I haven’t checked your model or anything, this is just a general comment:
The CBOW or Skip-Gram model of Word2Vec is unlikely to yield high accuracies. Although both models are designed a word given a context, or vice versa, that’s the really the primary tasks – that is, you don’t use Word2Vec to predict a word or a context in practice.
A high accuracy would imply, say for CBOW, that given a context you can predict the word with high probability. Language is just the flexible for that to happen. Sure, you try to force the network to learn to predict the word but “only” to get useful word vectors.
In short, for Word2Vec – and for embedding solutions in general, I would assume, the absolute accuracy of the model is not really meaningful.