Source code for nemo.collections.nlp.modules.common.sequence_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, LogprobsType, NeuralType

__all__ = ['SequenceClassifier']

[docs]class SequenceClassifier(Classifier): @property def output_types(self) -> Optional[Dict[str, NeuralType]]: if not self.log_softmax: return {"logits": NeuralType(('B', 'D'), LogitsType())} else: return {"log_probs": NeuralType(('B', 'D'), LogprobsType())} def __init__( self, hidden_size: int, num_classes: int, num_layers: int = 2, activation: str = 'relu', log_softmax: bool = True, dropout: float = 0.0, use_transformer_init: bool = True, idx_conditioned_on: int = 0, ): """ Initializes the SequenceClassifier module. Args: hidden_size: the hidden size of the mlp head on the top of the encoder num_classes: number of the classes 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 idx_conditioned_on: index of the token to use as the sequence representation for the classification task, default is the first token """ super().__init__(hidden_size=hidden_size, dropout=dropout) self.log_softmax = log_softmax self._idx_conditioned_on = idx_conditioned_on self.mlp = MultiLayerPerceptron( hidden_size=hidden_size, num_classes=num_classes, 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) logits = self.mlp(hidden_states[:, self._idx_conditioned_on]) return logits