--- license: apache-2.0 task_categories: - question-answering - table-question-answering - text-generation language: - en tags: - synthetic - SQL - text-to-SQL - code size_categories: - 100K

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# synthetic_text_to_sql **gretelai/synthetic_text_to_sql** is a rich dataset of high quality synthetic Text-to-SQL samples, designed and generated using [Gretel Navigator](https://gretel.ai/gretel-navigator), and released under Apache 2.0. Please see our [release blogpost](https://gretel.ai/blog/synthetic-text-to-sql-dataset) for more details. The dataset includes:

105,851 records partitioned into 100,000 train and 5,851 test records
~23M total tokens, including ~12M SQL tokens
Coverage across 100 distinct domains/verticals
Comprehensive array of SQL tasks: data definition, retrieval, manipulation, analytics & reporting
Wide range of SQL complexity levels, including subqueries, single joins, multiple joins, aggregations, window functions, set operations
Database context, including table and view create statements
Natural language explanations of what the SQL query is doing
Contextual tags to optimize model training

As of April 2024, gretelai/synthetic_text_to_sql dataset stands as the largest and most diverse synthetic Text-to-SQL dataset available to-date. It is not just a milestone in the world of synthetic data; it's an invitation to the broader AI community. We invite developers, researchers, and data enthusiasts to take the dataset for a spin, and build upon it. If you end up using this dataset, drop us a note in the [Synthetic Data Discord](https://gretel.ai/discord) community. We'd love to hear what you are building! This release is also merely a glimpse into the capabilities of Gretel. The real value of synthetic data lies in the ability to design and iterate on data to address specific data gaps, incorporate unique business logic, and to infuse with use-case-specific context. We invite you to explore Gretel tools and capabilities to accelerate your journey towards [data-centric AI](https://datacentricai.org/). ## Dataset Details ### Schema The dataset includes 11 fields shown below:

### Example ``` { "id": 39325, "domain": "public health", "domain_description": "Community health statistics, infectious disease tracking data, healthcare access metrics, and public health policy analysis.", "sql_complexity": "aggregation", "sql_complexity_description": "aggregation functions (COUNT, SUM, AVG, MIN, MAX, etc.), and HAVING clause", "sql_task_type": "analytics and reporting", "sql_task_type_description": "generating reports, dashboards, and analytical insights", "sql_prompt": "What is the total number of hospital beds in each state?", "sql_context": "CREATE TABLE Beds (State VARCHAR(50), Beds INT); INSERT INTO Beds (State, Beds) VALUES ('California', 100000), ('Texas', 85000), ('New York', 70000);", "sql": "SELECT State, SUM(Beds) FROM Beds GROUP BY State;", "sql_explanation": "This query calculates the total number of hospital beds in each state in the Beds table. It does this by using the SUM function on the Beds column and grouping the results by the State column." } ``` ### Dataset Description

Breakdown of text to SQL dataset features and corresponding data types and token counts

Breakdown by SQL complexity

Breakdown by SQL task type

Domain Distribution

Token Distributions

Word clouds for the natural language prompt, database context, SQL, and SQL explanation

### Data Quality Assessment In order to assess the quality of our Text-to-SQL data, we leveraged the [LLM-as-a-judge technique](https://arxiv.org/pdf/2306.05685.pdf) (see also our [blog](https://gretel.ai/blog/synthetic-text-to-sql-dataset) for more details). We holistically evaluate the quality of SQL across 1,000 randomly chosen samples of data. We use GPT-4 to score samples from our Text-to-SQL dataset and compare results to 1,000 randomly chosen samples from the [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) dataset, which is an extension of the [Spider](https://huggingface.co/datasets/spider) dataset, and includes database context for an apples-to-apples comparison. We observe that our dataset consistently scores higher on: - Compliance with SQL Standards: +54.6% - SQL Correctness: +34.5% - Adherence to Instructions: +8.5% LLM-as-a-judge evaluation

LLM-as-a-judge comparison of gretelai/synthetict_text_to_sql with b-mc2/sql-create-context dataset across five different criteria: (i) Adherence to Instructions, (ii) SQL Correctness, (iii) Readability and Maintanability, (iv) Scalability, and (v) Compliance with Standards

See the [grading rubric](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/blob/main/llm_as_a_judge_rubric.txt) with explicit criteria used for the LLM-as-a-judge evaluation. We also include two examples of LLM judgements for the b-mc2/sql-create-context dataset: - [example 1](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/blob/main/bmc2_llm_judge_example_1.txt) - [example 2](https://huggingface.co/datasets/gretelai/synthetic_text_to_sql/blob/main/bmc2_llm_judge_example_2.txt) In addition to the above, the parsability and validity of SQL in both sql_context and sql fields has been verified using a python SQL Parser/Transpiler [sqlglot](https://github.com/tobymao/sqlglot) and a SQL format/syntax/semantics validator [sqlvalidator](https://github.com/David-Wobrock/sqlvalidator):

Breakdown of SQL parsability and validity for gretelai/synthetict_text_to_sql and b-mc2/sql-create-context

## Citation ``` @software{gretel-synthetic-text-to-sql-2024, author = {Meyer, Yev and Emadi, Marjan and Nathawani, Dhruv and Ramaswamy, Lipika and Boyd, Kendrick and Van Segbroeck, Maarten and Grossman, Matthew and Mlocek, Piotr and Newberry, Drew}, title = {{Synthetic-Text-To-SQL}: A synthetic dataset for training language models to generate SQL queries from natural language prompts}, month = {April}, year = {2024}, url = {https://huggingface.co/datasets/gretelai/synthetic-text-to-sql} } ```

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0xca91f20a...	CoT Reasoning Mens Mental Health	12.12 KB	5FHneW46xGXgs5m...	1.03 PALI	11/14/2025, 7:59:06 AM
0x26e7586c...	Awesome ChatGPT Prompts [CSV dataset]	12.11 KB	5FHneW46xGXgs5m...	1.47 PALI	11/14/2025, 7:59:18 AM
0xaf81a601...	synthetic_text_to_sql	12.11 KB	5FHneW46xGXgs5m...	1.15 PALI	11/14/2025, 7:59:30 AM