natural-sql-7b
Maintainer: chatdb
95
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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
The natural-sql-7b model by ChatDB is a powerful text-to-SQL generation model that outperforms other models of similar size in its space. It has excellent performance on complex, compound SQL questions and can handle tasks that other models struggle with. The model is trained to convert natural language instructions into SQL queries, making it a valuable tool for non-technical users to interact with databases.
Similar models include pipSQL-1.3b by PipableAi, which also focuses on text-to-SQL generation, and the SQLCoder and SQLCoder2 models developed by Defog, which are state-of-the-art large language models for natural language to SQL conversion.
Model inputs and outputs
Inputs
- Natural language instructions: The model takes in natural language questions or instructions and converts them into SQL queries.
Outputs
- SQL queries: The model generates SQL queries based on the provided natural language input.
Capabilities
The natural-sql-7b model has exceptional performance in text-to-SQL tasks, outperforming models of similar size. It can handle complex, compound questions that often trip up other models. For example, the model can generate SQL queries to find the total revenue from customers in New York compared to San Francisco, including the difference between the two.
What can I use it for?
The natural-sql-7b model is a valuable tool for non-technical users to interact with databases. It can be used in a variety of applications, such as:
- Business intelligence and data analysis: Users can ask natural language questions about the data in their database and get the corresponding SQL queries, allowing them to quickly generate insights without needing to learn SQL.
- Customer support: The model can be used to build chatbots that can help customers find information in a database by understanding their natural language requests.
- Productivity tools: The model can be integrated into productivity software, allowing users to quickly generate SQL queries to extract the data they need.
Things to try
One interesting aspect of the natural-sql-7b model is its ability to handle complex, compound questions. Try asking the model questions that involve multiple steps or conditions, such as "Find the top 3 best-selling products by revenue, but only for products with a price above the average product price." The model should be able to generate the appropriate SQL query to answer this type of complex question.
Another interesting thing to try is fine-tuning the model on a specific database schema or domain. By training the model on data more closely related to the task at hand, you may be able to further improve its performance and tailor it to your specific needs.
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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