How to evaluate Word2Vec model
One of the ways of evaluating the Word2Vec model would be to apply the K-Means algorithm on the features generated by the Word2Vec. Along with that create your own manual labels/ground truth representing the instances/records. You can calculate the accuracy of the model by comparing the clustered result tags with the ground truth label. 
Eg: CLuter 0 - Positive -{"This is a good restaurant", "Good food here", "Not so good dinner"}
    Cluster 1 - Negative - {"This is a fantastic hotel", "food was stale"}
Now, compare the tags/labels generated by the clusters with the ground truth values of the instances/sentences in the clusters and calculate the accuracy.
Hi have my own corpus and I train several Word2Vec models on it.
What is the best way to evaluate them one against each-other and choose the best one? (Not manually obviously - I am looking for various measures).
It worth noting that the embedding is for items and not word, therefore I can't use any existing benchmarks.
Thanks!