如何从word2vec保存的模型中加载某些单词的矢量?

问题描述 投票:0回答:1

如何从先前训练的word2vec模型中找到相应的单词向量?

data = {'one': array([-0.06590105,  0.01573388,  0.00682817,  0.53970253, -0.20303348,
   -0.24792041,  0.08682659, -0.45504045,  0.89248925,  0.0655603 ,
   ......
   -0.8175681 ,  0.27659689,  0.22305458,  0.39095637,  0.43375066,
    0.36215973,  0.4040089 , -0.72396156,  0.3385369 , -0.600869  ],
  dtype=float32),
 'two': array([ 0.04694849,  0.13303463, -0.12208422,  0.02010536,  0.05969441,
   -0.04734801, -0.08465996,  0.10344813,  0.03990637,  0.07126121,
    ......
    0.31673026,  0.22282903, -0.18084198, -0.07555179,  0.22873943,
   -0.72985399, -0.05103955, -0.10911274, -0.27275378,  0.01439812],
  dtype=float32),
 'three': array([-0.21048863,  0.4945509 , -0.15050395, -0.29089224, -0.29454648,
    0.3420335 , -0.3419629 ,  0.87303966,  0.21656844, -0.07530259,
    ......
   -0.80034876,  0.02006451,  0.5299498 , -0.6286509 , -0.6182588 ,
   -1.0569025 ,  0.4557548 ,  0.4697938 ,  0.8928275 , -0.7877308 ],
  dtype=float32),
  'four': ......
}

现在我想获得像

word = "one"
wordvector = data.get_vector(word)

并返回

[-0.06590105,  0.01573388,  0.00682817,  0.53970253, -0.20303348,
   -0.24792041,  0.08682659, -0.45504045,  0.89248925,  0.0655603 ,
   ......
   -0.8175681 ,  0.27659689,  0.22305458,  0.39095637,  0.43375066,
    0.36215973,  0.4040089 , -0.72396156,  0.3385369 , -0.600869  ]
machine-learning nlp word2vec
1个回答
0
投票
one_array = data['one']
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