text_plain_output_1.png
w2v_model = Word2Vec(vector_size=dim_w2v, alpha=alpha, min_alpha=alpha_min, window=wnd, min_count=mincount, sample=sample, sg=sg, negative=ngt, workers=threads)
word_freq = {dct[k]: v for k, v in dct.cfs.items()}
w2v_model.build_vocab_from_freq(word_freq)
num_samples = dct.num_docs
w2v_model.train(corpus_w2v, total_examples=num_samples, epochs=epochs)