I have a DataFrame in which the index are words and I have 100 columns with float number such that for each word I have its embedding as a 100d vector. I would like to convert my DataFrame object into a gensim model object so that I can use its methods; specially gensim.models.keyedvectors.most_similar() so that I can search for similar words within my subset.
Which is the preferred way of doing that?
Thanks