{
    "info": {
        "author": "yilei.wang",
        "author_email": "stevewyl@163.com",
        "bugtrack_url": null,
        "classifiers": [
            "Programming Language :: Java",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "# Chunk\u5206\u8bcd\u5668\u4f7f\u7528\u6307\u5357\n\n\u73af\u5883\u4f9d\u8d56\uff1apython 3.6 (\u6682\u65f6\u53ea\u652f\u6301python3)\n\n## \u4e3b\u8981\u529f\u80fd\n\n1. \u80fd\u591f\u8f93\u51fa\u540d\u8bcd\u77ed\u8bed\n2. \u652f\u6301\u8bcd\u6027\u8f93\u51fa\uff0c\u540d\u8bcd\u77ed\u8bed\u8bcd\u6027\u4e3anp\n3. \u652f\u6301\u540d\u8bcd\u77ed\u8bed\u4ee5\u9650\u5b9a\u8bcd+\u4e2d\u5fc3\u8bcd\u7684\u5f62\u5f0f\u8f93\u51fa\n\n>\u4e0d\u53ef\u5206\u5272\u7684\u540d\u8bcd\u77ed\u8bed\u662f\u4e0d\u5b58\u5728\u9650\u5b9a\u8bcd+\u4e2d\u5fc3\u8bcd\u7684\u5f62\u5f0f\u7684\uff0c\u5982\u201c\u673a\u5668\u5b66\u4e60\u201d\uff0c\u800c\u201c\u7ecf\u5178\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u201d\u53ef\u62c6\u89e3\u4e3a\u201c\u7ecf\u5178_\u673a\u5668\u5b66\u4e60_\u7b97\u6cd5\u201d\n\n## Step 1 \u5b89\u88c5\u8f6f\u4ef6\u5305\n\n\u63a8\u8350\u65b0\u5efa\u4e00\u4e2apython\u7684\u865a\u62df\u73af\u5883\uff08\u53ef\u8df3\u8fc7\uff09\n\n```bash\nconda create --name chunk_seg python=3.6.5\n```\n\n### pip\u5b89\u88c5\n\n```bash\npip install git+https://www.github.com/keras-team/keras-contrib.git\npip install chunk-segmentor\n```\n\n### \u624b\u52a8\u5b89\u88c5\n\n```bash\ngit clone https://github.com/stevewyl/chunk_segmentor\ncd chunk_segmentor\npip install -r requirements.txt\npython setup.py install\n```\n\n### \u989d\u5916\u5b89\u88c5\n```bash\n# \u82e5\u4f60\u7684\u673a\u5668\u5b89\u88c5\u6709GPU\uff0c\u5229\u7528GPU\u52a0\u901f\u9884\u6d4b\u901f\u5ea6\npip install tensorflow-gpu==1.9.0\n```\n### \u5b89\u88c5\u9519\u8bef\n1. ImportError: cannot import name 'normalize_data_format'\n```bash\npip install -U keras\n```\n\n## Step 2 \u5982\u4f55\u4f7f\u7528\n\n* \u7b2c\u4e00\u6b21import\u7684\u65f6\u5019\uff0c\u4f1a\u81ea\u52a8\u4e0b\u8f7d\u6a21\u578b\u548c\u5b57\u5178\u6570\u636e  \n* \u652f\u6301\u5355\u53e5\u548c\u591a\u53e5\u6587\u672c\u7684\u8f93\u5165\u683c\u5f0f\uff0c\u5efa\u8bae\u4ee5\u5217\u8868\u7684\u5f62\u5f0f\u4f20\u5165\u5206\u8bcd\u5668\n\n```python\nfrom chunk_segmentor import Chunk_Segmentor\ncutter = Chunk_Segmentor()\ns = '\u8fd9\u662f\u4e00\u4e2a\u80fd\u591f\u8f93\u51fa\u540d\u8bcd\u77ed\u8bed\u7684\u5206\u8bcd\u5668\uff0c\u6b22\u8fce\u8bd5\u7528\uff01'\nres = [item for item in cutter.cut([s] * 10000)] # 1080ti\u4e0a\u8017\u65f612s\n\n# \u63d0\u4f9b\u4e24\u4e2a\u7248\u672c\uff0caccurate\u4e3a\u7cbe\u786e\u7248\uff0cfast\u4e3a\u5feb\u901f\u7248\u4f46\u53ec\u56de\u4f1a\u964d\u4f4e\u4e00\u4e9b\uff0c\u9ed8\u8ba4\u7cbe\u786e\u7248\ncutter = Chunk_Segmentor(mode='accurate')\ncutter = Chunk_Segmentor(mode='fast')\n# \u9650\u5b9a\u8bcd+\u4e2d\u5fc3\u8bcd\u7684\u5f62\u5f0f, \u9ed8\u8ba4\u5f00\u542f\ncutter.cut(s, qualifier=False)\n# \u662f\u5426\u8f93\u51fa\u8bcd\u6027\uff0c \u9ed8\u8ba4\u5f00\u542f\ncutter.cut(s, pos=False)\n\n# \u8f93\u51fa\u683c\u5f0f\uff08\u8bcd\u5217\u8868\uff0c\u8bcd\u6027\u5217\u8868\uff0cchunk\u96c6\u5408\uff09\n[\n    (\n        ['\u8fd9', '\u662f', '\u4e00\u4e2a', '\u80fd\u591f', '\u8f93\u51fa', '\u540d\u8bcd_\u77ed\u8bed', '\u7684', '\u5206\u8bcd\u5668', ',', '\u6b22\u8fce', '\u8bd5\u7528', '!'],\n        ['rzv', 'vshi', 'mq', 'v', 'vn', 'np', 'ude1', 'np', 'w', 'v', 'v', 'w'],\n        ['\u5206\u8bcd\u5668', '\u540d\u8bcd_\u77ed\u8bed']\n    )\n    ...\n]\n```\n\n## Step 3 \u540e\u7eed\u66f4\u65b0\n\n\u82e5\u5b58\u5728\u65b0\u7684\u6a21\u578b\u548c\u5b57\u5178\u6570\u636e\uff0c\u4f1a\u63d0\u793a\u4f60\u662f\u5426\u9700\u8981\u66f4\u65b0\n\n## To-Do Lists\n\n1. \u63d0\u5347\u9650\u5b9a\u8bcd\u548c\u540d\u8bcd\u77ed\u8bed\u7684\u51c6\u786e\u6027 ---> \u65b0\u7684\u6a21\u578b\n2. char\u6a21\u578b\u5b58\u5728GPU\u8c03\u7528\u5185\u5b58\u6ea2\u51fa\u7684\u95ee\u9898 ---> \u4f7f\u7528cnn\u63d0\u53d6Nchar\u4fe1\u606f\u6765\u4ee3\u66ffembedding\u7684\u65b9\u5f0f\uff0c\u7f29\u5c0f\u6a21\u578b\u89c4\u6a21\n3. \u81ea\u5b9a\u4e49\u5b57\u5178\uff0c\u652f\u6301\u4e0d\u540c\u7c92\u5ea6\u7684\u5207\u5206\n4. \u591a\u8fdb\u7a0b\u6a21\u578b\u52a0\u8f7d\u548c\u9884\u6d4b",
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