可以用gensim載入進來，但是需要記憶體足夠大。

#載入Google訓練的詞向量
import gensim
model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin',binary=True)
print(model['love'])

2 用Glove預訓練的詞向量也可以用gensim載入進來，只是在載入之前要多做一步操作，程式碼參考。

Glove300維的詞向量有5.25個G。

# 用gensim開啟glove詞向量需要在向量的開頭增加一行：所有的單詞數 詞向量的維度
import gensim
import os
import shutil
import hashlib
from sys import platform
#計算行數，就是單詞數
def getFileLineNums(filename):
	f = open(filename, 'r')
	count = 0
	for line in f:
		count += 1
	return count
 
#Linux或者Windows下開啟詞向量檔案，在開始增加一行
def prepend_line(infile, outfile, line):
	with open(infile, 'r') as old:
		with open(outfile, 'w') as new:
			new.write(str(line) + "\n")
			shutil.copyfileobj(old, new)
 
def prepend_slow(infile, outfile, line):
	with open(infile, 'r') as fin:
		with open(outfile, 'w') as fout:
			fout.write(line + "\n")
			for line in fin:
				fout.write(line)
 
def load(filename):
	num_lines = getFileLineNums(filename)
	gensim_file = 'glove_model.txt'
	gensim_first_line = "{} {}".format(num_lines, 300)
	# Prepends the line.
	if platform == "linux" or platform == "linux2":
		prepend_line(filename, gensim_file, gensim_first_line)
	else:
		prepend_slow(filename, gensim_file, gensim_first_line)
	
	model = gensim.models.KeyedVectors.load_word2vec_format(gensim_file)
 
load('glove.840B.300d.txt')
 

生成的glove_model.txt就是可以直接用gensim開啟的模型。