def main():
USE_GPU = True
if USE_GPU and torch.cuda.is_available():
torch.cuda.empty_cache()
device = torch.device('cuda')
else:
device = torch.device('cpu')
print('using device:', device)
dtype = torch.float32
'''
filename = 'curr_model_soumya_mat_it_es'
f = open(filename, 'rb')
model = pickle.load(f)
f.close()
'''
n_epochs = 5
n_refinement = 5
batch_size = 32
model = UMWE(dtype, device, batch_size, n_epochs, n_refinement)
model.build_model()
model.discrim_fit()
filename = 'curr_model_soumya_mat_it_es'
f = open(filename, 'wb')
pickle.dump(model, f)
f.close()
eval_ = Evaluator(model)
print(eval_.clws('es', 'en'))
print(eval_.clws('en', 'es'))
print(eval_.clws('it', 'en'))
eval_.word_translation('es', 'en')
eval_.word_translation('en', 'es')
eval_.word_translation('it', 'en')
model.mpsr_refine()
print(eval_.clws('es', 'en'))
print(eval_.clws('en', 'es'))
print(eval_.clws('it', 'en'))
eval_.word_translation('es', 'en')
eval_.word_translation('en', 'es')
eval_.word_translation('it', 'en')
filename = 'curr_model_soumya_mpsr_it_es'
f = open(filename, 'wb')
pickle.dump(model, f)
f.close()
for lang in model.src_langs.values():
model.export_embeddings(lang, model.embs, "txt", "20th")
def main():
USE_GPU = True
if USE_GPU and torch.cuda.is_available():
torch.cuda.empty_cache()
device = torch.device('cuda')
else:
device = torch.device('cpu')
print('using device:', device)
dtype = torch.float32
# =============================================================================
# filename = 'curr_model'
# f = open(filename, 'rb')
# model = pickle.load(f)
# f.close()
#
# =============================================================================
model = UMWE(dtype, device, 32, 2)
model.build_model()
# model.discrim_fit()
# filename = 'curr_model'
# f = open(filename, 'wb')
# pickle.dump(model, f)
# f.close()
# =============================================================================
model.mpsr_refine()
# =============================================================================
# =============================================================================
# for lang in model.src_langs.values():
# model.export_embeddings(lang, model.embs, "txt")
# =============================================================================
model.export_embeddings('es', model.embs, "txt")
eval_ = Evaluator(model)
print(eval_.clws('es', 'en'))
eval_.word_translation('es', 'en')
