Add dataset card README.md

README.md

@@ -1,3 +1,138 @@
----
-license: mit
----
+---
+license: mit
+language:
+  - ny
+  - en
+pretty_name: Chichewa Text-to-SQL
+size_categories:
+  - n<1K
+task_categories:
+  - text2text-generation
+  - 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},
+  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)