Duplicate from seeklhy/SynSQL-2.5M

Co-authored-by: lihaoyang <seeklhy@users.noreply.huggingface.co>

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README.md

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+---
+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 lihaoyang.cs@ruc.edu.cn.

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