Koyipooo/SynSQL-2.5M

---
license: apache-2.0
task_categories:
- table-question-answering
- translation
- text2text-generation
language:
- en
tags:
- Text-to-SQL
- NL2SQL
- Text2SQL
- SQL
size_categories:
- 1M<n<10M
---
# SynSQL-2.5M - The First Million-Scale Cross-Domain Text-to-SQL Dataset

We introduce the first million-scale text-to-SQL dataset, **SynSQL-2.5M**, containing over **2.5 million diverse and high-quality data samples**, spanning more than **16,000 databases from various domains**.

Building on SynSQL-2.5M, we introduce **OmniSQL**, a family of powerful text-to-SQL models available in three sizes: **7B, 14B, and 32B**. During the fine-tuning process, we also integrate training sets from Spider and BIRD, which provide high-quality, human-labeled data.

**Paper Link:** [https://arxiv.org/abs/2503.02240](https://arxiv.org/abs/2503.02240)

**GitHub Link:** [https://github.com/RUCKBReasoning/OmniSQL](https://github.com/RUCKBReasoning/OmniSQL)

## Downloads
| **Model and Dataset** | **Download Latest** |
|-----------|------------------|
| SynSQL-2.5M | [✨ Modelscope](https://www.modelscope.cn/datasets/seeklhy/SynSQL-2.5M/summary), [🤗 HuggingFace](https://huggingface.co/datasets/seeklhy/SynSQL-2.5M) |
| OmniSQL-7B | [✨ Modelscope](https://modelscope.cn/models/seeklhy/OmniSQL-7B), [🤗 HuggingFace](https://huggingface.co/seeklhy/OmniSQL-7B) |
| OmniSQL-14B | [✨ Modelscope](https://modelscope.cn/models/seeklhy/OmniSQL-14B), [🤗 HuggingFace](https://huggingface.co/seeklhy/OmniSQL-14B) |
| OmniSQL-32B | [✨ Modelscope](https://modelscope.cn/models/seeklhy/OmniSQL-32B), [🤗 HuggingFace](https://huggingface.co/seeklhy/OmniSQL-32B) |

## Statistics about SynSQL-2.5M
SynSQL-2.5M is a high-quality synthetic text-to-SQL dataset, generated entirely using open-source LLMs and released under Apache 2.0.

The dataset includes:
- 2,544,390 diverse and complex text-to-SQL samples, each consisting of a `<database, question, SQL query, chain-of-thought solution>` quad.
- Coverage of 16,583 synthetic databases from realistic scenarios.
- A wide range of SQL complexity levels: `simple, moderate, complex, highly complex`, from single-table queries to advanced multi-table joins, functions, and common table expressions.
- A variety of linguistic styles in natural language questions: `formal, colloquial, imperative, interrogative, descriptive, concise, vague, metaphorical, and conversational`.
- Chain-of-thought (CoT) solutions provided for all data samples.

For more statistics and quality evaluations, refer to our paper. As of March 2025, SynSQL-2.5M is the largest and most diverse synthetic text-to-SQL dataset to date. It represents a significant milestone in the text-to-SQL community. We encourage researchers, practitioners, and data enthusiasts to explore and build models using this dataset. *If you find it useful, please consider giving us a star or citing our work. Your feedback is our greatest motivation to continue advancing.*

## Limitations
SynSQL-2.5M is an English dataset focused on the SQLite database engine, so its performance in multi-language and multi-SQL dialect scenarios may be limited. However, you can synthesize new data samples using our proposed framework to suit your scenarios. After synthesizing a new dataset, you can use OmniSQL for further fine-tuning, as it is a strong starting point for text-to-SQL capabilities.

## Contact
If you have any questions, we encourage you to either create Github issues or get in touch with Haoyang Li at [e-mail].