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        "description": "# Pywordseg\n\u57fa\u65bc BiLSTM \u53ca ELMo \u7684 State-of-the-art \u958b\u6e90\u4e2d\u6587\u65b7\u8a5e\u7cfb\u7d71\u3002  \nAn open source state-of-the-art Chinese word segmentation system with BiLSTM and ELMo.  \n\n- arXiv paper link: https://arxiv.org/abs/1901.05816\n- PyPI page: https://pypi.org/project/pywordseg/\n\n## Performance\n![](https://i.imgur.com/4WflkYS.png)\n- \u6b64\u5c08\u6848\u63d0\u4f9b\u5716\u4e2d\u7684 \"character level ELMo\" model \u4ee5\u53ca \"baseline\" model\uff0c\u5176\u4e2d \"character level ELMo\" model \u662f\u7576\u524d\u6e96\u78ba\u7387\u6700\u9ad8\u3002\u9019\u5169\u500b model \u90fd\u8d0f\u904e\u76ee\u524d\u5e38\u7528\u7684\u65b7\u8a5e\u7cfb\u7d71 [Jieba](https://github.com/fxsjy/jieba) (HMM-based) \u53ca [CKIP](http://ckipsvr.iis.sinica.edu.tw/) (rule-based) \u8a31\u591a\u3002  \n- This repo provides the \"character level ELMo\" model and \"baseline\" model in the figure. Our \"character level ELMo\" model outperforms the previous state-of-the-art Chinese word segmentation (Ma et al. 2018), and also largely outerform \"[Jieba](https://github.com/fxsjy/jieba)\" and \"[CKIP](http://ckipsvr.iis.sinica.edu.tw/)\", which are most popular toolkits in processing simplified/traditional Chinese text.\n\n![](https://i.imgur.com/0vCz0ui.png)\n- \u7576\u8655\u7406\u8a13\u7df4\u6642\u672a\u898b\u904e\u7684\u8a5e\u6642\uff0c\"character level ELMo\" model \u4ecd\u7136\u4fdd\u6709\u4e0d\u932f\u7684\u6b63\u78ba\u7387\uff0c\u76f8\u8f03\u65bc\"baseline\" model\u3002  \n- When considering OOV accuracy, our \"character level ELMo\" model outperforms our \"baseline\" model about 5%.\n\n## Usage\n### Requirements\n- python >= 3.6 (do not use 3.5)\n- pytorch 0.4\n- overrides\n\n### Install with Pip\n  - `$ pip install pywordseg`\n  - the module will automatically download the models while your first import within 1 minute.\n  - if you use MacOS and encounter the [urllib.error.URLError](https://stackoverflow.com/questions/49183801/ssl-certificate-verify-failed-with-urllib) problem when downloading your models,  \n  try `$ sudo /Applications/Python\\ 3.6/Install\\ Certificates.command` to bypass the certificate issue.\n\n### Install manually\n  - `$ git clone https://github.com/voidism/pywordseg`\n  - download [ELMoForManyLangs.zip](https://www.dropbox.com/s/eiya6ztmjopprsm/ELMoForManyLangs.zip?dl=0) and unzip it to the `pywordseg/pywordseg` (the code of the ELMo model is from [HIT-SCIR](https://github.com/HIT-SCIR/ELMoForManyLangs), training by myself in character-level)\n  - `$ pip install .` under the main directory\n\n### Segment!\n  ```python\n  # import the module\n  from pywordseg import *\n\n  # declare the segmentor.\n  seg = Wordseg(batch_size=64, device=\"cuda:0\", embedding='elmo', elmo_use_cuda=True, mode=\"TW\")\n\n  # input is a list of raw sentences.\n  seg.cut([\"\u4eca\u5929\u5929\u6c23\u771f\u597d\u554a!\", \"\u6f6e\u6c34\u9000\u4e86\u5c31\u77e5\u9053\uff0c\u8ab0\u6c92\u7a7f\u8932\u5b50\u3002\"])\n\n  # will return a list of lists of the segmented sentences.\n  # [['\u4eca\u5929', '\u5929\u6c23', '\u771f', '\u597d', '\u554a', '!'], ['\u6f6e\u6c34', '\u9000', '\u4e86', '\u5c31', '\u77e5\u9053', ',', '\u8ab0', '\u6c92', '\u7a7f', '\u8932\u5b50', '\u3002']]\n  ```\n#### Parameters:\n  - **batch_size**: batch size for the word segmentation model, default: `64`.\n  - **device**: the CPU/GPU device to run you model, default: `'cpu'`.\n  - **embedding**: (default: `'w2v'`) \n    - `'elmo'`: the loaded model will be the \"character level ELMo\" model above, which runs slow.\n    - `'w2v'`: the loaded model will be the \"baseline model\" above, which runs faster than `'elmo'`.\n  - **elmo_use_cuda**: if you want your ELMo model be accelerated on GPU, use `True`, otherwise the ELMo model will be run on CPU. This param is no use when `embedding='w2v'`. default: `True`.\n  - **mode**: `WordSeg` will load different model according to the mode as listed below: (default: `TW`)\n    - `TW`: trained on AS corpus, from CKIP, Academia Sinica, Taiwan.\n    - `HK`: trained on CityU corpus, from City University of Hong Kong, Hong Kong SAR.\n    - `CN_MSR`: trained on MSR corpus, from Microsoft Research, China.\n    - `CN_PKU` or `CN`: trained on PKU corpus, from Peking University, China.\n\n### TODO\n- \u76ee\u524d\u53ea\u652f\u63f4\u7e41\u9ad4\u4e2d\u6587(\u5373\u4f7f\u9078\u64c7CN mode\uff0c\u6587\u5b57\u4e5f\u8981\u8f49\u63db\u6210\u7e41\u9ad4\u624d\u80fd\u904b\u4f5c\uff0c\u76ee\u524d\u8a13\u7df4\u8cc7\u6599\u90fd\u662f\u7d93\u904e [OpenCC](https://github.com/BYVoid/OpenCC) \u8f49\u63db\u7684)\uff0c\u65e5\u5f8c\u6703\u52a0\u5165\u7c21\u9ad4\u4e2d\u6587\u3002\n\n\n",
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