Project: T5 Model Fine-Tuning and Application

This project demonstrates the fine-tuning of the T5-small model from Hugging Face for text-to-text tasks, integration with MySQL, and schema generation. The repository is structured to include a Streamlit application, database testing, and fine-tuning notebooks.

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📂 Directory Structure

• src/

  • weights: Download from givem link below.
  • app_version4.py: Contains the Streamlit application for user interaction.

• testing/

  • connect.py: Tests the connection to the MySQL database.
  • getschema.py: Generates the schema required to feed data into the fine-tuned model.

• finetune.ipynb
  Notebook for fine-tuning the T5-large model. The model has been trained for one epoch, achieving promising results.

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📋 Requirements

To run this project, ensure you have the following dependencies installed:

streamlit
pandas
sqlalchemy
transformers
torch
evaluate
datasets
numpy
scikit-learn
pymysql

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Note:

We have used the weights we got by doing the finetuning in app_version4.py, and used pretrained model in app_version3.py You also have to do the configeration for Mysql Data base for the streamlit app to work properly. Keep the weights in a weights folder in the root folder of the project. Download the weights from https://drive.google.com/drive/folders/1kWGmSJI7Q2o4qnt2K85HHsuhJxF9Cjkh?usp=drive_link