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https://github.com/sohv/sql_chatbot
https://github.com/sohv/sql_chatbot
Last synced: about 1 month ago
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- Host: GitHub
- URL: https://github.com/sohv/sql_chatbot
- Owner: sohv
- Created: 2024-04-01T12:47:54.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-04-06T11:33:17.000Z (10 months ago)
- Last Synced: 2024-04-07T11:37:55.882Z (10 months ago)
- Language: Jupyter Notebook
- Size: 665 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## SQL Query Generator Chatbot ##
This repository contains the code and resources for building a chatbot that generates SQL queries based on user prompts. We fine-tuned the GPT-2 model on a dataset sourced from the Hugging Face library and trained it using TensorFlow. The fine-tuned model is saved in a designated folder within this repository.
## Requirements: ##
* Python 3.x
* TensorFlow
* Hugging Face Transformers
* Other dependencies as specified in requirements.txt## Installation: ##
1. Clone this repository to your local machine:
https://github.com/sohv/sql_chatbot.git
2. Navigate to the cloned directory:
cd sql_chatbot3. Install the required dependencies:
pip install -r requirements.txt
## Usage: ##
Ensure that the fine-tuned GPT-2 model is saved in the designated folder (models/ by default).Run the chatbot script:
python chatbot.py
The chatbot will prompt you to enter your query requirements. Based on your input, it will generate SQL queries.## Training: ##
If you want to fine-tune the model further or train it from scratch:*Prepare your dataset in the required format.
*Use the provided scripts or adapt them for your dataset.
*Fine-tune the GPT-2 model using TensorFlow.## Model ##
The fine-tuned GPT-2 model is saved in the models/ folder. This model is used by the chatbot to generate SQL queries based on user input.## Contributing ##
Contributions are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or create a pull request.## Acknowledgments ##
*This project utilizes the Hugging Face Transformers library.
*Inspiration for this project came from the need for an intelligent SQL query generator.
*Thanks to the open-source community for providing valuable resources and tools.