Datasets:
snips_built_in_intents

	# coding=utf-8
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	# http://www.apache.org/licenses/LICENSE-2.0
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	# Lint as: python3
	"""Snips built in intents (2016-12-built-in-intents) dataset."""


	import json

	import datasets
	from datasets.tasks import TextClassification


	_DESCRIPTION = """\
	Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at
	https://github.com/sonos/nlu-benchmark 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes. The
	related paper mentioned on the github page is https://arxiv.org/abs/1805.10190 and a related Medium post is
	https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d .
	"""

	_CITATION = """\
	@article{DBLP:journals/corr/abs-1805-10190,
	author = {Alice Coucke and
	Alaa Saade and
	Adrien Ball and
	Th{\'{e}}odore Bluche and
	Alexandre Caulier and
	David Leroy and
	Cl{\'{e}}ment Doumouro and
	Thibault Gisselbrecht and
	Francesco Caltagirone and
	Thibaut Lavril and
	Ma{\"{e}}l Primet and
	Joseph Dureau},
	title = {Snips Voice Platform: an embedded Spoken Language Understanding system
	for private-by-design voice interfaces},
	journal = {CoRR},
	volume = {abs/1805.10190},
	year = {2018},
	url = {http://arxiv.org/abs/1805.10190},
	archivePrefix = {arXiv},
	eprint = {1805.10190},
	timestamp = {Mon, 13 Aug 2018 16:46:59 +0200},
	biburl = {https://dblp.org/rec/journals/corr/abs-1805-10190.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DOWNLOAD_URL = (
	"https://raw.githubusercontent.com/sonos/nlu-benchmark/master/2016-12-built-in-intents/benchmark_data.json"
	)


	class SnipsBuiltInIntents(datasets.GeneratorBasedBuilder):
	"""Snips built in intents (2016-12-built-in-intents) dataset."""

	def _info(self):
	# ToDo: Consider adding an alternate configuration for the entity slots. The default is to only return the intent labels.

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"text": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=[
	"ComparePlaces",
	"RequestRide",
	"GetWeather",
	"SearchPlace",
	"GetPlaceDetails",
	"ShareCurrentLocation",
	"GetTrafficInformation",
	"BookRestaurant",
	"GetDirections",
	"ShareETA",
	]
	),
	}
	),
	homepage="https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents",
	citation=_CITATION,
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	# Note: The source dataset doesn't have a train-test split.
	# ToDo: Consider splitting the data into train-test sets and re-hosting.
	samples_path = dl_manager.download_and_extract(_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": samples_path}),
	]

	def _generate_examples(self, filepath):
	"""Snips built in intent examples."""
	num_examples = 0

	with open(filepath, encoding="utf-8") as file_obj:
	snips_dict = json.load(file_obj)
	domains = snips_dict["domains"]

	for domain_dict in domains:
	intents = domain_dict["intents"]

	for intent_dict in intents:
	label = intent_dict["benchmark"]["Snips"]["original_intent_name"]
	queries = intent_dict["queries"]

	for query_dict in queries:
	query_text = query_dict["text"]

	yield num_examples, {"text": query_text, "label": label}
	num_examples += 1 # Explicitly keep track of the number of examples.