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        "author": "Chao-Ming Wang",
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        "description": "\n======\nOujago\n======\n\nCoding makes life easier. This is a factory contains commonly used\nalgorithms.\n\n\nInstallation\n============\n\nInstall ``oujago`` using pip:\n\n.. code-block:: bash\n\n    $> pip install oujago\n\nInstall from source code:\n\n.. code-block:: bash\n\n    $> python setup.py clean --all install\n\n\nDownload data from `BaiDuYun <https://pan.baidu.com/s/1i57RVLj>`_:\n\n.. code-block::\n\n    https://pan.baidu.com/s/1i57RVLj\n\n\n\nDocumentation\n=============\n\nAvailable online documents: `latest <http://oujago.readthedocs.io/en/latest/>`_,\n`stable <http://oujago.readthedocs.io/en/stable/>`_,\nand `stable <http://oujago.readthedocs.io/en/develop/>`_.\n\n\nNLP Part\n========\n\nHanzi Converter\n---------------\n\n\u7e41\u7b80\u8f6c\u6362\u5668.\n\n.. code-block:: shell\n\n    >>> from oujago.nlp import FJConvert\n    >>> FJConvert.to_tradition('\u7e41\u7b80\u8f6c\u6362\u5668')\n    '\u7e41\u7c21\u8f49\u63db\u5668'\n    >>> FJConvert.to_simplify('\u7e41\u7c21\u8f49\u63db\u5668')\n    '\u7e41\u7b80\u8f6c\u6362\u5668'\n    >>> FJConvert.same('\u7e41\u7b80\u8f6c\u6362\u5668', '\u7e41\u7c21\u8f49\u63db\u5668')\n    >>> True\n    >>> FJConvert.same('\u7e41\u7b80\u8f6c\u6362\u5668', '\u7e41\u7c21\u8f49\u63db')\n    >>> False\n\n\nChinese Segment\n---------------\n\nSupport ``jieba``, ``LTP``, ``thulac``, ``pynlpir`` etc. public segmentation methods.\n\n.. code-block:: shell\n\n    >>> from oujago.nlp import seg\n    >>>\n    >>> sentence = \"\u8fd9\u662f\u4e00\u4e2a\u4f38\u624b\u4e0d\u89c1\u4e94\u6307\u7684\u9ed1\u591c\u3002\u6211\u53eb\u5b59\u609f\u7a7a\uff0c\u6211\u7231\u5317\u4eac\uff0c\u6211\u7231Python\u548cC++\u3002\"\n    >>> seg(sentence, mode='ltp')\n    ['\u8fd9', '\u662f', '\u4e00\u4e2a', '\u4f38\u624b', '\u4e0d', '\u89c1', '\u4e94', '\u6307', '\u7684', '\u9ed1\u591c', '\u3002', '\u6211', '\u53eb', '\u5b59\u609f\u7a7a',\n    '\uff0c', '\u6211', '\u7231', '\u5317\u4eac', '\uff0c', '\u6211', '\u7231', 'Python', '\u548c', 'C', '+', '+', '\u3002']\n    >>> seg(sentence, mode='jieba')\n    ['\u8fd9\u662f', '\u4e00\u4e2a', '\u4f38\u624b\u4e0d\u89c1\u4e94\u6307', '\u7684', '\u9ed1\u591c', '\u3002', '\u6211', '\u53eb', '\u5b59\u609f\u7a7a', '\uff0c', '\u6211', '\u7231',\n    '\u5317\u4eac', '\uff0c', '\u6211', '\u7231', 'Python', '\u548c', 'C++', '\u3002']\n    >>> seg(sentence, mode='thulac')\n    ['\u8fd9', '\u662f', '\u4e00\u4e2a', '\u4f38\u624b\u4e0d\u89c1\u4e94\u6307', '\u7684', '\u9ed1\u591c', '\u3002', '\u6211', '\u53eb', '\u5b59\u609f\u7a7a', '\uff0c',\n    '\u6211', '\u7231', '\u5317\u4eac', '\uff0c', '\u6211', '\u7231', 'Python', '\u548c', 'C', '+', '+', '\u3002']\n    >>> seg(sentence, mode='nlpir')\n    ['\u8fd9', '\u662f', '\u4e00\u4e2a', '\u4f38\u624b', '\u4e0d\u89c1', '\u4e94\u6307', '\u7684', '\u9ed1\u591c', '\u3002', '\u6211', '\u53eb', '\u5b59\u609f\u7a7a',\n    '\uff0c', '\u6211', '\u7231', '\u5317\u4eac', '\uff0c', '\u6211', '\u7231', 'Python', '\u548c', 'C++', '\u3002']\n    >>>\n    >>> seg(\"\u8fd9\u662f\u4e00\u4e2a\u4f38\u624b\u4e0d\u89c1\u4e94\u6307\u7684\u9ed1\u591c\u3002\")\n    ['\u8fd9\u662f', '\u4e00\u4e2a', '\u4f38\u624b\u4e0d\u89c1\u4e94\u6307', '\u7684', '\u9ed1\u591c', '\u3002']\n    >>> seg(\"\u8fd9\u662f\u4e00\u4e2a\u4f38\u624b\u4e0d\u89c1\u4e94\u6307\u7684\u9ed1\u591c\u3002\", mode='ltp')\n    ['\u8fd9', '\u662f', '\u4e00\u4e2a', '\u4f38\u624b', '\u4e0d', '\u89c1', '\u4e94', '\u6307', '\u7684', '\u9ed1\u591c', '\u3002']\n    >>> seg('\u6211\u4e0d\u559c\u6b22\u65e5\u672c\u548c\u670d', mode='jieba')\n    ['\u6211', '\u4e0d', '\u559c\u6b22', '\u65e5\u672c', '\u548c\u670d']\n    >>> seg('\u6211\u4e0d\u559c\u6b22\u65e5\u672c\u548c\u670d', mode='ltp')\n    ['\u6211', '\u4e0d', '\u559c\u6b22', '\u65e5\u672c', '\u548c\u670d']\n\n\nPart-of-Speech\n--------------\n\n.. code-block:: shell\n\n    >>> from oujago.nlp.postag import pos\n    >>> pos('\u6211\u4e0d\u559c\u6b22\u65e5\u672c\u548c\u670d', mode='jieba')\n    ['r', 'd', 'v', 'ns', 'nz']\n    >>> pos('\u6211\u4e0d\u559c\u6b22\u65e5\u672c\u548c\u670d', mode='ltp')\n    ['r', 'd', 'v', 'ns', 'n']\n\n\nNN Part\n=======\n\nSRU (PyTorch)\n-------------\n\nRequire packages: ``cupy``, ``pynvrtc``, ``pytorch``.\nComes from `<Training RNNs as Fast as CNNs> <https://arxiv.org/abs/1709.02755>`_ .\n\nThe usage of SRU is similar to ``torch.nn.LSTM``.\n\n.. code-block:: python\n\n    import torch\n    from torch.autograd import Variable\n    from oujago.nn.sru import SRU, SRUCell\n\n    # input has length 20, batch size 32 and dimension 128\n    x = Variable(torch.FloatTensor(20, 32, 128).cuda())\n\n    input_size, hidden_size = 128, 128\n\n    rnn = SRU(input_size, hidden_size,\n        num_layers = 2,          # number of stacking RNN layers\n        dropout = 0.0,           # dropout applied between RNN layers\n        rnn_dropout = 0.0,       # variational dropout applied on linear transformation\n        use_tanh = 1,            # use tanh?\n        use_relu = 0,            # use ReLU?\n        bidirectional = False    # bidirectional RNN ?\n    )\n    rnn.cuda()\n\n    output, hidden = rnn(x)      # forward pass\n\n    # output is (length, batch size, hidden size * number of directions)\n    # hidden is (layers, batch size, hidden size * number of directions)\n\n\nSee Language Modeling example: `sru_language_modeling.py <apps/sru_language_modeling>`_\n\n\nUtils Part\n==========\n\nCommon Utils\n------------\n\n\nCheck weather this ``object`` is an iterable.\n\n.. code-block:: shell\n\n    >>> from oujago.utils.common import is_iterable\n    >>> is_iterable([1, 2])\n    True\n    >>> is_iterable((1, 2))\n    True\n    >>> is_iterable(\"123\")\n    True\n    >>> is_iterable(123)\n    False\n\n\nTime Utils\n----------\n\nGet current time.\n\n.. code-block:: shell\n\n    >>> from oujago.utils.time import now\n    >>> now()\n    \"2017-04-26-16-44-56\"\n    >>>\n    >>> from oujago.utils.time import today\n    >>> today()\n    \"2017-04-26\"\n\nChange the total time into the normal time format.\n\n.. code-block:: shell\n\n    >>> from oujago.utils.time import time_format\n    >>> time_format(36)\n    \"36 s\"\n    >>> time_format(90)\n    \"1 min 30 s \"\n    >>> time_format(5420)\n    \"1 h 30 min 20 s\"\n    >>> time_format(20.5)\n    \"20 s 500 ms\"\n    >>> time_format(864023)\n    '10 d 23 s'\n\n\n\n\n\nChange Log\n==========\n\n\n0.1.13\n------\n\n* PyTorch alexnet, at `2018.03.30`.\n* PyTorch densenet, at `2018.03.30`.\n* PyTorch inception, at `2018.03.30`.\n* PyTorch resnet, at `2018.03.30`.\n* PyTorch squeezenet, at `2018.03.30`.\n* PyTorch vgg, at `2018.03.30`.\n* keras resnet, at `2018.03.30`.\n\n\n0.1.12\n------\n\n* PyTorch SRU Layer  , at `2018.01.21`.\n* Format README  , at `2018.01.21`.\n\n0.1.9\n-----\n\n* NLP moran NER    , at `2017.07.06`.\n* NLP thulac segment     , at `2017.07.06`.\n* NLP thulac postag     , at `2017.07.06`.\n\n\n0.1.8\n-----\n\n* NLP moran segment    , at `2017.06.26`.\n* NLP moran postag    , at `2017.06.26`.\n\n\n0.1.7\n-----\n\n* NLP jieba segment    , at `2017.06.20`.\n* NLP LTP segment     , at `2017.06.20`.\n* NLP jieba POSTag    , at `2017.06.20`.\n* NLP LTP POSTag    , at `2017.06.20`.\n* NLP LTP NER    , at `2017.06.20`.\n* NLP LTP Dependecy Parse    , at `2017.06.20`.\n* NLP LTP Semantic Role Labeling    , at `2017.06.20`.\n\n\n0.1.6\n-----\n\n* Hanzi Converter    , at `2017.06.19`.\n* Chinese Stopwords    , at `2017.06.19`.\n\n",
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