Check out the app live at Streamlit Cloud: StreamLit link

The webapp is based on the Efficient Estimation of Word Representations in Vector Space paper. Read it here.

Screencast

screencast.webm

Features:

1. Upload your own text corpus, or even a CSV dataset.
2. Train the Word2Vec on the fly using custom parameters.
3. Choose either PCA or TSNE as your dimensionality reduction technique.
4. Visualize the word in either 2-D or 3-D space.
5. Get similar words for each word, with similarity scores.
6. Option to tune the number of words you wish to see for each input.

Steps to install locally:

1. Setup a virtual environment using Conda or any other method you prefer.
2. Install the dependencies from requirements.txt.
3. Run the following in the terminal.

   pip install -U pip setuptools wheel
   pip install -U spacy
   python -m spacy download en_core_web_sm
   

4. Run streamlit run app.py in the terminal to launch the web app.