else:
pretrained_bert_model = nemo_nlp.huggingface.BERT(
pretrained_model_name=args.pretrained_bert_model, factory=nf)
hidden_size = pretrained_bert_model.local_parameters["hidden_size"]
data_desc = JointIntentSlotDataDesc(args.data_dir,
args.do_lower_case,
args.dataset_name,
args.none_slot_label,
args.pad_label)
# Create sentence classification loss on top
classifier = nemo_nlp.JointIntentSlotClassifier(
hidden_size=hidden_size,
num_intents=data_desc.num_intents,
num_slots=data_desc.num_slots,
dropout=args.fc_dropout)
if args.class_balancing == 'weighted_loss':
# Using weighted loss will enable weighted loss for both intents and slots
# Use the intent_loss_weight hyperparameter to adjust intent loss to
# prevent overfitting or underfitting.
loss_fn = nemo_nlp.JointIntentSlotLoss(
num_slots=data_desc.num_slots,
slot_classes_loss_weights=data_desc.slot_weights,
intent_classes_loss_weights=data_desc.intent_weights,
intent_loss_weight=args.intent_loss_weight)
else:
loss_fn = nemo_nlp.JointIntentSlotLoss(num_slots=data_desc.num_slots)
# Evaluation pipeline
nf.logger.info("Loading eval data...")
data_layer = nemo_nlp.BertJointIntentSlotDataLayer(
input_file=f'{data_desc.data_dir}/{args.eval_file_prefix}.tsv',
slot_file=f'{data_desc.data_dir}/{args.eval_file_prefix}_slots.tsv',
pad_label=data_desc.pad_label,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
shuffle=False,
batch_size=args.batch_size,
num_workers=0,
local_rank=args.local_rank)
classifier = nemo_nlp.JointIntentSlotClassifier(
hidden_size=hidden_size,
num_intents=data_desc.num_intents,
num_slots=data_desc.num_slots)
ids, type_ids, \
input_mask, loss_mask, subtokens_mask, \
intents, slots = data_layer()
hidden_states = pretrained_bert_model(input_ids=ids,
token_type_ids=type_ids,
attention_mask=input_mask)
intent_logits, slot_logits = classifier(hidden_states=hidden_states)
###########################################################################
# Instantiate an optimizer to perform `infer` action
evaluated_tensors = nf.infer(
