This is an example of word embedding. We implemented Mikolov's Skip-gram model and Continuous-BoW model with Hierarchical softmax and Negative sampling.
train_word2vec.py to train and get
word2vec.model which includes embedding data.
You can find top-5 nearest embedding vectors using
This example is based on the following word embedding implementation in C++. https://code.google.com/p/word2vec/