sentiment_analysis_model¶
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class
BoWModel(vocab_size, num_classes, emb_dim=128, padding_idx=0, hidden_size=128, fc_hidden_size=96)[source]¶ 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 representations 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: The vocab size that used to create the embedding. :type vocab_size: int :param num_class: The num class of the classifier. :type num_class: int :param emb_dim: The size of the embedding, default value is 128. :type emb_dim: int. optional :param padding_idx: The padding value in the embedding, the padding_idx of embedding value willnot be updated, the default value is 0.
- Parameters
hidden_size (int, optional) – The output size of linear that after the bow, default value is 128.
fc_hidden_size (int, optional) – The output size of linear that after the first linear, default value is 96.
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class
LSTMModel(vocab_size, num_classes, emb_dim=128, padding_idx=0, lstm_hidden_size=198, direction='forward', lstm_layers=1, dropout_rate=0.0, pooling_type=None, fc_hidden_size=96)[source]¶ 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 representations 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: The vocab size that used to create the embedding. :type vocab_size: int :param num_class: The num class of the classifier. :type num_class: int :param emb_dim: The size of the embedding, default value is 128. :type emb_dim: int. optional :param padding_idx: The padding value in the embedding, the padding_idx of embedding value willnot be updated, the default value is 0.
- Parameters
lstm_hidden_size (int, optional) – The output size of the lstm, default value 198.
direction (string, optional) – The direction of lstm, default value is
forward.lstm_layers (string, optional) – The num of lstm layer.
dropout (float, optional) – The dropout rate of lstm.
pooling_type (float, optional) – The pooling type of lstm. Default value is None, if
pooling_typeis None, then the LSTMEncoder will return the hidden state of the last time step at last layer as a single vector.