SpaCy: how to load Google news word2vec vectors?

Question

I've tried several methods of loading the google news word2vec vectors (https://code.google.com/archive/p/word2vec/):

en_nlp = spacy.load('en',vector=False) en_nlp.vocab.load_vectors_from_bin_loc('GoogleNews-vectors-negative300.bin') 

The above gives:

MemoryError: Error assigning 18446744072820359357 bytes 

I've also tried with the .gz packed vectors; or by loading and saving them with gensim to a new format:

from gensim.models.word2vec import Word2Vec model = Word2Vec.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True) model.save_word2vec_format('googlenews2.txt') 

This file then contains the words and their word vectors on each line. I tried to load them with:

en_nlp.vocab.load_vectors('googlenews2.txt') 

but it returns "0".

What is the correct way to do this?

Update:

I can load my own created file into spacy. I use a test.txt file with "string 0.0 0.0 ...." on each line. Then zip this txt with .bzip2 to test.txt.bz2. Then I create a spacy compatible binary file:

spacy.vocab.write_binary_vectors('test.txt.bz2', 'test.bin') 

That I can load into spacy:

nlp.vocab.load_vectors_from_bin_loc('test.bin') 

This works! However, when I do the same process for the googlenews2.txt, I get the following error:

lib/python3.6/site-packages/spacy/cfile.pyx in spacy.cfile.CFile.read_into (spacy/cfile.cpp:1279)()  OSError:  

Answer 1

For spacy 1.x, load Google news vectors into gensim and convert to a new format (each line in .txt contains a single vector: string, vec):

from gensim.models.word2vec import Word2Vec from gensim.models import KeyedVectors model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin', binary=True) model.wv.save_word2vec_format('googlenews.txt') 

Remove the first line of the .txt:

tail -n +2 googlenews.txt > googlenews.new && mv -f googlenews.new googlenews.txt 

Compress the txt as .bz2:

bzip2 googlenews.txt 

Create a SpaCy compatible binary file:

spacy.vocab.write_binary_vectors('googlenews.txt.bz2','googlenews.bin') 

Move the googlenews.bin to /lib/python/site-packages/spacy/data/en_google-1.0.0/vocab/googlenews.bin of your python environment.

Then load the wordvectors:

import spacy nlp = spacy.load('en',vectors='en_google') 

or load them after later:

nlp.vocab.load_vectors_from_bin_loc('googlenews.bin') 

Answer 2

I know that this question has already been answered, but I am going to offer a simpler solution. This solution will load google news vectors into a blank spacy nlp object.

import gensim import spacy  # Path to google news vectors google_news_path = "path\to\google\news\\GoogleNews-vectors-negative300.bin.gz"  # Load google news vecs in gensim model = gensim.models.KeyedVectors.load_word2vec_format(gn_path, binary=True)  # Init blank english spacy nlp object nlp = spacy.blank('en')  # Loop through range of all indexes, get words associated with each index. # The words in the keys list will correspond to the order of the google embed matrix keys = [] for idx in range(3000000):     keys.append(model.index2word[idx])  # Set the vectors for our nlp object to the google news vectors nlp.vocab.vectors = spacy.vocab.Vectors(data=model.syn0, keys=keys)  >>> nlp.vocab.vectors.shape (3000000, 300)