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# 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 warnings
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):
warnings.warn(
"Dataset 'banking77' is deprecated and will be deleted. Use 'PolyAI/banking77' instead.",
FutureWarning,
)
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(_TRAIN_DOWNLOAD_URL)
test_path = dl_manager.download(_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}
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