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.
- 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.
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Clone the repository:
git clone https://github.com/habeeb3579/Word-Vector-Visualizer.git
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Cd into the repo
cd Word-Vector-Visualizer
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.