Datasets:
Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
License:
metadata
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- intent-classification
- multi-class-classification
paperswithcode_id: null
pretty_name: BANKING77
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': activate_my_card
'1': age_limit
'2': apple_pay_or_google_pay
'3': atm_support
'4': automatic_top_up
'5': balance_not_updated_after_bank_transfer
'6': balance_not_updated_after_cheque_or_cash_deposit
'7': beneficiary_not_allowed
'8': cancel_transfer
'9': card_about_to_expire
'10': card_acceptance
'11': card_arrival
'12': card_delivery_estimate
'13': card_linking
'14': card_not_working
'15': card_payment_fee_charged
'16': card_payment_not_recognised
'17': card_payment_wrong_exchange_rate
'18': card_swallowed
'19': cash_withdrawal_charge
'20': cash_withdrawal_not_recognised
'21': change_pin
'22': compromised_card
'23': contactless_not_working
'24': country_support
'25': declined_card_payment
'26': declined_cash_withdrawal
'27': declined_transfer
'28': direct_debit_payment_not_recognised
'29': disposable_card_limits
'30': edit_personal_details
'31': exchange_charge
'32': exchange_rate
'33': exchange_via_app
'34': extra_charge_on_statement
'35': failed_transfer
'36': fiat_currency_support
'37': get_disposable_virtual_card
'38': get_physical_card
'39': getting_spare_card
'40': getting_virtual_card
'41': lost_or_stolen_card
'42': lost_or_stolen_phone
'43': order_physical_card
'44': passcode_forgotten
'45': pending_card_payment
'46': pending_cash_withdrawal
'47': pending_top_up
'48': pending_transfer
'49': pin_blocked
'50': receiving_money
'51': Refund_not_showing_up
'52': request_refund
'53': reverted_card_payment?
'54': supported_cards_and_currencies
'55': terminate_account
'56': top_up_by_bank_transfer_charge
'57': top_up_by_card_charge
'58': top_up_by_cash_or_cheque
'59': top_up_failed
'60': top_up_limits
'61': top_up_reverted
'62': topping_up_by_card
'63': transaction_charged_twice
'64': transfer_fee_charged
'65': transfer_into_account
'66': transfer_not_received_by_recipient
'67': transfer_timing
'68': unable_to_verify_identity
'69': verify_my_identity
'70': verify_source_of_funds
'71': verify_top_up
'72': virtual_card_not_working
'73': visa_or_mastercard
'74': why_verify_identity
'75': wrong_amount_of_cash_received
'76': wrong_exchange_rate_for_cash_withdrawal
splits:
- name: train
num_bytes: 715036
num_examples: 10003
- name: test
num_bytes: 204014
num_examples: 3080
download_size: 1079034
dataset_size: 919050
Dataset Card for BANKING77
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
Dataset Summary
Dataset composed of online banking queries annotated with their corresponding intents.
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.
Supported Tasks and Leaderboards
Intent classification, intent detection
Languages
English
Dataset Structure
Data Instances
An example of 'train' looks as follows:
{
'label': 11, # integer label corresponding to "card_arrival" intent
'text': 'I am still waiting on my card?'
}
Data Fields
text: a string feature.label: One of classification labels (0-76) corresponding to unique intents.
Intent names are mapped to label in the following way:
| label | intent (category) |
|---|---|
| 0 | activate_my_card |
| 1 | age_limit |
| 2 | apple_pay_or_google_pay |
| 3 | atm_support |
| 4 | automatic_top_up |
| 5 | balance_not_updated_after_bank_transfer |
| 6 | balance_not_updated_after_cheque_or_cash_deposit |
| 7 | beneficiary_not_allowed |
| 8 | cancel_transfer |
| 9 | card_about_to_expire |
| 10 | card_acceptance |
| 11 | card_arrival |
| 12 | card_delivery_estimate |
| 13 | card_linking |
| 14 | card_not_working |
| 15 | card_payment_fee_charged |
| 16 | card_payment_not_recognised |
| 17 | card_payment_wrong_exchange_rate |
| 18 | card_swallowed |
| 19 | cash_withdrawal_charge |
| 20 | cash_withdrawal_not_recognised |
| 21 | change_pin |
| 22 | compromised_card |
| 23 | contactless_not_working |
| 24 | country_support |
| 25 | declined_card_payment |
| 26 | declined_cash_withdrawal |
| 27 | declined_transfer |
| 28 | direct_debit_payment_not_recognised |
| 29 | disposable_card_limits |
| 30 | edit_personal_details |
| 31 | exchange_charge |
| 32 | exchange_rate |
| 33 | exchange_via_app |
| 34 | extra_charge_on_statement |
| 35 | failed_transfer |
| 36 | fiat_currency_support |
| 37 | get_disposable_virtual_card |
| 38 | get_physical_card |
| 39 | getting_spare_card |
| 40 | getting_virtual_card |
| 41 | lost_or_stolen_card |
| 42 | lost_or_stolen_phone |
| 43 | order_physical_card |
| 44 | passcode_forgotten |
| 45 | pending_card_payment |
| 46 | pending_cash_withdrawal |
| 47 | pending_top_up |
| 48 | pending_transfer |
| 49 | pin_blocked |
| 50 | receiving_money |
| 51 | Refund_not_showing_up |
| 52 | request_refund |
| 53 | reverted_card_payment? |
| 54 | supported_cards_and_currencies |
| 55 | terminate_account |
| 56 | top_up_by_bank_transfer_charge |
| 57 | top_up_by_card_charge |
| 58 | top_up_by_cash_or_cheque |
| 59 | top_up_failed |
| 60 | top_up_limits |
| 61 | top_up_reverted |
| 62 | topping_up_by_card |
| 63 | transaction_charged_twice |
| 64 | transfer_fee_charged |
| 65 | transfer_into_account |
| 66 | transfer_not_received_by_recipient |
| 67 | transfer_timing |
| 68 | unable_to_verify_identity |
| 69 | verify_my_identity |
| 70 | verify_source_of_funds |
| 71 | verify_top_up |
| 72 | virtual_card_not_working |
| 73 | visa_or_mastercard |
| 74 | why_verify_identity |
| 75 | wrong_amount_of_cash_received |
| 76 | wrong_exchange_rate_for_cash_withdrawal |
Data Splits
| Dataset statistics | Train | Test |
|---|---|---|
| Number of examples | 10 003 | 3 080 |
| Average character length | 59.5 | 54.2 |
| Number of intents | 77 | 77 |
| Number of domains | 1 | 1 |
Dataset Creation
Curation Rationale
Previous intent detection datasets such as Web Apps, Ask Ubuntu, the Chatbot Corpus or SNIPS are limited to small number of classes (<10), which oversimplifies the intent detection task and does not emulate the true environment of commercial systems. Although there exist large scale multi-domain datasets (HWU64 and CLINC150), the examples per each domain may not sufficiently capture the full complexity of each domain as encountered "in the wild". This dataset tries to fill the gap and provides a very fine-grained set of intents in a single-domain i.e. banking. Its focus on fine-grained single-domain intent detection makes it complementary to the other two multi-domain datasets.
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
The dataset does not contain any additional annotations.
Who are the annotators?
[N/A]
Personal and Sensitive Information
[N/A]
Considerations for Using the Data
Social Impact of Dataset
The purpose of this dataset it to help develop better intent detection systems.
Any comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77.
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
Licensing Information
Creative Commons Attribution 4.0 International
Citation Information
@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}
}
Contributions
Thanks to @dkajtoch for adding this dataset.