else:
sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000)
with open(w2i_file, 'w') as f:
json.dump(word2idx, f)
V = len(word2idx)
model = Glove(80, V, 10)
# model.fit(sentences, cc_matrix=cc_matrix, epochs=20) # coordinate descent
model.fit(
sentences,
cc_matrix=cc_matrix,
learning_rate=3*10e-5,
reg=0.01,
epochs=2000,
gd=True,
use_theano=False
) # gradient descent
model.save(we_file)
if __name__ == '__main__':
we = 'glove_model_50.npz'
w2i = 'glove_word2idx_50.json'
main(we, w2i)
for concat in (True, False):
print "** concat:", concat
find_analogies('king', 'man', 'woman', concat, we, w2i)
find_analogies('france', 'paris', 'london', concat, we, w2i)
find_analogies('france', 'paris', 'rome', concat, we, w2i)
find_analogies('paris', 'france', 'italy', concat, we, w2i)
word2idx = json.load(f)
sentences = [] # dummy - we won't actually use it
else:
sentences, word2idx = get_wikipedia_data(n_files=n_files, n_vocab=2000)
with open(w2i_file, 'w') as f:
json.dump(word2idx, f)
V = len(word2idx)
model = Glove(80, V, 10)
# model.fit(sentences, cc_matrix=cc_matrix, epochs=20) # coordinate descent
model.fit(sentences,
cc_matrix=cc_matrix,
learning_rate=3 * 10e-5,
reg=0.01,
epochs=2000,
gd=True,
use_theano=False) # gradient descent
model.save(we_file)
if __name__ == '__main__':
we = 'glove_model_50.npz'
w2i = 'glove_word2idx_50.json'
main(we, w2i)
for concat in (True, False):
print "** concat:", concat
find_analogies('king', 'man', 'woman', concat, we, w2i)
find_analogies('france', 'paris', 'london', concat, we, w2i)
find_analogies('france', 'paris', 'rome', concat, we, w2i)
find_analogies('paris', 'france', 'italy', concat, we, w2i)
model.fit(
sentences,
cc_matrix=cc_matrix,
learning_rate=3 * 10e-5,
reg=0.1,
epochs=10,
gd=True,
use_theano=False,
use_tensorflow=True,
)
model.save(we_file)
if __name__ == '__main__':
# we = 'glove_model_50.npz'
# w2i = 'glove_word2idx_50.json'
we = 'glove_model_brown.npz'
w2i = 'glove_word2idx_brown.json'
main(we, w2i, use_brown=True)
for concat in (True, False):
print "** concat:", concat
find_analogies('king', 'man', 'woman', concat, we, w2i)
find_analogies('france', 'paris', 'london', concat, we, w2i)
find_analogies('france', 'paris', 'rome', concat, we, w2i)
find_analogies('paris', 'france', 'italy', concat, we, w2i)
find_analogies('france', 'french', 'english', concat, we, w2i)
find_analogies('japan', 'japanese', 'chinese', concat, we, w2i)
find_analogies('japan', 'japanese', 'italian', concat, we, w2i)
find_analogies('japan', 'japanese', 'australian', concat, we, w2i)
find_analogies('december', 'november', 'june', concat, we, w2i)
model.fit(
sentences,
cc_matrix=cc_matrix,
learning_rate=3*10e-5,
reg=0.01,
epochs=500,
gd=True,
use_theano=False,
use_tensorflow=True,
)
model.save(we_file)
if __name__ == '__main__':
# we = 'glove_model_50.npz'
# w2i = 'glove_word2idx_50.json'
we = 'glove_model_brown.npz'
w2i = 'glove_word2idx_brown.json'
main(we, w2i, use_brown=True)
for concat in (True, False):
print "** concat:", concat
find_analogies('king', 'man', 'woman', concat, we, w2i)
find_analogies('france', 'paris', 'london', concat, we, w2i)
find_analogies('france', 'paris', 'rome', concat, we, w2i)
find_analogies('paris', 'france', 'italy', concat, we, w2i)
find_analogies('france', 'french', 'english', concat, we, w2i)
find_analogies('japan', 'japanese', 'chinese', concat, we, w2i)
find_analogies('japan', 'japanese', 'italian', concat, we, w2i)
find_analogies('japan', 'japanese', 'australian', concat, we, w2i)
find_analogies('december', 'november', 'june', concat, we, w2i)
