def main():
args = get_args()
args.n_gpu = 1
set_seed(args)
# Construct tokenizer
tokenizer = CharTokenizer([])
tokenizer.load(args.load_vocab)
args.vocab_size = len(tokenizer)
logger.info(f"args: {json.dumps(args.__dict__, indent=2, sort_keys=True)}")
# GPU setting
os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Construct model
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
logger.info(
f"# of model parameters: {sum(p.numel() for p in model.parameters()) * 1e-6:.2f}M"
)
# Load data
noisy_sents = read_strings(os.path.join('sejong_corpus', args.noisy_file))
clean_sents = read_strings(os.path.join('sejong_corpus', args.clean_file))
sents_annotation = ['None'] * len(noisy_sents)
pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
} for noisy, clean, annot in zip(noisy_sents, clean_sents,
sents_annotation)]
# Train-validation split
train_data, valid_data = train_test_split(
pairs, test_size=args.val_ratio,
random_state=args.seed) # test: about 1000
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
train(model, tokenizer, train_data, valid_data, args, eos=args.eos_setting)
with open(os.path.join(path, 'vocab_unlabeled.txt'), 'w') as f:
for token in vocab_unlabeled:
f.write(token + '\n')
with open(os.path.join(path, 'vocab_clean.txt'), 'w') as f:
for token in vocab_clean:
f.write(token + '\n')
with open(os.path.join(path, 'vocab_total.txt'), 'w') as f:
for token in vocab_total:
f.write(token + '\n')
print("vocab saved!")
nsml.bind(save)
data_dir = os.path.join(DATASET_PATH, 'train')
noisy_sents = read_strings(os.path.join(data_dir, "train_data", "train_data"))
unlabeled_sents = read_strings(os.path.join(data_dir, "train_data", "train_corpus"))
clean_sents = read_strings(os.path.join(data_dir, "train_label"))
noisy_counter = Counter()
for x in noisy_sents:
noisy_counter.update(x)
unlabeled_counter = Counter()
for x in unlabeled_sents:
unlabeled_counter.update(x)
clean_counter = Counter()
for x in clean_sents:
clean_counter.update(x)
from train import get_args, correct
from tokenizer import CharTokenizer
import torch
# args = get_args()
# args.eval_batch_size = 64
# args.vocab_size = 1300
# args.device = 'cuda:0'
# # load model (clean -> noisy)
# model = TransformerModel(
# vocab_size=args.vocab_size,
# hidden_size=args.hidden_size,
# num_attention_heads=args.num_attention_heads,
# num_encoder_layers=args.num_encoder_layers,
# num_decoder_layers=args.num_decoder_layers,
# intermediate_size=args.intermediate_size,
# dropout=args.dropout,
# ).to(args.device)
# model.load_state_dict(torch.load('reverse/model.pt', map_location=args.device))
# tokenizer = CharTokenizer([])
# tokenizer.load('reverse/vocab.txt')
clean_sents = read_strings('sejong_corpus/sejong_clean.txt')
# noisy_sents = correct(model, tokenizer, clean_sents, args, eos=args.eos_setting, length_limit=0.1)
noisy_sents = noise(clean_sents)
write_strings('sejong_corpus/sejong_noisy.txt', noisy_sents)
def load(self, path):
i2c = read_strings(path)
self.init(i2c)
def main():
args = get_args()
logger.info(f"args: {json.dumps(args.__dict__, indent=2, sort_keys=True)}")
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.n_gpu = torch.cuda.device_count()
set_seed(args)
if args.tokenizer == 'char':
tokenizer = CharTokenizer([])
if args.tokenizer == 'kobert':
print("koBERT tokenizer")
tokenizer = KoBertTokenizer.from_pretrained('monologg/kobert')
args.vocab_size = tokenizer.vocab_size
print(args.vocab_size)
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
logger.info(f"# of model parameters: {sum(p.numel() for p in model.parameters()) * 1e-6:.2f}M")
eos_setting = args.eos_setting
bind_nsml(model, tokenizer, args, eos=eos_setting)
if args.pause:
nsml.paused(scope=locals())
#train_data, valid_data = None, None
if args.mode == "train" or args.mode == "pretrain" or args.mode == "semi-train":
if args.mode == "train":
noisy_sents = read_strings(os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(os.path.join(args.data_dir, "train_data", "train_annotation"))
clean_sents = read_strings(os.path.join(args.data_dir, "train_label"))
if args.mode == "semi-train":
noisy_sents = read_strings(os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(os.path.join(args.data_dir, "train_data", "train_annotation"))
clean_sents = read_strings(os.path.join(args.data_dir, "train_label"))
checkpoint = 'generated_data'
sess = 't0005/rush1-1/'+str(args.semi_dataset)
# five copy
#sess = 't0005/rush1-1/209'
# one copy
#sess = 't0005/rush1-1/224'
semi_noisy_sents, semi_clean_sents = load_generated_data(checkpoint=checkpoint, session=sess)
semi_sents_annotation = ['None'] * len(semi_noisy_sents)
if args.mode == "pretrain":
print("PRETRAIN MODE ON!!")
checkpoint = 'generated_data'
sess = 't0005/rush1-1/113'
noisy_sents, clean_sents = load_generated_data(checkpoint=checkpoint, session=sess)
sents_annotation = ['None']*len(noisy_sents)
error_type_counter = Counter()
for annotation in sents_annotation:
error_type_counter += Counter(annotation.split(','))
print(error_type_counter)
# cleaning noise 버전
# pairs = [{"noisy": preprocess_sentence(noisy), "clean": clean} for noisy, clean in zip(noisy_sents, clean_sents)]
# original 버전
if args.mode == "semi-train":
pairs = [{"noisy": noisy, "clean": clean, "annotation": annot} for noisy, clean, annot in
zip(noisy_sents, clean_sents, sents_annotation)]
semi_pairs = [{"noisy": noisy, "clean": clean, "annotation": annot} for noisy, clean, annot in
zip(semi_noisy_sents, semi_clean_sents, semi_sents_annotation)]
train_data = pairs[:-args.num_val_data]+semi_pairs
valid_data = pairs[-args.num_val_data:]
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
train_sents = [x['noisy'] for x in train_data] + [x['clean'] for x in train_data]
tokenizer = CharTokenizer.from_strings(train_sents, args.vocab_size)
bind_nsml(model, tokenizer, args, eos=eos_setting)
else:
pairs = [{"noisy": noisy, "clean": clean, "annotation": annot} for noisy, clean, annot in zip(noisy_sents, clean_sents, sents_annotation)]
train_data, valid_data = pairs[:-args.num_val_data], pairs[-args.num_val_data:]
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
train_sents = [x['noisy'] for x in train_data] + [x['clean'] for x in train_data]
#tokenizer = CharTokenizer.from_strings(train_sents, args.vocab_size)
bind_nsml(model, tokenizer, args,eos=eos_setting)
## to load pretrained model
nsml.load(checkpoint='best', session='t0005/rush1-2/79')
#print(tokenizer.vocab)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=1)
if args.mode == "train" or args.mode == "pretrain" or args.mode == 'semi-train':
train(model, tokenizer, train_data, valid_data, args, eos=eos_setting)
def load(dirname, *args):
noisy_sents = read_strings(os.path.join(dirname, 'generated_data'))
clean_sents = read_strings(os.path.join(dirname, 'generated_label'))
generated_data.save(noisy_sents, clean_sents)
def main():
args = get_args()
logger.info(f"args: {json.dumps(args.__dict__, indent=2, sort_keys=True)}")
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.n_gpu = torch.cuda.device_count()
set_seed(args)
tokenizer = CharTokenizer([])
configuration = BertConfig(vocab_size=args.vocab_size)
model = BertForMaskedLM(configuration).to(args.device)
'''
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
'''
logger.info(
f"# of model parameters: {sum(p.numel() for p in model.parameters()) * 1e-6:.2f}M"
)
bind_nsml(model, tokenizer, args)
if args.pause:
nsml.paused(scope=locals())
if args.mode == "train" or args.mode == "pretrain":
if args.mode == "train":
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(
os.path.join(args.data_dir, "train_data", "train_annotation"))
clean_sents = read_strings(
os.path.join(args.data_dir, "train_label"))
if args.mode == "pretrain":
print("PRETRAIN MODE ON!!")
checkpoint = 'generated_data'
sess = 't0005/rush1-1/113'
noisy_sents, clean_sents = load_generated_data(
checkpoint=checkpoint, session=sess)
sents_annotation = ['None'] * len(noisy_sents)
error_type_counter = Counter()
for annotation in sents_annotation:
error_type_counter += Counter(annotation.split(','))
print(error_type_counter)
# cleaning noise 버전
# pairs = [{"noisy": preprocess_sentence(noisy), "clean": clean} for noisy, clean in zip(noisy_sents, clean_sents)]
# original 버전
pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
} for noisy, clean, annot in zip(noisy_sents, clean_sents,
sents_annotation)]
train_data, valid_data = pairs[:-args.num_val_data], pairs[
-args.num_val_data:]
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
train_sents = [x['noisy']
for x in train_data] + [x['clean'] for x in train_data]
tokenizer = CharTokenizer.from_strings(train_sents, args.vocab_size)
bind_nsml(model, tokenizer, args)
## to load pretrained model
#nsml.load(checkpoint='best', session='t0005/rush1-1/177')
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=1)
if args.mode == "train" or args.mode == "pretrain":
train(model, tokenizer, train_data, valid_data, args)
# save_generated_data test
from noise_generation import save_generated_data, load_generated_data, noise
from data_loader import read_strings
import os
from nsml import DATASET_PATH
import random
data_dir = os.path.join(DATASET_PATH, 'train')
## 데이터 copy를 몇개 만들것인지 정함. train_label clean data에서 noise만든것을 몇번 반복할건지
num_copy = 5
clean_sents = read_strings(os.path.join(data_dir, "train_label"))*num_copy
noisy_sents = noise(clean_sents)
shuffle_idxs = list(range(len(clean_sents)))
random.shuffle(shuffle_idxs)
noise_sents_shuf = []
clean_sents_shuf = []
for i in shuffle_idxs:
noise_sents_shuf.append(noisy_sents[i])
clean_sents_shuf.append(clean_sents[i])
save_generated_data(noise_sents_shuf, clean_sents_shuf)
## 현재 돌리는 세션의 이름을 넣으면 됨 't0005/rush1-1/(세션 번호)'. 그 위치에서 만들어진 데이터셋이 잘 로드되는지 확인할 수 있음
checkpoint = 'generated_data'
sess = 't0005/rush1-1/230'
loaded_noisy_sents, loaded_clean_sents = load_generated_data(checkpoint=checkpoint, session=sess)
def main():
args = get_args()
logger.info(f"args: {json.dumps(args.__dict__, indent=2, sort_keys=True)}")
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
args.n_gpu = torch.cuda.device_count()
set_seed(args)
if args.tokenizer == 'char':
tokenizer = CharTokenizer([])
if args.tokenizer == 'kobert':
print("koBERT tokenizer")
tokenizer = KoBertTokenizer.from_pretrained('monologg/kobert')
args.vocab_size = tokenizer.vocab_size
print(args.vocab_size)
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
logger.info(
f"# of model parameters: {sum(p.numel() for p in model.parameters()) * 1e-6:.2f}M"
)
eos_setting = args.eos_setting
bind_nsml(model, tokenizer, args, eos=eos_setting)
if args.pause:
nsml.paused(scope=locals())
#train_data, valid_data = None, None
if args.mode == "train":
noisy_sents_labeled = read_strings(
os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation_labeled = read_strings(
os.path.join(args.data_dir, "train_data", "train_annotation"))
clean_sents_labeled = read_strings(
os.path.join(args.data_dir, "train_label"))
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_corpus"))
pairs = noisy_sents
pairs_labeled = clean_sents_labeled
train_data, valid_data = pairs + noisy_sents_labeled[:-args.
num_val_data] + pairs_labeled[:-args.num_val_data], pairs_labeled[
-args.
num_val_data:]
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
train_sents = [x for x in train_data]
if args.tokenizer == 'char':
tokenizer = CharTokenizer.from_strings(train_sents,
args.vocab_size)
print("===vocab size: ", len(tokenizer))
args.vocab_size = len(tokenizer)
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
bind_nsml(model, tokenizer, args, eos=eos_setting)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=1)
if args.mode == "train":
train(model, tokenizer, train_data, valid_data, args, eos=eos_setting)
def main():
# from pathlib import Path
# print("File Path:", Path(__file__).absolute())
# print("Directory Path:", Path().absolute())
args = get_args()
args.n_gpu = 1
# noisy_sents_1 = read_strings(os.path.join(args.data_dir, "train_data", "train_data"))
# clean_sents = read_strings(os.path.join(args.data_dir, "train_label"))
# noisy_sents_2 = read_strings(os.path.join(args.data_dir, "train_data", "train_corpus"))
#
# noisy_sents = noisy_sents_1 + noisy_sents_2
# noise_space_ratio = []
#
# for sentence in noisy_sents:
# noise_space_ratio.append(sentence.count(' ') / len(sentence))
#
# clean_space_ratio = []
# for sentence in clean_sents:
# clean_space_ratio.append(sentence.count(' ') / len(sentence))
#
# print("noise_space_ratio: {}, clean_space_ratio: {}".format(sum(noise_space_ratio) / len(noise_space_ratio),
# sum(clean_space_ratio) / len(clean_space_ratio)))
# ##########
# ##for local
# args.num_workers=0
# args.train_batch_size = 4
# args.eval_batch_size = 4
# args.eval_interval = 10
# ##########
set_seed(args)
if args.tokenizer == 'char':
tokenizer = CharTokenizer([])
if args.tokenizer == 'kobert':
print("koBERT tokenizer")
tokenizer = KoBertTokenizer.from_pretrained('monologg/kobert')
args.vocab_size = tokenizer.vocab_size
print(args.vocab_size)
if args.load_vocab != "":
tokenizer.load(args.load_vocab)
args.vocab_size = tokenizer.__len__()
logger.info(f"args: {json.dumps(args.__dict__, indent=2, sort_keys=True)}")
os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda
args.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
logger.info(
f"# of model parameters: {sum(p.numel() for p in model.parameters()) * 1e-6:.2f}M"
)
eos_setting = args.eos_setting
bind_nsml(model, tokenizer, args, eos=eos_setting)
if args.pause:
nsml.paused(scope=locals())
if args.mode != 'test' and args.averaging != "":
sess = 't0005/rush1-3/37'
checkpoints = ["4500", "6500", "7500", "8000"]
nsml.load(checkpoint=checkpoints[0], session=sess)
args.vocab_size = tokenizer.__len__()
print(args.vocab_size)
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
params = model.named_parameters()
new_dict_params = dict(params)
for checkpoint in checkpoints:
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
for name, param in params:
new_dict_params[name] += param / len(checkpoints)
model.load_state_dict(new_dict_params, strict=False)
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.save('best')
elif args.mode == 'eval':
print("I'm in EVAL")
checkpoint = 'best'
sess = 't0005/rush1-3/507'
nsml.load(checkpoint=checkpoint, session=sess)
args.vocab_size = tokenizer.__len__()
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
model.eval()
#noisy_sents = open("./naver_data_clean.txt", "r", encoding='utf-8').read().splitlines()
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_corpus"))
valid_noisy = noisy_sents[:1000]
prediction = correct_beam(model,
tokenizer,
valid_noisy,
args,
eos=True,
length_limit=0.15)
for i, pred in enumerate(prediction[:1000]):
print("noisy_input: {}, pred: {}".format(valid_noisy[i], pred))
# bind_txt(prediction)
# nsml.save('prediction')
# with open('naver_data_clean_again.txt', 'w',encoding='utf-8') as f:
# for i, pred in enumerate(prediction):
# if i%500==0: print(i)
# f.write("%s\n" % pred)
## only works when char tokenizer
##TODO: kobert tokenizer, different vocabsize if it is needed
elif args.mode != 'test' and args.resubmit != "":
checkpoint = 'best'
sess = 't0005/rush1-3/' + args.resubmit
print(sess)
model = None
tokenizer = CharTokenizer([])
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
args.vocab_size = len(tokenizer)
print(args.vocab_size)
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
bind_nsml(model, tokenizer, args, eos=eos_setting)
nsml.load(checkpoint=checkpoint, session=sess)
bind_nsml(model, tokenizer, args, eos=eos_setting)
########## testing loaded model & tokenizer ###############
# model.eval()
# noisy_sents = read_strings(os.path.join(args.data_dir, "train_data", "train_data"))
# valid_noisy = noisy_sents[-10:]
#
# prediction = correct(model, tokenizer, valid_noisy, args, eos=True, length_limit=0.1)
#
# for pred in prediction:
# print(pred)
##################
nsml.save("best")
else:
#train_data, valid_data = None, None
if args.mode == "train" or args.mode == "pretrain" or args.mode == "semi-train":
if args.mode == "train":
# noisy_sents = open("./noisy_sejong_500k.txt", "r", encoding='utf-8').read().splitlines()[:20000]
# clean_sents = open("./clean_sejong_500k.txt", "r", encoding='utf-8').read().splitlines()[:20000]
# sents_annotation = ['None'] * len(noisy_sents)
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(
os.path.join(args.data_dir, "train_data",
"train_annotation"))
clean_sents = read_strings(
os.path.join(args.data_dir, "train_label"))
if args.mode == "semi-train":
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(
os.path.join(args.data_dir, "train_data",
"train_annotation"))
clean_sents = read_strings(
os.path.join(args.data_dir, "train_label"))
checkpoint = 'generated_data'
sess = 't0005/rush1-1/' + str(args.semi_dataset)
# five copy
#sess = 't0005/rush1-1/209'
# one copy
#sess = 't0005/rush1-1/224'
semi_noisy_sents, semi_clean_sents = load_generated_data(
checkpoint=checkpoint, session=sess)
semi_sents_annotation = ['None'] * len(semi_noisy_sents)
if args.mode == "pretrain":
print("PRETRAIN MODE ON!!")
noisy_sents = read_strings(
os.path.join('sejong_corpus', args.noisy_file))
clean_sents = read_strings(
os.path.join('sejong_corpus', args.clean_file))
# checkpoint = 'generated_data'
# sess = 't0005/rush1-1/113'
# noisy_sents, clean_sents = load_generated_data(checkpoint=checkpoint, session=sess)
sents_annotation = ['None'] * len(noisy_sents)
error_type_counter = Counter()
for annotation in sents_annotation:
error_type_counter += Counter(annotation.split(','))
print(error_type_counter)
# cleaning noise 버전
# pairs = [{"noisy": preprocess_sentence(noisy), "clean": clean} for noisy, clean in zip(noisy_sents, clean_sents)]
# original 버전
if args.mode == "semi-train":
pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
}
for noisy, clean, annot in zip(
noisy_sents, clean_sents, sents_annotation)]
semi_pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
} for noisy, clean, annot in zip(
semi_noisy_sents, semi_clean_sents, semi_sents_annotation)]
train_data = pairs[:-args.num_val_data] + semi_pairs
valid_data = pairs[-args.num_val_data:]
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
train_sents = [x['noisy'] for x in train_data
] + [x['clean'] for x in train_data]
tokenizer = CharTokenizer.from_strings(train_sents,
args.vocab_size)
bind_nsml(model, tokenizer, args, eos=eos_setting)
else:
pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
}
for noisy, clean, annot in zip(
noisy_sents, clean_sents, sents_annotation)]
train_data, valid_data = train_test_split(
pairs, test_size=args.val_ratio,
random_state=args.seed) # test: about 1000
logger.info(f"# of train data: {len(train_data)}")
logger.info(f"# of valid data: {len(valid_data)}")
# print("validation: ", valid_data)
train_sents = [x['noisy'] for x in train_data
] + [x['clean'] for x in train_data]
# train_sents = [x['clean'] for x in train_data]
if args.load_model != "" and args.mode == "train": # Load pretrained model
print("load pretrained model")
model.load_state_dict(
torch.load(args.load_model, map_location=args.device))
if args.freeze:
model.token_embeddings.weight.requires_grad = False
model.decoder_embeddings.weight.requires_grad = False
if args.tokenizer == 'char' and args.load_vocab == "":
tokenizer = CharTokenizer.from_strings(
train_sents, args.vocab_size)
print(
f'tokenizer loaded from strings. len={len(tokenizer)}.'
)
bind_nsml(model, tokenizer, args, eos=eos_setting)
if args.tokenizer == 'char' and tokenizer is not None:
tokenizer.save('vocab.txt')
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=1)
if args.mode == "train" or args.mode == "pretrain" or args.mode == 'semi-train':
train(model,
tokenizer,
train_data,
valid_data,
args,
eos=eos_setting)
args.vocab_size = tokenizer.__len__()
model = TransformerModel(
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
num_encoder_layers=args.num_encoder_layers,
num_decoder_layers=args.num_decoder_layers,
intermediate_size=args.intermediate_size,
dropout=args.dropout,
).to(args.device)
bind_nsml(model, tokenizer, args)
nsml.load(checkpoint=checkpoint, session=sess)
noisy_sents = read_strings(
os.path.join(args.data_dir, "train_data", "train_data"))
sents_annotation = read_strings(
os.path.join(args.data_dir, "train_data", "train_annotation"))
clean_sents = read_strings(os.path.join(args.data_dir, "train_label"))
pairs = [{
"noisy": noisy,
"clean": clean,
"annotation": annot
} for noisy, clean, annot in zip(noisy_sents, clean_sents, sents_annotation)]
train_data, valid_data = pairs[:-1000], pairs[-1000:]
#train_data, valid_data = train_test_split(pairs, test_size=args.val_ratio, random_state=2020)
# 길이가 작은 순으로 sort
valid_data.sort(key=lambda x: len(x['noisy']))
for i in range(0, len(valid_data), 100):
try:
