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Tiny DSL to generate training dataset for NLU engines

Project description

Installation

pip

$ pip install pychatl

source

$ git clone https://github.com/atlassistant/chatl.git
$ cd chatl/python
$ python setup.py install

or

$ pip install -e .

Usage

From the terminal

usage: pychatl [-h] [--version] [-a ADAPTER] [-m MERGE] [--pretty]
             files [files ...]

Generates training dataset from a simple DSL.

positional arguments:
  files                 One or more DSL files to process

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  -a ADAPTER, --adapter ADAPTER
                        Name of the adapter to use
  -m MERGE, --merge MERGE
                        Options file to merge with the final result
  --pretty              Pretty output

From the code

from pychatl import parse

result = parse("""
%[get_forecast]
  will it rain in @[city] @[dateStart]

~[new york]
  ny
  nyc

@[dateStart](type=snips/datetime)
  at the end of the day
  tomorrow
  today

@[city]
  ~[new york]
  paris
""")

# Now you got a parsed dataset so you may want to process it for a specific NLU engines

from pychatl.adapters import snips

snips_dataset = snips(result) # Or give options with `snips(result, language='en')`

# And now you got your dataset ready to be fitted within snips-nlu!

Testing

$ pip install -e .[test]
$ python -m nose --with-doctest --with-coverage --cover-package=pychatl

Project details


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Files for pychatl, version 2.0.4
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