eosphoros-ai/awesome-text2sql

This repository is a curated collection of research papers, tutorials, datasets, and code libraries focused on a problem called Text-to-SQL. The core idea behind Text-to-SQL is simple: instead of requiring someone to know how to write SQL (the technical language used to query databases), a system can take a plain English question and convert it automatically into the right SQL command. For example, a question like "how many customers bought something last month" would be turned into a precise database query without the person typing a single line of code. The collection is organized into clearly labeled sections. There are papers grouped by topic: surveys that give a broad overview of the field, classic models from earlier research, and more recent models built on large language AI systems. There are also sections covering fine-tuning techniques (ways to adapt AI models to specific databases), datasets used to train and test these systems, and evaluation tools for measuring how well a given approach performs. A leaderboard in the README shows the top-performing systems on standard benchmarks like WikiSQL, Spider, and BIRD. These benchmarks test how accurately a model can translate a question into correct SQL across different types of databases. The scores and linked papers let researchers quickly see who is leading and which techniques are being used. The repository also links to practical projects: open-source tools and demos that actually implement Text-to-SQL for real use cases, not just research prototypes. This section is useful if you want to try a working system rather than read academic papers. This is a reference hub for researchers, engineers, and anyone building products that let non-technical users query data with plain language. It is not a standalone tool itself. The full README is longer than what was shown.