GatorSched Project

Overview

GatorSched is an innovative project that leverages the power of GPT-2 small model to process and generate responses to queries related to lifelog data. It aims to extract relevant schedule and historical information from structured lifelog data, offering personalized recommendations and insights.

Structure

• source/: Contains the main scripts and data for running GatorSched.
• TimelineQA/: A Git submodule containing the TimelineQA code, with an additional dataGenCode folder for data generation.

Getting Started

To use GatorSched, clone this repository along with the TimelineQA submodule:

git clone --recurse-submodules git@github.com:Vveanta/GatorSched.git

Directory Contents

• source/inference.ipynb: Notebook to generate answers for the provided questions using the trained model.

• source/myimplementation.ipynb: Notebook for training and fine-tuning the GPT-2 model on lifelog data.

• source/questions.txt: Text file containing sample questions for model testing.

• source/test_data/: Folder containing test data used to train the model.

• TimelineQA/: Submodule from the TimelineQA project with added dataGenCode folder.

  • dataGenCode/datagenerator.py: Script for generating various datasets with different verbosity, sparsity, and time duration.

Usage

Data Generation

To generate new lifelog data, navigate to the TimelineQA submodule and use the datagenerator.py script:

cd TimelineQA/dataGenCode
python datagenerator.py

Model Training and Fine-Tuning

Run the myimplementation.ipynb notebook in the source folder to train and fine-tune the GPT-2 model on your lifelog data. This notebook processes the data, trains the model, and saves the trained model for inference.

Generating Responses

After training the model, use the inference.ipynb notebook to generate responses for questions listed in questions.txt. The notebook outputs the answers in answers.txt, forming a question-answer pair for each query.