Datasets:
File size: 3,667 Bytes
8654bfc 08f8d3d ebd4b6f 8654bfc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 | ---
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:
```json
{
"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
```python
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:
```bibtex
@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](https://opensource.org/licenses/MIT)
|