Data Science, Machine Learning, NLP, Transformers, Word Embeddings.
vectors from Google model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True) king = model['king'] # king - man + woman = queen print(model.most_similar(positive=['woman', 'king'], negative=['man'])) Check how similar are vectors for words ‘woman’