text-to-sql-with-table-schema
Maintainer: juierror
64
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
text-to-sql-with-table-schema is an AI model developed by juierror that can translate natural language questions into SQL queries, given the schema of the tables involved. This model is an upgraded version of a previous model that supports multiple tables and the "<" sign, using the Flan-T5 model as a base.
Similar models include t5-base-finetuned-wikiSQL, which is a T5-base model fine-tuned on the WikiSQL dataset for English to SQL translation, and natural-sql-7b, a large language model with strong performance on text-to-SQL tasks.
Model inputs and outputs
Inputs
- Question: A natural language question about data in a database
- Table: A list of the database table names that the question refers to
Outputs
- SQL query: The SQL query that answers the given natural language question, based on the provided table schema
Capabilities
The text-to-sql-with-table-schema model has the capability to translate a wide range of natural language questions into SQL queries, including complex queries involving multiple tables. It can handle questions that require aggregations, filtering, and other SQL operations.
What can I use it for?
You can use this model to build applications that allow users to interact with a database using natural language. For example, you could create a chatbot or voice interface that allows users to query a database and get the results in a user-friendly way, without requiring them to learn SQL. This could be useful in a variety of domains, such as business intelligence, customer service, or data analysis.
Things to try
One interesting thing to try with this model is to see how it handles complex, compound questions that involve multiple tables and advanced SQL operations. You could also experiment with fine-tuning the model on your own dataset to see if it can improve performance on specific types of queries or domains.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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