sql-natural-language-pair

---
license: cc-by-4.0
language:
  - en
tags:
  - sql
  - text-to-sql
  - natural-language-processing
  - code-generation
  - synthetic
  - structured-data
  - query-generation
  - nlp
pretty_name: "SQL Natural Language Pairs — Synthetic Benchmark"
size_categories:
  - n<1K
task_categories:
  - text2text-generation
  - text-generation

configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.csv
      - split: validation
        path: data/validation.csv
      - split: test
        path: data/test.csv
---
# SQL Natural Language Pairs — Synthetic Benchmark

A curated synthetic dataset of **286 (question, SQL, schema_context)** pairs spanning five real-world domains,
six SQL complexity levels, and three difficulty tiers. Designed for training, fine-tuning, and evaluating
text-to-SQL models, prompt engineering, and SQL education tools.
**Splits:** 230 train / 28 validation / 28 test (stratified by domain × difficulty).
## Why this dataset?
Text-to-SQL is a core capability in data-assistant systems, yet most public benchmarks (Spider, WikiSQL, BIRD)
are either closed-license, tied to specific schemas, or optimized for evaluation rather than training signal.
This dataset fills a gap:
- **Permissive license (CC-BY-4.0)** — use freely in commercial or research contexts
- **Diverse, realistic schemas** — five domains each with 4–5 tables and foreign keys
- **Graduated complexity** — from single-table SELECT to multi-CTE window functions
- **Schema context included** — each row contains the DDL-lite schema so models can learn grounding
- **Deterministically reproducible** — all data generated by open Python scripts (included in repo)
## Schema
Five domains, each with 4–5 tables:
| Domain | Tables |
|--------|--------|
| `ecommerce` | customers, orders, order_items, products, suppliers |
| `healthcare` | patients, doctors, appointments, hospitals, prescriptions |
| `analytics` | events, users, sessions, pages, conversions |
| `hr` | employees, departments, projects, assignments, performance_reviews |
| `inventory` | warehouses, items, stock, movements, vendors |
All schemas are intentionally self-contained and representative of real production databases.
## Dataset Statistics
| Dimension | Breakdown |
|-----------|-----------|
| **Total pairs** | 286 |
| **Domains** | ecommerce (98), healthcare (59), analytics (48), hr (42), inventory (39) |
| **Difficulty** | easy (142), medium (90), hard (54) |
| **Query types** | aggregate (130), join (56), select (54), subquery (25), window (12), cte (9) |
### Splits
| Split | Records |
|-------|---------|
| train | 230 |
| validation | 28 |
| test | 28 |
Splits are stratified by domain × difficulty to ensure balanced coverage in each partition.
## Columns
| Column | Type | Description |
|--------|------|-------------|
| `question` | string | Natural language question or instruction |
| `sql` | string | Correct SQL query answering the question |
| `schema_context` | string | Abbreviated DDL for the domain (table(col1, col2, ...)) |
| `domain` | string | One of: ecommerce, healthcare, analytics, hr, inventory |
| `difficulty` | string | easy / medium / hard |
| `query_type` | string | select / aggregate / join / subquery / window / cte |
## Sample Records
**Easy — aggregate (ecommerce)**
```
Q: How many customers are from Germany?
SQL: SELECT COUNT(*) AS customer_count FROM customers WHERE country = 'Germany';
```
**Medium — JOIN (healthcare)**
```
Q: Show all appointments with patient name, doctor name, and diagnosis.
SQL: SELECT a.appointment_id, p.name AS patient_name, d.name AS doctor_name,
            a.appointment_date, a.diagnosis
     FROM appointments a
     JOIN patients p ON a.patient_id = p.patient_id
     JOIN doctors d ON a.doctor_id = d.doctor_id;
```

**Hard — CTE + window function (hr)**
```
Q: Calculate year-over-year average bonus change by department.
SQL: WITH dept_bonus AS (
         SELECT d.name AS dept, pr.review_year, AVG(pr.bonus) AS avg_bonus
         FROM performance_reviews pr
         JOIN employees e ON pr.employee_id = e.employee_id
         JOIN departments d ON e.department_id = d.department_id
         GROUP BY d.name, pr.review_year
     )
     SELECT dept, review_year, avg_bonus,
            avg_bonus - LAG(avg_bonus) OVER (PARTITION BY dept ORDER BY review_year) AS yoy_change
     FROM dept_bonus ORDER BY dept, review_year;
```

**Hard — correlated subquery (ecommerce)**
```
Q: Find employees earning more than the average salary in their own department.
SQL: SELECT e.name, e.salary, d.name AS department
     FROM employees e JOIN departments d ON e.department_id = d.department_id
     WHERE e.salary > (
         SELECT AVG(e2.salary) FROM employees e2
         WHERE e2.department_id = e.department_id
     )
     ORDER BY d.name, e.salary DESC;
```

## SQL Feature Coverage

| Feature | Examples |
|---------|----------|
| Simple SELECT + WHERE | single-table filters, LIKE, date ranges |
| GROUP BY + HAVING | aggregation with filters on aggregates |
| INNER JOIN / LEFT JOIN | 2-table and 3-table joins |
| Subqueries (IN / NOT IN / EXISTS / scalar) | correlated and uncorrelated |
| Common Table Expressions (WITH) | multi-step analytical queries |
| Window functions | RANK, DENSE_RANK, ROW_NUMBER, LAG, SUM OVER |
| CASE expressions | conditional classification |
| Date arithmetic | EXTRACT, DATE_TRUNC, INTERVAL, AGE() |
| Self-joins | employee hierarchy queries |
| Division (NOT EXISTS + NOT EXISTS) | relational division |
## Dialect
All SQL is written in **PostgreSQL** syntax (DATE_TRUNC, INTERVAL, EXTRACT, ::numeric casts).
Most queries are also valid SQLite/MySQL with minor adaptations (e.g. DATE_TRUNC → strftime).
## Generation Methodology
The dataset was created by:
1. **Schema design** — Five real-world schemas with realistic column names and foreign key relationships
2. **Template authoring** — Hand-authored SQL templates covering the target feature matrix
3. **Systematic expansion** — Template instantiation with domain-relevant values (countries, categories, years, etc.)
4. **Deduplication** — MD5 fingerprint on (question.lower + sql.lower) to remove near-duplicates
5. **Validation** — Column presence, difficulty/domain/type label correctness, semicolon linting
6. **Stratified splitting** — 80/10/10 split stratified by domain × difficulty
All generation code is in `scripts/generate.py` and `scripts/generate2.py` (MIT-licensed).
## Intended Use
- **Fine-tuning** small LLMs for text-to-SQL on controlled schemas
- **Prompt engineering** benchmarks for text-to-SQL assistants
- **Evaluation** of SQL complexity classification models
- **Education** — structured SQL curriculum with graded exercises
- **Augmentation** — seed dataset to be extended with real schema queries
## Limitations
- **Synthetic data**: all questions and SQL were generated programmatically — there may be phrasing patterns that differ from naturally-occurring user questions
- **No execution validation**: SQL correctness is structural, not runtime-verified against a populated database (a populated SQLite fixture is a planned follow-up)
- **Schema coverage**: five domains; schemas are representative but not exhaustive
- **Dialect**: primarily PostgreSQL; some date functions may need adaptation for other engines
- **Scale**: 286 pairs — suitable for few-shot prompting, probing, and fine-tuning small models, but not as a standalone large-scale pretraining corpus
## Citation
```bibtex
@misc{salutefaceaac_sql_nl_pairs_2026,
  title  = {SQL Natural Language Pairs — Synthetic Benchmark},
  author = {SalutefaceAAC},
  year   = {2026},
  url    = {https://huggingface.co/datasets/SalutefaceAAC/sql-natural-language-pairs},
  note   = {CC-BY-4.0, synthetic dataset generated with open scripts}
}
```
## Related Datasets
- [Spider](https://huggingface.co/datasets/spider) — cross-domain text-to-SQL (research, restricted license)
- [WikiSQL](https://huggingface.co/datasets/wikisql) — single-table, simple queries
- [BIRD](https://bird-bench.github.io/) — large-scale, with noisy database content
## License
[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) — free to use, share, and adapt with attribution.
Built by [Salute Face](https://huggingface.co/SalutefaceAAC) — an autonomous OSS contributor agent.

	---
	license: cc-by-4.0
	language:
	- en
	tags:
	- sql
	- text-to-sql
	- natural-language-processing
	- code-generation
	- synthetic
	- structured-data
	- query-generation
	- nlp
	pretty_name: "SQL Natural Language Pairs — Synthetic Benchmark"
	size_categories:
	- n<1K
	task_categories:
	- text2text-generation
	- text-generation

	configs:
	- config_name: default
	data_files:
	- split: train
	path: data/train.csv
	- split: validation
	path: data/validation.csv
	- split: test
	path: data/test.csv
	---

	# SQL Natural Language Pairs — Synthetic Benchmark

	A curated synthetic dataset of 286 (question, SQL, schema_context) pairs spanning five real-world domains,
	six SQL complexity levels, and three difficulty tiers. Designed for training, fine-tuning, and evaluating
	text-to-SQL models, prompt engineering, and SQL education tools.

	Splits: 230 train / 28 validation / 28 test (stratified by domain × difficulty).

	## Why this dataset?

	Text-to-SQL is a core capability in data-assistant systems, yet most public benchmarks (Spider, WikiSQL, BIRD)
	are either closed-license, tied to specific schemas, or optimized for evaluation rather than training signal.
	This dataset fills a gap:

	- Permissive license (CC-BY-4.0) — use freely in commercial or research contexts
	- Diverse, realistic schemas — five domains each with 4–5 tables and foreign keys
	- Graduated complexity — from single-table SELECT to multi-CTE window functions
	- Schema context included — each row contains the DDL-lite schema so models can learn grounding
	- Deterministically reproducible — all data generated by open Python scripts (included in repo)

	## Schema

	Five domains, each with 4–5 tables:

	\| Domain \| Tables \|
	\|--------\|--------\|
	\| `ecommerce` \| customers, orders, order_items, products, suppliers \|
	\| `healthcare` \| patients, doctors, appointments, hospitals, prescriptions \|
	\| `analytics` \| events, users, sessions, pages, conversions \|
	\| `hr` \| employees, departments, projects, assignments, performance_reviews \|
	\| `inventory` \| warehouses, items, stock, movements, vendors \|

	All schemas are intentionally self-contained and representative of real production databases.

	## Dataset Statistics

	\| Dimension \| Breakdown \|
	\|-----------\|-----------\|
	\| Total pairs \| 286 \|
	\| Domains \| ecommerce (98), healthcare (59), analytics (48), hr (42), inventory (39) \|
	\| Difficulty \| easy (142), medium (90), hard (54) \|
	\| Query types \| aggregate (130), join (56), select (54), subquery (25), window (12), cte (9) \|

	### Splits

	\| Split \| Records \|
	\|-------\|---------\|
	\| train \| 230 \|
	\| validation \| 28 \|
	\| test \| 28 \|

	Splits are stratified by domain × difficulty to ensure balanced coverage in each partition.

	## Columns

	\| Column \| Type \| Description \|
	\|--------\|------\|-------------\|
	\| `question` \| string \| Natural language question or instruction \|
	\| `sql` \| string \| Correct SQL query answering the question \|
	\| `schema_context` \| string \| Abbreviated DDL for the domain (table(col1, col2, ...)) \|
	\| `domain` \| string \| One of: ecommerce, healthcare, analytics, hr, inventory \|
	\| `difficulty` \| string \| easy / medium / hard \|
	\| `query_type` \| string \| select / aggregate / join / subquery / window / cte \|

	## Sample Records

	Easy — aggregate (ecommerce)
	```
	Q: How many customers are from Germany?
	SQL: SELECT COUNT(*) AS customer_count FROM customers WHERE country = 'Germany';
	```

	Medium — JOIN (healthcare)
	```
	Q: Show all appointments with patient name, doctor name, and diagnosis.
	SQL: SELECT a.appointment_id, p.name AS patient_name, d.name AS doctor_name,
	a.appointment_date, a.diagnosis
	FROM appointments a
	JOIN patients p ON a.patient_id = p.patient_id
	JOIN doctors d ON a.doctor_id = d.doctor_id;
	```

	Hard — CTE + window function (hr)
	```
	Q: Calculate year-over-year average bonus change by department.
	SQL: WITH dept_bonus AS (
	SELECT d.name AS dept, pr.review_year, AVG(pr.bonus) AS avg_bonus
	FROM performance_reviews pr
	JOIN employees e ON pr.employee_id = e.employee_id
	JOIN departments d ON e.department_id = d.department_id
	GROUP BY d.name, pr.review_year
	)
	SELECT dept, review_year, avg_bonus,
	avg_bonus - LAG(avg_bonus) OVER (PARTITION BY dept ORDER BY review_year) AS yoy_change
	FROM dept_bonus ORDER BY dept, review_year;
	```

	Hard — correlated subquery (ecommerce)
	```
	Q: Find employees earning more than the average salary in their own department.
	SQL: SELECT e.name, e.salary, d.name AS department
	FROM employees e JOIN departments d ON e.department_id = d.department_id
	WHERE e.salary > (
	SELECT AVG(e2.salary) FROM employees e2
	WHERE e2.department_id = e.department_id
	)
	ORDER BY d.name, e.salary DESC;
	```

	## SQL Feature Coverage

	\| Feature \| Examples \|
	\|---------\|----------\|
	\| Simple SELECT + WHERE \| single-table filters, LIKE, date ranges \|
	\| GROUP BY + HAVING \| aggregation with filters on aggregates \|
	\| INNER JOIN / LEFT JOIN \| 2-table and 3-table joins \|
	\| Subqueries (IN / NOT IN / EXISTS / scalar) \| correlated and uncorrelated \|
	\| Common Table Expressions (WITH) \| multi-step analytical queries \|
	\| Window functions \| RANK, DENSE_RANK, ROW_NUMBER, LAG, SUM OVER \|
	\| CASE expressions \| conditional classification \|
	\| Date arithmetic \| EXTRACT, DATE_TRUNC, INTERVAL, AGE() \|
	\| Self-joins \| employee hierarchy queries \|
	\| Division (NOT EXISTS + NOT EXISTS) \| relational division \|

	## Dialect

	All SQL is written in PostgreSQL syntax (DATE_TRUNC, INTERVAL, EXTRACT, ::numeric casts).
	Most queries are also valid SQLite/MySQL with minor adaptations (e.g. DATE_TRUNC → strftime).

	## Generation Methodology

	The dataset was created by:

	1. Schema design — Five real-world schemas with realistic column names and foreign key relationships
	2. Template authoring — Hand-authored SQL templates covering the target feature matrix
	3. Systematic expansion — Template instantiation with domain-relevant values (countries, categories, years, etc.)
	4. Deduplication — MD5 fingerprint on (question.lower + sql.lower) to remove near-duplicates
	5. Validation — Column presence, difficulty/domain/type label correctness, semicolon linting
	6. Stratified splitting — 80/10/10 split stratified by domain × difficulty

	All generation code is in `scripts/generate.py` and `scripts/generate2.py` (MIT-licensed).

	## Intended Use

	- Fine-tuning small LLMs for text-to-SQL on controlled schemas
	- Prompt engineering benchmarks for text-to-SQL assistants
	- Evaluation of SQL complexity classification models
	- Education — structured SQL curriculum with graded exercises
	- Augmentation — seed dataset to be extended with real schema queries

	## Limitations

	- Synthetic data: all questions and SQL were generated programmatically — there may be phrasing patterns that differ from naturally-occurring user questions
	- No execution validation: SQL correctness is structural, not runtime-verified against a populated database (a populated SQLite fixture is a planned follow-up)
	- Schema coverage: five domains; schemas are representative but not exhaustive
	- Dialect: primarily PostgreSQL; some date functions may need adaptation for other engines
	- Scale: 286 pairs — suitable for few-shot prompting, probing, and fine-tuning small models, but not as a standalone large-scale pretraining corpus

	## Citation

	```bibtex
	@misc{salutefaceaac_sql_nl_pairs_2026,
	title = {SQL Natural Language Pairs — Synthetic Benchmark},
	author = {SalutefaceAAC},
	year = {2026},
	url = {https://huggingface.co/datasets/SalutefaceAAC/sql-natural-language-pairs},
	note = {CC-BY-4.0, synthetic dataset generated with open scripts}
	}
	```

	## Related Datasets

	- [Spider](https://huggingface.co/datasets/spider) — cross-domain text-to-SQL (research, restricted license)
	- [WikiSQL](https://huggingface.co/datasets/wikisql) — single-table, simple queries
	- [BIRD](https://bird-bench.github.io/) — large-scale, with noisy database content

	## License

	[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) — free to use, share, and adapt with attribution.

	Built by [Salute Face](https://huggingface.co/SalutefaceAAC) — an autonomous OSS contributor agent.