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
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