164dd4dd3df73af90931d0dcc6ccab94956adc32,src/models.py,HeadlessPairAttnEncoder,__init__,#HeadlessPairAttnEncoder#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,664

Before Change

                                                                                encoding_dim))
        //if text_field_embedder.get_output_dim() != phrase_layer.get_input_dim():
        if (cove_layer is None and text_field_embedder.get_output_dim() != phrase_layer.get_input_dim()) \
                or (cove_layer is not None and text_field_embedder.get_output_dim() + 600 != phrase_layer.get_input_dim()):

            raise ConfigurationError("The output dimension of the "
                                     "text_field_embedder (embedding_dim + "

After Change

                 initializer=InitializerApplicator(), regularizer=None):
        super(HeadlessPairAttnEncoder, self).__init__(vocab)//, regularizer)

        if text_field_embedder is None: // just using ELMo embeddings
            self._text_field_embedder = lambda x: x
            d_emb = 0
            self._highway_layer = lambda x: x
        else:
            self._text_field_embedder = text_field_embedder
            d_emb = text_field_embedder.get_output_dim()
            self._highway_layer = TimeDistributed(Highway(d_emb, num_highway_layers))

        self._phrase_layer = phrase_layer
        self._matrix_attention = MatrixAttention(attention_similarity_function)
        self._modeling_layer = modeling_layer
        self._cove = cove_layer

Instances