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 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 = "xlm-roberta-large"
tokenizer = XLMRobertaTokenizer.from_pretrained(TOK_NAME)
special_tokens_dict = {'additional_special_tokens': ["#", "@", '₩', '^']}
num_added_toks = tokenizer.add_special_tokens(special_tokens_dict)
# load my model
MODEL_NAME = args.model_dir # model dir.
model = XLMRobertaForSequenceClassification.from_pretrained(MODEL_NAME)
model.resize_token_embeddings(len(tokenizer))
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
pred_answer = inference(model, test_dataset, device)
output = pd.DataFrame(pred_answer, columns=['pred'])
output.to_csv('./prediction/submission.csv', index=False)
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 = "bert-base-multilingual-cased"
#tokenizer = AutoTokenizer.from_pretrained(TOK_NAME)
tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
# load my model
MODEL_NAME = args.model_dir # model dir.
model = XLMRobertaForSequenceClassification.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
pred_answer = 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.to_csv('./prediction/roberta-submission13.csv', index=False)
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 = "xlm-roberta-large"
tokenizer = XLMRobertaTokenizer.from_pretrained(TOK_NAME)
# load my model
MODEL_NAME = args.model_dir # model dir.
p = Path('.').resolve() # /opt/ml
model_dir = p / args.model_dir
model = XLMRobertaForSequenceClassification.from_pretrained(model_dir)
model.resize_token_embeddings(len(tokenizer))
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
pred_answer = 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.to_csv(
'./prediction/submission.csv',
index=False,
)
def main(json_path, model_name_or_dir):
tokenizer = XLMRobertaTokenizerFast.from_pretrained(model_name_or_dir)
model = XLMRobertaForSequenceClassification.from_pretrained(
model_name_or_dir).to(DEVICE)
model.eval()
with open(json_path) as json_file:
data = json.load(json_file)
predictions = []
labels = []
for pair in data:
sentence = pair['text']
label = pair['sentiment']
inputs = tokenizer.encode(sentence,
padding=False,
truncation=True,
return_tensors='pt').to(DEVICE)
with torch.no_grad():
output = model(inputs).logits
prediction = torch.argmax(output, dim=-1)[0].item()
predictions.append(prediction)
labels.append(label)
print(metrics.classification_report(labels, predictions, digits=6))
def main(
output_dir,
logging_dir,
logging_steps,
large,
batch_size,
gradient_accumulation_steps,
learning_rate,
num_train_epochs,
warmup_ratio):
sst_train_dataset = load_dataset('glue', 'sst2', split='train')
sst_validation_dataset = load_dataset('glue', 'sst2', split='validation')
if large:
model_name = 'xlm-roberta-large'
else:
model_name = 'xlm-roberta-base'
tokenizer = XLMRobertaTokenizerFast.from_pretrained(model_name)
model = XLMRobertaForSequenceClassification.from_pretrained(model_name)
def preprocess_function(examples):
return tokenizer(examples['sentence'], padding=False, truncation=True)
sst_train_dataset = sst_train_dataset.map(preprocess_function, batched=True)
sst_validation_dataset = sst_validation_dataset.map(preprocess_function, batched=True)
training_args = TrainingArguments(
output_dir=output_dir,
do_train=True,
do_eval=True,
do_predict=False,
evaluation_strategy='epoch',
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
learning_rate=learning_rate,
num_train_epochs=num_train_epochs,
warmup_ratio=warmup_ratio,
logging_dir=logging_dir,
logging_strategy='steps',
logging_steps=logging_steps,
save_strategy='epoch',
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=sst_train_dataset,
eval_dataset=sst_validation_dataset,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
trainer.train()
def __init__(self, cache_dir=DEFAULT_CACHE_DIR, verbose=False):
from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
#download the model or load the model path
model_path = download_model('xlmr.ned',
cache_dir,
process_func=_unzip_process_func,
verbose=verbose)
self.classes = ['0', '1']
self.tokenizer = XLMRobertaTokenizer.from_pretrained(model_path)
self.model = XLMRobertaForSequenceClassification.from_pretrained(
model_path, num_labels=len(self.classes))
self.max_length = self.model.roberta.embeddings.position_embeddings.num_embeddings - 2
def __init__(self, config):
model_name = config.get("model_name", None)
model_path = config.get("model_path", None)
device = config.get("device", 0) # default on gpu 0
self.tokenizer = XLMRobertaTokenizer.from_pretrained(model_path)
# the default entailment id is 2 (contradiction is 0, neutral is 1)
self.contradiction_id = 0
self.entailment_id = 2
self.model = XLMRobertaForSequenceClassification.from_pretrained(
model_path)
self.model.eval()
self.model.half()
self.device = torch.device(
"cpu" if device < 0 else "cuda:{}".format(device))
if self.device.type == "cuda":
self.model = self.model.to(self.device)
def main(args):
"""
주어진 dataset tsv 파일과 같은 형태일 경우 inference 가능한 코드입니다.
"""
seed_everything(args.seed)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# load tokenizer
TOK_NAME = args.token
if TOK_NAME.startswith('xlm'):
tokenizer = XLMRobertaTokenizer.from_pretrained(TOK_NAME)
else:
tokenizer = AutoTokenizer.from_pretrained(TOK_NAME)
# load my model
MODEL_NAME = os.path.join(args.model_dir, args.model) # model dir.
if TOK_NAME.startswith('xlm'):
model = XLMRobertaForSequenceClassification.from_pretrained(MODEL_NAME)
else:
model = BertForSequenceClassification.from_pretrained(MODEL_NAME)
# load test datset
test_dataset_dir = "/opt/ml/input/data/test/test.tsv"
test_dataset, test_label = load_test_dataset(test_dataset_dir, model,
tokenizer, args)
test_dataset = RE_Dataset(test_dataset, test_label)
model.to(device)
# predict answer
batch_size = args.batch_size
logits, pred_answer = inference(model, test_dataset, device, batch_size)
# make csv file with predicted answer
# 아래 directory와 columns의 형태는 지켜주시기 바랍니다.
output = pd.DataFrame(pred_answer, columns=['pred'])
save_dir = os.path.join(args.output_dir, args.name)
os.makedirs(save_dir, exist_ok=True)
output.to_csv(os.path.join(save_dir, f'{args.name}.csv'), index=False)
np.save(os.path.join(save_dir, r'logits.npy'), logits)
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 main(train_json_path, val_json_path, model_name_or_dir, output_dir,
logging_dir, logging_steps, batch_size, gradient_accumulation_steps,
learning_rate, num_train_epochs, warmup_ratio):
tokenizer = XLMRobertaTokenizerFast.from_pretrained(model_name_or_dir)
model = XLMRobertaForSequenceClassification.from_pretrained(
model_name_or_dir)
sh_sentiment_train_dataset, sh_sentiment_val_dataset = create_sh_sentiment_dataset(
train_json_path, val_json_path, tokenizer)
training_args = TrainingArguments(
output_dir=output_dir,
do_train=True,
do_eval=True,
do_predict=False,
evaluation_strategy='epoch',
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
gradient_accumulation_steps=gradient_accumulation_steps,
learning_rate=learning_rate,
num_train_epochs=num_train_epochs,
warmup_ratio=warmup_ratio,
logging_dir=logging_dir,
logging_strategy='steps',
logging_steps=logging_steps,
save_strategy='epoch',
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=sh_sentiment_train_dataset,
eval_dataset=sh_sentiment_val_dataset,
tokenizer=tokenizer,
compute_metrics=compute_metrics,
)
trainer.train()
def main(
input_dir_path,
output_dir_path,
model_name_or_dir):
tokenizer = XLMRobertaTokenizerFast.from_pretrained(model_name_or_dir)
model = XLMRobertaForSequenceClassification.from_pretrained(model_name_or_dir).to(DEVICE)
model.eval()
os.makedirs(output_dir_path, exist_ok=True)
for file_name in os.listdir(input_dir_path):
if file_name.endswith('.json'):
count = 0
input_file_path = os.path.join(input_dir_path, file_name)
with open(input_file_path) as json_file:
data = json.load(json_file)
for session in data['sessions']:
for speech in session['speeches']:
content = []
for text in speech['content']:
inputs = tokenizer.encode(
text, padding=False, truncation=True, return_tensors='pt').to(DEVICE)
with torch.no_grad():
outputs = model(inputs).logits
predictions = torch.softmax(outputs, dim=-1)[0, 1].item()
content.append({ 'text': text, 'sentiment': round(predictions, 6) })
count += 1
speech['content'] = content
output_file_path = os.path.join(output_dir_path, file_name)
with open(output_file_path, 'w') as json_file:
json.dump(data, json_file)
print("File: {}, Count: {}".format(file_name, count))
def load_model(pretrained_name, model_loc=None, load_tuned=True, num_labels=2):
assert pretrained_name is not None
if load_tuned:
# load previously tuned model from disk
if model_loc is None:
model_dump_loc, model_state_dic_loc = generate_disk_location()
else:
model_dump_loc = model_loc
model = torch.load(model_dump_loc)
logger.info("loading model from {}".format(model_dump_loc))
else:
# load pretrained name from hugging face
model_name = config["model_name"]
if model_name == "bert":
model = BertForSequenceClassification.from_pretrained(
pretrained_name, num_labels=num_labels)
elif model_name == "roberta":
model = RobertaForSequenceClassification.from_pretrained(
pretrained_name, num_labels=num_labels)
elif model_name == "distillbert":
model = DistilBertForSequenceClassification.from_pretrained(
pretrained_name, num_labels=num_labels)
elif model_name == "xlmroberta":
model = XLMRobertaForSequenceClassification.from_pretrained(
pretrained_name, num_labels=num_labels)
elif model_name == "xlnet":
model = XLNetForSequenceClassification.from_pretrained(
pretrained_name, num_labels=num_labels)
else:
logger.error("unsupported model: {}".format(model_name))
logger.info("loading pretrained model")
tokenizer = AutoTokenizer.from_pretrained(pretrained_name)
logger.info("model config: {}".format(model.config))
return model, tokenizer
from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
import torch
from textattack.models.wrappers.huggingface_model_wrapper import HuggingFaceModelWrapper
tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-base')
raw_model = XLMRobertaForSequenceClassification.from_pretrained('xlm-roberta-base', num_labels=10)
raw_model.load_state_dict(torch.load('xlm_roberta_en/state_dict.pt', map_location='cuda:0'))
model = HuggingFaceModelWrapper(raw_model, tokenizer)
def train(args):
# load model and tokenizer
MODEL_NAME = "xlm-roberta-large"
tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_NAME)
# split dataset
dataset = pd.read_csv('/opt/ml/input/data/train/train.tsv',
delimiter='\t',
header=None)
train, dev = train_test_split(dataset, test_size=0.2, random_state=42)
train.to_csv('/opt/ml/input/data/train/train_train.tsv',
sep='\t',
header=None,
index=False)
dev.to_csv('/opt/ml/input/data/train/train_dev.tsv',
sep='\t',
header=None,
index=False)
# load dataset
train_dataset = load_data('/opt/ml/input/data/train/train_train.tsv',
args.root)
dev_dataset = load_data('/opt/ml/input/data/train/train_dev.tsv',
args.root)
train_label = train_dataset['label'].values
dev_label = dev_dataset['label'].values
# tokenizing dataset
tokenized_train = tokenized_dataset(train_dataset, tokenizer)
tokenized_dev = tokenized_dataset(dev_dataset, tokenizer)
# make dataset for pytorch.
RE_train_dataset = RE_Dataset(tokenized_train, train_label)
RE_dev_dataset = RE_Dataset(tokenized_dev, dev_label)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# setting model hyperparameter
bert_config = XLMRobertaConfig.from_pretrained(MODEL_NAME)
bert_config.num_labels = 42
model = XLMRobertaForSequenceClassification.from_pretrained(
MODEL_NAME, config=bert_config)
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/' + str(args.id),
save_total_limit=3,
save_steps=100,
num_train_epochs=10,
learning_rate=1e-5,
per_device_train_batch_size=32,
per_device_eval_batch_size=32,
warmup_steps=300,
weight_decay=0.01,
logging_dir='./logs/' + str(args.id),
logging_steps=100,
evaluation_strategy='steps',
eval_steps=100,
dataloader_num_workers=4,
label_smoothing_factor=0.5)
trainer = Trainer(model=model,
args=training_args,
train_dataset=RE_train_dataset,
eval_dataset=RE_dev_dataset,
compute_metrics=compute_metrics)
# train model
trainer.train()
def model_init():
"""Returns an initialized model for use in a Hugging Face Trainer."""
model = XLMRobertaForSequenceClassification.from_pretrained(
"xlm-roberta-base")
return model
# Create the DataLoader for our training set.
train_data = TensorDataset(train_inputs, train_masks, train_labels)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)
# Create the DataLoader for our validation set.
validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)
validation_sampler = SequentialSampler(validation_data)
validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)
# Load XLMRobertaForSequenceClassification, the pretrained XLMRoberta model with a single
# linear classification layer on top.
model = XLMRobertaForSequenceClassification.from_pretrained(
"xlm-roberta-base", # Use the 12-layer XLMRoberta 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.
)
# Tell pytorch to run this model on the GPU.
model.cuda()
# 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 = 1e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5
eps = 1e-8 # args.adam_epsilon - default is 1e-8.
)
def load_model(args):
if 'bert-base-multilingual' in args['model_checkpoint']:
# bert-base-multilingual-uncased or bert-base-multilingual-cased
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained(args['model_checkpoint'])
config = BertConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'xlm-mlm' in args['model_checkpoint']:
# xlm-mlm-100-1280
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = XLMTokenizer.from_pretrained(args['model_checkpoint'])
config = XLMConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = XLMForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = XLMForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = XLMForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'xlm-roberta' in args['model_checkpoint']:
# xlm-roberta-base or xlm-roberta-large
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = XLMRobertaTokenizer.from_pretrained(
args['model_checkpoint'])
config = XLMRobertaConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = XLMRobertaForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = XLMRobertaForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = XLMRobertaForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'fasttext' in args['model_checkpoint']:
# Prepare config & tokenizer
vocab_path = args['vocab_path']
config_path = None
word_tokenizer = args['word_tokenizer_class']()
emb_path = args['embedding_path'][args['model_checkpoint']]
_, vocab_map = load_vocab(vocab_path)
tokenizer = SimpleTokenizer(vocab_map,
word_tokenizer,
lower=args["lower"])
vocab_list = list(tokenizer.vocab.keys())
config = BertConfig.from_pretrained('bert-base-uncased')
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
config.num_hidden_layers = args["num_layers"]
embeddings = gen_embeddings(vocab_list, emb_path, emb_dim=300)
config.hidden_size = 300
config.num_attention_heads = 10
config.vocab_size = len(embeddings)
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'scratch' in args['model_checkpoint']:
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
config = BertConfig.from_pretrained("bert-base-uncased")
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
config.num_hidden_layers = args["num_layers"]
config.hidden_size = 300
config.num_attention_heads = 10
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config=config)
elif 'indobenchmark' in args['model_checkpoint']:
# indobenchmark models
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained(args['model_checkpoint'])
config = BertConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
model_class = None
if 'sequence_classification' == args['task']:
model_class = AlbertForSequenceClassification if 'lite' in args[
'model_checkpoint'] else BertForSequenceClassification
elif 'token_classification' == args['task']:
model_class = AlbertForWordClassification if 'lite' in args[
'model_checkpoint'] else BertForWordClassification
elif 'multi_label_classification' == args['task']:
model_class = AlbertForMultiLabelClassification if 'lite' in args[
'model_checkpoint'] else BertForMultiLabelClassification
model = model_class.from_pretrained(args['model_checkpoint'],
config=config)
return model, tokenizer, vocab_path, config_path
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
batch_size = 32
train_dataloader = DataLoader(train_dataset,
sampler=RandomSampler(train_dataset),
batch_size=batch_size)
validation_dataloader = DataLoader(val_dataset,
sampler=SequentialSampler(val_dataset),
batch_size=batch_size)
# Load BertForSequenceClassification, the pretrained BERT model with a single
# linear classification layer on top.
model = XLMRobertaForSequenceClassification.from_pretrained(
"./drive/MyDrive/XLM/XLMRoBERTa-B/",
#'xlm-roberta-base',
num_labels=2, # multi classification
output_attentions=False,
output_hidden_states=False)
# Freezing layers except the classifying layer
# for param in model.bert.parameters():
# param.requires_grad = False
# Use GPU
model.cuda()
# if layers frozen
#optimizer = AdamW(filter(lambda p: p.requires_grad, model.parameters()), lr = 5e-5, eps = 1e-8)
optimizer = AdamW(model.parameters(), lr=5e-5, eps=1e-8)
epochs = 8
def model_init():
model = XLMRobertaForSequenceClassification.from_pretrained(args.model_dir)
return model
collate_fn=train_dataset.spam_collate_func,
shuffle=False)
import random
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
model = XLMRobertaForSequenceClassification.from_pretrained( "xlm-roberta-large" ,
num_labels = 2 ,
output_attentions = False,
output_hidden_states = False, )
model.cuda()
optimizer = AdamW(model.parameters(),
lr = 1e-5,
eps = 1e-8,
weight_decay = 0.01
)
"""
optimizer = Adafactor( model.parameters(),
lr=1e-5,
eps=(1e-30, 1e-3),
clip_threshold=1.0,
import itertools
for model, wbool in list(itertools.product(models, wbools)):
loss_weighted = wbool
if model == 'MURIL':
# Using Huggingface MURIL version
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("simran-kh/muril-cased-temp")
model = AutoModelForSequenceClassification.from_pretrained("simran-kh/muril-cased-temp", num_labels=6)
model_name = 'MURIL_cased_temp_tamil'
if model == 'XLMR':
# Using XLM-Roberta-Base pretrained model
from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
model = XLMRobertaForSequenceClassification.from_pretrained('xlm-roberta-large', num_labels=6)
model_name = 'XLMroberta_large_tamil'
if model == 'XLMR_base':
# Using XLM-Roberta-Base pretrained model
from transformers import XLMRobertaTokenizer, XLMRobertaForSequenceClassification
tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-base')
model = XLMRobertaForSequenceClassification.from_pretrained('xlm-roberta-base', num_labels=6)
model_name = 'XLMroberta_base_tamil'
if model == 'mbertlarge':
# Using Multilingual Bert, bert-large-multilingual-cased pretrained
from transformers import BertTokenizer, BertForSequenceClassification
tokenizer = BertTokenizer.from_pretrained('distilbert-base-multilingual-cased')
model = BertForSequenceClassification.from_pretrained('distilbert-base-multilingual-cased', num_labels=6)
model_name = 'Distilbert_m_base_cased_tamil'
if model == 'XLMR_custom':
# Using XLMRoberta finetuning Custom Pretrained model, Vocab same => Tokenizer base
def train():
seed_everything(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# setting model hyperparameter
# config 자체에는 학습 weight 정보 없기 때문에, from_pretrained 사용해 weight 가져올 수 있다
# bert_config = BertConfig.from_pretrained(MODEL_NAME)
# bert_config.num_labels = 42
# model = BertForSequenceClassification.from_pretrained(MODEL_NAME, config=bert_config)
# Auto
model_config = XLMRobertaConfig.from_pretrained(args.model_name)
model_config.num_labels = 42
model = XLMRobertaForSequenceClassification.from_pretrained(
args.model_name, config=model_config)
# load model and tokenizer
# MODEL_NAME = "monologg/koelectra-base-v3-discriminator"
# roberta: https://huggingface.co/transformers/model_doc/xlmroberta.html
tokenizer = AutoTokenizer.from_pretrained(args.model_name)
# load dataset
dataset = load_data("/opt/ml/input/data/train/train.tsv")
# label = dataset['label'].values
train_dataset, val_dataset = train_test_split(dataset,
test_size=0.2,
random_state=args.seed)
tokenized_train = tokenized_dataset(train_dataset, tokenizer)
tokenized_val = tokenized_dataset(val_dataset, tokenizer)
tokenized_train_label = train_dataset['label'].values
tokenized_val_label = val_dataset['label'].values
# train_datasets = TokenDataset(train_dataset, tokenizer)
# val_datasets = TokenDataset(val_dataset, tokenizer)
RE_train_dataset = RE_Dataset(tokenized_train, tokenized_train_label)
RE_val_dataset = RE_Dataset(tokenized_val, tokenized_val_label)
# print(model.parameters)
model.to(device)
model = torch.nn.DataParallel(model)
train_loader = DataLoader(
RE_train_dataset,
batch_size=args.batch_size,
# num_workers=8,
pin_memory=torch.cuda.is_available(),
shuffle=True,
)
val_loader = DataLoader(
RE_val_dataset,
batch_size=args.batch_size,
# num_workers=8,
shuffle=False,
pin_memory=torch.cuda.is_available(),
)
optimizer = AdamW(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
loss_fn = LabelSmoothingLoss(smoothing=0.5)
# loss_fn = nn.CrossEntropyLoss()
# t_total = len(train_loader) * args.epoch
t_total = args.epoch
warmup_step = int(t_total * args.warmup_steps)
scheduler = get_cosine_schedule_with_warmup(optimizer,
num_warmup_steps=warmup_step,
num_training_steps=t_total)
log_dir = ""
log_list = glob("./logs/*")
if len(log_list) == 0:
log_dir = "./logs/exp1"
else:
log_list = [int(log[-1]) for log in log_list]
log_dir = "./logs/exp" + str(max(log_list) + 1)
logger = SummaryWriter(log_dir=log_dir)
scaler = GradScaler()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
import time
for epoch in tqdm(range(args.epoch)):
train_acc = 0.0
train_loss = 0.0
val_acc = 0.0
val_loss = 0.0
best_acc = 0.0
model.train()
for batch_id, batch in enumerate(tqdm(train_loader)):
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
labels = batch["labels"].to(device)
optimizer.zero_grad()
with autocast():
outputs = model(input_ids,
attention_mask=attention_mask,
labels=labels)
loss = loss_fn(outputs.logits, labels)
# loss.backward()
# optimizer.step()
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
scaler.step(optimizer)
scaler.update()
train_acc += compute_acc(outputs.logits.cpu(), labels.cpu())
train_loss += loss
if (batch_id + 1) % args.logging_steps == 0:
train_loss = train_loss.data.cpu().numpy()
print(
f"[Train] epoch {epoch + 1} | batch_id {batch_id + 1} | loss {(train_loss) / args.logging_steps:.4f} | train_acc {train_acc / args.logging_steps:.4f}"
)
logger.add_scalar("Train/loss",
train_loss / args.logging_steps,
epoch * len(train_loader) + batch_id)
logger.add_scalar("Train/acc", train_acc / args.logging_steps,
epoch * len(train_loader) + batch_id)
train_acc = 0.0
train_loss = 0.0
# scheduler.step()
print("\nStart Validation Step!")
with torch.no_grad():
model.eval()
for batch_id, batch in enumerate(tqdm(val_loader)):
input_ids = batch["input_ids"].to(device)
attention_mask = batch["attention_mask"].to(device)
labels = batch["labels"].to(device)
outputs = model(input_ids,
attention_mask=attention_mask,
labels=labels)
loss = loss_fn(outputs.logits, labels)
val_acc += compute_acc(outputs.logits.cpu(), labels.cpu())
val_loss += loss
print(
f"[Val] epoch {epoch + 1} | val_acc {val_acc / (batch_id + 1):.4f}"
)
logger.add_scalar("Val/loss", val_loss / (batch_id + 1), epoch)
logger.add_scalar("Val/acc", val_acc / (batch_id + 1), epoch)
if val_acc >= best_acc:
best_acc = val_acc
# torch.save(model.state_dict(), os.path.join(args.output_dir, "saved_" + str(epoch) + ".pth"))
torch.save(model.state_dict(),
os.path.join(args.output_dir, "best.pth"))
print("Saved best acc model...")
scheduler.step()
torch.save(model.state_dict(), os.path.join(args.output_dir, "last.pth"))
def train():
# load model and tokenizer
#MODEL_NAME = "bert-base-multilingual-cased"
MODEL_NAME = 'xlm-roberta-large'
tokenizer = XLMRobertaTokenizer.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")
#dev_dataset = load_data("./dataset/train/dev.tsv")
train_label = train_dataset['label'].values
#dev_label = dev_dataset['label'].values
# train_dataset, dev_dataset = load_fold(6)
# train_label = train_dataset['label'].values
#dev_label = dev_dataset['label'].values
# tokenizing dataset
tokenized_train = tokenized_dataset(train_dataset, tokenizer)
#tokenized_dev = tokenized_dataset(dev_dataset, tokenizer)
# make dataset for pytorch.
RE_train_dataset = RE_Dataset(tokenized_train, train_label)
#RE_dev_dataset = RE_Dataset(tokenized_dev, dev_label)
train_dataset, dev_dataset = torch.utils.data.random_split(
RE_train_dataset, [8000, 1001])
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# setting model hyperparameter
bert_config = XLMRobertaConfig.from_pretrained(MODEL_NAME)
bert_config.num_labels = 42
model = XLMRobertaForSequenceClassification.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=3, # number of total save model.
save_steps=300, # model saving step.
load_best_model_at_end=True,
num_train_epochs=10, # total number of training epochs
learning_rate=1e-5, # learning_rate
per_device_train_batch_size=16, # batch size per device during training
per_device_eval_batch_size=16, # batch size for evaluation
warmup_steps=300, # 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=300, # evaluation step.
dataloader_num_workers=4,
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()
model_checkpoint = "xlm-roberta-base"
metric_name = "matthews_correlation"
batch_size = 16
dataset = load_dataset("glue", task)
tokenizer = XLMRobertaTokenizer.from_pretrained("xlm-roberta-base")
def preprocess_function(examples):
return tokenizer(examples["sentence"], truncation=True)
encoded_dataset = dataset.map(preprocess_function, batched=True)
model = XLMRobertaForSequenceClassification.from_pretrained(model_checkpoint,
num_labels=2)
args = TrainingArguments(
"cola_checkpoints",
evaluation_strategy="epoch",
learning_rate=2e-5,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
num_train_epochs=5,
weight_decay=0.01,
load_best_model_at_end=True,
)
def compute_metrics(eval_pred):
labels = eval_pred.label_ids
def train():
# load model and tokenizer
MODEL_NAME = "xlm-roberta-large"
tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_NAME)
# load dataset
train_dataset = load_data("/opt/ml/input/data/train/train.tsv")
#dev_dataset = load_data("./dataset/train/dev.tsv")
train_label = train_dataset['label'].values
#dev_label = dev_dataset['label'].values
# tokenizing dataset
tokenized_train = tokenized_dataset(
train_dataset,
tokenizer) # keys: input_ids, token_type_ids, attention_mask
#tokenized_dev = tokenized_dataset(dev_dataset, tokenizer)
# make dataset for pytorch.
RE_train_dataset = RE_Dataset(tokenized_train, train_label)
#RE_dev_dataset = RE_Dataset(tokenized_dev, dev_label)
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print(f'RUNNING ON {device}')
# setting model hyperparameter
bert_config = XLMRobertaConfig.from_pretrained(MODEL_NAME)
bert_config.num_labels = 42
model = XLMRobertaForSequenceClassification.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=3, # number of total save model.
save_steps=500, # model saving step.
num_train_epochs=10, # total number of training epochs
learning_rate=1e-5, # learning_rate
per_device_train_batch_size=32, # batch size per device during training
per_device_eval_batch_size=32, # batch size for evaluation
warmup_steps=300, # 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 = 500, # evaluation step.
dataloader_num_workers=4,
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=RE_train_dataset, # training dataset
# eval_dataset=RE_dev_dataset, # evaluation dataset
compute_metrics=compute_metrics # define metrics function
)
# train model
trainer.train()
# differentiates padding from non-padding
attention_masks.append(encoded_dict['attention_mask'])
# Convert the lists into tensors.
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
labels = torch.tensor(labels)
batch_size = 1
prediction_data = TensorDataset(input_ids, attention_masks, labels)
prediction_sampler = SequentialSampler(prediction_data)
prediction_dataloader = DataLoader(prediction_data,
sampler=prediction_sampler,
batch_size=batch_size)
model = XLMRobertaForSequenceClassification.from_pretrained(
'./models/XLM/XLMRoBERTa-Multi')
model.to(device)
model.eval()
# Tracking variables
predictions, true_labels = [], []
for batch in prediction_dataloader:
# Add batch to GPU
batch = tuple(t.to(device) for t in batch)
# Unpack
b_input_ids, b_input_mask, b_labels = batch
with torch.no_grad():
# Get predictionss
outputs = model(b_input_ids,
def train(model_dir, args):
seed_everything(args.seed)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
print("This notebook use [%s]." % (device))
s_dir = args.model + str(
args.num_hidden_layers) + '-' + args.preprocess + '-epoch' + str(
args.epochs
) + '-' + args.scheduler + '-' + args.tokenize + '-' + str(
args.max_len) + '-' + str(args.seed)
save_dir = increment_path(os.path.join(model_dir, s_dir))
log_dir = increment_path(os.path.join('logs', s_dir))
# load model and tokenizer
MODEL_NAME = args.model
if MODEL_NAME.startswith('xlm'):
tokenizer = XLMRobertaTokenizer.from_pretrained(MODEL_NAME)
else:
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
# set neptune
set_neptune(save_dir, args)
# load dataset
dataset = load_data("/opt/ml/input/data/train/train.tsv")
labels = dataset['label'].values
# setting model hyperparameter
if MODEL_NAME.startswith('xlm'):
bert_config = XLMRobertaConfig.from_pretrained(MODEL_NAME)
else:
bert_config = BertConfig.from_pretrained(MODEL_NAME)
bert_config.num_labels = args.num_labels
bert_config.num_hidden_layers = args.num_hidden_layers
if MODEL_NAME.startswith('xlm'):
model = XLMRobertaForSequenceClassification.from_pretrained(
MODEL_NAME, config=bert_config)
else:
model = BertForSequenceClassification.from_pretrained(
MODEL_NAME, config=bert_config)
if args.drop >= 0:
model.dropout = nn.Dropout(p=args.drop)
# preprocess dataset
if args.preprocess != 'no':
pre_module = getattr(import_module("preprocess"), args.preprocess)
dataset = pre_module(dataset, model, tokenizer)
# make dataset for pytorch.
# train, val split
train_dataset, val_dataset = train_test_split(dataset,
test_size=args.val_ratio,
random_state=args.seed)
tok_module = getattr(import_module("load_data"), args.tokenize)
train_tokenized = tok_module(train_dataset,
tokenizer,
max_len=args.max_len)
val_tokenized = tok_module(val_dataset, tokenizer, max_len=args.max_len)
# make dataset for pytorch.
RE_train_dataset = RE_Dataset(
train_tokenized, train_dataset['label'].reset_index(drop='index'))
RE_val_dataset = RE_Dataset(val_tokenized,
val_dataset['label'].reset_index(drop='index'))
model.to(device)
# 사용한 option 외에도 다양한 option들이 있습니다.
# https://huggingface.co/transformers/main_classes/trainer.html#trainingarguments 참고해주세요.
training_args = TrainingArguments(
seed=args.seed,
output_dir=save_dir, # output directory
save_total_limit=2, # number of total save model.
save_steps=args.save_steps, # model saving step.
num_train_epochs=args.epochs, # total number of training epochs
learning_rate=args.lr, # learning_rate
per_device_train_batch_size=args.
batch_size, # batch size per device during training
per_device_eval_batch_size=16, # batch size for evaluation
lr_scheduler_type=args.scheduler,
warmup_steps=args.
warmup_steps, # number of warmup steps for learning rate scheduler
weight_decay=args.weight_decay, # strength of weight decay
logging_dir=log_dir, # 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=4,
label_smoothing_factor=args.smoothing_factor,
load_best_model_at_end=True,
metric_for_best_model='accuracy')
trainer = Trainer(
model=model, # the instantiated 🤗 Transformers model to be trained
args=training_args, # training arguments, defined above
train_dataset=RE_train_dataset, # training dataset
eval_dataset=RE_val_dataset, # evaluation dataset
compute_metrics=compute_metrics # define metrics function
)
# train model
trainer.train()
def load_model(args):
if 'albert-large-wwmlm-512' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-large-wwmlm-512/albert_large_model_bpe_wwmlm_512_vocab_uncased_30000.txt"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(
"../embeddings/albert-large-wwmlm-512/albert_large_model_bpe_wwmlm_512_albert_large_config.json"
)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-large-wwmlm-512/albert_large_model_bpe_wwmlm_512_pytorch_albert_large_512_629k.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'albert-base-wwmlm-512' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-base-wwmlm-512/albert_base_model_bpe_wwmlm_512_vocab_uncased_30000.txt"
config_path = "../embeddings/albert-base-wwmlm-512/albert_base_model_bpe_wwmlm_512_albert_base_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-base-wwmlm-512/albert_base_model_bpe_wwmlm_512_pytorch_model_albert_base_162k.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'albert-large-wwmlm-128' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-large-wwmlm-128/albert_large_model_bpe_wwmlm_128_vocab_uncased_30000.txt"
config_path = "../embeddings/albert-large-wwmlm-128/albert_large_model_bpe_wwmlm_128_albert_large_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-large-wwmlm-128/albert_large_model_bpe_wwmlm_128_pytorch_albert_large_128_500k.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'babert-bpe-mlm-large-512' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-large-512/babert_model_bpe_mlm_uncased_large_512_dup10-5_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-bpe-mlm-large-512/babert_model_bpe_mlm_uncased_large_512_dup10-5_bert_large_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-large-512/babert_model_bpe_mlm_uncased_large_512_dup10-5_pytorch_babert_uncased_large_512_dup10-5_1120k.bin",
config=config)
model.bert = bert_model.bert
elif 'albert-base-uncased-112500' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-base-uncased-112500/vocab.txt"
config_path = "../embeddings/albert-base-uncased-112500/bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-base-uncased-112500/albert_base_uncased_112500.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'albert-base-uncased-96000' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-base-uncased-96000/vocab.txt"
config_path = "../embeddings/albert-base-uncased-96000/bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-base-uncased-96000/albert_base_uncased_96000.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'albert-base-uncased-191k' == args['model_checkpoint']:
vocab_path = "../embeddings/albert-base-uncased-191k/pytorch_models_albert_base_uncased_191500_vocab_uncased_30000.txt"
config_path = "../embeddings/albert-base-uncased-191k/pytorch_models_albert_base_uncased_191500_albert_base_config.json"
tokenizer = BertTokenizer(vocab_path)
config = AlbertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = AlbertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = AlbertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = AlbertForMultiLabelClassification(config)
# Plug pretrained bert model
albert_model = AlbertModel.from_pretrained(
"../embeddings/albert-base-uncased-191k/pytorch_models_albert_base_uncased_191500_pytorch_model_albert_base_191k.bin",
from_tf=False,
config=config)
model.albert = albert_model
elif 'babert-opensubtitle' == args['model_checkpoint']:
# babert-opensubtitle
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-opensubtitle/vocab.txt"
config_path = "../embeddings/babert-opensubtitle/bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-opensubtitle/model.ckpt-1000000.index",
from_tf=True,
config=config)
model.bert = bert_model.bert
elif 'babert-bpe-mlm-large-uncased-1100k' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-large-uncased-1100k/pytorch_models_babert_uncased_large_1100k_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-bpe-mlm-large-uncased-1100k/pytorch_models_babert_uncased_large_1100k_bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-large-uncased-1100k/pytorch_models_babert_uncased_large_1100k_pytorch_model_babert_large_1100k.bin",
config=config)
model.bert = bert_model.bert
elif 'babert-bpe-mlm-large-uncased-1m' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-large-uncased-1m/pytorch_models_babert_uncased_large_1mil_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-bpe-mlm-large-uncased-1m/pytorch_models_babert_uncased_large_1mil_bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-large-uncased-1m/pytorch_models_babert_uncased_large_1mil_pytorch_model_babert_large_1mil.bin",
config=config)
model.bert = bert_model.bert
elif 'babert-base-512' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-base-512/pytorch_models_babert_base_512_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-base-512/pytorch_models_babert_base_512_bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-base-512/pytorch_models_babert_base_512_pytorch_model_babert_base_uncased_512.bin",
config=config)
model.bert = bert_model.bert
elif 'babert-bpe-mlm-large-uncased' == args['model_checkpoint']:
# babert_bpe
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-large-uncased/pytorch_models_babert_uncased_large_vocab_uncased_30522.txt"
config_path = "../embeddings/babert-bpe-mlm-large-uncased/pytorch_models_babert_uncased_large_bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-large-uncased/pytorch_models_babert_uncased_large_pytorch_model_babert_large_778500.bin",
config=config)
model.bert = bert_model.bert
elif 'babert-bpe-mlm-uncased-128-dup10-5' == args['model_checkpoint']:
# babert_bpe_wwmlm
# Prepare config & tokenizer
vocab_path = "../embeddings/babert-bpe-mlm-uncased-128-dup10-5/vocab.txt"
config_path = "../embeddings/babert-bpe-mlm-uncased-128-dup10-5/bert_config.json"
tokenizer = BertTokenizer(vocab_path)
config = BertConfig.from_json_file(config_path)
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
# Plug pretrained bert model
bert_model = BertForPreTraining.from_pretrained(
"../embeddings/babert-bpe-mlm-uncased-128-dup10-5/pytorch_model.bin",
config=config)
model.bert = bert_model.bert
elif 'bert-base-multilingual' in args['model_checkpoint']:
# bert-base-multilingual-uncased or bert-base-multilingual-cased
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained(args['model_checkpoint'])
config = BertConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'xlm-mlm' in args['model_checkpoint']:
# xlm-mlm-100-1280
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = XLMTokenizer.from_pretrained(args['model_checkpoint'])
config = XLMConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = XLMForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = XLMForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = XLMForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'xlm-roberta' in args['model_checkpoint']:
# xlm-roberta-base or xlm-roberta-large
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = XLMRobertaTokenizer.from_pretrained(
args['model_checkpoint'])
config = XLMRobertaConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = XLMRobertaForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = XLMRobertaForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = XLMRobertaForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'word2vec' in args['model_checkpoint'] or 'fasttext' in args[
'model_checkpoint']:
# Prepare config & tokenizer
vocab_path = args['vocab_path']
config_path = None
word_tokenizer = args['word_tokenizer_class']()
emb_path = args['embedding_path'][args['model_checkpoint']]
_, vocab_map = load_vocab(vocab_path)
tokenizer = SimpleTokenizer(vocab_map,
word_tokenizer,
lower=args["lower"])
vocab_list = list(tokenizer.vocab.keys())
config = BertConfig.from_pretrained('bert-base-uncased')
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
config.num_hidden_layers = args["num_layers"]
if args['model_checkpoint'] == 'word2vec-twitter':
embeddings = gen_embeddings(vocab_list, emb_path)
config.hidden_size = 400
config.num_attention_heads = 8
if args['model_checkpoint'] == 'fasttext-cc-id' or args[
'model_checkpoint'] == 'fasttext-cc-id-300-no-oov-uncased' or args[
'model_checkpoint'] == 'fasttext-4B-id-300-no-oov-uncased':
embeddings = gen_embeddings(vocab_list, emb_path, emb_dim=300)
config.hidden_size = 300
config.num_attention_heads = 10
config.vocab_size = len(embeddings)
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'token_classification' == args['task']:
model = BertForWordClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config)
model.bert.embeddings.word_embeddings.weight.data.copy_(
torch.FloatTensor(embeddings))
elif 'scratch' in args['model_checkpoint']:
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
config = BertConfig.from_pretrained("bert-base-uncased")
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
config.num_hidden_layers = args["num_layers"]
config.hidden_size = 300
config.num_attention_heads = 10
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification(config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification(config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification(config=config)
elif 'indobenchmark' in args['model_checkpoint']:
# indobenchmark models
# Prepare config & tokenizer
vocab_path, config_path = None, None
tokenizer = BertTokenizer.from_pretrained(args['model_checkpoint'])
config = BertConfig.from_pretrained(args['model_checkpoint'])
if type(args['num_labels']) == list:
config.num_labels = max(args['num_labels'])
config.num_labels_list = args['num_labels']
else:
config.num_labels = args['num_labels']
# Instantiate model
if 'sequence_classification' == args['task']:
model = BertForSequenceClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'token_classification' == args['task']:
model = BertForWordClassification.from_pretrained(
args['model_checkpoint'], config=config)
elif 'multi_label_classification' == args['task']:
model = BertForMultiLabelClassification.from_pretrained(
args['model_checkpoint'], config=config)
return model, tokenizer, vocab_path, config_path
def run_xlm(dataset, model, header):
test_set = pd.read_csv(dataset, encoding='utf-8')
X = test_set['text']
y = test_set[header]
input_ids = []
attention_masks = []
tokenizer = XLMRobertaTokenizer.from_pretrained(model, do_lower_case=True)
device = torch.device("cuda")
labels = []
for i in range(len(X)):
if not pd.isnull(X[i]):
encoded_dict = tokenizer.encode_plus(
X[i], # text.
add_special_tokens=True, # [CLS] and [SEP] tokens'
max_length=64,
pad_to_max_length=True, # Pad missing tokens with 0s
return_attention_mask=True,
return_tensors='pt', # pytorch tensors.
)
labels.append(y[i])
input_ids.append(encoded_dict['input_ids'])
# differentiates padding from non-padding
attention_masks.append(encoded_dict['attention_mask'])
# Convert the lists into tensors.
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
labels = torch.tensor(labels)
batch_size = 1
prediction_data = TensorDataset(input_ids, attention_masks, labels)
prediction_sampler = SequentialSampler(prediction_data)
prediction_dataloader = DataLoader(prediction_data,
sampler=prediction_sampler,
batch_size=batch_size)
model = XLMRobertaForSequenceClassification.from_pretrained(model)
model.to(device)
model.eval()
# Tracking variables
predictions, true_labels = [], []
for batch in prediction_dataloader:
# Add batch to GPU
batch = tuple(t.to(device) for t in batch)
# Unpack
b_input_ids, b_input_mask, b_labels = batch
with torch.no_grad():
# Get predictionss
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
logits = outputs[0]
# Retrieve data from GPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# Save predictions
predictions.append(logits)
true_labels.append(label_ids)
# Combine the results across all batches.
flat_predictions = np.concatenate(predictions, axis=0)
# For each sample, pick the label (0 or 1) with the higher score.
flat_predictions = np.argmax(flat_predictions, axis=1).flatten()
# Combine the correct labels for each batch into a single list.
flat_true_labels = np.concatenate(true_labels, axis=0)
# Check if predictions are correct
acc = np.sum(flat_predictions == flat_true_labels) / len(flat_predictions)
# print(flat_predictions)
# print(flat_true_labels)
# print(acc)
print("Accuracy: ", accuracy_score(flat_true_labels, flat_predictions))
print(
"Precision: ",
precision_score(flat_true_labels, flat_predictions,
average='weighted'))
print("Recall: ",
recall_score(flat_true_labels, flat_predictions, average='weighted'))
print("F1-score: ",
f1_score(flat_true_labels, flat_predictions, average='weighted'))