Gensim（word2vec模型）中哪些经过训练的嵌入向量应用于Tensorflow？非标准化的还是标准化的？

我想在神经网络（Tensorflow）中使用Gensim（word2vec模型）训练向量。有两种重量我可以用于此目的。第一组是

model.syn0

，第二组是

model.vectors\u norm

（调用

model.init\u sims（replace=True）

）。第二个是我们用来计算相似性的向量组。哪一个具有神经网络嵌入层的正确顺序（与

model.wv.indexword

和

model.wv.vocab[X].index

）和权重？

如果使用谷歌的

谷歌新闻向量

作为预训练模型，可以使用

model.syn0

。如果您使用Facebook的

fastText

word嵌入，您可以直接加载二进制文件。
下面是加载这两个实例的示例

加载谷歌新闻预训练嵌入：

model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz',binary=True,limit=500000) # To load the model first time.
model.wv.save_word2vec_format(model_path) #You can save the loaded model to binary file to load the model faster
model = gensim.models.KeyedVectors.load(model_path,mmap='r')
model.syn0norm = model.syn0
index2word_set = set(model.index2word)

model[word] gives the vector representation of the word which can be used to find similarity. 

import gensim
from gensim.models import FastText
model = FastText.load_fasttext_format('cc.en.300') # to load the model for first time.
model.save("fasttext_en_bin") # Save the model to binary file to load faster.
model = gensim.models.KeyedVectors.load("fasttext_en_bin",mmap="r")
index2word_set = set(model.index2word)

model[word] gives the vector representation of the word which can be used to find similarity. 

加载fastText预训练嵌入：

model = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz',binary=True,limit=500000) # To load the model first time.
model.wv.save_word2vec_format(model_path) #You can save the loaded model to binary file to load the model faster
model = gensim.models.KeyedVectors.load(model_path,mmap='r')
model.syn0norm = model.syn0
index2word_set = set(model.index2word)

model[word] gives the vector representation of the word which can be used to find similarity. 

import gensim
from gensim.models import FastText
model = FastText.load_fasttext_format('cc.en.300') # to load the model for first time.
model.save("fasttext_en_bin") # Save the model to binary file to load faster.
model = gensim.models.KeyedVectors.load("fasttext_en_bin",mmap="r")
index2word_set = set(model.index2word)

model[word] gives the vector representation of the word which can be used to find similarity. 

一般示例：

if word in index2word:
   feature_vec = model[word]

---

为什么要使用

model.syn0norm=model.syn0

？@Eghbal该行基本上阻止了重新计算归一化向量以提高速度。由于相似的运算必须在单位归一化向量上进行，我认为这里仍然没有回答主要问题。在神经网络中使用这些权重时，我们应该使用Gensim的归一化权重还是只使用

syn0

？您可以使用

syn0

。