Firstly, let’s talk about what is a Word2Vec model. Word2Vec is one of the most popular techniques to learn word embeddings using a shallow neural network. It was developed by Tomas Mikolov in 2013 at Google. For the algorithm Odd One Out that we are going to implement soon, we will use the Google pre-trained model: ‘Googlenews-vectors-negative300.bin’, which can be downloaded from here. This model can be loaded using the gensim module, by the following code:

from gensim.models import KeyedVectors

model = KeyedVectors.load_word2vec_format(‘GoogleNews-vectors-negative300.bin’,binary=True)
#model loaded

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