Simplify vectorizer

[mttk]

From

vectorizer = GloVe()
vectorizer.load_vocab(vocab)
embeddings = vectorizer.get_embedding_matrix(vocab)

to

embeddings = GloVe().load_vocab(vocab)

load_vocab(vocab) for a non-None vocab now returns the embedding matrix by default to avoid the second call.

Also, maybe create a custom classmethod which only returns the embedding matrix:
embeddings = GloVe.for_vocab(vocab) which would mask the constructor call.

[ivansmokovic]

Also, how do we handle <UNK> and '' in vectorizers. Should we let users define that in their custom_numericalize?

[mttk]

UNK should be a special symbol which is then randomly initialized in the vectorizer.

[ivansmokovic]

UNK should be a special symbol which is then randomly initialized in the vectorizer.e

Should vectorizer specifically check if vocab has those symbols and then generate randoms? Also, that would not be reproducible because for the same dataset if you load vectors two times you could get two different embeddings for those symbols.

[mttk]

Re 1.: if the vectorizer doesn't find a vector for a word in the vocab, it should randomly initialize it. This is done in token_to_vector (but currently initialized to zeros by default).
It is also reproducible given a fixed random seed, which is fine.

[mttk]

@ivansmokovic added doc notes, it seems that from python 3.6 and above (https://docs.python.org/3.6/whatsnew/3.6.html#other-language-changes) the insertion order is preserved by dictionaries. Since we support 3.6+, there is no need for OrderedDict or sth alike.

Also: set the default random vector for words not found in the vector file to be drawn from the normal distribution (as is standard practice).

[ivansmokovic]

Minor additions required.

[mttk]

@ivansmokovic revised wrt comments

[sskudar]

LGTM

[ivansmokovic]

LGTM

[ivansmokovic]

@mttk Feel free to merge.