def main():
parser = argparse.ArgumentParser()
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
dataset, max_length, label_length = reader.read_trigger_txt(
conf.trigger_file, -1)
reader.merge_labels(dataset)
trains = reader.read_txt(conf.train_all_file, conf.train_num)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
print(len(dataset))
if conf.context_emb == ContextEmb.bert:
print('Loading the BERT vectors for all datasets.')
conf.context_emb_size = load_bert_vec(
conf.trigger_file + "." + conf.context_emb.name + ".vec", dataset)
# setting for data
conf.use_iobes(trains)
conf.use_iobes(dataset)
conf.use_iobes(devs)
conf.use_iobes(tests)
conf.optimizer = opt.trig_optimizer
conf.build_label_idx(dataset)
conf.build_word_idx(trains, devs, tests)
conf.build_emb_table()
conf.map_insts_ids(dataset)
conf.map_insts_ids(trains)
conf.map_insts_ids(devs)
conf.map_insts_ids(tests)
dataset = reader.trigger_percentage(dataset, conf.percentage)
encoder = SoftMatcher(conf, label_length)
trainer = SoftMatcherTrainer(encoder, conf, devs, tests)
# matching module training
random.shuffle(dataset)
trainer.train_model(conf.num_epochs_soft, dataset)
logits, predicted, triggers = trainer.get_triggervec(dataset)
# all the trigger vectors, trigger type, string name of the trigger
triggers_remove = remove_duplicates(logits, predicted, triggers, dataset)
numbers = int(len(trains) * (1 - opt.unlabeled_percentage))
print("number of train instances : ", numbers)
initial_trains = trains[:numbers]
unlabeled_x = trains[numbers:]
for data in unlabeled_x:
data.output_ids = None
# sequence labeling module self-training
random.shuffle(dataset)
inference = SoftSequence(conf, encoder)
sequence_trainer = SoftSequenceTrainer(inference, conf, devs, tests,
triggers_remove)
sequence_trainer.self_training(conf.num_epochs, dataset, unlabeled_x)
default=100,
help="how much percentage of training dataset to use")
args = parser.parse_args()
for k in args.__dict__:
print(k + ": " + str(args.__dict__[k]))
return args
parser = argparse.ArgumentParser()
opt = parse_arguments(parser)
conf = Config(opt)
reader = Reader(conf.digit2zero)
dataset, max_length, label_length = reader.read_trigger_txt(
conf.trigger_file, -1)
reader.merge_labels(dataset)
devs = reader.read_txt(conf.dev_file, conf.dev_num)
tests = reader.read_txt(conf.test_file, conf.test_num)
print(len(dataset))
if conf.context_emb == ContextEmb.bert:
print('Loading the BERT vectors for all datasets.')
conf.context_emb_size = load_bert_vec(
conf.trigger_file + "." + conf.context_emb.name + ".vec", dataset)
# setting for data
conf.use_iobes(dataset)
conf.use_iobes(devs)
conf.use_iobes(tests)
conf.optimizer = opt.trig_optimizer