Word-Vectors

Overview

This repository implements different architectures for training word embeddings. The architectures include Continuous Bag-of-Words (CBOW), skip-gram, and Global Vectors for Word Representation (GloVe). Wikipedia articles is used as training data, while the Google Analogy dataset and the WordSim353 dataset is used for validating the word embeddings.

• Continuous Bag-of-Words (CBOW) architecture implementation
• Skip-gram architecture implementation
• Global Vectors for Word Representation (GloVe) architecture implementation

Setup

0. Install required python version 3.11
1. Install required packages pip install -r source/requirements.txt (We recommend using virtual environment, follow guide under Virtual Environment Setup below and skip this step)
2. Run program python source/main.py

Virtual Environment Setup

Windows

0. Get the package pip install virtualenv
1. Create a new empty instance of python environment py -3.11 -m venv ./.venv
2. Activate the environment source .venv/Scripts/activate
3. Install the packages required by this project pip install -r source/requirements.txt

Linux

0. Get the package pip install virtualenv
1. Create a new empty instance of python environment python -m venv ./.venv
2. Activate the environment source .venv/bin/activate
3. Install the packages required by this project pip install -r source/requirements.txt