Use gensim to load a word2vec model pretrained on google news and perform some simple actions with the word vectors.

gensim_word2vec_demo.py

from gensim.models import Word2Vec

# Load pretrained model (since intermediate data is not included, the model cannot be refined with additional data)
model = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True, norm_only=True)

dog = model['dog']
print(dog.shape)
print(dog[:10])

# Deal with an out of dictionary word: Михаил (Michail)
if 'Михаил' in model:
    print(model['Михаил'].shape)
else:
    print('{0} is an out of dictionary word'.format('Михаил'))

# Some predefined functions that show content related information for given words
print(model.most_similar(positive=['woman', 'king'], negative=['man']))

print(model.doesnt_match("breakfast cereal dinner lunch".split()))

print(model.similarity('woman', 'man'))

Reg "the model cannot be refined with additional data" - gensim has an online algorithm which allows to augment existing model (since 2015) https://rutumulkar.com/blog/2015/word2vec/

AttributeError: The vocab attribute was removed from KeyedVector in Gensim 4.0.0.
Use KeyedVector's .key_to_index dict, .index_to_key list, and methods .get_vecattr(key, attr) and .set_vecattr(key, attr, new_val) instead.
See https://github.com/RaRe-Technologies/gensim/wiki/Migrating-from-Gensim-3.x-to-4

Google Colab demo

Thank you for the Colab demo !!