Returning an empty array from get_embedding

[tobrun]

In case you come across a word that is not part of the original GloVe dataset.
We currently return an empty array.

def get_embedding( word ):
    try:
        return vectors[ indexes[ word ] ]
    except KeyError:
        return []

This however should be an array of zeros with a size matching the dataset sizes:

def get_embedding(word):
    try:
        return vectors[indexes[word]]
    except KeyError:
        return np.zeros(50) 

In this case I'm using numpy because I'm running this in a real python env for preprocessing but could be an floatarray size 50 with all zeros instead.

This solves issues like:

ValueError: all the input array dimensions for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 0 and the array at index 2 has size 50

[shubham0204]

Good observation @tobrun. I'll make this change to the codebase.