Word2Vec is a popular model family used in datascience. To be meaningful, it requires a model of millions of words. Loading those models in a normal laptop can take a hit on ram usage.
Why not providing a word2vec implementation trained on the GoogleNews-vectors-negative300 dataset as a service ?
Just hit the /word2vec?word=something
of this service ad you will get your word vector
docker pull vampolo/word2vec-service
docker run -P vampolo/word2vec-service
Go to http://localhost:8000/word2vec?word=something and you will get the array of the word you selected.
Since building this project takes quite a bit of time and resources, you can leverage docker-compose
to dowload published image
docker-compose pull
docker-compose up
Then go to http://localhost:8000/word2vec?word=something and you will get the array of the word you selected.