🧠 SQaLe: Enabling new Text-to-SQL models with our massive dataset

SQaLe is an extensive text-to-SQL dataset designed to overcome the limitations of existing resources by providing a large, diverse, and realistic foundation for training and evaluating models that convert natural language into SQL queries. Built from over 139,000 database schemas and more than 500,000 validated triples of schema, question, and query, SQaLe reflects real-world schema complexity and is accessible via the Hugging Face Hub for research and model fine-tuning. The dataset addresses the gap in current benchmarks by offering a scale that supports large language models (LLMs) and a realism that mirrors production database environments, with validated SQL queries ensuring consistency with corresponding natural-language questions. SQaLe's creation involved extending schemas sourced from SchemaPile and generating diverse natural-language questions and SQL queries, culminating in a resource that supports the training and evaluation of text-to-SQL models, schema understanding, and benchmark testing in realistic database contexts.

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