Source code for nemo.collections.nlp.modules.common.sequence_token_classifier

# 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.
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# distributed under the License is distributed on an "AS IS" BASIS,
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from typing import Dict, Optional

from 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']

[docs]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)
[docs] @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