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Text embedding classifier with a neural net

Source: R/obj_TEClassifierRegular.R
TEClassifierRegular.Rd

Abstract class for neural nets with 'pytorch'.

This class is deprecated. Please use an Object of class TEClassifierSequential instead.

Value

Objects of this class are used for assigning texts to classes/categories. For the creation and training of a classifier an object of class EmbeddedText or LargeDataSetForTextEmbeddings on the one hand and a factor on the other hand are necessary.

The object of class EmbeddedText or LargeDataSetForTextEmbeddings contains the numerical text representations (text embeddings) of the raw texts generated by an object of class TextEmbeddingModel. For supporting large data sets it is recommended to use LargeDataSetForTextEmbeddings instead of EmbeddedText.

The factor contains the classes/categories for every text. Missing values (unlabeled cases) are supported and can be used for pseudo labeling.

For predictions an object of class EmbeddedText or LargeDataSetForTextEmbeddings has to be used which was created with the same TextEmbeddingModel as for training.

Note

This model requires pad_value=0. If this condition is not met the padding value is switched automatically.

See also

Other Classification: TEClassifierParallel, TEClassifierParallelPrototype, TEClassifierProtoNet, TEClassifierSequential, TEClassifierSequentialPrototype

Super classes

aifeducation::AIFEMaster -> aifeducation::AIFEBaseModel -> aifeducation::ModelsBasedOnTextEmbeddings -> aifeducation::ClassifiersBasedOnTextEmbeddings -> aifeducation::TEClassifiersBasedOnRegular -> TEClassifierRegular

Methods

Public methods

Inherited methods

Method new()

Creating a new instance of this class.

Usage

TEClassifierRegular$new()

Returns

Returns an object of class TEClassifierRegular which is ready for configuration.

Method configure()

Creating a new instance of this class.

Usage

TEClassifierRegular$configure(
  name = NULL,
  label = NULL,
  text_embeddings = NULL,
  feature_extractor = NULL,
  target_levels = NULL,
  bias = TRUE,
  dense_size = 4L,
  dense_layers = 0L,
  rec_size = 4L,
  rec_layers = 2L,
  rec_type = "GRU",
  rec_bidirectional = FALSE,
  self_attention_heads = 0L,
  intermediate_size = NULL,
  attention_type = "Fourier",
  add_pos_embedding = TRUE,
  act_fct = "ELU",
  parametrizations = "None",
  rec_dropout = 0.1,
  repeat_encoder = 1L,
  dense_dropout = 0.4,
  encoder_dropout = 0.1
)

Arguments

name

string Name of the new model. Please refer to common name conventions. Free text can be used with parameter label. If set to NULL a unique ID is generated automatically. Allowed values: any

label

string Label for the new model. Here you can use free text. Allowed values: any

text_embeddings

EmbeddedText, LargeDataSetForTextEmbeddings Object of class EmbeddedText or LargeDataSetForTextEmbeddings.

feature_extractor

TEFeatureExtractor Object of class TEFeatureExtractor which should be used in order to reduce the number of dimensions of the text embeddings. If no feature extractor should be applied set NULL.

target_levels

vector containing the levels (categories or classes) within the target data. Please note that order matters. For ordinal data please ensure that the levels are sorted correctly with later levels indicating a higher category/class. For nominal data the order does not matter.

bias

bool If TRUE a bias term is added to all layers. If FALSE no bias term is added to the layers.

dense_size

int Number of neurons for each dense layer. Allowed values: 1 <= x

dense_layers

int Number of dense layers. Allowed values: 0 <= x

rec_size

int Number of neurons for each recurrent layer. Allowed values: 1 <= x

rec_layers

int Number of recurrent layers. Allowed values: 0 <= x

rec_type

string Type of the recurrent layers. rec_type='GRU' for Gated Recurrent Unit and rec_type='LSTM' for Long Short-Term Memory. Allowed values: 'GRU', 'LSTM'

rec_bidirectional

bool If TRUE a bidirectional version of the recurrent layers is used.

self_attention_heads

int determining the number of attention heads for a self-attention layer. Only relevant if attention_type='multihead' Allowed values: 0 <= x

intermediate_size

int determining the size of the projection layer within a each transformer encoder. Allowed values: 1 <= x

attention_type

string Choose the attention type. Allowed values: 'Fourier', 'MultiHead'

add_pos_embedding

bool TRUE if positional embedding should be used.

act_fct

string Activation function for all layers. Allowed values: 'ELU', 'LeakyReLU', 'ReLU', 'GELU', 'Sigmoid', 'Tanh', 'PReLU'

parametrizations

string Re-Parametrizations of the weights of layers. Allowed values: 'None', 'OrthogonalWeights', 'WeightNorm', 'SpectralNorm'

rec_dropout

double determining the dropout between recurrent layers. Allowed values: 0 <= x <= 0.6

repeat_encoder

int determining how many times the encoder should be added to the network. Allowed values: 0 <= x

dense_dropout

double determining the dropout between dense layers. Allowed values: 0 <= x <= 0.6

encoder_dropout

double determining the dropout for the dense projection within the transformer encoder layers. Allowed values: 0 <= x <= 0.6

Returns

Returns an object of class TEClassifierRegular which is ready for training.

Method clone()

The objects of this class are cloneable with this method.

Usage

TEClassifierRegular$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.