Datasets:

johneze
/

chichewa-text2sql

license: mit
language:
  - ny
  - en
pretty_name: Chichewa Text-to-SQL
size_categories:
  - n<1K
task_categories:
  - table-question-answering
tags:
  - text-to-sql
  - chichewa
  - low-resource-language
  - semantic-parsing
  - nlp
  - malawi
  - sql
  - qlora
  - few-shot

Chichewa Text-to-SQL

The first structured Text-to-SQL benchmark for Chichewa, a low-resource Bantu language spoken by over 12 million people in Malawi and neighboring regions. The dataset contains 400 manually curated natural language–SQL pairs in both Chichewa (Nyanja) and English, grounded in a unified relational SQLite database covering five real-world domains from Malawi.

Dataset Summary

This benchmark was constructed to investigate the adaptation of Large Language Models (LLMs) for Text-to-SQL generation in Chichewa. It supports systematic evaluation of zero-shot, few-shot (random and retrieval-augmented), and parameter-efficient fine-tuning (QLoRA) approaches for low-resource semantic parsing.

Key findings from the accompanying research:

English zero-shot execution accuracy: 20% → 50% (random few-shot) → 70% (RAG few-shot) → 76.7% (QLoRA)
Chichewa zero-shot execution accuracy: 0% across all models → 41.7% (RAG few-shot) → 41.7% (QLoRA)

Database Schema

The SQLite database (database/chichewa_text2sql.db) contains five tables:

Table	Description
`production`	Agricultural crop yield by district and season
`population`	Census data with geographic and demographic breakdowns
`mse_daily`	Malawi Stock Exchange daily trading data
`commodity_prices`	Commodity price data across markets
`food_insecurity`	Food insecurity indicators by region

Dataset Structure

Files

File	Description
`data/all.json`	Full dataset (400 examples)
`data/train.json`	Training split
`data/dev.json`	Development/validation split
`data/test.json`	Test split
`data/human_translations.csv`	Human-verified translations
`data/split_verification.json`	Split integrity verification
`database/chichewa_text2sql.db`	SQLite database
`database_tables_csv/`	Raw CSV files for each table

Data Fields

Each example contains:

{
  "id": 1,
  "question_en": "Which district produced the most Maize",
  "question_ny": "Ndi boma liti komwe anakolola chimanga chambiri?",
  "sql_statement": "SELECT district, MAX(yield) AS max_yield FROM production WHERE crop = 'Maize';",
  "sql_result": "[('Lilongwe', 444440.0)]",
  "difficulty_level": "easy",
  "table": "production"
}

Difficulty Levels

easy — single table, simple SELECT / WHERE / ORDER BY
medium — aggregations, GROUP BY, LIMIT
hard — multi-condition queries, subqueries, JOINs

Splits

Split	Size
Train	~280
Dev	~60
Test	~60
Total	400

Usage

import json

with open("data/train.json") as f:
    train = json.load(f)

print(train[0]["question_ny"])    # Chichewa question
print(train[0]["question_en"])    # English question
print(train[0]["sql_statement"])  # Ground-truth SQL

Citation

If you use this dataset, please cite:

@dataset{eze2026chichewa,
  author    = {Eze, John Emeka and Matekenya, Dunstan and Matthewe, Evance},
  title     = {Chichewa Text-to-SQL: A Low-Resource Benchmark for Semantic Parsing in Chichewa},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/johneze/chichewa-text2sql}
}

License

MIT