def get_ncrf_data_object(model_name): #, input_path, output_path):
data = Data()
model = MODEL_PATHS[model_name]
data.dset_dir = model['dset']
data.load(data.dset_dir)
data.HP_gpu = False
#data.raw_dir = input_path
#data.decode_dir = output_path
data.load_model_dir = model['model']
data.nbest = None
return data
parser.add_argument('--savedset', help='Dir of saved data setting')
parser.add_argument('--train', default="data/conll03/train.bmes")
parser.add_argument('--dev', default="data/conll03/dev.bmes")
parser.add_argument('--test', default="data/conll03/test.bmes")
parser.add_argument('--seg', default="True")
parser.add_argument('--raw')
parser.add_argument('--loadmodel')
parser.add_argument('--output')
args = parser.parse_args()
data = Data()
data.train_dir = args.train
data.dev_dir = args.dev
data.test_dir = args.test
data.model_dir = args.savemodel
data.dset_dir = args.savedset
print("aaa", data.dset_dir)
status = args.status.lower()
save_model_dir = args.savemodel
data.HP_gpu = torch.cuda.is_available()
print("Seed num:", seed_num)
data.number_normalized = True
data.word_emb_dir = "../data/glove.6B.100d.txt"
if status == 'train':
print("MODEL: train")
data_initialization(data)
data.use_char = True
data.HP_batch_size = 10
data.HP_lr = 0.015
data.char_seq_feature = "CNN"
def main():
parser = argparse.ArgumentParser(description='Tuning with NCRF++')
# parser.add_argument('--status', choices=['train', 'decode'], help='update algorithm', default='train')
parser.add_argument('--config', help='Configuration File', default='None')
parser.add_argument('--wordemb',
help='Embedding for words',
default='None')
parser.add_argument('--charemb',
help='Embedding for chars',
default='None')
parser.add_argument('--status',
choices=['train', 'decode'],
help='update algorithm',
default='train')
parser.add_argument('--savemodel',
default="data/model/saved_model.lstmcrf.")
parser.add_argument('--savedset', help='Dir of saved data setting')
parser.add_argument('--train', default="data/conll03/train.bmes")
parser.add_argument('--dev', default="data/conll03/dev.bmes")
parser.add_argument('--test', default="data/conll03/test.bmes")
parser.add_argument('--seg', default="True")
parser.add_argument('--random-seed', type=int, default=42)
parser.add_argument('--lr', type=float)
parser.add_argument('--batch-size', type=int)
parser.add_argument('--raw')
parser.add_argument('--loadmodel')
parser.add_argument('--output')
parser.add_argument('--output-tsv')
parser.add_argument('--model-prefix')
parser.add_argument('--cpu', action='store_true')
args = parser.parse_args()
# Set random seed
seed_num = args.random_seed
random.seed(seed_num)
torch.manual_seed(seed_num)
np.random.seed(seed_num)
data = Data()
data.random_seed = seed_num
data.HP_gpu = torch.cuda.is_available()
if args.config == 'None':
data.train_dir = args.train
data.dev_dir = args.dev
data.test_dir = args.test
data.model_dir = args.savemodel
data.dset_dir = args.savedset
print("Save dset directory:", data.dset_dir)
save_model_dir = args.savemodel
data.word_emb_dir = args.wordemb
data.char_emb_dir = args.charemb
if args.seg.lower() == 'true':
data.seg = True
else:
data.seg = False
print("Seed num:", seed_num)
else:
data.read_config(args.config)
if args.lr:
data.HP_lr = args.lr
if args.batch_size:
data.HP_batch_size = args.batch_size
data.output_tsv_path = args.output_tsv
if args.cpu:
data.HP_gpu = False
if args.model_prefix:
data.model_dir = args.model_prefix
# data.show_data_summary()
status = data.status.lower()
print("Seed num:", seed_num)
if status == 'train':
print("MODEL: train")
data_initialization(data)
data.generate_instance('train')
data.generate_instance('dev')
data.generate_instance('test')
data.build_pretrain_emb()
train(data)
elif status == 'decode':
print("MODEL: decode")
data.load(data.dset_dir)
data.read_config(args.config)
print(data.raw_dir)
# exit(0)
data.show_data_summary()
data.generate_instance('raw')
print("nbest: %s" % (data.nbest))
decode_results, pred_scores = load_model_decode(data, 'raw')
if data.nbest and not data.sentence_classification:
data.write_nbest_decoded_results(decode_results, pred_scores,
'raw')
else:
data.write_decoded_results(decode_results, 'raw')
else:
print(
"Invalid argument! Please use valid arguments! (train/test/decode)"
)
parser.add_argument('--savedset', help='Dir of saved data setting')
parser.add_argument('--train', default="data/conll03/train.bmes")
parser.add_argument('--dev', default="data/conll03/dev.bmes" )
parser.add_argument('--test', default="data/conll03/test.bmes")
parser.add_argument('--seg', default="True")
parser.add_argument('--raw')
parser.add_argument('--loadmodel')
parser.add_argument('--output')
args = parser.parse_args()
data = Data()
data.train_dir = args.train
data.dev_dir = args.dev
data.test_dir = args.test
data.model_dir = args.savemodel
data.dset_dir = args.savedset
print("aaa",data.dset_dir)
status = args.status.lower()
save_model_dir = args.savemodel
data.HP_gpu = torch.cuda.is_available()
print("Seed num:",seed_num)
data.number_normalized = True
data.word_emb_dir = "../data/glove.6B.100d.txt"
if status == 'train':
print("MODEL: train")
data_initialization(data)
data.use_char = True
data.HP_batch_size = 10
data.HP_lr = 0.015
data.char_seq_feature = "CNN"
