trl-lab/SQaLe-text-to-SQL-dataset
🧮 SQALE: A Large-Scale Semi-Synthetic Dataset SQALE is a large-scale, semi-synthetic Text-to-SQL dataset grounded in real-world database schemas. It was designed to push the boundaries of natural language to SQL generation, combining realistic schema diversity, complex query structures, and linguistically varied natural language questions. The dataset was introduced in the paper SQaLe: A Large Text-to-SQL Corpus Grounded in Real Schemas. The code for the generation pipeline of… See the full description on the dataset page: https://huggingface.co/datasets/trl-lab/SQaLe-text-to-SQL-dataset.
| Author / organization | trl-lab |
|---|---|
| License (as declared) | mit |
| Downloads | 1K (as reported by Hugging Face) |
| Likes | 21 |
| Created | 21 September 2025 |
| Last updated at source | 2 March 2026 |
Popularity, last 3 days
1,033 now (+88 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 17 Oct 2026 Description: 🧮 SQALE: A Large-Scale Semi-Synthetic Dataset SQALE is a large-scale, semi-synthetic Text-to-SQL dataset grounded in real-world database schemas. It was designed to push the boundaries of natural language to SQL generation, combining reali → 🧮 SQALE: A Large-Scale Semi-Synthetic Dataset SQALE is a large-scale, semi-synthetic Text-to-SQL dataset grounded in real-world database schemas. It was designed to push the boundaries of natural language to SQL generation, combining reali
Changes detected by Silytics between refreshes of the source.
More from trl-lab
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.