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Word Vector Embedding Visualization with SpaCy, Streamlit and Docker

This project demonstrates how to visualize word embeddings using SpaCy, Streamlit and Docker. Word embeddings are numerical representations of words in a high-dimensional space, often used in natural language processing (NLP) tasks. With this application, you can visualize word embeddings in 2D or 3D space using techniques like PCA, UMAP, or t-SNE.

Features

  • Visualize word embeddings in 2D or 3D space.
  • Select from different dimensionality reduction techniques: PCA, UMAP, t-SNE.
  • Select a pre-trained word embedding model or supply your own text for visualization.
  • Interactive plots with Streamlit and Plotly.
  • Dockerized for easy deployment.

Installation

  1. Clone the repository:

     git clone https://github.com/habeeb3579/Word-Vector-Visualizer.git

  2. Cd into the repo

     cd Word-Vector-Visualizer

Usage

Running Using Docker

Ensure you have Docker installed on your system.

Build the Docker image:

    docker build -t word-embedding-app:v1 .

Run the app:

    docker run -p 8501:8501 word-embedding-app:v1

If port 8501 is unavailable on your local machine, run:

     docker run -p <PORT>:8501 word-embedding-app:v1

where PORT is your preferred port.

The application will be accessible at http://localhost:8501.