Papers › DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL
DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL
Lixia Wu, Peng Li, Junhong Lou, Lei Fu
In addressing the pivotal role of translating natural language queries into SQL commands, we propose a suite of compact, fine-tuned models and self-refine mechanisms to democratize data access and analysis for non-expert users, mitigating risks associated with closed-source Large Language Models. Specifically, we constructed a dataset of over 20K sample for Text-to-SQL as well as the preference dateset, to improve the efficiency in the domain of SQL generation. To further ensure code validity, a code corrector was integrated into the model. Our system, DataGpt-sql, achieved 87.2\% accuracy on the spider-dev, respectively, showcasing the effectiveness of our solution in text-to-SQL conversion tasks. Our code, data, and models are available at \url{https://github.com/CainiaoTechAi/datagpt-sql-7b}
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-To-SQL | spider | datagpt-sql-7B + InvalidSQL-Feedback | Exact Match Accuracy (Dev) | 81.6 | #15 of 20 | Archive leaderboard | report |
| Text-To-SQL | spider | datagpt-sql-7B + InvalidSQL-Feedback | Execution Accuracy (Dev) | 87.2 | #15 of 20 | Archive leaderboard | report |
| Text-To-SQL | spider | datagpt-sql-7B | Exact Match Accuracy (Dev) | 80.3 | #16 of 20 | Archive leaderboard | report |
| Text-To-SQL | spider | datagpt-sql-7B | Execution Accuracy (Dev) | 84.8 | #16 of 20 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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