Datasets:
banking77

	# coding=utf-8
	# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""BANKING77 dataset."""


	import csv

	import datasets
	from datasets.tasks import TextClassification


	_CITATION = r"""\
	@inproceedings{Casanueva2020,
	author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic},
	title = {Efficient Intent Detection with Dual Sentence Encoders},
	year = {2020},
	month = {mar},
	note = {Data available at https://github.com/PolyAI-LDN/task-specific-datasets},
	url = {https://arxiv.org/abs/2003.04807},
	booktitle = {Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020}
	}
	""" # noqa: W605

	_DESCRIPTION = """\
	BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
	It comprises 13,083 customer service queries labeled with 77 intents.
	It focuses on fine-grained single-domain intent detection.
	"""

	_HOMEPAGE = "https://github.com/PolyAI-LDN/task-specific-datasets"

	_LICENSE = "Creative Commons Attribution 4.0 International"

	_TRAIN_DOWNLOAD_URL = (
	"https://raw.githubusercontent.com/PolyAI-LDN/task-specific-datasets/master/banking_data/train.csv"
	)
	_TEST_DOWNLOAD_URL = "https://raw.githubusercontent.com/PolyAI-LDN/task-specific-datasets/master/banking_data/test.csv"


	class Banking77(datasets.GeneratorBasedBuilder):
	"""BANKING77 dataset."""

	VERSION = datasets.Version("1.1.0")

	def _info(self):
	features = datasets.Features(
	{
	"text": datasets.Value("string"),
	"label": datasets.features.ClassLabel(
	names=[
	"activate_my_card",
	"age_limit",
	"apple_pay_or_google_pay",
	"atm_support",
	"automatic_top_up",
	"balance_not_updated_after_bank_transfer",
	"balance_not_updated_after_cheque_or_cash_deposit",
	"beneficiary_not_allowed",
	"cancel_transfer",
	"card_about_to_expire",
	"card_acceptance",
	"card_arrival",
	"card_delivery_estimate",
	"card_linking",
	"card_not_working",
	"card_payment_fee_charged",
	"card_payment_not_recognised",
	"card_payment_wrong_exchange_rate",
	"card_swallowed",
	"cash_withdrawal_charge",
	"cash_withdrawal_not_recognised",
	"change_pin",
	"compromised_card",
	"contactless_not_working",
	"country_support",
	"declined_card_payment",
	"declined_cash_withdrawal",
	"declined_transfer",
	"direct_debit_payment_not_recognised",
	"disposable_card_limits",
	"edit_personal_details",
	"exchange_charge",
	"exchange_rate",
	"exchange_via_app",
	"extra_charge_on_statement",
	"failed_transfer",
	"fiat_currency_support",
	"get_disposable_virtual_card",
	"get_physical_card",
	"getting_spare_card",
	"getting_virtual_card",
	"lost_or_stolen_card",
	"lost_or_stolen_phone",
	"order_physical_card",
	"passcode_forgotten",
	"pending_card_payment",
	"pending_cash_withdrawal",
	"pending_top_up",
	"pending_transfer",
	"pin_blocked",
	"receiving_money",
	"Refund_not_showing_up",
	"request_refund",
	"reverted_card_payment?",
	"supported_cards_and_currencies",
	"terminate_account",
	"top_up_by_bank_transfer_charge",
	"top_up_by_card_charge",
	"top_up_by_cash_or_cheque",
	"top_up_failed",
	"top_up_limits",
	"top_up_reverted",
	"topping_up_by_card",
	"transaction_charged_twice",
	"transfer_fee_charged",
	"transfer_into_account",
	"transfer_not_received_by_recipient",
	"transfer_timing",
	"unable_to_verify_identity",
	"verify_my_identity",
	"verify_source_of_funds",
	"verify_top_up",
	"virtual_card_not_working",
	"visa_or_mastercard",
	"why_verify_identity",
	"wrong_amount_of_cash_received",
	"wrong_exchange_rate_for_cash_withdrawal",
	]
	),
	}
	)
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	task_templates=[TextClassification(text_column="text", label_column="label")],
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL)
	test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL)
	return [
	datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}),
	datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}),
	]

	def _generate_examples(self, filepath):
	"""Yields examples as (key, example) tuples."""
	with open(filepath, encoding="utf-8") as f:
	csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
	# call next to skip header
	next(csv_reader)
	for id_, row in enumerate(csv_reader):
	text, label = row
	yield id_, {"text": text, "label": label}