Model Details
Model Description
This is a model for SQL query generation based on the Hugging Face 🤗 transformers library, specifically utilizing the T5 model architecture. The model is trained to generate SQL queries given a context and a question related to a database schema.
- Developed by: Yusuf Abdulakeem
- Model type: Text-to-Text Generation (T5)
- Language(s) (NLP): English
- Finetuned from model: T5-small
Uses
Direct Use
The model can be directly used to generate SQL queries based on provided context and questions.
Downstream Use
The model can be integrated into applications for automating SQL query generation tasks in various database-related applications.
Out-of-Scope Use
Use cases requiring precise and complex SQL query generation beyond the model's training data may be out of scope.
Bias, Risks, and Limitations
Users should be cautious about the model's output and verify generated SQL queries for correctness. Limitations may include difficulty handling complex queries or rare schema types.
Recommendations
Users should be made aware of the potential risks, biases, and limitations of the model. Further validation and testing are recommended for critical applications.
How to Get Started with the Model
Use the provided Python code to train and utilize the model.
Training Details
Training Data
The training data consists of SQL-related datasets, potentially containing various database schema contexts, questions, and corresponding SQL queries.
Training Procedure
- Preprocessing: Data preprocessing involves tokenization and formatting of the input context, questions, and output SQL queries.
Training Hyperparameters
- Training regime: AdamW optimizer with a learning rate of 0.0001.
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model's performance can be evaluated using separate testing datasets containing context, questions, and ground truth SQL queries.
Factors
Evaluation factors may include query correctness, semantic similarity, and query execution efficiency.
Metrics
Evaluation metrics may include accuracy, precision, recall, and F1 score for generated SQL queries.
Results
Evaluation results on testing datasets are needed to assess the model's performance accurately.
Summary
Model Examination
Detailed analysis of the model's architecture, parameters, and performance metrics is recommended.
Technical Specifications
Model Architecture and Objective
The model is based on the T5 architecture, which is designed for text-to-text tasks. Its objective is to generate SQL queries from given context and questions.
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