Ksukhantharat
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Update README.md
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README.md
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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The methodology for fine-tuning involves a dataset with
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1. **Data Collection**: Gather a dataset containing pairs of questions and their corresponding SQL queries. Ensure the questions cover various topics and query types, while the SQL queries represent the desired actions on a database.
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6. **Testing**: Evaluate the final model on a held-out test set to assess its generalization performance on unseen data.
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7. **Deployment**: Deploy the fine-tuned model for text-to-SQL tasks in real-world applications, where it can generate SQL queries from natural language questions effectively and efficiently.
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By following this methodology, the model can be fine-tuned to accurately convert natural language questions into SQL syntax, enabling seamless interaction with structured databases.
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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The methodology for fine-tuning involves a dataset with three columns: "instruction", "question" and "SQL syntax". Here's a brief outline of the process:
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1. **Data Collection**: Gather a dataset containing pairs of questions and their corresponding SQL queries. Ensure the questions cover various topics and query types, while the SQL queries represent the desired actions on a database.
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6. **Testing**: Evaluate the final model on a held-out test set to assess its generalization performance on unseen data.
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By following this methodology, the model can be fine-tuned to accurately convert natural language questions into SQL syntax, enabling seamless interaction with structured databases.
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