def define_model(name, config=None, location=None):
# config가 있으면 처음 training하는 경우, 없으면 체크포인트 불러오기
if name in [
"bert-base-multilingual-cased",
"sangrimlee/bert-base-multilingual-cased-korquad",
"kykim/bert-kor-base", "monologg/kobert"
]:
return BertForSequenceClassification.from_pretrained(
name, config=config
) if config else BertForSequenceClassification.from_pretrained(
location)
elif name in [
"monologg/koelectra-base-v3-discriminator",
"kykim/electra-kor-base"
]:
return ElectraForSequenceClassification.from_pretrained(
name, config=config
) if config else ElectraForSequenceClassification.from_pretrained(
location)
elif name in ["xlm-roberta-large"]:
return XLMRobertaForSequenceClassification.from_pretrained(
name, config=config
) if config else XLMRobertaForSequenceClassification.from_pretrained(
location)
elif name in ["kykim/funnel-kor-base"]:
return FunnelForSequenceClassification.from_pretrained(
name, config=config
) if config else FunnelForSequenceClassification.from_pretrained(
location)
def load_model(device):
checkpoint = os.path.dirname(os.path.realpath(__file__))+'/ckpt/koelectra-base-v3-ckpt1/s2_checkpoint-7300'
model = ElectraForSequenceClassification.from_pretrained(checkpoint)
model.to(device)
print('model loaded')
return model
def get_text_reader(reader_name, task, num_labels):
# AILAW Corpus is korean dataset.
# So, model is fixed to Korean Model such as multilingual-BERT, kobert, koelectra, etc.
if reader_name == "bert":
if task == "classification":
model_name = "bert-base-multilingual-cased"
text_reader = BertForSequenceClassification.from_pretrained(model_name, num_labels=num_labels)
else: # ner
model_name = "bert-base-multilingual-cased"
text_reader = BertForTokenClassification.from_pretrained(model_name, num_labels=num_labels)
elif reader_name == "kobert":
if task == "classification":
model_name = "monologg/kobert"
text_reader = BertForSequenceClassification.from_pretrained(model_name, num_labels=num_labels)
else: # ner
model_name = "monologg/kobert"
text_reader = BertForTokenClassification.from_pretrained(model_name, num_labels=num_labels)
elif reader_name == "koelectra":
if task == "classification":
model_name = "monologg/koelectra-base-discriminator"
text_reader = ElectraForSequenceClassification.from_pretrained(model_name, num_labels=num_labels)
else: # ner
model_name = "monologg/koelectra-base-discriminator"
text_reader = ElectraForTokenClassification.from_pretrained(model_name, num_labels=num_labels)
else:
raise KeyError(reader_name)
return text_reader
def _init_deep_model(self, model_type, model_path, num_labels, num_regs=None):
if 'roberta' in model_type:
tokenizer = RobertaTokenizer.from_pretrained(model_path)
config = RobertaConfig.from_pretrained(model_path)
config.num_labels = num_labels
model = RobertaForSequenceClassification.from_pretrained(model_path, config=config)
model.eval()
model.to(self.device)
elif 'electra_multitask' in model_type:
tokenizer = ElectraTokenizer.from_pretrained(model_path)
tokenizer.add_special_tokens({'additional_special_tokens': ['[VALUES]']})
config = ElectraConfig.from_pretrained(model_path)
config.num_labels = num_labels
config.num_regs = num_regs
config.vocab_size = len(tokenizer)
model = ElectraForSequenceClassificationMultiTask.from_pretrained(model_path, config=config)
model.eval()
model.to(self.device)
elif 'electra' in model_type:
tokenizer = ElectraTokenizer.from_pretrained(model_path)
config = ElectraConfig.from_pretrained(model_path)
config.num_labels = num_labels
model = ElectraForSequenceClassification.from_pretrained(model_path, config=config)
model.eval()
model.to(self.device)
else:
raise NotImplementedError()
return config, tokenizer, model
def main(args):
"""
주어진 dataset tsv 파일과 같은 형태일 경우 inference 가능한 코드입니다.
"""
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# load tokenizer
TOK_NAME = "monologg/koelectra-base-v3-discriminator"
#tokenizer = AutoTokenizer.from_pretrained(TOK_NAME)
tokenizer = ElectraTokenizer.from_pretrained(TOK_NAME)
# load my model
MODEL_NAME = args.model_dir # model dir.
model = ElectraForSequenceClassification.from_pretrained(args.model_dir)
model.parameters
model.to(device)
# load test datset
test_dataset_dir = "/opt/ml/input/data/test/test.tsv"
test_dataset, test_label = load_test_dataset(test_dataset_dir, tokenizer)
test_dataset = RE_Dataset(test_dataset ,test_label)
# predict answer
logits, predictions = inference(model, test_dataset, device)
# make csv file with predicted answer
# 아래 directory와 columns의 형태는 지켜주시기 바랍니다.
output = pd.DataFrame(predictions, columns=['pred'])
output.to_csv('./prediction/koelectra-submission6.csv', index=False)
def predict_pair(model_args, data_args, training_args):
# Set seed
set_seed(training_args.seed)
if 'roberta' in model_args.model_type:
tokenizer = RobertaTokenizer.from_pretrained(model_args.tokenizer_name_or_path)
config = RobertaConfig.from_pretrained(model_args.model_name_or_path)
config.num_labels = data_args.num_labels
model = RobertaForSequenceClassification.from_pretrained(model_args.model_name_or_path, config=config)
elif 'electra' in model_args.model_type:
tokenizer = ElectraTokenizer.from_pretrained(model_args.tokenizer_name_or_path)
config = ElectraConfig.from_pretrained(model_args.model_name_or_path)
config.num_labels = data_args.num_labels
model = ElectraForSequenceClassification.from_pretrained(model_args.model_name_or_path, config=config)
else:
# default -> bert
tokenizer = BertTokenizer.from_pretrained(model_args.tokenizer_name_or_path)
config = BertConfig.from_pretrained(model_args.model_name_or_path)
config.num_labels = data_args.num_labels
model = BertForSequenceClassification.from_pretrained(model_args.model_name_or_path, config=config)
model.to(training_args.device)
test_df = pickle.load(open(data_args.test_data_file, 'rb'))
test_dataset = get_dataset(data_args, tokenizer, test_df, model_args.model_type)
data_collator = MyDataCollator()
if training_args.local_rank != -1:
sampler = SequentialDistributedSampler(test_dataset)
model = torch.nn.DataParallel(model)
else:
n_gpu = torch.cuda.device_count()
if n_gpu > 1:
model = torch.nn.DataParallel(model)
sampler = SequentialSampler(test_dataset)
print(len(test_dataset))
dataloader = DataLoader(
test_dataset,
sampler=sampler,
batch_size=training_args.eval_batch_size,
collate_fn=data_collator,
)
model.eval()
all_probs = []
for inputs in tqdm(dataloader):
for k, v in inputs.items():
inputs[k] = v.to(training_args.device)
inputs.pop('labels')
with torch.no_grad():
outputs = model(**inputs)
logits = outputs[0]
probs = torch.softmax(logits, dim=-1)
maxp, maxi = torch.max(probs, dim=-1)
result = [(_i, _p) for _p, _i in zip(maxp, maxi)]
all_probs.extend(result)
with open('./{}_{}.answer_classify.result'.format(data_args.data_type, model_args.model_type), 'w', encoding='utf-8') as fout:
for i in range(len(test_df)):
fout.write('{} | {} | {} | {} | {}\n'.format(test_df[i][0], test_df[i][1], test_df[i][2], all_probs[i][0], all_probs[i][1]))
def main(task='mrpc',
base_train_cfg='config/QDElectra_pretrain.json',
train_cfg='config/train_mrpc.json',
model_cfg='config/QDElectra_base.json',
data_file='../glue/MRPC/train.tsv',
model_file=None,
data_parallel=True,
vocab='../uncased_L-12_H-768_A-12/vocab.txt',
log_dir='../exp/electra/pretrain/runs',
save_dir='../exp/bert/mrpc',
mode='train',
pred_distill=True):
train_cfg_dict = json.load(open(base_train_cfg, "r"))
train_cfg_dict.update(json.load(open(train_cfg, "r")))
train_cfg = ElectraConfig().from_dict(train_cfg_dict)
# train_cfg = ElectraConfig().from_json_file(train_cfg)
model_cfg = ElectraConfig().from_json_file(model_cfg)
output_mode, train_cfg.n_epochs, max_len = get_task_params(task)
set_seeds(train_cfg.seed)
tokenizer = tokenization.FullTokenizer(vocab_file=vocab, do_lower_case=True)
TaskDataset = dataset_class(task) # task dataset class according to the task
num_labels = len(TaskDataset.labels)
pipeline = [
Tokenizing(tokenizer.convert_to_unicode, tokenizer.tokenize),
AddSpecialTokensWithTruncation(max_len),
TokenIndexing(tokenizer.convert_tokens_to_ids, TaskDataset.labels, output_mode, max_len)
]
data_set = TaskDataset(data_file, pipeline)
data_iter = DataLoader(data_set, batch_size=train_cfg.batch_size, shuffle=True)
t_discriminator = ElectraForSequenceClassification.from_pretrained(
'google/electra-base-discriminator'
)
s_discriminator = QuantizedElectraForSequenceClassification.from_pretrained(
'google/electra-small-discriminator', config=model_cfg
)
model = DistillElectraForSequenceClassification(t_discriminator, s_discriminator, model_cfg)
optimizer = optim.optim4GPU(train_cfg, model)
writer = SummaryWriter(log_dir=log_dir) # for tensorboardX
base_trainer_args = (train_cfg, model_cfg, model, data_iter, optimizer, save_dir, get_device())
trainer = QuantizedDistillElectraTrainer(writer, *base_trainer_args)
if mode == 'train':
trainer.train(model_file, None, data_parallel)
elif mode == 'eval':
input_ids, attention_mask, token_type_ids, label_ids = TokenIndexing(tokenizer.convert_tokens_to_ids,
TaskDataset.labels,
output_mode,
max_len)
_, eval_labels = get_tensor_data(output_mode, input_ids, attention_mask, token_type_ids, label_ids)
results = trainer.eval(model_file, output_mode, eval_labels, num_labels, data_parallel)
total_accuracy = torch.cat(results).mean().item()
print('Accuracy:', total_accuracy)
def index():
if request.values.get("txt"):
from transformers import AutoTokenizer, AutoModel, ElectraForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(
"/srv/electra-ka-fake-news-tagging/")
model = ElectraForSequenceClassification.from_pretrained(
"/srv/electra-ka-fake-news-tagging/")
inputs = tokenizer(request.values.get("txt"), return_tensors="pt")
return str(model(**inputs)[0].tolist())
return 'no text was sent'
def __init__(self, cache_dir=DEFAULT_CACHE_DIR, verbose=False):
from transformers import ElectraTokenizer, ElectraForSequenceClassification
# download the model or load the model path
model_path = download_model('electra.offensive',
cache_dir,
process_func=_unzip_process_func,
verbose=verbose)
self.classes = ['NOT', 'OFF']
self.tokenizer = ElectraTokenizer.from_pretrained(model_path)
self.model = ElectraForSequenceClassification.from_pretrained(
model_path, num_labels=len(self.classes))
self.max_length = self.model.electra.embeddings.position_embeddings.num_embeddings
def __init__(self, batch_size, output_size, hidden_size):
super(KEA_ELECTRA, self).__init__()
options_name = "google/electra-base-discriminator"
self.encoder = ElectraForSequenceClassification.from_pretrained(
options_name, num_labels=output_size)
self.batch_size = batch_size
self.output_size = output_size
self.hidden_size = hidden_size
self.a = nn.Linear(512, hidden_size) #512 is the size of lexicon_vec
self.v = nn.Linear(512, hidden_size) #512 is the size of lexicon_vec
self.d = nn.Linear(512, hidden_size) #512 is the size of lexicon_vec
self.dropout = nn.Dropout(0.1)
self.fc1 = nn.Linear(hidden_size, 384)
self.label = nn.Linear(384, output_size)
def __init__(self, batch_size, output_size, hidden_size):
super(KEA_Electra_Word_level, self).__init__()
options_name = "google/electra-base-discriminator"
self.encoder = ElectraForSequenceClassification.from_pretrained(
options_name, num_labels=output_size)
self.batch_size = batch_size
self.output_size = output_size
self.hidden_size = hidden_size
self.bilstm = nn.LSTM(hidden_size + 3,
int(hidden_size / 2),
dropout=0.2,
bidirectional=True)
self.dropout = nn.Dropout(0.1)
self.fc1 = nn.Linear(hidden_size, 384)
self.label = nn.Linear(384, output_size)
def __init__(self,
input_ids_batch,
attention_mask,
hidden_size=768,
num_classes=6,
dr_rate=None,
params=None):
super(ElectraClassifier, self).__init__()
self.dr_rate = dr_rate
self.device = torch.device(
"cuda:0") if torch.cuda.is_available() else torch.device("cpu")
self.electramodel = ElectraForSequenceClassification.from_pretrained(
"monologg/koelectra-small-v2-discriminator")
self.attention_mask = attention_mask
self.classifier = nn.Linear(hidden_size, num_classes)
if dr_rate:
self.dropout = nn.Dropout(p=dr_rate)
def __call_model_torch(self):
if self.model_to_use.lower() == 'bert':
self.config = BertConfig(num_labels=2)
self.model = BertForSequenceClassification.from_pretrained(
'bert-base-uncased', config=self.config)
elif self.model_to_use.lower() == 'albert':
self.config = AlbertConfig(num_labels=2)
self.model = AlbertForSequenceClassification.from_pretrained(
'albert-base-v1', config=self.config)
elif self.model_to_use.lower() == 'electra':
self.config = ElectraConfig(num_labels=2)
self.model = ElectraForSequenceClassification.from_pretrained(
'google/electra-small-discriminator', config=self.config)
elif self.model_to_use.lower() == 'distilbert':
self.config = DistilBertConfig(num_labels=2)
self.model = DistilBertForSequenceClassification.from_pretrained(
'distilbert-base-uncased', config=self.config)
else:
print('Model not avaiable yet.')
def __init__(self,
config: ElectraConfig,
embeddings,
discriminator=None,
embed_layer=None):
super().__init__()
self.embed_layer = nn.Embedding(num_embeddings=config.vocab_size,
embedding_dim=config.embedding_size,
padding_idx=config.vocab_size - 1)
if embed_layer:
self.embed_layer.load_state_dict(torch.load(embed_layer))
else:
self.embed_layer.weight = nn.Parameter(embeddings)
if discriminator:
self.discriminator = ElectraForSequenceClassification.from_pretrained(
discriminator, config=config)
else:
self.discriminator = ElectraForSequenceClassification(config)
self.softmax = nn.Softmax(1)
def __init__(
self,
model_dir,
vocab_dir="skplanet/dialog-koelectra-small-discriminator",
label_list=None,
cuda=False,
):
if cuda:
device = "cuda" if torch.cuda.is_available() else "cpu"
else:
device = "cpu"
self.device = torch.device(device)
self.model = ElectraForSequenceClassification.from_pretrained(model_dir)
self.model.to(self.device)
self.model.eval()
self.tokenizer = ElectraTokenizer.from_pretrained(
vocab_dir, do_lower_case=False
)
self.label_list = None
if label_list:
self.label_list = label_list
def call(self):
if self.model_to_use.lower() == 'bert':
self.model = BertForSequenceClassification.from_pretrained(
'bert-base-uncased',
num_labels=2,
output_attentions=False,
output_hidden_states=False)
print('Bert Cargado.')
print(self.model)
elif self.model_to_use.lower() == 'albert':
self.model = AlbertForSequenceClassification.from_pretrained(
'albert-base-v1',
num_labels=2,
output_attentions=False,
output_hidden_states=False)
elif self.model_to_use.lower() == 'electra':
self.model = ElectraForSequenceClassification.from_pretrained(
'google/electra-small-discriminator',
num_labels=2,
output_attentions=False,
output_hidden_states=False)
elif self.model_to_use.lower() == 'distilbert':
self.model = DistilBertForSequenceClassification.from_pretrained(
'distilbert-base-uncased',
num_labels=2,
output_attentions=False,
output_hidden_states=False)
else:
print('Model not avaiable right now.')
self.model.to(self.device)
self.optimizer = AdamW(self.model.parameters(),
lr=self.learning_rate,
eps=self.epsilon)
self.total_steps = len(self.train_dataloader) * self.epochs
self.scheduler = get_linear_schedule_with_warmup(
self.optimizer,
num_warmup_steps=0,
num_training_steps=self.total_steps)
def main(args):
"""
주어진 dataset tsv 파일과 같은 형태일 경우 inference 가능한 코드입니다.
"""
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# load tokenizer
TOK_NAME = args.model_name
tokenizer = AutoTokenizer.from_pretrained(TOK_NAME)
# load my model
model_dir = f'./results/{args.id}/checkpoint-{args.checkpoint}'
if args.model_type == 'bert':
model = BertForSequenceClassification.from_pretrained(model_dir)
elif args.model_type == 'electra':
model = ElectraForSequenceClassification.from_pretrained(model_dir)
elif args.model_type == 'roberta':
model = XLMRobertaForSequenceClassification.from_pretrained(model_dir)
model.parameters
model.to(device)
# load test datset
# root = "/opt/ml"
# root = "/content/drive/MyDrive/Boostcamp/Stage2_KLUE"
root = args.root
test_dataset, test_label = load_test_dataset(root, tokenizer)
test_dataset = RE_Dataset(test_dataset, test_label)
# predict answer
pred_answer = inference(model, test_dataset, device)
# logits, predictions = inference(model, test_dataset, device)
# make csv file with predicted answer
# 아래 directory와 columns의 형태는 지켜주시기 바랍니다.
output = pd.DataFrame(pred_answer, columns=['pred'])
# output = pd.DataFrame(predictions, columns=['pred'])
output.to_csv(f'./results/{args.id}/submission{args.id}.csv', index=False)
# np.save(f'./results/{args.id}/logits{args.id}.npy', logits)
print('File saved')
def pretrained_tokenizer_and_model(self):
print(f'Model Class : {self.model_type}')
if self.model_type == 'bert':
pretrained_model = 'bert-base-uncased'
self.tokenizer = BertTokenizer.from_pretrained(pretrained_model)
self.model = BertForSequenceClassification.from_pretrained(
pretrained_model, num_labels=self.labels_count)
elif self.model_type == 'roberta':
pretrained_model = 'roberta-base'
self.tokenizer = RobertaTokenizer.from_pretrained(pretrained_model)
self.model = RobertaForSequenceClassification.from_pretrained(
pretrained_model, num_labels=self.labels_count)
elif self.model_type == 'distilbert':
pretrained_model = 'distilbert-base-uncased'
self.tokenizer = DistilBertTokenizer.from_pretrained(
pretrained_model)
self.model = DistilBertForSequenceClassification.from_pretrained(
pretrained_model, num_labels=self.labels_count)
elif self.model_type == 'electra':
pretrained_model = 'google/electra-small-discriminator'
self.tokenizer = ElectraTokenizer.from_pretrained(pretrained_model)
self.model = ElectraForSequenceClassification.from_pretrained(
pretrained_model, num_labels=self.labels_count)
if self.device.type == 'cuda': self.model.cuda()
from transformers import ElectraForSequenceClassification, ElectraTokenizerFast, Trainer, TrainingArguments
from nlp import load_dataset
import torch
import numpy as np
model = ElectraForSequenceClassification.from_pretrained(
'google/electra-small-discriminator')
tokenizer = ElectraTokenizerFast.from_pretrained(
'google/electra-small-discriminator')
import random
def tokenize(batch):
return tokenizer(batch['text'],
truncation=True,
max_length=256,
add_special_tokens=True,
padding='max_length',
return_attention_mask=True)
train_dataset = load_dataset(
'json',
data_files={'train': 'dataset_full_question/quanta_train.json'},
field='questions')['train']
train_dataset = train_dataset.map(
lambda example: {'label': [0 if example['difficulty'] == 'School' else 1]})
train_dataset = train_dataset.map(tokenize,
batched=True,
batch_size=len(train_dataset))
def main(args):
nsmc = h5py.File(f'{args.data_root}/nsmc.h5', 'r')
train_dataset = nsmc['train']
test_dataset = nsmc['test']
print('\n====================== Dataset Summary ======================\n')
print(f"Train Label : {train_dataset['label']}")
print(f"Train Input Ids : {train_dataset['input_ids']}")
print(f"Train Attention Mask : {train_dataset['attention_mask']}")
print(f"Test Label : {test_dataset['label']}")
print(f"Test Input Ids : {test_dataset['input_ids']}")
print(f"Test Attention Mask : {test_dataset['attention_mask']}")
print('\n=============================================================\n')
train_label = np.array(train_dataset['label'])
train_input_ids = np.array(train_dataset['input_ids'])
train_attention_mask = np.array(train_dataset['attention_mask'])
test_label = np.array(test_dataset['label'])
test_input_ids = np.array(test_dataset['input_ids'])
test_attention_mask = np.array(test_dataset['attention_mask'])
nsmc.close()
train_dataset = NSMCDataset(train_label, train_input_ids,
train_attention_mask)
test_dataset = NSMCDataset(test_label, test_input_ids, test_attention_mask)
train_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.n_workers)
test_loader = DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.n_workers)
if torch.cuda.is_available() and args.cuda:
device = torch.device('cuda')
else:
device = torch.device('cpu')
model = ElectraForSequenceClassification.from_pretrained(
"monologg/koelectra-base-v3-discriminator")
model = nn.parallel.DataParallel(model)
model.to(device)
optimizer = AdamW(model.parameters(), lr=args.lr)
# Plot Loss and Images in Tensorboard
experiment_dir = 'logs/{}@{}'.format(
'NSMC',
datetime.now().strftime("%d.%m.%Y-%H:%M:%S"))
os.makedirs(f"{experiment_dir}/checkpoints", exist_ok=True)
writer = SummaryWriter(os.path.join(experiment_dir, "tb"))
metric_dict = defaultdict(list)
metric_dict_epoch_train = defaultdict(list)
metric_dict_epoch_test = defaultdict(list)
##########################################
################ Training ################
##########################################
n_iters_total = 0
for epoch in range(args.n_epochs):
total_loss_train = 0.0
correct = 0
total = 0
model.train()
for idx, (label, input_ids,
attention_masks) in tqdm(enumerate(train_loader),
total=len(train_loader)):
optimizer.zero_grad()
label = label.to(device)
input_ids = input_ids.to(device)
attention_masks = attention_masks.to(device)
output = model(input_ids, attention_masks)[0] # (batch_size, 2)
_, pred = torch.max(output, 1) # (batch_size)
loss = F.cross_entropy(output, label)
loss.backward()
optimizer.step()
total_loss_train += loss.item()
correct += (pred == label).sum()
total += len(label)
train_accuracy = correct.float() / total
if n_iters_total % 300 == 0:
print(f"Batch Loss : {loss} / Accuracy : {train_accuracy}")
metric_dict['train_loss'].append(loss.item())
metric_dict['train_accuracy'].append(train_accuracy.item())
n_iters_total += 1
for title, value in metric_dict.items():
writer.add_scalar('train/{}'.format(title), value[-1],
n_iters_total)
train_accuracy = correct.float() / total
metric_dict_epoch_train['train_total_loss_epoch'].append(
total_loss_train)
metric_dict_epoch_train['train_accuracy_epoch'].append(train_accuracy)
for title, value in metric_dict_epoch_train.items():
writer.add_scalar('train/{}'.format(title), value[-1], epoch)
print(
f"Epoch : {epoch} / Train Loss : {total_loss_train} / Accuracy : {train_accuracy}"
)
##########################################
################## Test ##################
##########################################
test_correct = 0
test_total = 0
total_loss_test = 0.0
model.eval()
with torch.no_grad():
for idx, (label, input_ids,
attention_masks) in tqdm(enumerate(test_loader),
total=len(test_loader)):
label = label.to(device)
input_ids = input_ids.to(device)
attention_masks = attention_masks.to(device)
output = model(input_ids, attention_masks)[0]
_, pred = torch.max(output, 1) # values, indices
loss = F.cross_entropy(output, label)
total_loss_test += loss
test_correct += (pred == label).sum()
test_total += len(label)
test_accuracy = test_correct.float() / test_total
metric_dict_epoch_test['test_total_loss_epoch'].append(
total_loss_test)
metric_dict_epoch_test['test_accuracy_epoch'].append(test_accuracy)
for title, value in metric_dict_epoch_test.items():
writer.add_scalar('test/{}'.format(title), value[-1], epoch)
print(f"Test Accuracy : {test_accuracy}")
torch.save(
{
'epoch': epoch,
'model_state_dict': model.module.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'train_accuracy': train_accuracy,
'test_accuracy': test_accuracy,
}, os.path.join(experiment_dir, "checkpoints", str(epoch)))
def model_setting():
model = ElectraForSequenceClassification.from_pretrained(MODEL_NAME).to(
device)
#model.load_state_dict(torch.load(pre_MODEL_NAME))
model.load_state_dict(torch.load(pre_MODEL_NAME, map_location=device))
return model
def main():
nsmc = h5py.File('../data/nsmc.h5', 'r')
train_dataset = nsmc['train']
test_dataset = nsmc['test']
print('\n====================== Dataset Summary ======================\n')
print(f"Train Label : {train_dataset['label']}")
print(f"Train Input Ids : {train_dataset['input_ids']}")
print(f"Train Attention Mask : {train_dataset['attention_mask']}")
print(f"Test Label : {test_dataset['label']}")
print(f"Test Input Ids : {test_dataset['input_ids']}")
print(f"Test Attention Mask : {test_dataset['attention_mask']}")
print('\n=============================================================\n')
train_label = np.array(train_dataset['label'])
train_input_ids = np.array(train_dataset['input_ids'])
train_attention_mask = np.array(train_dataset['attention_mask'])
test_label = np.array(test_dataset['label'])
test_input_ids = np.array(test_dataset['input_ids'])
test_attention_mask = np.array(test_dataset['attention_mask'])
nsmc.close()
train_dataset = NSMCDataset(train_label, train_input_ids,
train_attention_mask)
test_dataset = NSMCDataset(test_label, test_input_ids, test_attention_mask)
train_loader = DataLoader(train_dataset,
batch_size=55,
shuffle=True,
num_workers=8)
test_loader = DataLoader(test_dataset,
batch_size=55,
shuffle=False,
num_workers=8)
model = ElectraForSequenceClassification.from_pretrained(
"monologg/koelectra-base-v3-discriminator")
parameters = filter(lambda p: p.requires_grad, model.parameters())
args = add_argument()
model_engine, _, _, _ = deepspeed.initialize(args=args,
model=model,
model_parameters=parameters)
losses = []
accuracies = []
for epoch in range(args.epochs):
total_loss = 0.0
correct = 0
total = 0
batches = 0
for idx, (label, input_ids,
attention_masks) in tqdm(enumerate(train_loader),
total=len(train_loader)):
label = label.to(model_engine.local_rank)
input_ids = input_ids.to(model_engine.local_rank)
attention_masks = attention_masks.to(model_engine.local_rank)
# Model Inference
output = model_engine(input_ids, attention_masks)[0]
_, pred = torch.max(output, 1)
loss = F.cross_entropy(output, label)
model_engine.backward(loss)
model_engine.step()
total_loss += loss.item()
correct += (pred == label).sum()
total += len(label)
batches += 1
if batches % 100 == 0:
print(
f"Batch Loss : {total_loss} / Accuracy : {correct.float() / total}"
)
losses.append(total_loss)
accuracies.append(correct.float() / total)
print(
f"Epoch : {epoch} / Train Loss : {total_loss} / Accuracy : {correct.float() / total}"
)
test_correct = 0
test_total = 0
with torch.no_grad():
for idx, (label, input_ids,
attention_masks) in tqdm(enumerate(test_loader),
total=len(test_loader)):
label = label.to(model_engine.local_rank)
input_ids = input_ids.to(model_engine.local_rank)
attention_masks = attention_masks.to(model_engine.local_rank)
# Model Inference
output = model_engine(input_ids, attention_masks)[0]
_, pred = torch.max(output, 1)
test_correct += (pred == label).sum()
test_total += len(label)
print(f"Test Accuracy : {test_correct.float() / test_total}")
model_engine.save_checkpoint('../weights', f"KoELECTRA_{epoch}")
def main():
args = parse_args()
os.makedirs(args.output_dir, exist_ok=True)
set_seed(args.seed)
logging.basicConfig(level=logging.INFO,
format='%(asctime)s :: %(levelname)s :: %(message)s')
if args.numnet_model is not None:
config = BertConfig.from_pretrained(
args.model_name, num_labels=1) # 1 label for regression
# if args.contrastive:
# model = ContrastiveElectra.from_pretrained(args.model_name, config=config)
# else:
model = BertForSequenceClassification.from_pretrained(args.model_name,
config=config)
state_dicts = torch.load(args.numnet_model)
if "model" in state_dicts:
logging.info("Loading in mutual electra format state_dicts.")
model.load_state_dict(state_dicts["model"], strict=False)
else:
logging.info("Loading model weights only.")
model.load_state_dict(state_dicts, strict=False)
else:
config = ElectraConfig.from_pretrained(
args.model_name, num_labels=1) # 1 label for regression
model = ElectraForSequenceClassification.from_pretrained(
args.model_name, config=config)
if args.local_model_path is not None:
state_dicts = torch.load(args.local_model_path)
model.load_state_dict(state_dicts["model"])
tokenizer = ElectraTokenizer.from_pretrained(args.model_name,
do_lower_case=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
# TODO enable multi-gpu training if necessary
pretrain_train_dataset = DapoDataset(args.data_dir, "train",
tokenizer) if args.pretrain else None
pretrain_dev_dataset = DapoDataset(args.data_dir, "dev",
tokenizer) if args.pretrain else None
if args.train:
if args.contrastive:
train_dataset = ContrastiveDataset(args.data_dir, "train",
tokenizer)
train_dataloader = DataLoader(train_dataset,
batch_size=args.train_batch_size,
shuffle=False,
num_workers=8,
collate_fn=mutual_contrast_collate)
dev_dataset = ContrastiveDataset(
args.data_dir, "dev",
tokenizer) if args.eval or args.test else None
dev_dataloader = DataLoader(dev_dataset,
batch_size=args.train_batch_size,
shuffle=False,
num_workers=8,
collate_fn=mutual_contrast_collate
) if dev_dataset is not None else None
else:
train_dataset = MutualDataset(args.data_dir, "train", tokenizer)
train_dataloader = DataLoader(train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
num_workers=8,
collate_fn=mutual_collate)
dev_dataset = MutualDataset(
args.data_dir, "dev",
tokenizer) if args.eval or args.test else None
dev_dataloader = DataLoader(
dev_dataset,
batch_size=args.train_batch_size,
shuffle=False,
num_workers=8,
collate_fn=mutual_collate) if dev_dataset is not None else None
else:
train_dataset, train_dataloader = None, None
# TODO: add test_dataset if we want to submit to leaderboard
pretrain_train_dataloader = DataLoader(
pretrain_train_dataset,
batch_size=args.train_batch_size,
shuffle=True,
num_workers=8,
collate_fn=dapo_collate
) if pretrain_train_dataset is not None else None
pretrain_dev_dataloader = DataLoader(
pretrain_dev_dataset,
batch_size=args.train_batch_size,
shuffle=False,
num_workers=8,
collate_fn=dapo_collate) if pretrain_dev_dataset is not None else None
# currently eval_batch_size = train_batch_size
if args.pretrain:
logging.info("Start pretraining...")
args.eval = True
trainer = Trainer(args, model, device, pretrain_train_dataloader,
pretrain_dev_dataloader)
trainer.train()
return # fine-tuning should be done separately
if args.train:
logging.info("Start training...")
trainer = Trainer(args, model, device, train_dataloader,
dev_dataloader)
trainer.train()
# TODO: currently testing is on the dev set
if args.test:
logging.info("Start testing...")
tester = Tester(args, model, device, dev_dataset, dev_dataloader)
tester.test()
def main(args):
if not os.path.isdir('CMDs'):
os.mkdir('CMDs')
with open('CMDs/train.cmd', 'a') as f:
f.write(' '.join(sys.argv) + '\n')
f.write('--------------------------------\n')
# Set the seed value all over the place to make this reproducible.
seed_val = args.seed
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
# Choose device
device = get_default_device()
prompts_train_idxs = np.loadtxt(args.train_prompts_idxs_path, dtype=np.int64)
topics_dist = np.loadtxt(args.unique_prompts_distribution_path, dtype=np.int32)
# Normalise
topics_dist = topics_dist / np.linalg.norm(topics_dist, 1)
# Load the BERT tokenizer.
print('Loading BERT tokenizer...')
tokenizer = ElectraTokenizer.from_pretrained('google/electra-base-discriminator', do_lower_case=True)
with open(args.unique_prompts_path) as f:
topics = f.readlines()
# Remove whitespaces and convert to lowercase
topics = [x.strip().lower() for x in topics]
with open(args.train_resps_path) as f:
responses = f.readlines()
# Remove whitespaces and convert to lower case
responses = [x.strip().lower() for x in responses]
# Tokenize all the prompts and the responses and then map the tokens to their word IDs
topic_ids = []
for sent in topics:
encoded_sent = tokenizer.encode(sent, add_special_tokens=True)
topic_ids.append(encoded_sent)
resp_ids = []
for sent in responses:
encoded_sent = tokenizer.encode(sent, add_special_tokens=True)
resp_ids.append(encoded_sent)
MAX_LEN_topic = max([len(sen) for sen in topic_ids])
MAX_LEN_resp = max([len(sen) for sen in resp_ids])
print('\nPadding token: "{:}", ID: {:}'.format(tokenizer.pad_token, tokenizer.pad_token_id))
# Pad our input tokens with value 0.
# "post" indicates that we want to pad and truncate at the end of the sequence,
# as opposed to the beginning.
topic_ids = pad_sequences(topic_ids, maxlen=MAX_LEN_topic, dtype="long",
value=0, truncating="post", padding="post")
resp_ids = pad_sequences(resp_ids, maxlen=MAX_LEN_resp, dtype="long",
value=0, truncating="post", padding="post")
# The attention mask simply makes it explicit which tokens are actual words versus which are padding.
attention_masks_topic = []
# For each sentence...
for sent in topic_ids:
# Create the attention mask.
# - If a token ID is 0, then it's padding, set the mask to 0.
# - If a token ID is > 0, then it's a real token, set the mask to 1.
att_mask = [int(token_id > 0) for token_id in sent]
# Store the attention mask for this sentence.
attention_masks_topic.append(att_mask)
attention_masks_resp = []
for sent in resp_ids:
# Create the attention mask.
# - If a token ID is 0, then it's padding, set the mask to 0.
# - If a token ID is > 0, then it's a real token, set the mask to 1.
att_mask = [int(token_id > 0) for token_id in sent]
# Store the attention mask for this sentence.
attention_masks_resp.append(att_mask)
# Convert to torch tensors
prompts_train_idxs = torch.from_numpy(prompts_train_idxs)
prompts_train_idxs = prompts_train_idxs.long()
topic_ids = torch.tensor(topic_ids)
topic_ids = topic_ids.long()
topic_ids = topic_ids.to(device)
attention_masks_topic = torch.tensor(attention_masks_topic)
attention_masks_topic = attention_masks_topic.long()
attention_masks_topic = attention_masks_topic.to(device)
resp_ids = torch.tensor(resp_ids)
resp_ids = resp_ids.long()
resp_ids = resp_ids.to(device)
attention_masks_resp = torch.tensor(attention_masks_resp)
attention_masks_resp = attention_masks_resp.long()
attention_masks_resp = attention_masks_resp.to(device)
# Create the DataLoader for our training set.
print(prompts_train_idxs.size(0))
print(resp_ids.size(0))
print(attention_masks_resp.size(0))
train_data = TensorDataset(prompts_train_idxs, resp_ids, attention_masks_resp)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.batch_size)
# Load BertForSequenceClassification, the pretrained BERT model with a single
# linear classification layer on top.
model = ElectraForSequenceClassification.from_pretrained(
"google/electra-base-discriminator", # Use the 12-layer BERT model, with an uncased vocab.
num_labels = 2, # The number of output labels--2 for binary classification.
# You can increase this for multi-class tasks.
output_attentions = False, # Whether the model returns attentions weights.
output_hidden_states = False, # Whether the model returns all hidden-states.
)
model.to(device)
# Note: AdamW is a class from the huggingface library (as opposed to pytorch)
# I believe the 'W' stands for 'Weight Decay fix"
optimizer = AdamW(model.parameters(),
lr = args.learning_rate,
eps = args.adam_epsilon
)
loss_values = []
# Total number of training steps is number of batches * number of epochs.
total_steps = len(train_dataloader) * args.n_epochs
# Create the learning rate scheduler.
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps = 0, # Default value in run_glue.py
num_training_steps = total_steps)
for epoch in range(args.n_epochs):
# Perform one full pass over the training set.
print("")
print('======== Epoch {:} / {:} ========'.format(epoch + 1, args.n_epochs))
print('Training...')
# Measure how long the training epoch takes.
t0 = time.time()
# Reset the total loss for this epoch.
total_loss = 0
model.train()
# For each batch of training data...
for step, batch in enumerate(train_dataloader):
# Progress update every 40 batches.
if step % 40 == 0 and not step == 0:
# Calculate elapsed time in minutes.
elapsed = format_time(time.time() - t0)
# Report progress.
print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))
p_id = batch[0].to(device)
r = batch[1].to(device)
r_msk = batch[2].to(device)
# Perform dynamic shuffling
p_id, r, r_msk, y_true, batch_size = _shuffle(p_id, r, r_msk, topics_dist, args.num_topics, device)
# Get the prompts from the topics
p, p_msk = _get_prompts(p_id, topic_ids, attention_masks_topic)
p, p_msk = p.to(device), p_msk.to(device)
# Concatenate prompts and responses
pr_resp, pr_resp_msk = _join_pr_resp(p, p_msk, r, r_msk, args.reverse)
pr_resp, pr_resp_msk = pr_resp.to(device), pr_resp_msk.to(device)
model.zero_grad()
# Perform a forward pass (evaluate the model on this training batch).
# This will return the loss (rather than the model output) because we
# have provided the `labels`.
# The documentation for this `model` function is here:
# https://huggingface.co/transformers/v2.2.0/model_doc/bert.html#transformers.BertForSequenceClassification
outputs = model(pr_resp, token_type_ids=None, attention_mask=pr_resp_msk, labels=y_true)
# The call to `model` always returns a tuple, so we need to pull the
# loss value out of the tuple.
loss = outputs[0]
# Accumulate the training loss over all of the batches so that we can
# calculate the average loss at the end. `loss` is a Tensor containing a
# single value; the `.item()` function just returns the Python value
# from the tensor.
total_loss += loss.item()
# Perform a backward pass to calculate the gradients.
loss.backward()
# Clip the norm of the gradients to 1.0.
# This is to help prevent the "exploding gradients" problem.
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# Update parameters and take a step using the computed gradient.
# The optimizer dictates the "update rule"--how the parameters are
# modified based on their gradients, the learning rate, etc.
optimizer.step()
# Update the learning rate.
scheduler.step()
# Calculate the average loss over the training data.
avg_train_loss = total_loss / len(train_dataloader)
# Store the loss value for plotting the learning curve.
loss_values.append(avg_train_loss)
print("")
print(" Average training loss: {0:.2f}".format(avg_train_loss))
print(" Training epoch took: {:}".format(format_time(time.time() - t0)))
# NEED TO DO THE VALIDATION CODE NOW - see the rest of the tutorial at
# https://medium.com/@aniruddha.choudhury94/part-2-bert-fine-tuning-tutorial-with-pytorch-for-text-classification-on-the-corpus-of-linguistic-18057ce330e1
# Save the model to a file
file_path = args.save_path+'electra_classifier_seed'+str(args.seed)+'.pt'
torch.save(model, file_path)
import os
import torch
import numpy as np
from flask import Flask, request, jsonify
from transformers import (ElectraTokenizer, ElectraForSequenceClassification)
app = Flask(__name__)
device = "cuda" if torch.cuda.is_available() else "cpu"
max_seq_length = int(os.getenv("PODOLI_MAX_LENGTH", 128))
model = ElectraForSequenceClassification.from_pretrained('model')
model.to(device)
tokenizer = ElectraTokenizer.from_pretrained(
"monologg/koelectra-small-v3-discriminator",
do_lower_case=False
)
def featurize(comments):
tokens_a = tokenizer.tokenize(comments)
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[0:(max_seq_length - 2)]
tokens = []
segment_ids = []
tokens.append("[CLS]")
segment_ids.append(0)
for token in tokens_a:
tokens.append(token)
segment_ids.append(0)
input_ids = inputs['input_ids'][0]
attention_mask = inputs['attention_mask'][0]
return input_ids, attention_mask, y
#학습에는 train만 사용함, test는 감점대상
train_dataset = NSMC_Dataset("ratings_train.txt","train")
test_dataset = NSMC_Dataset("ratings_test.txt","train")
sample_dataset = NSMC_Dataset("ko_data.csv","sample")
tmpstr = '훌륭하다. 초한지 얼른 읽어보고 다시 봐야겠다. 연출 훌륭하다 껄껄 한신의 토사구팽은 슬펐다'
print( train_dataset.clean_text( txt = tmpstr) )
"""# 모델 생성 (Create Model)"""
model = ElectraForSequenceClassification.from_pretrained("monologg/koelectra-base-v3-discriminator").to(device)
model.cuda()
# 한번 실행해보기
#text, attention_mask, y = train_dataset[0]
#model(text.unsqueeze(0).to(device), attention_mask=attention_mask.unsqueeze(0).to(device))
try:
model.load_state_dict(torch.load("model.pt"))
except:
print("error - model.load_state_dict(torch.load('model.pt'))")
else:
print("success - model.load_state_dict(torch.load('model.pt'))")
# 모델 레이어 보기
model
def train():
# load model and tokenizer
#MODEL_NAME = "bert-base-multilingual-cased"
MODEL_NAME = "monologg/koelectra-base-v3-discriminator"
tokenizer = ElectraTokenizer.from_pretrained(MODEL_NAME)
print(tokenizer.tokenize("이순신은 조선 중기의 무신이다."))
print(tokenizer.tokenize("아버지가방에들어가신다."))
tokenized_str = tokenizer.tokenize("이순신은 조선 중기의 무신이다." +
tokenizer.sep_token + "아버지가방에들어가신다.")
print(tokenized_str)
# load dataset
train_dataset = load_data("/opt/ml/input/data/train/train.tsv")
train_label = train_dataset['label'].values
# tokenizing dataset
tokenized_train = tokenized_dataset(train_dataset, tokenizer)
# make dataset for pytorch.
RE_train_dataset = RE_Dataset(tokenized_train, train_label)
train_dataset, dev_dataset = torch.utils.data.random_split(
RE_train_dataset, [7000, 2001])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# setting model hyperparameter
bert_config = ElectraConfig.from_pretrained(MODEL_NAME)
bert_config.num_labels = 42
model = ElectraForSequenceClassification.from_pretrained(
MODEL_NAME, config=bert_config)
#model.parameters
model.to(device)
# 사용한 option 외에도 다양한 option들이 있습니다.
# https://huggingface.co/transformers/main_classes/trainer.html#trainingarguments 참고해주세요.
training_args = TrainingArguments(
output_dir='./results', # output directory
save_total_limit=4, # number of total save model.
load_best_model_at_end=True,
save_steps=100, # model saving step.
num_train_epochs=10, # total number of training epochs
learning_rate=5e-5, # learning_rate
per_device_train_batch_size=8, # batch size per device during training
per_device_eval_batch_size=8, # batch size for evaluation
warmup_steps=500, # number of warmup steps for learning rate scheduler
weight_decay=0.01, # strength of weight decay
logging_dir='./logs', # directory for storing logs
logging_steps=100, # log saving step.
evaluation_strategy=
'steps', # evaluation strategy to adopt during training
# `no`: No evaluation during training.
# `steps`: Evaluate every `eval_steps`.
# `epoch`: Evaluate every end of epoch.
eval_steps=100, # evaluation step.
dataloader_num_workers=3,
label_smoothing_factor=0.5)
trainer = Trainer(
model=model, # the instantiated 🤗 Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=dev_dataset, # evaluation dataset
compute_metrics=compute_metrics, # define metrics function
)
# train model
trainer.train()
from transformers import ElectraTokenizer, ElectraForSequenceClassification, pipeline
from pprint import pprint
tokenizer = ElectraTokenizer.from_pretrained(
"monologg/koelectra-small-finetuned-nsmc")
model = ElectraForSequenceClassification.from_pretrained(
"monologg/koelectra-small-finetuned-nsmc")
nsmc = pipeline("sentiment-analysis", tokenizer=tokenizer, model=model)
texts = [
"이 영화는 미쳤다. 넷플릭스가 일상화된 시대에 극장이 존재해야하는 이유를 증명해준다.",
"촬영감독의 영혼까지 갈아넣은 마스터피스",
"보면서 화가날수있습니다.",
"아니 그래서 무슨말이 하고싶은거야 ㅋㅋㅋ",
]
pprint(nsmc(texts))
from transformers import ElectraForSequenceClassification, ElectraTokenizerFast, Trainer, TrainingArguments
from nlp import load_dataset
import torch
import numpy as np
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
model = ElectraForSequenceClassification.from_pretrained(
'models/ELECTRA_last_line')
tokenizer = ElectraTokenizerFast.from_pretrained(
'google/electra-small-discriminator')
def tokenize(batch):
return tokenizer(batch['text'],
truncation=True,
max_length=128,
add_special_tokens=True,
padding='max_length',
return_attention_mask=True)
test_dataset = load_dataset(
'json',
data_files={'test': 'dataset_last_line/quanta_test.json'},
field='questions')['test']
test_dataset = test_dataset.map(
lambda example: {'label': [0 if example['difficulty'] == 'School' else 1]})
test_dataset = test_dataset.map(tokenize,
batched=True,
batch_size=len(test_dataset))
test_dataset.set_format('torch',
# tmpstr = 'Come on. Hello? I?m sorry you have the wrong number. Okay, I?ll call you later dad. I love you.'
# print( train_dataset.clean_text( txt = tmpstr) )
# test_emotion = 'joy'
# if test_emotion in train_dataset.emotion_dic.keys() :
# print( train_dataset.emotion_dic[test_emotion] )
# else :
# print(0)
# print( train_dataset.__getitem__(11790) )
# print( sample_dataset.__getitem__(10) )
"""# 모델 생성 (Create Model)"""
model = ElectraForSequenceClassification.from_pretrained("google/electra-base-discriminator", num_labels=8).to(device)
#model.cuda()
# 한번 실행해보기
#text, attention_mask, y = train_dataset[0]
#model(text.unsqueeze(0).to(device), attention_mask=attention_mask.unsqueeze(0).to(device))
try:
model.load_state_dict(torch.load("model.pt"))
#model.load_state_dict(torch.load("/content/drive/MyDrive/Colab Notebooks/model_daniel021_friends_electra_base_epoch4.pt"))
#model.load_state_dict(torch.load("/content/drive/MyDrive/Colab Notebooks/model_daniel021_friends_electra_large_epoch8.pt"))
except:
print("error - model.load_state_dict(torch.load(...))")
else:
print("success - model.load_state_dict(torch.load(...))")
