Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks

Editor's note: This post is only one part of a far more thorough and in-depth original, found here, which covers much more than what is included here. While our implementations are decent enough, they are not optimized enough to work well on large corpora. The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model. We will leverage the same on our Bible corpus. In our workflow, we will tokenize our normalized corpus and then focus on the following four parameters in the Word2Vec model to build it.