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        "author": "Feynman",
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            "License :: OSI Approved :: MIT License",
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        "description": "\nThis is a project for later lazy work!\nOnly support for python3, \u2639\ufe0f, but maybe you can try in python2\n\n# Install\n\u547d\u4ee4\u884c\u76f4\u63a5\u5b89\u88c5\n```bash\npip install poros\n```\n\u4ece\u4ee3\u7801\u5e93\u5b89\u88c5\n```bash\ngit clone https://github.com/diqiuzhuanzhuan/poros.git\ncd poros\npython setup install\n```\n\n\nSome code is from other people, and some is from me.\n\n# bert_model\nusage:\n- create pretrain data\n\n```python\nfrom poros.bert_model import create_pretraining_data\n\n>>> create_pretraining_data.create_data(input_file=\"./test_data/sample_text.txt\",\noutput_file=\"./test_data/output\", vocab_file=\"./test_data/vocab.txt\")\n```\n\n- pretrain bert model\n```python\nfrom poros.bert_model import pretrain\n>>> pretrain.run(input_file=\"./test_data/output\",  bert_config_file=\"./test_data/bert_config.json\", \noutput_dir=\"./output\")\n\n```\n- prepare a trained model, tell classifier model\n- prepare train.csv and test.csv, its format is like this: \"id, text1, label\", but remember no header!\n- init the model, the code is like below\n````python\nfrom poros.bert_model.run_classifier import SimpleClassifierModel\n>>> model = SimpleClassifierModel(\n    bert_config_file=\"./data/chinese_L-12_H-768_A-12/bert_config.json\",      \n     vocab_file=\"./data/chinese_L-12_H-768_A-12/vocab.txt\",                   \n     output_dir=\"./output\",                                                   \n     max_seq_length=512,                                                      \n     train_file=\"./data/train.csv\",                                           \n     dev_file=\"./data/dev.csv\",                                               \n     init_checkpoint=\"./data/chinese_L-12_H-768_A-12/bert_model.ckpt\",        \n     label_list=[\"0\", \"1\", \"2\", \"3\"]                                                  \n    )\n````\n\n# poros_dataset\nsome operations about tensor\n```python\nfrom poros.poros_dataset import about_tensor\nimport tensorflow as tf\n>>> A = tf.constant(value=[0])\n>>> print(about_tensor.get_shape(A))\n[1]\n\n```\n\n\n# poros_chars\nProvide a list of small functions\n\nusage:\n- convert chinese words into arabic number:\n```python\nfrom poros.poros_chars import chinese_to_arabic\n>>> print(chinese_to_arabic.NumberAdapter.convert(\"\u56db\u5343\u4e09\u767e\u4e07\"))\n43000000\n\n```\n\n\n\n",
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