How to remove a word completely from a Word2Vec model in gensim?

Question

Given a model, e.g.

from gensim.models.word2vec import Word2Vec


documents = [\"Human machine interface for lab abc computer applications\",
\"A survey of u         


        

Answer 1

Note that this does not trim the model per se. It trims the KeyedVectors object that the similarity look-ups is based on.

Suppose you only want to keep the top 5000 words in your model.

wv = w2v_model.wv
words_to_trim = wv.index2word[5000:]
# In op's case 
# words_to_trim = ['graph'] 
ids_to_trim = [wv.vocab[w].index for w in words_to_trim]

for w in words_to_trim:
    del wv.vocab[w]

wv.vectors = np.delete(wv.vectors, ids_to_trim, axis=0)
wv.init_sims(replace=True)

for i in sorted(ids_to_trim, reverse=True):
    del(wv.index2word[i])

This does the job because the BaseKeyedVectors class contains the following attributes: self.vectors, self.vectors_norm, self.vocab, self.vector_size, self.index2word.

The advantage of this is that if you write the KeyedVectors using methods such as save_word2vec_format(), the file is much smaller.