# -*- coding: utf-8 -*-

import numpy as np
from gensim.models.keyedvectors import KeyedVectors

embedding_matrix = np.load("embedding")
model = KeyedVectors.load_word2vec_format("/DB/rhome/zkli/data/weibo.withstop.vector.copy")

model.syn0 = embedding_matrix

model.save_word2vec_format("new.vector")

e4b5ab54c1175c2e0fa7b303cffecac4ef1c215e
<mask token>

<mask token>
model.save_word2vec_format('new.vector')

<mask token>
embedding_matrix = np.load('embedding')
model = KeyedVectors.load_word2vec_format(
    '/DB/rhome/zkli/data/weibo.withstop.vector.copy')
model.syn0 = embedding_matrix
model.save_word2vec_format('new.vector')

import numpy as np
from gensim.models.keyedvectors import KeyedVectors
embedding_matrix = np.load('embedding')
model = KeyedVectors.load_word2vec_format(
    '/DB/rhome/zkli/data/weibo.withstop.vector.copy')
model.syn0 = embedding_matrix
model.save_word2vec_format('new.vector')

# -*- coding: utf-8 -*-

import numpy as np
from gensim.models.keyedvectors import KeyedVectors

embedding_matrix = np.load("embedding")
model = KeyedVectors.load_word2vec_format("/DB/rhome/zkli/data/weibo.withstop.vector.copy")

model.syn0 = embedding_matrix

model.save_word2vec_format("new.vector")
