Skip to content

Latest commit

 

History

History
46 lines (35 loc) · 1.96 KB

README.md

File metadata and controls

46 lines (35 loc) · 1.96 KB
Raw

Masters Thesis Research Project: A Novel Approach to Improve Question Answering System using GPT-3 🎓🔍

Overview 📜

This repository showcases my Masters Thesis Research Project where I delved into innovative techniques to enhance Question Answering Systems. The cutting-edge models BERT and OpenAI GPT-3 were employed, and their performance was evaluated using the Stanford Question Answering Dataset (SQuAD).

Objectives 🎯

  • Investigate the efficacy of BERT and GPT-3 in the domain of Question Answering.
  • Analyze accuracy and loss metrics for both models.
  • Compare the performance of BERT and GPT-3 based on SQuAD evaluations.

Key Features 🛠️

  • Data Preprocessing: Adapted the SQuAD dataset to suit the model requirements.
  • Model Training: Trained BERT and GPT-3 using the processed SQuAD dataset.
  • Evaluation Metrics: Calculated accuracy and loss for performance assessment.
  • Comparative Analysis: Comprehensive comparison of BERT and GPT-3 performance metrics.

Installation 🖥️

  1. Clone the repository:
git clone https://github.com/rhakbari/ms-thesis-gpt-3.git
  1. Navigate to the project directory:
cd ms-thesis-gpt-3

Usage 📊

  1. Data Preparation: Ensure SQuAD dataset is correctly located.
  2. Model Training: Execute training scripts for BERT and GPT-3.
  3. Evaluation: Run evaluation scripts for accuracy and loss calculations.
  4. Comparison: Use analysis tools for BERT vs. GPT-3 performance assessment.

Results 📈

Detailed insights into BERT and GPT-3 performance metrics, including graphical representations and statistical analyses.

Conclusion 📝

Summarization of research findings, highlighting strengths and limitations of BERT and GPT-3 in Question Answering Systems.

Future Work 🔮

Discussion on potential research directions and methodology enhancements.

Contributors 👥

For inquiries or feedback, please reach out.