task_categories:text-classification language:en size_categories:1K<n<10K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
---
dataset_info:
  features:
  - name: text
    dtype: string
  - name: labels
    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': blocked_change_pin
          '9': cancel_transfer
          '10': card_about_to_expire
          '11': card_acceptance
          '12': card_arrival_delivery_estimate
          '13': card_linking
          '14': card_not_working
          '15': card_swallowed
          '16': compromised_card_lost
          '17': country_support
          '18': declined_cash_withdrawal
          '19': declined_reverted_card_payment
          '20': edit_personal_details
          '21': exchange_charge
          '22': exchange_rate
          '23': extra_charge_on_statement
          '24': failed_transfer
          '25': fee_charged
          '26': fiat_currency_support
          '27': get_physical_card
          '28': getting_spare_card
          '29': getting_virtual_card
          '30': movement_not_recognized
          '31': passcode_forgotten
          '32': pending_transfer_topup_cardpayment
          '33': receiving_money
          '34': request_refund
          '35': supported_cards_and_currencies
          '36': terminate_account
          '37': top_up_card_cash_transfer
          '38': top_up_failed_reverted_limits
          '39': transaction_charged_twice
          '40': transfer_into_account
          '41': transfer_not_received_by_recipient
          '42': transfer_timing
          '43': verify_my_identity
          '44': verify_source_of_funds
          '45': verify_top_up
          '46': visa_or_mastercard
          '47': wrong_amount_of_cash_received
          '48': wrong_exchange_rate
  splits:
  - name: train
    num_bytes: 378768
    num_examples: 5515
  - name: test
    num_bytes: 65493
    num_examples: 985
  download_size: 192748
  dataset_size: 444261
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
task_categories:
- text-classification
language:
- en
---
# Dataset Card for BANKING49

BANKING49 is a refined and optimized version of the well-known BANKING77 dataset, designed to improve the quality and usability of intent classification in banking-related NLP tasks. This dataset is the result of meticulous cleaning and reorganization efforts aimed at enhancing its reliability and practical application.

### Dataset Description

It addresses the limitations of the original BANKING77 dataset by applying advanced cleaning techniques and rethinking the categorization of intents.

Key improvements include:

- **Elimination of Mislabelled Data**: Leveraging the powerful CleanLab methodology, mislabelled examples were systematically identified and removed. This ensures a more consistent and accurate dataset, reducing noise for downstream applications.

- **Refined Intent Categories**: Intents that were overly granular or overlapping in BANKING77 were carefully reviewed and merged where appropriate. This results in 49 distinct and well-defined intents that better align with actual user needs and interactions.>


- **Curated by**: Pilar Llera
- **Language(s)**: English (`en`)
- **Tasks**: Intent classification for banking-related queries.

  
### Source Data 

This dataset is built upon BANKING77, a benchmark dataset for intent classification, and extends its usability by addressing key issues. 
For more details about the original dataset, refer to its Hugging Face page.

## Uses

BANKING49 is ideal for:

- Intent Classification: Train and evaluate models for intent recognition in the banking domain.
- Customer Support Optimization: Enhance chatbots or virtual assistants by understanding customer queries with greater precision.
- NLP Research: Use this clean and reliable dataset as a benchmark for banking-related tasks.


## Dataset Structure

The dataset contains two splits:

- **Train**: 5,515 examples  
- **Test**: 985 examples  

#### Features

- **Text**: Customer queries in string format.  
- **Labels**: Class labels representing 49 intents.

### Curation Rationale

The dataset was created to address challenges in noisy labels and overlapping intents in BANKING77, making it more reliable for real-world applications.

