Word embeddings capture semantic and syntactic similarities of words, represented as vectors. Word2Vec is a popular implementation of word embeddings; it takes as input a large corpus of text and learns a model that maps unique words in that corpus to other contextually relevant words. After training, Word2Vec's internal vector representation of words in the corpus map unique words to a vector space, which are then used in many downstream tasks. Training these models requires significant computational resources (t…