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\u0026 Syllable Segmentation","自然語言處理-泰語"],"sub_categories":["Word Segmentation","函式庫"],"readme":"# Cutkum ['คัดคำ']\nCutkum ('คัดคำ') is a python code for Thai Word-Segmentation using Recurrent Neural Network (RNN) based on Tensorflow library. \n\nCutkum is trained on BEST2010, a 5 Millions Thai words corpus by NECTEC (https://www.nectec.or.th/). It also comes with an already trained model, and can be used right out of the box. Cutkum is still a work-in-progress project. Evaluated on the 10% hold-out data from BEST2010 corpus (~600,000 words), the included trained model currently performs at \n\n98.0% recall, 96.3% precision, 97.1% F-measure (character-level)\nRC: 0.988, PC: 0.966, FC: 0.977\n95% recall, 95% precision and 95.0% F-measure (word-level -- same evaluation method as BEST2010)\n\n# Update :D\n\nA major update\n\n1. now you dont have to load the model seperately, just do `pip install` and Cutkum is ready to use out of the box.\n2. the included model is now smaller, faster, and have higher accuracy. :)\n\n# Requirements\n* python = 2.7, 3.0+\n* tensorflow = 1.4+\n\n# Installation\n\n`cutkum` can be installed using `pip` \n\n```\npip install cutkum\n\n```\n\n# Usages\n\nOnce installed, you can use `cutkum` within your python code to tokenize thai sentences. \n\n```\n\n\u003e\u003e\u003e from cutkum.tokenizer import Cutkum\n\n\u003e\u003e\u003e ck = Cutkum()\n\u003e\u003e\u003e words = ck.tokenize(\"สารานุกรมไทยสำหรับเยาวชนฯ\")\n\n# python 3.0\n\u003e\u003e\u003e words\n['สารานุกรม', 'ไทย', 'สำหรับ', 'เยาวชน', 'ฯ']\n\n# python 2.7\n\u003e\u003e\u003e print(\"|\".join(words)) \n# สารานุกรม|ไทย|สำหรับ|เยาวชน|ฯ\n\n```\n\nYou can also use `cutkum` straight from the command line.\n\n```\nusage: cutkum [-h] [-v]\n              (-s SENTENCE | -i INPUT_FILE | -id INPUT_DIR)\n              [-o OUTPUT_FILE | -od OUTPUT_DIR] [--max | --viterbi]\n```\n\n```\ncutkum -s \"ล่าสุดกระทรวงพาณิชย์ได้ประกาศตัวเลขการส่งออกของไทย\"\n\n# output as\nล่าสุด|กระทรวงพาณิชย์|ได้|ประกาศ|ตัว|เลข|การ|ส่ง|ออก|ของ|ไทย\n```\n\n\n`cutkum` can also be used to segment text within a file (with -i), or to segment all the files within a given directory (with -id).\n\n```\ncutkum -i input.txt -o output.txt\ncutkum -id input_dir -od output_dir\n```\n\n## Citation\n\n```\nPucktada Treeratpituk (2017). Cutkum: Thai Word-Segmentation with LSTM in Tensorflow. May 5, 2017. See https://github.com/pucktada/cutkum\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details\n\n## To Do\n* Improve performance, with better better model, and better included trained-model\n* Improve the speed when processing big file\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpucktada%2Fcutkum","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpucktada%2Fcutkum","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpucktada%2Fcutkum/lists"}