def main():
parser = argparse.ArgumentParser()
parser.add_argument('input_file', type=argparse.FileType())
parser.add_argument('output_file', type=argparse.FileType('wb'))
parser.add_argument('vocabulary_size', type=int)
parser.add_argument('context_size', type=int)
parser.add_argument('feature_size', type=int)
parser.add_argument('hidden_size', type=int)
parser.add_argument('iterations', type=int)
args = parser.parse_args()
sentences = list(prepend_caret(lower(tokenize(args.input_file))))
nplm = NPLM(args.vocabulary_size, args.feature_size, args.context_size, args.hidden_size)
nplm.train(sentences, args.iterations)
pickle.dump(nplm, args.output_file)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input_file', type=argparse.FileType())
parser.add_argument('output_file', type=argparse.FileType('wb'))
parser.add_argument('vocabulary_size', type=int)
parser.add_argument('context_size', type=int)
parser.add_argument('feature_size', type=int)
parser.add_argument('hidden_size', type=int)
parser.add_argument('iterations', type=int)
args = parser.parse_args()
sentences = list(prepend_caret(lower(tokenize(args.input_file))))
nplm = NPLM(args.vocabulary_size, args.feature_size, args.context_size,
args.hidden_size)
nplm.train(sentences, args.iterations)
pickle.dump(nplm, args.output_file)
def test_prepend_caret(self):
sentences = [['i', 'am', 'beautiful']]
actual = list(prepend_caret(sentences))
expected = [['^', 'i', 'am', 'beautiful']]
self.assertSequenceEqual(expected, actual)
