\u4f9b\u7684\u63a5\u53e3\u4e00\u81f4\uff0c\u5982\u4e0b\u6240\u793a\uff1a\n\n#### \u5bfc\u5165\u63a5\u53e3\n\n```python\nfrom lightnlp.we import SkipGramBaseModule, SkipGramNegativeSamplingModule, SkipGramHierarchicalSoftmaxModule  # \u5206\u522b\u5bfc\u5165skip_gram\u4e0d\u540c\u6a21\u578b\n```\n\n#### \u8bad\u7ec3\n\n```python\n# skip_gram_model = SkipGramHierarchicalSoftmaxModule()\nskip_gram_model = SkipGramNegativeSamplingModule()\n# skip_gram_model = SkipGramBaseModule()\n\ntrain_path = '/home/lightsmile/NLP/corpus/novel/test.txt'\ndev_path = '/home/lightsmile/NLP/corpus/novel/test.txt'\n\nskip_gram_model.train(train_path, dev_path=dev_path, save_path='./skip_gram_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\nskip_gram_model.load('./skip_gram_saves')\n\nskip_gram_model.test(dev_path)\n```\n\n#### \u9884\u6d4b\n\n```python\ntest_target = '\u65cf\u957f'\nprint(skip_gram_model.evaluate(test_target, '\u4ed6'))\nprint(skip_gram_model.evaluate(test_target, '\u63d0\u9632'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\uff1a\n\n```python\n1.0\n0.002815224463120103\n```\n\n#### \u4fdd\u5b58\u8bcd\u5411\u91cf\n\n```python\nskip_gram_model.save_embeddings('./skip_gram_saves/skip_gram_ns.bin')\n```\n\n`./skip_gram_saves/skip_gram_ns.bin`\u6587\u4ef6\u5185\u5bb9\uff1a\n\n```bash\n623 300\n<unk> 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-0.45588523 -2.1563365 0.6449749 0.06401941 -0.5306914 1.9065568 -0.8465525 2.175783 0.6279667 -0.18118665 -0.7002306 0.08241815 -1.2743592 0.86315835 0.2589759 -0.11746242 -2.0128748 0.85062236 1.7910615 -0.23783809 0.22933501 0.8359954 -0.16953708 0.711695 -0.13198276 1.3160635 0.48212075 -0.83564043\n<pad> 0.9462378 -1.0530922 0.26814827 0.75049055 -0.43643618 0.90060383 0.38048416 0.3394666 -0.6542603 -1.2994871 0.2035602 0.13271607 -0.0821392 0.6386408 -0.53183955 -0.21759015 1.3303281 -0.4926919 1.2892267 0.49860442 1.6501433 -0.5349831 1.7068337 1.1600994 0.4631011 1.0019102 -0.080210954 0.35248953 0.88543874 0.08718851 -0.50338817 -1.4847835 -0.5894625 -1.0142589 -0.37832302 -1.6291661 -0.12362847 1.8569889 0.47709444 0.6944984 1.5645366 -1.643663 -0.4542581 -1.7151413 -0.8393249 0.9062153 -0.047601987 -1.101938 -0.68224543 -0.39662254 0.5475226 -1.2819566 -0.86349916 -0.07766274 -0.27872422 -0.8497833 1.7615329 0.2950122 0.68848085 -0.26785335 0.08160306 0.5527327 -1.1441914 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0.72340196 -0.46958932 -0.3729695 -0.012005955 0.4500639 0.2354974 -0.44309667 -0.30639353 1.7849098 -0.47391504 -0.097441934 -0.87036467 0.4343567 -0.73129076 0.34823084 -0.9211271 -1.4157289 -0.14143807 -0.17118739 0.20365688 0.49579987 -0.28146592 0.17587937 0.73483443 -0.7240221 0.21285006 -1.1389073 -0.872867 -0.808176 0.78133965 0.778077 0.84429437 -0.6242826 -1.6780285 0.89954937 -1.0216842 0.69884956 -0.47699523 -1.1182262 -0.94061846 0.8274227 0.77821773 -0.6390419 -0.03573271 0.24811082 -0.19128142 -0.95383316 1.0210499 0.31598154 0.935698 0.12872082 -0.079226725 -0.68159103 0.47343037 0.5274688 -1.1747869 -0.6254046 1.3211188 -0.6488405 -0.16827887 0.45877635 0.7407617 0.5414452 1.4700226 -0.17359328 0.43262202 0.13622835 -0.04152306 0.7327739 0.38002792 0.44764686 0.7599607 1.3506728 -1.2795128 0.5494145 -0.9258237 -0.10960347 1.3573207 0.87325376\n\u7684 -0.20160268 -0.21241257 -0.4043321 2.1909928 0.49679247 -0.1325275 0.42584014 0.24683614 -0.9702372 -1.8741518 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-0.36281568 0.07450257 0.018646415 -0.7296821 -0.9214585 -0.5921474 -0.30300933 -0.6692921 0.43340573 0.78606945 -0.34040892 0.2503277 0.037954286 0.40688336 1.2049704 0.10686994 1.8918518 1.416152 0.9152621\n\u4e86 -0.11100567 1.8244103 0.99881077 -0.1217553 -1.2965926 2.5752037 -0.52939695 -0.024460727 1.481322 -1.6454383 -0.32412064 -0.812192 -0.6247802 0.5879142 -0.99333626 -0.09178917 1.2610664 0.17786814 -0.09755645 -1.381767 0.3328385 -0.28843904 -0.8976809 0.51493317 -0.2354604 0.7267729 -0.2759223 -0.875182 0.80424625 -0.68562067 0.345338 -0.36612466 0.68599254 -0.69162357 -0.74803936 0.4015563 -0.19871116 0.64606947 1.5207865 -0.5436914 0.7913142 -0.8087305 0.8900298 -1.2114931 0.29318812 0.010917914 0.794381 0.22564566 0.45246655 0.035850927 0.90022266 0.30116358 -0.08802621 0.22181591 -1.3465856 0.45377666 -1.1994032 -1.1599623 -0.90402436 0.4413774 -0.60787785 -0.18609983 -0.28758097 0.86769056 1.070895 0.18053351 1.5689834 -0.825002 -0.1844389 0.8352371 -2.0554366 -1.1158409 -1.1282442 0.3164764 0.81705356 1.3285462 -1.1886504 0.34115657 1.4381564 0.18249284 -2.3909829 -0.7105743 -1.3165226 2.6451735 0.044927556 1.8649187 -0.7606436 1.3267876 -0.8695353 -0.82985395 0.53350765 -1.5758001 0.07124073 0.3113274 -0.31943765 0.19381034 -0.40180263 -1.2200341 -0.46648136 -0.9460392 1.56903 -0.023224937 -0.109002806 -0.122972146 -0.38673204 -0.34158748 0.8986403 -1.3978604 0.30148593 0.10867346 0.5560802 0.6666235 -0.9128663 -0.11801989 -1.6656537 0.470698 1.1878209 0.21174078 -1.3163888 -0.88419724 1.2939 1.0864538 -0.59195155 1.3402531 0.9949826 1.0850434 1.9927737 -0.05526331 -0.9249912 -0.99546725 0.19423905 0.34155425 0.116912715 0.46196705 -0.22920796 0.62712306 0.19182688 2.545757 0.0005199494 0.047344636 1.4041449 -0.4366798 -0.30238223 -0.8592163 -0.1450108 -1.0397685 -1.7507579 0.35720333 -1.0890079 -1.0088387 -2.166981 -0.119711794 -0.894902 -0.34313017 -0.83818877 1.8535222 -0.35545102 1.1037723 -0.30759943 -0.073416024 -0.6099894 -0.082894325 0.5696355 0.02636172 -0.4934832 0.8380404 0.6522451 0.23057912 -0.97358364 -0.20894456 -0.6702974 -1.350783 -1.494697 0.6830102 -0.6583244 1.4899143 1.3386569 0.8388928 0.53304636 0.10571614 -1.6693112 0.34666675 -0.18932551 -1.1207005 -0.13619985 -0.37456858 1.6312733 -1.0417099 0.18789463 0.76180553 0.3472593 0.746827 -0.59724313 1.1076568 0.39835903 0.3250713 -0.890195 0.851977 -1.8276579 -0.5058599 0.038222674 -0.40975145 0.34742656 -0.656784 0.52408326 -0.24545604 0.6244086 1.5422938 -0.11375929 0.4439602 1.194646 0.108786985 -1.2637025 0.30540422 -0.6331694 -0.10961318 -0.269253 0.3094217 0.37967274 0.4198166 -0.7752912 0.93305284 -0.51205474 0.10966317 0.42575598 1.3847312 0.09545336 0.35880265 1.3952161 -0.50513256 0.6978953 -0.42724264 0.61427826 0.42278302 1.3981684 -0.21110083 -1.2695825 0.3271231 -0.18441366 -0.18477333 -1.5454911 0.89642715 -1.0348141 0.9462476 -0.97564644 0.33486953 0.015225394 0.66027576 -0.016567787 0.38754386 0.33223125 -0.01711932 0.81019586 1.8340786 -0.8252963 0.53832585 0.7248964 1.0357465 -1.7218229 0.7785235 -0.059777193 -1.6390696 -0.13061148 1.6316975 0.9055566 0.31447434 -0.050311707 -0.14748432 -1.152082 -0.32038185 -0.20986848 -0.3397427 0.7406922 0.19800813 -0.37535998 0.8428346 -0.74992263 -1.8344332 1.7500505 -0.019035367 -0.17899665 -1.7635467 0.27003205 -0.07612512 -0.0870243 0.9975619 -0.7678579 -0.51703197 -1.0690825 1.4577312 1.6575297 -0.5548624 0.60410196 1.4686142 0.36586297 0.55307645 -0.72146875 -1.1415323 1.8086451 -1.4277877\n\u2026 1.5090982 -0.12030965 -0.49108917 0.553509 0.8591837 0.36316723 0.67062587 -0.35389656 -0.37601423 0.4347592 -0.21231161 -0.0190618 -0.092252515 -0.8948071 -0.35531363 -0.6883051 0.98320943 -1.3508446 1.3872787 0.038260926 1.3760954 -0.96063673 -0.40200645 1.1017556 0.3531388 1.588081 1.8033375 -0.269804 -0.57227546 -0.93687373 0.21478365 -0.3091859 -0.28627992 -0.44842216 0.28986707 0.50668955 -0.12714297 0.40329775 0.2786915 -0.15945554 -0.072181724 -1.1862227 0.43608934 0.5287543 -0.15116562 0.28055522 0.33410925 -0.2543533 0.13481624 0.822775 -0.25385746 -1.9061172 -0.33296132 0.6996039 -0.02980973 0.15660484 0.92750925 0.14352156 0.51745534 0.83292395 -2.6289806 0.19450875 -0.19730678 0.69921744 0.6158621 -0.55871856 -0.8584412 -0.12929948 0.029738523 0.3390188 -1.9727246 -0.78936666 -2.1812136 -1.0392722 2.047485 0.32518044 -0.18576339 1.2273303 -0.1372615 -0.10400256 0.7829299 0.614227 1.2265315 -0.32849684 -1.829169 2.396865 -0.18662089 0.41651145 0.67483145 -0.25038102 0.91003495 -0.623002 0.8042123 -0.29000333 0.5248564 -0.5109571 -1.2964711 -0.68491465 -1.663857 0.89274037 0.38988277 -0.021962767 0.103607625 -0.010340261 -0.24129027 -0.19017185 -0.30158663 -1.6015892 0.15792845 -0.25693515 0.17915845 0.5200748 0.4175489 -0.4977316 -0.6749868 -0.5071436 -0.8346771 -0.88110495 0.025185872 -0.4367816 0.04352713 0.39762473 -0.46531925 -0.43724746 -0.7177102 -0.3682369 0.561147 -0.39390326 0.35378152 0.41432992 0.26307502 0.24112928 0.37295812 0.34381458 0.035766784 -1.3103579 -1.791835 0.022306077 1.1904793 -0.58638793 0.14341566 -0.30051395 0.40474305 1.1653486 -1.51402 -0.8725059 0.31818748 -0.47912052 -0.78443474 -1.8794175 1.0267793 0.5685301 -0.5732909 0.46504852 0.9766738 0.32158154 -0.6603262 0.110638835 0.899724 0.6087294 -0.29793775 0.031041417 -0.29028177 0.95917696 0.029481404 -1.0456511 0.20962428 2.5877905 0.30777544 1.4205157 -0.5609667 1.3714185 -1.6550691 -0.3033392 -1.0271896 0.7320804 1.7060531 -0.20705949 -0.25557384 -0.1358627 0.08981131 0.16696481 -0.23829576 1.0140337 0.6376786 -0.8297083 0.6914956 1.2876294 1.1641147 0.5714225 -0.5951064 -0.40035194 0.05664461 -0.07712284 0.5560351 -0.09779525 -0.16741481 -1.0268891 -0.7811151 0.9260142 -0.3978502 0.50975585 0.2891867 -0.46284804 0.40699214 -0.6509738 -1.0038178 -0.2018522 0.8137981 -0.22531015 1.2800273 -1.543597 0.50349194 -0.65495837 1.5992006 -0.15456946 -0.39201578 1.024163 0.05098209 1.2978015 -0.6115874 -0.09561463 -1.5342172 0.80156255 -1.1532938 -0.21155114 -1.1255947 0.49859214 -0.8305734 0.76576126 1.0035298 -1.2507325 0.034969218 -2.0024865 1.3010553 -0.0128532 -1.8367712 -0.47018003 -1.626798 0.5764391 -0.10373195 0.013681615 0.6368426 -0.27328128 -0.72183806 -1.1314341 -1.4741199 -0.7339853 1.0301492 -0.013149526 -1.26929 -0.5952624 -1.0160192 -0.00892491 0.52773505 0.1007514 0.047913335 -1.2911471 -0.942016 1.6295937 1.5446011 0.4706358 -0.41318798 -0.8102736 1.1772252 -1.1744756 0.39915764 -1.1615202 1.1721202 -0.68552047 -0.7426144 -1.2427216 0.06827004 -2.04893 1.0171161 -0.18418719 -0.17167264 0.01688471 -0.5838595 0.50578314 -0.34972578 0.5766365 0.23884809 -0.1105168 0.0633554 2.8148367 0.26426396 0.51255965 -0.8230475 0.5432819 0.122535184 0.04558672 0.79334635 0.6718906 -0.8498224 0.3440884 0.87352926 0.46972504 0.33059952 0.9709751\n\u5c11\u5973 0.43625292 -0.49615338 -0.80635625 -0.95218086 -0.7034901 -0.24982916 -0.19628817 -0.6407552 1.1144902 -0.18383415 0.240521 -0.48174432 -0.43238354 1.5460479 0.17876568 1.1772094 0.7226882 -0.40163878 -0.27529833 0.6727029 0.6616228 -0.4649058 -0.82819325 0.077967666 0.22773252 0.47824302 1.263238 0.16044211 -0.8465441 -0.03908252 1.5012993 -0.6464531 1.2720991 0.7075523 0.2655532 -0.16548817 0.18449956 -1.57685 -0.3937183 1.1220738 -0.024133356 0.061765857 0.9063283 0.63651776 -0.9984391 1.7219917 -0.16911109 0.3950257 -0.44229308 -0.042594746 -0.18409568 1.5873259 1.5174317 -0.7761925 0.85540444 -0.25851408 -1.7153916 0.21526498 -0.30503598 -1.0740207 -0.26189303 -0.7361771 0.3404945 0.6267011 1.6266515 -0.7249698 -2.5369713 -0.040242277 -0.1521137 -1.2230408 0.105983995 0.24729799 0.66424936 0.7428047 0.8017003 -1.6448103 -1.3638207 0.22201629 -1.6191676 1.7650356 0.8733405 1.2922136 0.15274213 0.56989926 1.3315401 0.3242414 -1.5333288 0.9968748 -0.26946962 -1.7603902 -0.06785398 -1.4466665 -0.2051215 1.0579334 -1.6114388 -0.6311598 -0.42203426 1.2702805 0.049536392 -1.5223664 -1.1988252 -0.005043749 1.2519345 -0.1971786 -0.15249117 -0.09027343 -0.68423694 0.05290712 1.1976366 -0.55118096 0.43239573 -0.3921759 -0.10146248 -0.84204507 -1.5519683 0.41450986 0.08121465 0.70506895 0.36502063 -1.8334937 0.0016303719 0.8337098 0.8315608 2.1455774 0.5289709 -0.15538014 0.18361539 1.5023578 -1.5350997 -0.34078425 -0.17852718 -0.2980528 0.3791773 0.13815962 1.0244071 0.3535323 0.45246312 -1.0676222 0.26734406 -0.7833891 -0.4830871 -0.81460315 0.40275338 -0.4218575 1.2936684 0.36921188 2.0835738 0.29924822 0.607953 0.13717254 1.3793758 -0.17895296 0.25180477 1.0935096 -2.4911408 0.059239827 1.4227028 1.0269765 -0.012899189 0.667102 -1.654389 0.32084498 -1.3945848 0.9240499 0.14125341 -0.40186697 0.46305555 1.4799619 -0.1761365 0.08784475 -0.16064519 -0.32681388 -0.9726041 -0.46950138 -0.10297061 -0.1305801 1.2899957 -0.20281254 1.5096477 0.579964 -2.1962826 -1.0500919 -0.75640684 -0.68146974 -0.61898047 -0.038223892 -0.63422275 -0.40183118 0.71116716 1.7054831 1.2117803 0.5540193 -0.7673277 0.8996408 2.1318727 1.3070806 0.3919676 -0.19198467 0.37812284 -1.5805892 1.622781 0.4241692 0.9778187 0.5272743 0.6481839 -0.91835433 -1.2837874 -1.0056475 -0.03336126 1.7704506 0.59232867 -0.6739266 0.4252755 -0.04943613 1.933676 -1.0441738 0.4929349 -0.5993543 -0.97701305 0.6164371 -1.2127788 1.2599823 -0.5473247 0.93479854 -0.6774386 0.59664416 -0.5358335 -0.6079708 0.7937759 0.5176709 0.2346288 -2.056608 -0.35982183 -0.6090097 0.8602409 0.055992365 -0.6665505 0.6803273 0.8781159 -0.028397428 0.6073012 0.5945208 0.7166259 -0.48727062 0.25150546 0.06475472 -0.33076963 -0.9388699 -0.47334203 1.1939013 0.78029764 0.022830477 -1.198792 1.2806718 0.1218006 0.05305545 1.0819892 -0.8576298 1.796153 -0.05783273 1.4125075 0.22831114 -0.14899425 -0.5525253 -0.8165426 0.043676265 -0.641531 -0.37138024 1.5661736 -1.2548814 0.12986626 -0.9875852 0.4007069 -0.23187949 0.4992489 0.2498534 -1.1453637 1.7015793 -0.91252553 0.07962135 0.7060094 -2.1144807 0.18295327 0.30965614 -0.36403483 0.39038125 -0.580957 0.33897293 -0.1780094 -0.03921564 0.55165535 -0.44981298 0.706237 0.13913499 -0.35856977 -0.20512235 -0.3393937 -1.9689944 2.374302 0.087832846\n```\n\n### cb\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.tg import CB\n\ncb_model = CB()\n\ntrain_path = '/home/lightsmile/NLP/corpus/chatbot/chat.train.sample.tsv'\ndev_path = '/home/lightsmile/NLP/corpus/chatbot/chat.test.sample.tsv'\nvec_path = '/home/lightsmile/NLP/embedding/word/sgns.zhihu.bigram-char'\n\ncb_model.train(train_path, vectors_path=vec_path, dev_path=train_path, save_path='./cb_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\ncb_model.load('./cb_saves')\n\ncb_model.test(train_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nprint(cb_model.predict('\u6211\u8fd8\u559c\u6b22\u5979,\u600e\u4e48\u529e'))\nprint(cb_model.predict('\u600e\u4e48\u4e86'))\nprint(cb_model.predict('\u5f00\u5fc3\u4e00\u70b9'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\u4e3a\uff1a\n\n```bash\n('\u6211\u4f60\u544a\u8bc9\u5979\uff1f\u53d1\u77ed\u4fe1\u8fd8\u662f\u6253\u7535\u8bdd\uff1f', 0.8164891742422521)\n('\u6211\u96be\u8fc7\uff0c\u5b89\u6170\u6211', 0.5596837521537591)\n('\u55ef\u4f1a\u7684', 0.595637918475396)\n```\n\n### mt\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.tg import MT\n\nmt_model = MT()\n\ntrain_path = '/home/lightsmile/NLP/corpus/translation/mt.train.sample.tsv'\ndev_path = '/home/lightsmile/NLP/corpus/translation/mt.test.sample.tsv'\nsource_vec_path = '/home/lightsmile/NLP/embedding/english/glove.6B.100d.txt'\ntarget_vec_path = '/home/lightsmile/NLP/embedding/word/sgns.zhihu.bigram-char'\n\nmt_model.train(train_path, source_vectors_path=source_vec_path, target_vectors_path=target_vec_path,\n               dev_path=train_path, save_path='./mt_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\nmt_model.load('./mt_saves')\n\nmt_model.test(train_path)\n```\n\n#### \u9884\u6d4b\n\n```python\nprint(mt_model.predict('Hello!'))\nprint(mt_model.predict('Wait!'))\n```\n\n\u9884\u6d4b\u7ed3\u679c\u4e3a\uff1a\n\n```bash\n('\u4f60\u597d\u3002', 0.6664615107892047)\n('\uff01', 0.661789059638977)\n```\n\n### ts\n\n#### \u8bad\u7ec3\n\n```python\nfrom lightnlp.tg import TS\n\nts_model = TS()\n\ntrain_path = '/home/lightsmile/NLP/corpus/text_summarization/ts.train.sample.tsv'\ndev_path = '/home/lightsmile/NLP/corpus/text_summarization/ts.test.sample.tsv'\nvec_path = '/home/lightsmile/NLP/embedding/word/sgns.zhihu.bigram-char'\n\nts_model.train(train_path, vectors_path=vec_path, dev_path=train_path, save_path='./ts_saves')\n```\n\n#### \u6d4b\u8bd5\n\n```python\nts_model.load('./ts_saves')\n\nts_model.test(train_path)\n```\n\n#### \u9884\u6d4b\n\n```python\ntest_str = \"\"\"\n            \u8fd1\u65e5\uff0c\u56e0\u5929\u6c14\u592a\u70ed\uff0c\u5b89\u5fbd\u4e00\u8001\u592a\u5728\u4e70\u8089\u8def\u4e0a\u7a81\u7136\u773c\u524d\u4e00\u9ed1\uff0c\u6454\u5012\u5728\u5730\u3002\u5979\u6015\u522b\u4eba\u4e0d\u6276\u5979\uff0c\u8fde\u5fd9\u8bf4\"\u5feb\u6276\u6211\u8d77\u6765\uff0c\u6211\u4e0d\u8bb9\u4f60\uff0c\u5730\u4e0a\u592a\u70ed\u6211\u8981\u719f\u4e86\uff01\"\u8fd9\u4e00\u558a\u5468\u56f4\u4eba\u90fd\u7b11\u4e86\uff0c\u8001\u4eba\u968f\u540e\u88ab\u6276\u5230\u8def\u8fb9\u4f11\u606f\u3002(\u988d\u5dde\u665a\u62a5)[\u8bdd\u7b52]\u6700\u8fd1\u8001\u4eba\u5c3d\u91cf\u907f\u514d\u51fa\u95e8!\n            \"\"\"\n\nprint(ts_model.predict(test_str))\n```\n\n\u9884\u6d4b\u7ed3\u679c\u4e3a\uff1a\n\n```bash\n('\uff0c\u6211\u4e0d\u8bb9\u4f60\uff0c\u5730\u4e0a\u592a\u70ed\u6211\u8981\u719f\u4e86\uff01[\u5141\u60b2]', 0.03261186463844203)\n```\n\n### \u8bcd\u5411\u91cf\n\n\u672c\u6846\u67b6\u4e5f\u63d0\u4f9b\u4e86\u7c7b\u4f3cgensim\u7684\u52a0\u8f7d\u8bcd\u5411\u91cf\u5e76\u5f97\u5230\u76f8\u4f3c\u8bcd\u6c47\u7684\u529f\u80fd\uff0c\u4f7f\u7528\u793a\u4f8b\u5982\u4e0b\uff1a\n\n```python\nfrom lightnlp.utils.word_vector import WordVectors\n\nvector_path = '/home/lightsmile/Projects/MyGithub/lightNLP/examples/cbow_saves/cbow_base.bin'\nword_vectors = WordVectors(vector_path)\nprint(word_vectors.get_similar_words('\u5c11\u5973', dis_type='cos'))\n```\n\n\u8f93\u51fa\u7ed3\u679c\u4e3a\uff1a\n\n```bash\n[('\u5c11\u5973', 0.9999998807907104), ('\u5632\u7b11', 0.17718511819839478), ('\u540c\u9f84\u4eba', 0.17244181036949158)]\n```\n## \u9879\u76ee\u7ec4\u7ec7\u7ed3\u6784\n\n### \u9879\u76ee\u67b6\u6784\n- base\n    - config.py\n    - model.py\n    - module.py\n    - tool.py\n- sl\uff0c\u5e8f\u5217\u6807\u6ce8\n    - ner\uff0c\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\n    - cws\uff0c\u4e2d\u6587\u5206\u8bcd\n    - pos\uff0c\u8bcd\u6027\u6807\u6ce8\n    - srl\uff0c\u8bed\u4e49\u89d2\u8272\u6807\u6ce8\n- sp\uff0c\u7ed3\u6784\u5206\u6790\n    - tdp\uff0c\u57fa\u4e8e\u8f6c\u79fb\u7684\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\n    - gdp\uff0c\u57fa\u4e8e\u56fe\u7684\u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\n- sr\uff0c\u53e5\u5b50\u5173\u7cfb\n    - ss\uff0c\u53e5\u5b50\u76f8\u4f3c\u5ea6\n    - te\uff0c\u6587\u672c\u8574\u542b\n- tc\uff0c\u6587\u672c\u5206\u7c7b\n    - re, \u5173\u7cfb\u62bd\u53d6\n    - sa\uff0c\u60c5\u611f\u5206\u6790\n- tg\uff0c\u6587\u672c\u751f\u6210\n    - cb, \u804a\u5929\u673a\u5668\u4eba\n    - lm\uff0c\u8bed\u8a00\u6a21\u578b\n    - mt\uff0c\u673a\u5668\u7ffb\u8bd1\n    - ts\uff0c\u6587\u672c\u6458\u8981\n- utils\n- we\uff0c\u8bcd\u5411\u91cf\n    - cbow\uff0c \u8bcd\u888b\u6a21\u578b\n    - skip_gram\uff0c\u8df3\u5b57\u6a21\u578b\n\n### \u67b6\u6784\u8bf4\u660e\n#### base\u76ee\u5f55\n\u653e\u4e00\u4e9b\u57fa\u7840\u7684\u6a21\u5757\u5b9e\u73b0\uff0c\u5176\u4ed6\u7684\u9ad8\u5c42\u4e1a\u52a1\u6a21\u578b\u4ee5\u53ca\u76f8\u5173\u8bad\u7ec3\u4ee3\u7801\u90fd\u4ece\u6b64module\u7ee7\u627f\u76f8\u5e94\u7236\u7c7b\u3002\n##### config\n\u5b58\u653e\u6a21\u578b\u8bad\u7ec3\u76f8\u5173\u7684\u8d85\u53c2\u6570\u7b49\u914d\u7f6e\u4fe1\u606f\n##### model\n\u6a21\u578b\u7684\u5b9e\u73b0\u62bd\u8c61\u57fa\u7c7b\uff0c\u5305\u542b`base.model.BaseConfig`\u548c`base.model.BaseModel`\uff0c\u5305\u542b`load`\u3001`save`\u7b49\u65b9\u6cd5\n##### module\n\u4e1a\u52a1\u6a21\u5757\u7684\u8bad\u7ec3\u9a8c\u8bc1\u6d4b\u8bd5\u7b49\u5b9e\u73b0\u62bd\u8c61\u57fa\u7c7b\uff0c\u5305\u542b`base.module.Module`\uff0c\u5305\u542b`train`\u3001`load`\u3001`_validate`\u3001`test`\u7b49\u65b9\u6cd5\n##### tool\n\u4e1a\u52a1\u6a21\u5757\u7684\u6570\u636e\u5904\u7406\u62bd\u8c61\u57fa\u7c7b\uff0c\u5305\u542b`base.tool.Tool`\uff0c\u5305\u542b`get_dataset`\u3001`get_vectors`\u3001`get_vocab`\u3001`get_iterator`\u3001`get_score`\u7b49\u65b9\u6cd5\n#### util\u76ee\u5f55\n\u653e\u4e00\u4e9b\u901a\u7528\u7684\u65b9\u6cd5\n\n## todo\n\n### \u4e1a\u52a1\n\n### \u5de5\u7a0b\n\n- [ ] \u589e\u52a0\u65ad\u70b9\u91cd\u8bad\u529f\u80fd\u3002\n- [ ] \u589e\u52a0earlyStopping\u3002\n- [x] \u91cd\u6784\u9879\u76ee\u7ed3\u6784\uff0c\u5c06\u76f8\u540c\u5197\u4f59\u7684\u5730\u65b9\u5408\u5e76\u8d77\u6765\uff0c\u4fdd\u6301\u9879\u76ee\u7ed3\u6784\u6e05\u6670\n- [ ] \u73b0\u5728\u6a21\u578b\u4fdd\u5b58\u7684\u8def\u5f84\u548c\u540d\u5b57\u9ed8\u8ba4\u4e00\u81f4\uff0c\u4f1a\u51b2\u7a81\uff0c\u63a5\u4e0b\u6765\u6bcf\u4e2a\u6a21\u578b\u90fd\u6709\u81ea\u5df1\u7684`name`\u3002\n\n### \u529f\u80fd\n\n- [x] \u589e\u52a0CBOW\u8bcd\u5411\u91cf\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0skip_gram\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [ ] \u589e\u52a0Elmo\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [ ] \u589e\u52a0GloVe\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [ ] \u589e\u52a0GPT\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [ ] \u589e\u52a0Bert\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u60c5\u611f\u5206\u6790\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u6587\u672c\u8574\u542b\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u6587\u672c\u751f\u6210\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u8bed\u8a00\u6a21\u578b\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u4f9d\u5b58\u5206\u6790\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u5173\u7cfb\u62bd\u53d6\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u4e2d\u6587\u5206\u8bcd\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u8bcd\u6027\u6807\u6ce8\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u4e8b\u4ef6\u62bd\u53d6\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [ ] \u589e\u52a0\u5c5e\u6027\u62bd\u53d6\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [ ] \u589e\u52a0\u6307\u4ee3\u6d88\u89e3\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u81ea\u52a8\u6458\u8981\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801 \n- [x] \u589e\u52a0\u673a\u5668\u7ffb\u8bd1\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801 \n- [ ] \u589e\u52a0\u9605\u8bfb\u7406\u89e3\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u53e5\u5b50\u76f8\u4f3c\u5ea6\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u5e8f\u5217\u5230\u5e8f\u5217\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [ ] \u589e\u52a0\u5173\u952e\u8bcd\u62bd\u53d6\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u804a\u5929\u673a\u5668\u4eba\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u8bad\u7ec3\u9884\u6d4b\u4ee3\u7801\n- [x] \u589e\u52a0\u547d\u540d\u5b9e\u4f53\u8bc6\u522b\u76f8\u5173\u6a21\u578b\u4ee5\u53ca\u9884\u6d4b\u8bad\u7ec3\u4ee3\u7801\n\n## \u53c2\u8003\n\n### Deep Learning\n\n- [What's the difference between \u201chidden\u201d and \u201coutput\u201d in PyTorch LSTM?](https://stackoverflow.com/questions/48302810/whats-the-difference-between-hidden-and-output-in-pytorch-lstm)\n- [What's the difference between LSTM() and LSTMCell()?](https://stackoverflow.com/questions/48187283/whats-the-difference-between-lstm-and-lstmcell)\n- [What is the difference between Luong Attention and Bahdanau Attention?](https://stackoverflow.com/questions/44238154/what-is-the-difference-between-luong-attention-and-bahdanau-attention)\n- [\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6\u6280\u672f\u5256\u6790[\u8f6c]](https://aiuai.cn/aifarm904.html)\n- [Attention? Attention!](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html)\n\n### NLP\n\n- [\u57fa\u4e8e\u8868\u793a\u5b66\u4e60\u7684\u4fe1\u606f\u62bd\u53d6\u65b9\u6cd5\u6d45\u6790](https://www.jiqizhixin.com/articles/2016-11-15-5)\n- [\u77e5\u8bc6\u62bd\u53d6-\u5b9e\u4f53\u53ca\u5173\u7cfb\u62bd\u53d6](http://www.shuang0420.com/2018/09/15/%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E5%AE%9E%E4%BD%93%E5%8F%8A%E5%85%B3%E7%B3%BB%E6%8A%BD%E5%8F%96/)\n- [\u77e5\u8bc6\u62bd\u53d6-\u4e8b\u4ef6\u62bd\u53d6](http://www.shuang0420.com/2018/10/15/%E7%9F%A5%E8%AF%86%E6%8A%BD%E5%8F%96-%E4%BA%8B%E4%BB%B6%E6%8A%BD%E5%8F%96/)\n\n\n### \u77e5\u8bc6\u56fe\u8c31\n\n- [\u7ffb\u8bd1\u6a21\u578b(Trans\u7cfb\u5217)\u7684\u77e5\u8bc6\u8868\u793a\u5b66\u4e60](https://mp.weixin.qq.com/s/STflo3c8nyG6iHh9dEeKOQ)\n- [\u77e5\u8bc6\u56fe\u8c31\u5411\u91cf\u5316\u8868\u793a](https://zhuanlan.zhihu.com/p/30320631)\n\n### Pytorch\u6559\u7a0b\n\n- [PyTorch \u5e38\u7528\u65b9\u6cd5\u603b\u7ed34\uff1a\u5f20\u91cf\u7ef4\u5ea6\u64cd\u4f5c\uff08\u62fc\u63a5\u3001\u7ef4\u5ea6\u6269\u5c55\u3001\u538b\u7f29\u3001\u8f6c\u7f6e\u3001\u91cd\u590d\u2026\u2026\uff09](https://zhuanlan.zhihu.com/p/31495102)\n- [Pytorch\u4e2d\u7684RNN\u4e4bpack_padded_sequence()\u548cpad_packed_sequence()](https://www.cnblogs.com/sbj123456789/p/9834018.html)\n- [pytorch\u5b66\u4e60\u7b14\u8bb0\uff08\u4e8c\uff09\uff1agradient](https://blog.csdn.net/u012436149/article/details/54645162)\n- [torch.multinomial()\u7406\u89e3](https://blog.csdn.net/monchin/article/details/79787621)\n- [Pytorch \u7ec6\u8282\u8bb0\u5f55](https://www.cnblogs.com/king-lps/p/8570021.html)\n- [What does flatten_parameters() do?](https://stackoverflow.com/questions/53231571/what-does-flatten-parameters-do)\n- [\u5173\u4e8ePytorch\u7684\u4e8c\u7ef4tensor\u7684gather\u548cscatter_\u64cd\u4f5c\u7528\u6cd5\u5206\u6790](https://www.cnblogs.com/HongjianChen/p/9450987.html)\n- [Pytorch scatter_ \u7406\u89e3\u8f74\u7684\u542b\u4e49](https://blog.csdn.net/qq_16234613/article/details/79827006)\n- [\u2018model.eval()\u2019 vs \u2018with torch.no_grad()\u2019](https://discuss.pytorch.org/t/model-eval-vs-with-torch-no-grad/19615)\n- [\u5230\u5e95\u4ec0\u4e48\u662f\u751f\u6210\u5f0f\u5bf9\u6297\u7f51\u7edcGAN\uff1f](https://www.msra.cn/zh-cn/news/features/gan-20170511)\n\n### torchtext\u4ecb\u7ecd\n\n- [torchtext](https://github.com/pytorch/text)\n- [A Tutorial on Torchtext](http://anie.me/On-Torchtext/)\n- [Torchtext \u8be6\u7ec6\u4ecb\u7ecd](https://zhuanlan.zhihu.com/p/37223078)\n- [torchtext\u5165\u95e8\u6559\u7a0b\uff0c\u8f7b\u677e\u73a9\u8f6c\u6587\u672c\u6570\u636e\u5904\u7406](https://zhuanlan.zhihu.com/p/31139113)\n\n### \u5176\u4ed6\u5de5\u5177\u6a21\u5757\n\n- [python\u7684Tqdm\u6a21\u5757](https://blog.csdn.net/langb2014/article/details/54798823)\n- [pytorch-crf](https://github.com/kmkurn/pytorch-crf)\n\n### \u8bcd\u5411\u91cf\n\n- [ChineseEmbedding](https://github.com/liuhuanyong/ChineseEmbedding)\n- [pytorch_word2vec](https://github.com/weberrr/pytorch_word2vec)\n- [pytorch-word-embedding](https://github.com/jeffchy/pytorch-word-embedding)\n\n### \u6570\u636e\u96c6\n\n- [Chinese-Literature-NER-RE-Dataset](https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset)\n- [ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference)\n- [\u4e2d\u6587\u77ed\u6587\u672c\u6458\u8981\u6570\u636e\u96c6](https://www.jianshu.com/p/8f52352f0748)\n- [\u673a\u5668\u7ffb\u8bd1\u8bed\u6599\u5e93\u5927\u5168(\u514d\u8d39\u4e0b)](https://mlln.cn/2018/08/26/%E6%9C%BA%E5%99%A8%E7%BF%BB%E8%AF%91%E8%AF%AD%E6%96%99%E5%BA%93%E5%A4%A7%E5%85%A8(%E5%85%8D%E8%B4%B9%E4%B8%8B)/)\n- [\u4e2d\u6587\u516c\u5f00\u804a\u5929\u8bed\u6599\u5e93](https://github.com/codemayq/chinese_chatbot_corpus)\n\n### \u5e8f\u5217\u6807\u6ce8\n\n- [a-PyTorch-Tutorial-to-Sequence-Labeling](https://github.com/sgrvinod/a-PyTorch-Tutorial-to-Sequence-Labeling)\n- [sequence_tagging](https://github.com/AdolHong/sequence_tagging)\n\n### \u6587\u672c\u5206\u7c7b\n\n- [chinese_text_cnn](https://github.com/bigboNed3/chinese_text_cnn)\n\n### \u547d\u540d\u5b9e\u4f53\u8bc6\u522b\n\n- [sequence_tagging](https://github.com/AdolHong/sequence_tagging)\n\n### \u5173\u7cfb\u62bd\u53d6\n\n- [ChineseNRE](https://github.com/buppt/ChineseNRE)\n- [pytorch-pcnn](https://github.com/ShomyLiu/pytorch-pcnn)\n- [\u5173\u7cfb\u62bd\u53d6(\u5206\u7c7b)\u603b\u7ed3](http://shomy.top/2018/02/28/relation-extraction/)\n\n### \u4e8b\u4ef6\u62bd\u53d6\n\n\u8fd9\u91cc\u76ee\u524d\u7c97\u6d45\u7684\u5c06\u8bed\u4e49\u89d2\u8272\u6807\u6ce8\u6280\u672f\u5b9e\u73b0\u7b49\u540c\u4e8e\u4e8b\u4ef6\u62bd\u53d6\u4efb\u52a1\u3002\n\n- [\u8bed\u4e49\u89d2\u8272\u6807\u6ce8](http://wiki.jikexueyuan.com/project/deep-learning/wordSence-identify.html)\n- [iobes_iob \u4e0e iob_ranges \u51fd\u6570\u501f\u9274](https://github.com/glample/tagger/blob/master/utils.py)\n- [BiRNN-SRL](https://github.com/zxplkyy/BiRNN-SRL)\n- [chinese_semantic_role_labeling](https://github.com/Nrgeup/chinese_semantic_role_labeling)\n\n### \u8bed\u8a00\u6a21\u578b\n\n- [char-rnn.pytorch](https://github.com/spro/char-rnn.pytorch)\n- [Simple Word-based Language Model in PyTorch](https://github.com/deeplearningathome/pytorch-language-model)\n- [PyTorch \u4e2d\u7ea7\u7bc7\uff085\uff09\uff1a\u8bed\u8a00\u6a21\u578b\uff08Language Model (RNN-LM)\uff09](https://shenxiaohai.me/2018/10/20/pytorch-tutorial-intermediate-05/)\n\n### \u6587\u672c\u751f\u6210\n\n- [\u597d\u73a9\u7684\u6587\u672c\u751f\u6210](https://www.msra.cn/zh-cn/news/features/ruihua-song-20161226)\n- [\u57fa\u4e8e\u6df1\u5ea6\u5b66\u4e60\u7684\u6587\u672c\u751f\u6210\u8fc7\u7a0b](https://puke3615.github.io/2018/08/10/ML-Text-Generator/)\n\n### \u8bed\u53e5\u76f8\u4f3c\u5ea6\n\n- [siamese_lstm](https://github.com/WEAINE/siamese_lstm)\n- [sentence-similarity](https://github.com/yanqiangmiffy/sentence-similarity)\n\n\n### \u6587\u672c\u8574\u542b\n\n- [ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference)\n\n### \u4e2d\u6587\u5206\u8bcd\n- [\u4e2d\u6587\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e2d\u6587\u5206\u8bcd\u8bad\u7ec3\u8bed\u6599](https://download.csdn.net/download/qq_36330643/10514771)\n- [\u4e2d\u6587\u5206\u8bcd\u3001\u8bcd\u6027\u6807\u6ce8\u8054\u5408\u6a21\u578b](https://zhuanlan.zhihu.com/p/56988686)\n- [pytorch_Joint-Word-Segmentation-and-POS-Tagging](https://github.com/bamtercelboo/pytorch_Joint-Word-Segmentation-and-POS-Tagging)\n\n### \u8bcd\u6027\u6807\u6ce8\n\n- [\u5e38\u89c1\u4e2d\u6587\u8bcd\u6027\u6807\u6ce8\u96c6\u6574\u7406](https://blog.csdn.net/qq_41853758/article/details/82924325)\n- [\u5206\u8bcd\uff1a\u8bcd\u6027\u6807\u6ce8\u5317\u5927\u6807\u51c6](https://blog.csdn.net/zhoubl668/article/details/6942251)\n- [ICTCLAS \u6c49\u8bed\u8bcd\u6027\u6807\u6ce8\u96c6 \u4e2d\u79d1\u9662](https://blog.csdn.net/memray/article/details/14105643)\n- [\u4e2d\u6587\u6587\u672c\u8bed\u6599\u5e93\u6574\u7406](https://www.jianshu.com/p/206caa232ded)\n- [\u4e2d\u6587\u5206\u8bcd\u3001\u8bcd\u6027\u6807\u6ce8\u8054\u5408\u6a21\u578b](https://zhuanlan.zhihu.com/p/56988686)\n- [pytorch_Joint-Word-Segmentation-and-POS-Tagging](https://github.com/bamtercelboo/pytorch_Joint-Word-Segmentation-and-POS-Tagging)\n\n### \u6307\u4ee3\u6d88\u89e3\n\n- [AllenNLP\u7cfb\u5217\u6587\u7ae0\u4e4b\u56db\uff1a\u6307\u4ee3\u6d88\u89e3](https://blog.csdn.net/sparkexpert/article/details/79868335)\n\n### \u4f9d\u5b58\u53e5\u6cd5\u5206\u6790\n\n- [\u6c49\u8bed\u6811\u5e93](http://www.hankcs.com/nlp/corpus/chinese-treebank.html#h3-6)\n- [Deep Biaffine Attention for Neural Dependency Parsing](https://arxiv.org/abs/1611.01734)\n- [\u4e2d\u6587\u53e5\u6cd5\u7ed3\u6784](https://xiaoxiaoaurora.github.io/2018/07/03/%E4%B8%AD%E6%96%87%E5%8F%A5%E6%B3%95%E7%BB%93%E6%9E%84/)\n- [\u53e5\u6cd5\u5206\u6790\u4e4b\u4f9d\u5b58\u53e5\u6cd5](https://nlpcs.com/article/syntactic-parsing-by-dependency)\n- [Deep Biaffine Attention for Neural Dependency Parsing, hankcs\u7b80\u8981\u89e3\u8bfb](http://www.hankcs.com/nlp/parsing/deep-biaffine-attention-for-neural-dependency-parsing.html)\n- [Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations](https://www.transacl.org/ojs/index.php/tacl/article/viewFile/885/198)\n- [biaffine-parser](https://github.com/zysite/biaffine-parser)\n- [DeepDependencyParsingProblemSet](https://github.com/rguthrie3/DeepDependencyParsingProblemSet)\n\n### \u81ea\u52a8\u6458\u8981\n\n- [\u5e72\u8d27\uff5c\u5f53\u6df1\u5ea6\u5b66\u4e60\u9047\u89c1\u81ea\u52a8\u6587\u672c\u6458\u8981\uff0cseq2seq+attention](https://blog.csdn.net/Mbx8X9u/article/details/80491214)\n\n### \u9605\u8bfb\u7406\u89e3\n\n- [ASReader\uff1a\u4e00\u4e2a\u7ecf\u5178\u7684\u673a\u5668\u9605\u8bfb\u7406\u89e3\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b](https://www.imooc.com/article/28709)\n\n### \u804a\u5929\u673a\u5668\u4eba\n\n- [ywk991112 / pytorch-chatbot](https://github.com/ywk991112/pytorch-chatbot)\n- [keon / seq2seq](https://github.com/keon/seq2seq)\n- [bentrevett / pytorch-seq2seq](https://github.com/bentrevett/pytorch-seq2seq)\n- [IBM / pytorch-seq2seq](https://github.com/IBM/pytorch-seq2seq)\n\n### \u5176\u4ed6\n\n- [\u57fa\u4e8e\u8ddd\u79bb\u7684\u7b97\u6cd5 \u66fc\u54c8\u987f\uff0c\u6b27\u6c0f\u7b49](https://www.jianshu.com/p/bbe6dfac9bc7)\n- [\u5728\u5206\u7c7b\u4e2d\u5982\u4f55\u5904\u7406\u8bad\u7ec3\u96c6\u4e2d\u4e0d\u5e73\u8861\u95ee\u9898](https://blog.csdn.net/heyongluoyao8/article/details/49408131)\n- [Python-Pandas \u5982\u4f55shuffle\uff08\u6253\u4e71\uff09\u6570\u636e\uff1f](https://blog.csdn.net/qq_22238533/article/details/70917102)\n- [Python DataFrame \u5982\u4f55\u5220\u9664\u539f\u6765\u7684\u7d22\u5f15\uff0c\u91cd\u65b0\u5efa\u7acb\u7d22\u5f15](https://www.cnblogs.com/xubing-613/p/6119162.html)\n- [Pandas\u5728\u8bfb\u53d6csv\u65f6\u5982\u4f55\u8bbe\u7f6e\u5217\u540d--\u5e38\u7528\u65b9\u6cd5\u96c6\u9526](https://zhuanlan.zhihu.com/p/44503744)\n- [Python\u4e2d__repr__\u548c__str__\u533a\u522b](https://blog.csdn.net/luckytanggu/article/details/53649156)\n- [Python3:ImportError: No module named 'compiler.ast'](https://blog.csdn.net/w5688414/article/details/78489277)\n- [Automated Python 2 to 3 code translation](https://docs.python.org/2/library/2to3.html)\n- [git \u62c9\u53d6\u8fdc\u7a0b\u5206\u652f\u5230\u672c\u5730](https://blog.csdn.net/carfge/article/details/79691360)\n- [OpenCC - \u7b80\u4f53\u7e41\u4f53\u8f6c\u6362](https://www.jianshu.com/p/834a02d085b6)\n\n\n",
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