def _set_data_desc_to_cfg(self, cfg, data_dir, train_ds, validation_ds):
""" Method creates IntentSlotDataDesc and copies generated values to cfg.data_desc. """
# Save data from data desc to config - so it can be reused later, e.g. in inference.
data_desc = IntentSlotDataDesc(data_dir=data_dir, modes=[train_ds.prefix, validation_ds.prefix])
OmegaConf.set_struct(cfg, False)
if not hasattr(cfg, "data_desc") or cfg.data_desc is None:
cfg.data_desc = {}
# Intents.
cfg.data_desc.intent_labels = list(data_desc.intents_label_ids.keys())
cfg.data_desc.intent_label_ids = data_desc.intents_label_ids
cfg.data_desc.intent_weights = data_desc.intent_weights
# Slots.
cfg.data_desc.slot_labels = list(data_desc.slots_label_ids.keys())
cfg.data_desc.slot_label_ids = data_desc.slots_label_ids
cfg.data_desc.slot_weights = data_desc.slot_weights
cfg.data_desc.pad_label = data_desc.pad_label
# for older(pre - 1.0.0.b3) configs compatibility
if not hasattr(cfg, "class_labels") or cfg.class_labels is None:
cfg.class_labels = {}
cfg.class_labels = OmegaConf.create(
{'intent_labels_file': 'intent_labels.csv', 'slot_labels_file': 'slot_labels.csv'}
)
slot_labels_file = os.path.join(data_dir, cfg.class_labels.slot_labels_file)
intent_labels_file = os.path.join(data_dir, cfg.class_labels.intent_labels_file)
self._save_label_ids(data_desc.slots_label_ids, slot_labels_file)
self._save_label_ids(data_desc.intents_label_ids, intent_labels_file)
self.register_artifact(cfg.class_labels.intent_labels_file, intent_labels_file)
self.register_artifact(cfg.class_labels.slot_labels_file, slot_labels_file)
OmegaConf.set_struct(cfg, True)
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
""" Initializes BERT Joint Intent and Slot model.
"""
self.data_dir = cfg.data_dir
self.max_seq_length = cfg.language_model.max_seq_length
self.data_desc = IntentSlotDataDesc(
data_dir=cfg.data_dir,
modes=[cfg.train_ds.prefix, cfg.validation_ds.prefix])
self._setup_tokenizer(cfg.tokenizer)
# init superclass
super().__init__(cfg=cfg, trainer=trainer)
# initialize Bert model
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,
)
self.classifier = SequenceTokenClassifier(
hidden_size=self.bert_model.config.hidden_size,
num_intents=self.data_desc.num_intents,
num_slots=self.data_desc.num_slots,
dropout=cfg.head.fc_dropout,
num_layers=cfg.head.num_output_layers,
log_softmax=False,
)
# define losses
if cfg.class_balancing == 'weighted_loss':
# You may need to increase the number of epochs for convergence when using weighted_loss
self.intent_loss = CrossEntropyLoss(
logits_ndim=2, weight=self.data_desc.intent_weights)
self.slot_loss = CrossEntropyLoss(
logits_ndim=3, weight=self.data_desc.slot_weights)
else:
self.intent_loss = CrossEntropyLoss(logits_ndim=2)
self.slot_loss = CrossEntropyLoss(logits_ndim=3)
self.total_loss = AggregatorLoss(
num_inputs=2,
weights=[cfg.intent_loss_weight, 1.0 - cfg.intent_loss_weight])
# setup to track metrics
self.intent_classification_report = ClassificationReport(
self.data_desc.num_intents, self.data_desc.intents_label_ids)
self.slot_classification_report = ClassificationReport(
self.data_desc.num_slots, self.data_desc.slots_label_ids)
# Optimizer setup needs to happen after all model weights are ready
self.setup_optimization(cfg.optim)
def _set_data_desc_to_cfg(self, cfg, data_dir, train_ds, validation_ds):
""" Method creates IntentSlotDataDesc and copies generated values to cfg.data_desc. """
# Save data from data desc to config - so it can be reused later, e.g. in inference.
data_desc = IntentSlotDataDesc(data_dir=data_dir, modes=[train_ds.prefix, validation_ds.prefix])
OmegaConf.set_struct(cfg, False)
if not hasattr(cfg, "data_desc") or cfg.data_desc is None:
cfg.data_desc = {}
# Intents.
cfg.data_desc.intent_labels = list(data_desc.intents_label_ids.keys())
cfg.data_desc.intent_label_ids = data_desc.intents_label_ids
cfg.data_desc.intent_weights = data_desc.intent_weights
# Slots.
cfg.data_desc.slot_labels = list(data_desc.slots_label_ids.keys())
cfg.data_desc.slot_label_ids = data_desc.slots_label_ids
cfg.data_desc.slot_weights = data_desc.slot_weights
cfg.data_desc.pad_label = data_desc.pad_label
OmegaConf.set_struct(cfg, True)