from gensim.models import Word2Vec sentences = [["I", "love", "dogs"], ["Cats", "are", "pretty"]] model = Word2Vec(sentences, min_count=1)
similar_words = model.wv.most_similar(positive=["dogs"]) print(similar_words)
from gensim.models import Word2Vec model1 = Word2Vec(sentences1, min_count=1) model2 = Word2Vec(sentences2, min_count=1) model_combined = model1 + model2Python gensim.models Word2Vec is a package library that is used for training and building word embedding models. It is mainly used for natural language processing tasks such as text classification, document analysis, and sentiment analysis.