def vectorize(sent):
tokens = text.tokenize(sent)
vocab = data.vocab()
ixs = [vocab[t] for t in tokens]
embeddings = data.glove()
_vectors = [embeddings[ix].reshape(1, -1) for ix in ixs]
return np.concatenate(_vectors, axis=0).sum(axis=0)
def __init__(self):
self.vocab = data.vocab()
def __init__(self, args=None):
self.vocab = data.vocab()
self.n_training_points = None
def __init__(self):
self.vocab = data.vocab()
self.collate_for_rnn = CollateSentsForRNN()
def __init__(self):
self.vocab = data.vocab()
self.tokenizer = text.tokenize
self.pad_ix = 0
"""Build GloVe vectors for ARCT data."""
import os
import numpy as np
from arct import data
import glovar
from util import text
if __name__ == '__main__':
print('Creating GloVe embeddings...')
vocab = data.vocab()
embeddings = text.create_glove_embeddings(vocab)
save_path = os.path.join(glovar.ARCT_DIR, 'glove.npy')
np.save(save_path, embeddings)
