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natural-language-to-sql

This project aims to develop a system that converts natural language questions into SQL queries, enabling non-technical users to query databases easily. The task is fundamental in applications such as chatbots, voice assistants, and user-friendly database interfaces. The system is built using the WikiSQL dataset, which includes tables, natural language questions, and their corresponding SQL queries.

Objectives

  • Create a pipeline that accurately transforms natural language questions into SQL queries.
  • Utilize pre-trained transformer models (e.g., BART) and fine-tune them for the Text-to-SQL task.
  • Evaluate performance using standard metrics like exact match accuracy and execution accuracy.

Measure system performance using key metrics:

  • Exact Match (EM): Achieved 21%, reflecting the proportion of queries perfectly matching references.
  • BLEU Score: Scored 0.598, showing moderate similarity to reference queries.
  • ROUGE-L: Scored 0.86, indicating high overlap in sequence structure
  • Analyze the system's strengths and weaknesses to suggest improvements.