How Can I Count Word Frequencies In Word2vec's Training Model?

I need to count the frequency of each word in word2vec's training model. I want to have output that looks like this: term count apple 123004 country 4432180 runs 620102 ..

Solution 1:

Which word2vec implementation are you using?

In the popular gensim library, after a Word2Vec model has its vocabulary established (either by doing its full training, or after build_vocab() has been called), the model's wv property contains a KeyedVectors-type object, which as a property vocab which is a dict of Vocab-type objects, which have a count property of the word's frequency in the scanned corpus.

So you could get roughly what you seek with something like:

w2v_model = Word2Vec(your_corpus, ...)
for word in w2v_model.wv.vocab:
    print((word, w2v_model.wv.vocab[word].count))

Plain sets of word-vectors (such as those loaded via gensim's load_word2vec_format() method) won't have accurate counts, but are by convention usually internally ordered from most-frequent to least-frequent.