pip-sql-1.3b
Maintainer: PipableAI
72
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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 pip-sql-1.3b model, developed by PipableAI, is a 1.3 billion parameter SQL model that outperforms most SQL expert models and even GPT-3.5 on popular benchmarks. It is a distilled version of the DeepSeek base model, trained using a combination of softmax cross entropy, modified policy gradient, and Q loss in an EM setup. This novel training approach has enabled the model to achieve exceptional performance on text-to-SQL tasks.
Compared to similar models like distilbert-base-cased-distilled-squad, sqlcoder-70b-alpha, and sqlcoder, the pip-sql-1.3b model stands out for its significant performance improvements on SQL-related tasks. It leverages a unique training approach to deliver state-of-the-art results, making it a valuable tool for analysts and developers working with SQL databases.
Model inputs and outputs
Inputs
- Schema: The schema of the database that the SQL query will be executed against.
- Question: The natural language question that the model will attempt to translate into a SQL query.
Outputs
- SQL query: The SQL query generated by the model based on the provided schema and question.
Capabilities
The pip-sql-1.3b model excels at translating natural language questions into SQL queries. It outperforms most SQL expert models and even GPT-3.5 on popular benchmarks like Semantic Evaluation for Text-to-SQL with Distilled Test Suites and Defog SQL-Eval. For example, on the Semantic Evaluation benchmark, the pip-sql-1.3b model achieves an overall accuracy of 42.1% on the "hard" and "extra" difficulty questions, significantly higher than the 31% accuracy of GPT-3.5.
What can I use it for?
The pip-sql-1.3b model can be a valuable tool for developers, analysts, and anyone working with SQL databases. It can be used to quickly generate SQL queries based on natural language questions, saving time and effort. This can be particularly useful for non-technical users who need to extract data from a database but are not proficient in SQL.
Additionally, the model's strong performance on SQL-related tasks makes it a compelling choice for building applications that require natural language processing capabilities for database interactions, such as chatbots, voice assistants, or data visualization tools.
Things to try
One interesting aspect of the pip-sql-1.3b model is its use of a novel training approach that combines softmax cross entropy, modified policy gradient, and Q loss in an EM setup. This approach has enabled the model to achieve exceptional performance on text-to-SQL tasks, outperforming even much larger models like GPT-3.5.
Researchers and developers interested in advancing the state of the art in natural language processing for database interactions could explore ways to further refine or build upon this training approach. Additionally, testing the model's performance on a wider range of SQL-related tasks or evaluating its robustness to different types of database schemas and queries could provide valuable insights into its capabilities and limitations.
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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