CS399 Module 7: AI

Author: Wonjoon Jun
Forked From: Prof. Paulus at ERAU

Welcome to CS399 Module 7, where we dive into the fascinating world of Neural Word Embeddings using Python. This project, forked from the esteemed Prof. Paulus, aims to explore the intricacies of linguistic models through practical, hands-on experience.

Table of Contents

• Introduction
• Compatibility Notice
• Installation
• Usage
• The Reason For Choosing this Data Model
• Acknowledgments

Introduction

main.py is a program to remove any outliers within the list of words that are provided.

Compatibility Notice

This version introduces typing_extensions for enhanced compatibility with Python versions prior to 3.10, diverging from the traditional typing module to ensure a smoother experience across different Python environments.

Important: If you've directly downloaded the files from GitHub, ensure that short_glove.txt is present and contains approximately 300MB of data to avoid any runtime issues. You can click on the button right top corner to download raw. The loading for the download takes around 15 seconds, please be patient. Thank you. If the git lfs doesn't work use the link:

Download short_glove.txt: https://1drv.ms/t/s!Ah33FZcNviz2nEUrmg5MO5vD6Yr9?e=E4VJj1

Installation

To get started, ensure you have Python 3.9 or later installed. Then, follow these steps:

1. Clone the repository to your local machine.
2. Install the required dependencies:

   pip install -r requirements.txt

3. Run using streamlit

   streamlit run main.py
   

The Reason For Choosing this Data Model

The GloVe model was chosen because it was opensource, and it yielded the best results over the other language models.

Acknowledgements

1. https://blog.esciencecenter.nl/king-man-woman-king-9a7fd2935a85
2. https://www.geeksforgeeks.org/detect-and-remove-the-outliers-using-python/
3. https://www.assemblyai.com/blog/6-best-ai-playgrounds/
4. https://fasttext.cc/docs/en/english-vectors.html