In spacy, how to use your own word2vec model created in gensim?

Question

I have trained my own word2vec model in gensim and I am trying to load that model in spacy. First, I need to save it in my disk and then try to load an init-model in spacy but u

Answer 1

Train and save your model in plain-text format:

from gensim.test.utils import common_texts, get_tmpfile
from gensim.models import Word2Vec

path = get_tmpfile("./data/word2vec.model")

model = Word2Vec(common_texts, size=100, window=5, min_count=1, workers=4)
model.wv.save_word2vec_format("./data/word2vec.txt")

Gzip the text file:

gzip word2vec.txt

Which produces a word2vec.txt.gz file.

Run the following command:

python -m spacy init-model en ./data/spacy.word2vec.model --vectors-loc word2vec.txt.gz

Load the vectors using:

nlp = spacy.load('./data/spacy.word2vec.model/')

Answer 2

As explained here, you can import custom word vectors that trained using Gensim, Fast Text, or Tomas Mikolov's original word2vec implementation, by creating a model using:

wget https://s3-us-west-1.amazonaws.com/fasttext-vectors/word-vectors-v2/cc.la.300.vec.gz
python -m spacy init-model en your_model --vectors-loc cc.la.300.vec.gz

then you can load you model, nlp = spacy.load('your_model') and use it!

Also see the similar question that answered here.