simnet

class SimNet(network, vocab_size, num_classes, emb_dim=128, pad_token_id=0)

Bases: paddle.fluid.dygraph.layers.Layer

forward(query, title, query_seq_len=None, title_seq_len=None)

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments

class BoWModel(vocab_size, num_classes, emb_dim=128, padding_idx=0, fc_hidden_size=128)

Bases: paddle.fluid.dygraph.layers.Layer

This class implements the Bag of Words Classification Network model to classify texts. At a high level, the model starts by embedding the tokens and running them through a word embedding. Then, we encode these epresentations with a BoWEncoder. Lastly, we take the output of the encoder to create a final representation, which is passed through some feed-forward layers to output a logits (output_layer). :param vocab_size (obj: int): The vocabulary size. :param emb_dim (obj: int, optional, defaults to 128): The embedding dimension. :param padding_idx (obj: int, optinal, defaults to 0) : The pad token index. :param hidden_size (obj: int, optional, defaults to 128): The first full-connected layer hidden size. :param fc_hidden_size (obj: int, optional, defaults to 96): The second full-connected layer hidden size. :param num_classes (obj: int): All the labels that the data has.

forward(query, title, query_seq_len=None, title_seq_len=None)

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments

class LSTMModel(vocab_size, num_classes, emb_dim=128, padding_idx=0, lstm_hidden_size=128, direction='forward', lstm_layers=1, dropout_rate=0.0, pooling_type=None, fc_hidden_size=128)

Bases: paddle.fluid.dygraph.layers.Layer

forward(query, title, query_seq_len, title_seq_len)

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments

class GRUModel(vocab_size, num_classes, emb_dim=128, padding_idx=0, gru_hidden_size=128, direction='forward', gru_layers=1, dropout_rate=0.0, pooling_type=None, fc_hidden_size=96)

Bases: paddle.fluid.dygraph.layers.Layer

forward(query, title, query_seq_len, title_seq_len)

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments

class CNNModel(vocab_size, num_classes, emb_dim=128, padding_idx=0, num_filter=256, ngram_filter_sizes=(3), fc_hidden_size=128)

Bases: paddle.fluid.dygraph.layers.Layer

This class implements the

Convolution Neural Network model.

At a high level, the model starts by embedding the tokens and running them through a word embedding. Then, we encode these epresentations with a CNNEncoder. The CNN has one convolution layer for each ngram filter size. Each convolution operation gives out a vector of size num_filter. The number of times a convolution layer will be used is num_tokens - ngram_size + 1. The corresponding maxpooling layer aggregates all these outputs from the convolution layer and outputs the max. Lastly, we take the output of the encoder to create a final representation, which is passed through some feed-forward layers to output a logits (output_layer). :param vocab_size (obj: int): The vocabulary size. :param emb_dim (obj: int, optional, defaults to 128): The embedding dimension. :param padding_idx (obj: int, optinal, defaults to 0) : The pad token index. :param num_classes (obj: int): All the labels that the data has.

forward(query, title, query_seq_len=None, title_seq_len=None)

Defines the computation performed at every call. Should be overridden by all subclasses.

Parameters

• *inputs (tuple) – unpacked tuple arguments

• **kwargs (dict) – unpacked dict arguments