pychatl 1.2.2

Tiny DSL to generate training dataset for NLU engines

Tiny DSL to generate training dataset for NLU engines. Based on the javascript implementation of chatl.

Installation

pip

$ pip install pychatl

source

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

or

$ pip install -e .

Usage

From the terminal

$ pychatl .\example\forecast.dsl .\example\lights.dsl -a snips -o '{ \"language\": \"en\" }'

From the code

from pychatl import parse

result = parse("""
# pychatl is really easy to understand.
#
# You can defines:
#   - Intents
#   - Entities (with or without variants)
#   - Synonyms
#   - Comments (only at the top level)

# Inside an intent, you got training data.
# Training data can refer to one or more entities and/or synonyms, they will be used
# by generators to generate all possible permutations and training samples.

%[my_intent]
  ~[greet] some training data @[date]
  another training data that uses an @[entity] at @[date#with_variant]

~[greet]
  hi
  hello

# Entities contains available samples and could refer to a synonym.

@[entity]
  some value
  other value
  ~[a synonym]

# Synonyms contains only raw values

~[a synonym]
  possible synonym
  another one

# Entities and intents can define arbitrary properties that will be made available
# to generators.
# For snips, `snips:type` and `extensible` are used for example.

@[date](snips:type=snips/datetime)
  tomorrow
  today

# Variants is used only to generate training sample with specific values that should
# maps to the same entity name, here `date`. Props will be merged with the root entity.

@[date#with_variant]
  the end of the day
  nine o clock
  twenty past five
""")

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

from pychatl.postprocess 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!

Adapters

For now, only the snips adapter has been done. Here is a list of adapters and their respective properties:

adapter	snips
type (1)	✔️ with snips:type
extensible (2)	✔️

1. Specific type of the entity to use (such as datetime, temperature and so on)

2. Are values outside of training samples allowed?

Testing

$ pip install -e .[test]
$ python -m nose --with-doctest -v