def __init__(self, cfg: DictConfig, trainer: Trainer = None):
"""
Initializes model to use BERT model for GLUE tasks.
"""
self.data_dir = cfg.dataset.data_dir
if not os.path.exists(self.data_dir):
raise FileNotFoundError(
"GLUE datasets not found. For more details on how to get the data, see: "
"https://gist.github.com/W4ngatang/60c2bdb54d156a41194446737ce03e2e"
)
if cfg.task_name not in cfg.supported_tasks:
raise ValueError(
f'{cfg.task_name} not in supported task. Choose from {cfg.supported_tasks}'
)
self.task_name = cfg.task_name
# MNLI task has two separate dev sets: matched and mismatched
cfg.train_ds.file_name = os.path.join(self.data_dir,
cfg.train_ds.file_name)
if self.task_name == "mnli":
cfg.validation_ds.file_name = [
os.path.join(self.data_dir, 'dev_matched.tsv'),
os.path.join(self.data_dir, 'dev_mismatched.tsv'),
]
else:
cfg.validation_ds.file_name = os.path.join(
self.data_dir, cfg.validation_ds.file_name)
logging.info(
f'Using {cfg.validation_ds.file_name} for model evaluation.')
self._setup_tokenizer(cfg.tokenizer)
super().__init__(cfg=cfg, trainer=trainer)
num_labels = GLUE_TASKS_NUM_LABELS[self.task_name]
self.bert_model = get_lm_model(
pretrained_model_name=cfg.language_model.pretrained_model_name,
config_file=cfg.language_model.config_file,
config_dict=OmegaConf.to_container(cfg.language_model.config)
if cfg.language_model.config else None,
checkpoint_file=cfg.language_model.lm_checkpoint,
)
# uses [CLS] token for classification (the first token)
if self.task_name == "sts-b":
self.pooler = SequenceRegression(
hidden_size=self.bert_model.config.hidden_size)
self.loss = MSELoss()
else:
self.pooler = SequenceClassifier(
hidden_size=self.bert_model.config.hidden_size,
num_classes=num_labels,
log_softmax=False)
self.loss = CrossEntropyLoss()
# Optimizer setup needs to happen after all model weights are ready
self.setup_optimization(cfg.optim)
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
"""
Initializes model to use BERT model for GLUE tasks.
"""
if cfg.task_name not in cfg.supported_tasks:
raise ValueError(
f'{cfg.task_name} not in supported task. Choose from {cfg.supported_tasks}'
)
self.task_name = cfg.task_name
# needed to setup validation on multiple datasets
# MNLI task has two separate dev sets: matched and mismatched
if not self._is_model_being_restored():
if self.task_name == "mnli":
cfg.validation_ds.ds_item = [
os.path.join(cfg.dataset.data_dir, 'dev_matched.tsv'),
os.path.join(cfg.dataset.data_dir, 'dev_mismatched.tsv'),
]
else:
cfg.validation_ds.ds_item = os.path.join(
cfg.dataset.data_dir, cfg.validation_ds.ds_item)
cfg.train_ds.ds_item = os.path.join(cfg.dataset.data_dir,
cfg.train_ds.ds_item)
logging.info(
f'Using {cfg.validation_ds.ds_item} for model evaluation.')
self.setup_tokenizer(cfg.tokenizer)
super().__init__(cfg=cfg, trainer=trainer)
num_labels = GLUE_TASKS_NUM_LABELS[self.task_name]
self.bert_model = get_lm_model(
pretrained_model_name=cfg.language_model.pretrained_model_name,
config_file=self.register_artifact('language_model.config_file',
cfg.language_model.config_file),
config_dict=OmegaConf.to_container(cfg.language_model.config)
if cfg.language_model.config else None,
checkpoint_file=cfg.language_model.lm_checkpoint,
vocab_file=self.register_artifact('tokenizer.vocab_file',
cfg.tokenizer.vocab_file),
)
# uses [CLS] token for classification (the first token)
if self.task_name == "sts-b":
self.pooler = SequenceRegression(
hidden_size=self.bert_model.config.hidden_size)
self.loss = MSELoss()
else:
self.pooler = SequenceClassifier(
hidden_size=self.bert_model.config.hidden_size,
num_classes=num_labels,
log_softmax=False)
self.loss = CrossEntropyLoss()
def _setup_loss(self):
return MSELoss()