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# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Dict, Optional
from nemo.collections.common.parts import MultiLayerPerceptron
from nemo.collections.nlp.modules.common.classifier import Classifier
from nemo.core.classes import typecheck
from nemo.core.neural_types import LogitsType, NeuralType
__all__ = ['SequenceTokenClassifier']
class SequenceTokenClassifier(Classifier):
@property
def output_types(self) -> Optional[Dict[str, NeuralType]]:
return {
"intent_logits": NeuralType(('B', 'D'), LogitsType()),
"slot_logits": NeuralType(('B', 'T', 'D'), LogitsType()),
}
def __init__(
self,
hidden_size: int,
num_intents: int,
num_slots: int,
num_layers: int = 2,
activation: str = 'relu',
log_softmax: bool = False,
dropout: float = 0.0,
use_transformer_init: bool = True,
):
"""
Initializes the SequenceTokenClassifier module, could be used for tasks that train sequence and
token classifiers jointly, for example, for intent detection and slot tagging task.
Args:
hidden_size: hidden size of the mlp head on the top of the encoder
num_intents: number of the intents to predict
num_slots: number of the slots to predict
num_layers: number of the linear layers of the mlp head on the top of the encoder
activation: type of activations between layers of the mlp head
log_softmax: applies the log softmax on the output
dropout: the dropout used for the mlp head
use_transformer_init: initializes the weights with the same approach used in Transformer
"""
super().__init__(hidden_size=hidden_size, dropout=dropout)
self.intent_mlp = MultiLayerPerceptron(
hidden_size=hidden_size,
num_classes=num_intents,
num_layers=num_layers,
activation=activation,
log_softmax=log_softmax,
)
self.slot_mlp = MultiLayerPerceptron(
hidden_size=hidden_size,
num_classes=num_slots,
num_layers=num_layers,
activation=activation,
log_softmax=log_softmax,
)
self.post_init(use_transformer_init=use_transformer_init)
@typecheck()
def forward(self, hidden_states):
hidden_states = self.dropout(hidden_states)
# intent is classified by first hidden position
intent_logits = self.intent_mlp(hidden_states[:, 0])
slot_logits = self.slot_mlp(hidden_states)
return intent_logits, slot_logits
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