Datasets:

clinc
/

clinc_oos

	"""An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction"""


	import json
	import textwrap

	import datasets


	_CITATION = """\
	@inproceedings{larson-etal-2019-evaluation,
	title = "An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction",
	author = "Larson, Stefan and
	Mahendran, Anish and
	Peper, Joseph J. and
	Clarke, Christopher and
	Lee, Andrew and
	Hill, Parker and
	Kummerfeld, Jonathan K. and
	Leach, Kevin and
	Laurenzano, Michael A. and
	Tang, Lingjia and
	Mars, Jason",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
	year = "2019",
	url = "https://www.aclweb.org/anthology/D19-1131"
	}
	"""

	_DESCRIPTION = """\
	This dataset is for evaluating the performance of intent classification systems in the
	presence of "out-of-scope" queries. By "out-of-scope", we mean queries that do not fall
	into any of the system-supported intent classes. Most datasets include only data that is
	"in-scope". Our dataset includes both in-scope and out-of-scope data. You might also know
	the term "out-of-scope" by other terms, including "out-of-domain" or "out-of-distribution".
	"""

	_DESCRIPTIONS = {
	"small": textwrap.dedent(
	"""\
	Small, in which there are only 50 training queries per each in-scope intent
	"""
	),
	"imbalanced": textwrap.dedent(
	"""\
	Imbalanced, in which intents have either 25, 50, 75, or 100 training queries.
	"""
	),
	"plus": textwrap.dedent(
	"""\
	OOS+, in which there are 250 out-of-scope training examples, rather than 100.
	"""
	),
	}

	_URL = "https://github.com/clinc/oos-eval/"

	# Source:
	# - https://raw.githubusercontent.com/clinc/oos-eval/master/data/data_small.json
	# - https://raw.githubusercontent.com/clinc/oos-eval/master/data/data_imbalanced.json
	# - https://raw.githubusercontent.com/clinc/oos-eval/master/data/data_oos_plus.json
	_DATA_URLS = {
	"small": "data/data_small.json.gz",
	"imbalanced": "data/data_imbalanced.json.gz",
	"plus": "data/data_oos_plus.json.gz",
	}


	class ClincConfig(datasets.BuilderConfig):

	"""BuilderConfig for CLINC150"""

	def __init__(self, description, data_url, citation, url, **kwrags):
	"""
	Args:
	description: `string`, brief description of the dataset
	data_url: `dictionary`, dict with url for each split of data.
	citation: `string`, citation for the dataset.
	url: `string`, url for information about the dataset.
	**kwrags: keyword arguments frowarded to super
	"""
	super(ClincConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwrags)
	self.description = description
	self.data_url = data_url
	self.citation = citation
	self.url = url


	class ClincOos(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	ClincConfig(
	name=name, description=_DESCRIPTIONS[name], data_url=_DATA_URLS[name], citation=_CITATION, url=_URL
	)
	for name in ["small", "imbalanced", "plus"]
	]

	def _info(self):
	features = {}
	features["text"] = datasets.Value("string")
	labels_list = [
	"restaurant_reviews",
	"nutrition_info",
	"account_blocked",
	"oil_change_how",
	"time",
	"weather",
	"redeem_rewards",
	"interest_rate",
	"gas_type",
	"accept_reservations",
	"smart_home",
	"user_name",
	"report_lost_card",
	"repeat",
	"whisper_mode",
	"what_are_your_hobbies",
	"order",
	"jump_start",
	"schedule_meeting",
	"meeting_schedule",
	"freeze_account",
	"what_song",
	"meaning_of_life",
	"restaurant_reservation",
	"traffic",
	"make_call",
	"text",
	"bill_balance",
	"improve_credit_score",
	"change_language",
	"no",
	"measurement_conversion",
	"timer",
	"flip_coin",
	"do_you_have_pets",
	"balance",
	"tell_joke",
	"last_maintenance",
	"exchange_rate",
	"uber",
	"car_rental",
	"credit_limit",
	"oos",
	"shopping_list",
	"expiration_date",
	"routing",
	"meal_suggestion",
	"tire_change",
	"todo_list",
	"card_declined",
	"rewards_balance",
	"change_accent",
	"vaccines",
	"reminder_update",
	"food_last",
	"change_ai_name",
	"bill_due",
	"who_do_you_work_for",
	"share_location",
	"international_visa",
	"calendar",
	"translate",
	"carry_on",
	"book_flight",
	"insurance_change",
	"todo_list_update",
	"timezone",
	"cancel_reservation",
	"transactions",
	"credit_score",
	"report_fraud",
	"spending_history",
	"directions",
	"spelling",
	"insurance",
	"what_is_your_name",
	"reminder",
	"where_are_you_from",
	"distance",
	"payday",
	"flight_status",
	"find_phone",
	"greeting",
	"alarm",
	"order_status",
	"confirm_reservation",
	"cook_time",
	"damaged_card",
	"reset_settings",
	"pin_change",
	"replacement_card_duration",
	"new_card",
	"roll_dice",
	"income",
	"taxes",
	"date",
	"who_made_you",
	"pto_request",
	"tire_pressure",
	"how_old_are_you",
	"rollover_401k",
	"pto_request_status",
	"how_busy",
	"application_status",
	"recipe",
	"calendar_update",
	"play_music",
	"yes",
	"direct_deposit",
	"credit_limit_change",
	"gas",
	"pay_bill",
	"ingredients_list",
	"lost_luggage",
	"goodbye",
	"what_can_i_ask_you",
	"book_hotel",
	"are_you_a_bot",
	"next_song",
	"change_speed",
	"plug_type",
	"maybe",
	"w2",
	"oil_change_when",
	"thank_you",
	"shopping_list_update",
	"pto_balance",
	"order_checks",
	"travel_alert",
	"fun_fact",
	"sync_device",
	"schedule_maintenance",
	"apr",
	"transfer",
	"ingredient_substitution",
	"calories",
	"current_location",
	"international_fees",
	"calculator",
	"definition",
	"next_holiday",
	"update_playlist",
	"mpg",
	"min_payment",
	"change_user_name",
	"restaurant_suggestion",
	"travel_notification",
	"cancel",
	"pto_used",
	"travel_suggestion",
	"change_volume",
	]
	features["intent"] = datasets.ClassLabel(names=labels_list)

	return datasets.DatasetInfo(
	description=_DESCRIPTION + "\n" + self.config.description,
	features=datasets.Features(features),
	homepage=self.config.url,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	file_ = dl_manager.download_and_extract(self.config.data_url)

	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": file_, "split": "train"}),
	datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": file_, "split": "val"}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": file_, "split": "test"}),
	]

	def _generate_examples(self, filepath, split):
	with open(filepath, encoding="utf-8") as f:
	j = json.load(f)
	for id_, row in enumerate(j[split] + j["oos_" + split]):
	yield id_, {"text": row[0], "intent": row[1]}