def main():
parser = argparse.ArgumentParser(
description="Dependency-Guided LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains = reader.read_conll(conf.train_file, -1, True)
devs = reader.read_conll(conf.dev_file, conf.dev_num, False)
tests = reader.read_conll(conf.test_file, conf.test_num, False)
if conf.context_emb != ContextEmb.none:
print('Loading the {} vectors for all datasets.'.format(
conf.context_emb.name))
conf.context_emb_size = reader.load_elmo_vec(
conf.train_file.replace(".sd", "").replace(".ud", "").replace(
".sud", "").replace(".predsd", "").replace(
".predud", "").replace(".stud", "").replace(".ssd", "") +
"." + conf.context_emb.name + ".vec", trains)
reader.load_elmo_vec(
conf.dev_file.replace(".sd", "").replace(".ud", "").replace(
".sud", "").replace(".predsd", "").replace(
".predud", "").replace(".stud", "").replace(".ssd", "") +
"." + conf.context_emb.name + ".vec", devs)
reader.load_elmo_vec(
conf.test_file.replace(".sd", "").replace(".ud", "").replace(
".sud", "").replace(".predsd", "").replace(
".predud", "").replace(".stud", "").replace(".ssd", "") +
"." + conf.context_emb.name + ".vec", tests)
conf.use_iobes(trains + devs + tests)
conf.build_label_idx(trains)
conf.build_deplabel_idx(trains + devs + tests)
print("# deplabels: ", len(conf.deplabels))
print("dep label 2idx: ", conf.deplabel2idx)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(trains + devs + tests)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
if conf.train_num != -1:
random.shuffle(trains)
trains = trains[:conf.train_num]
learn_from_insts(conf, conf.num_epochs, trains, devs, tests)
else:
## Load the trained model.
test_model(conf, tests)
# pass
print(opt.mode)
def main():
parser = argparse.ArgumentParser(
description="Dependency-Guided LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains = reader.read_conll(conf.train_file, -1, True)
devs = reader.read_conll(conf.dev_file, conf.dev_num, False)
tests = reader.read_conll(conf.test_file, conf.test_num, False)
conf.use_iobes(trains)
conf.build_label_idx(trains)
conf.build_deplabel_idx(trains + devs + tests)
print("# deplabels: ", len(conf.deplabels))
print("dep label 2idx: ", conf.deplabel2idx)
conf.build_word_idx(trains + devs + tests)
conf.build_emb_table()
conf.map_insts_ids(trains)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
if conf.train_num != -1:
random.shuffle(trains)
trains = trains[:conf.train_num]
learn_from_insts(conf, conf.num_epochs, trains)
print(opt.mode)
def main():
TASKS = ['ner_german', 'ner']
USE_DEV = True
char_set = set()
for task in TASKS:
t = __import__(task)
data_list = [t.TRAIN_DATA, t.DEV_DATA, t.TEST_DATA]
char_index, _ = t.create_char_index(data_list)
for k, v in char_index.items():
char_set.add(k)
char_index, char_cnt = {}, 0
for char in char_set:
char_index[char] = char_cnt
char_cnt += 1
for i, task in enumerate(TASKS):
t = __import__(task)
word_index, word_cnt = t.create_word_index(
[t.TRAIN_DATA, t.DEV_DATA, t.TEST_DATA])
wx, y, m = t.read_data(t.TRAIN_DATA, word_index)
if USE_DEV and task == 'ner':
dev_wx, dev_y, dev_m = t.read_data(t.TEST_DATA, word_index)
wx, y, m = np.vstack((wx, dev_wx)), np.vstack(
(y, dev_y)), np.vstack((m, dev_m))
twx, ty, tm = t.read_data(t.DEV_DATA, word_index)
x, cm = t.read_char_data(t.TRAIN_DATA, char_index)
if USE_DEV and task == 'ner':
dev_x, dev_cm = t.read_char_data(t.TEST_DATA, char_index)
x, cm = np.vstack((x, dev_x)), np.vstack((cm, dev_cm))
tx, tcm = t.read_char_data(t.DEV_DATA, char_index)
if task == 'ner':
list_prefix = t.read_list()
gaze = t.read_list_data(t.TRAIN_DATA, list_prefix)
tgaze = t.read_list_data(t.DEV_DATA, list_prefix)
if USE_DEV:
dev_gaze = t.read_list_data(t.TEST_DATA, list_prefix)
gaze = np.vstack((gaze, dev_gaze))
else:
gaze, tgaze = None, None
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains = reader.read_txt(conf.train_file, conf.train_num, True)
devs = reader.read_txt(conf.dev_file, conf.dev_num, False)
tests = reader.read_txt(conf.test_file, conf.test_num, False)
trains_target = reader.read_txt(conf.train_target_file_file,
conf.train_num, True)
if conf.context_emb != ContextEmb.none:
print('Loading the elmo vectors for all datasets.')
conf.context_emb_size = reader.load_elmo_vec(
conf.train_file + "." + conf.context_emb.name + ".vec", trains)
reader.load_elmo_vec(
conf.dev_file + "." + conf.context_emb.name + ".vec", devs)
reader.load_elmo_vec(
conf.test_file + "." + conf.context_emb.name + ".vec", tests)
conf.use_iobes(trains)
conf.use_iobes(devs)
conf.use_iobes(tests)
conf.build_label_idx(trains)
conf.use_iobes(trains_target)
conf.build_label_idx_target(trains_target)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
ids_train = conf.map_insts_ids(trains)
ids_dev = conf.map_insts_ids(devs)
ids_test = conf.map_insts_ids(tests)
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
learn_from_insts(conf, conf.num_epochs, trains, devs, tests)
else:
## Load the trained model.
test_model(conf, tests)
# pass
print(opt.mode)
def main():
print('Reading arguments')
parser = argparse.ArgumentParser(description="LSTM CRF implementation")
opt = parse_arguments(parser)
conf = Config(opt)
conf_conll = Config_conll(opt)
conf_ontonotes = Config_ontonotes(opt)
reader = Reader(conf.digit2zero)
setSeed(opt, conf.seed)
trains_0 = reader.read_conll(conf.train_file_1, 0, conf.train_num, True)
devs_0 = reader.read_conll(conf.dev_file_1, 0, conf.dev_num, False)
tests_0 = reader.read_conll(conf.test_file_1, 0, conf.test_num, False)
trains_1 = reader.read_conll(conf.train_file_2, 1, conf.train_num, True)
devs_1 = reader.read_conll(conf.dev_file_2, 1, conf.dev_num, False)
tests_1 = reader.read_conll(conf.test_file_2, 1, conf.test_num, False)
trains_all = trains_0 + trains_1
devs_all = devs_0 + devs_1
tests_all = tests_0 + tests_1
if conf.context_emb != ContextEmb.none:
print('Loading the elmo vectors for all datasets.')
conf.context_emb_size = reader.load_elmo_vec(
conf.train_file_1 + "." + conf.context_emb.name + ".vec", trains_1)
reader.load_elmo_vec(
conf.dev_file_1 + "." + conf.context_emb.name + ".vec", devs_1)
reader.load_elmo_vec(
conf.test_file_1 + "." + conf.context_emb.name + ".vec", tests_1)
conf.use_iobes(trains_all)
conf.use_iobes(devs_all)
conf.use_iobes(tests_all)
conf.build_label_idx(trains_all)
conf.build_word_idx(trains_all, devs_all, tests_all)
conf.build_emb_table()
ids_train = conf.map_insts_ids(trains_all)
ids_dev = conf.map_insts_ids(devs_all)
ids_test = conf.map_insts_ids(tests_all)
conf_conll.label_size = conf.label_size_0
conf_conll.label2idx = conf.label2idx_0
conf_conll.idx2labels = conf.idx2labels_0
conf_ontonotes.label_size = conf.label_size_1
conf_ontonotes.label2idx = conf.label2idx_1
conf_ontonotes.idx2labels = conf.idx2labels_1
print("num chars: " + str(conf.num_char))
# print(str(config.char2idx))
print("num words: " + str(len(conf.word2idx)))
# print(config.word2idx)
if opt.mode == "train":
learn_from_insts(conf, conf_conll, conf_ontonotes, conf.num_epochs,
trains_all, devs_all, tests_all)
else:
## Load the trained model.
test_model(conf, tests_all)
# pass
print(opt.mode)