
# Dataset Card for BANKING77

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
- **Repository:** [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
- **Paper:** [ArXiv](https://arxiv.org/abs/2003.04807)
- **Leaderboard:**
- **Point of Contact:**

### 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](https://github.com/xliuhw/NLU-Evaluation-Data) and [CLINC150](https://github.com/clinc/oos-eval)), 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

[PolyAI](https://github.com/PolyAI-LDN)

### 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](https://github.com/dkajtoch) for adding this dataset.

task_categories:text-classification task_ids:intent-classification task_ids:multi-class-classification annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:cc-by-4.0 arxiv:2003.04807 region:us
{"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"], "pretty_name": "BANKING77", "extended": ["original"]}
TAGS
#task_categories-text-classification #task_ids-intent-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2003.04807 #region-us 

Dataset Card for BANKING77
==========================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: Github
* Repository: Github
* Paper: ArXiv
* Leaderboard:
* Point of Contact:


### 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:


### 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:



### Data Splits


Dataset statistics: Number of examples, Train: 10 003, Test: 3 080
Dataset statistics: Average character length, Train: 59.5, Test: 54.2
Dataset statistics: Number of intents, Train: 77, Test: 77
Dataset statistics: Number of domains, Train: 1, Test: 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


#### Who are the source language producers?


### 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


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


PolyAI


### Licensing Information


Creative Commons Attribution 4.0 International


### Contributions


Thanks to @dkajtoch for adding this dataset.



### 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: ### 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: ### Data Splits


Dataset statistics: Number of examples, Train: 10 003, Test: 3 080
Dataset statistics: Average character length, Train: 59.5, Test: 54.2
Dataset statistics: Number of intents, Train: 77, Test: 77
Dataset statistics: Number of domains, Train: 1, Test: 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 #### Who are the source language producers? ### 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 ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


PolyAI ### Licensing Information


Creative Commons Attribution 4.0 International ### Contributions


Thanks to @dkajtoch for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-intent-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2003.04807 #region-us 
 ### 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: ### 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: ### Data Splits


Dataset statistics: Number of examples, Train: 10 003, Test: 3 080
Dataset statistics: Average character length, Train: 59.5, Test: 54.2
Dataset statistics: Number of intents, Train: 77, Test: 77
Dataset statistics: Number of domains, Train: 1, Test: 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 #### Who are the source language producers? ### 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 ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


PolyAI ### Licensing Information


Creative Commons Attribution 4.0 International ### Contributions


Thanks to @dkajtoch for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-intent-classification #task_ids-multi-class-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-cc-by-4.0 #arxiv-2003.04807 #region-us 
### 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:### 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:### Data Splits


Dataset statistics: Number of examples, Train: 10 003, Test: 3 080
Dataset statistics: Average character length, Train: 59.5, Test: 54.2
Dataset statistics: Number of intents, Train: 77, Test: 77
Dataset statistics: Number of domains, Train: 1, Test: 1


Dataset Creation
----------------