---
license: unknown
language:
  - pcm
  - en
task_categories:
  - text-generation
  - question-answering
size_categories:
  - 100K<n<1M
pretty_name: Nigerian Pidgin Bank Customer Support Dataset
tags:
  - pidgin
  - nigerian-pidgin
  - banking
  - customer-support
  - conversational
  - fintech
  - low-resource-language
---

# Nigerian Pidgin Bank Customer Support Dataset

## Overview

This dataset contains 150,000 single-turn customer support conversations for a Nigerian retail bank, written primarily in Nigerian Pidgin English (pcm) with code-switched English. Each example simulates a customer inquiry about common banking issues — such as failed transfers, POS/ATM dispense errors, USSD (`*737#`) banking, and internet banking login/password resets — paired with an assistant response grounded in a bank help-centre style script. Personally identifiable information such as the bank's name has been redacted/blanked from the text (the file is named `150k_pidgin_pii_safe.jsonl`).

## Dataset Structure

- **Total rows:** 150,000 (verified by counting lines in the JSONL file)
- **File:** `150k_pidgin_pii_safe.jsonl`
- **Field:** each JSON line has a single top-level key, `messages`, containing a list of exactly 3 chat-style turns:
  - `role: "system"` — a fixed instruction defining the assistant persona ("You are &nbsp;Customer Support. Provide clear, safe, and accurate guidance..."). Only 1 unique system prompt appears across the whole dataset.
  - `role: "user"` — the customer's message, typically in Nigerian Pidgin (e.g. *"POS debit fail but I was charge. Wetin I go do? Ref TRX5581265757"*). There are 56,375 unique user messages (about 38% of rows), built from a set of query patterns parameterized with randomized transaction references (`TRX##########`), amounts (in ₦), and channel names (POS, ATM, USSD, Internet Banking).
  - `role: "assistant"` — the support response. Only 4 unique assistant response templates occur in the entire dataset, covering: (1) failed transfer/reversal guidance, (2) POS/ATM dispense-error timelines, (3) `*737#` USSD transfer codes, and (4) internet banking / app password reset steps. Some templates contain unfilled placeholders (`{ref}`, `{amt}`).

Every row has the identical structure `[system, user, assistant]` — there are no multi-turn conversations in this release.

## Data Format

Single file in **JSON Lines (`.jsonl`)** format, one JSON object per line, in the OpenAI-style chat `messages` schema (`role` + `content`). File size is approximately 122 MB.

## Intended Use

This dataset is intended for fine-tuning or evaluating conversational AI / customer-support chatbots that need to understand and respond appropriately to Nigerian Pidgin English banking queries — a low-resource language setting that is underrepresented in most NLP training data. It can support tasks such as intent classification, response generation, and instruction-tuning for Pidgin-aware assistants in the fintech domain.

## Limitations

- **Highly templated responses:** with only 4 distinct assistant responses across 150,000 rows, the dataset offers very limited diversity on the answer side. Models trained on it as-is are likely to reproduce these 4 templates verbatim rather than generate genuinely varied, context-sensitive answers.
- **Synthetic construction:** the repetitive structure (fixed system prompt, parameterized user query templates, a handful of answer templates) strongly suggests the data was synthetically generated/templated rather than collected from real customer-agent transcripts. It should not be treated as naturalistic conversational data.
- **Redacted bank identity:** the bank's name and app name have been blanked out (leaving artifacts like double spaces and "the app"), which may affect fluency and downstream response quality if used verbatim.
- **Some unfilled placeholders:** a subset of assistant responses retain literal `{ref}` / `{amt}` placeholders instead of realized values.
- **Single-turn only:** there is no multi-turn dialogue or follow-up context in this release.
- **No documented license or collection methodology** was found in the repository at the time of this review.

## Author

Curated by [Ephraimmm](https://huggingface.co/Ephraimmm)