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        "author": "Julien LEICHER",
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        "description": "chatl: python flavor\n====================\n\nInstallation\n------------\n\npip\n~~~\n\n.. code-block:: bash\n\n  $ pip install pychatl\n\nsource\n~~~~~~\n\n.. code-block:: bash\n\n  $ git clone https://github.com/atlassistant/chatl.git\n  $ cd chatl/python\n  $ python setup.py install\n\nor\n\n.. code-block:: bash\n\n  $ pip install -e .\n\nUsage\n-----\n\nFrom the terminal\n~~~~~~~~~~~~~~~~~\n\n.. code-block:: bash\n\n  usage: pychatl [-h] [--version] [-a ADAPTER] [-m MERGE] [--pretty]\n               files [files ...]\n\n  Generates training dataset from a simple DSL.\n\n  positional arguments:\n    files                 One or more DSL files to process\n\n  optional arguments:\n    -h, --help            show this help message and exit\n    --version             show program's version number and exit\n    -a ADAPTER, --adapter ADAPTER\n                          Name of the adapter to use\n    -m MERGE, --merge MERGE\n                          Options file to merge with the final result\n    --pretty              Pretty output\n\nFrom the code\n~~~~~~~~~~~~~\n\n.. code-block:: python\n\n  from pychatl import parse\n\n  result = parse(\"\"\"\n  %[get_forecast]\n    will it rain in @[city] @[dateStart]\n\n  ~[new york]\n    ny\n    nyc\n\n  @[dateStart](type=snips/datetime)\n    at the end of the day\n    tomorrow\n    today\n\n  @[city]\n    ~[new york]\n    paris\n  \"\"\")\n\n  # Now you got a parsed dataset so you may want to process it for a specific NLU engines\n\n  from pychatl.adapters import snips\n\n  snips_dataset = snips(result) # Or give options with `snips(result, language='en')`\n\n  # And now you got your dataset ready to be fitted within snips-nlu!\n\nTesting\n-------\n\n.. code-block:: bash\n\n  $ pip install -e .[test]\n  $ python -m nose --with-doctest --with-coverage --cover-package=pychatl",
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