def __call__(self, word_embeddings, segment_ids=None, column_ids=None, row_ids=None,
prev_label_ids=None, column_ranks=None, inv_column_ranks=None,
numeric_relations=None, ** kwargs):
inputs = tf_utils.pack_inputs([word_embeddings, segment_ids, column_ids,
row_ids, prev_label_ids, column_ranks,
inv_column_ranks, numeric_relations])
return super(EmbeddingPostprocessor, self).__call__(inputs, **kwargs)
def __call__(self, input_word_ids, input_mask=None, segment_ids=None,
column_ids=None, row_ids=None, prev_label_ids=None, column_ranks=None,
inv_column_ranks=None, numeric_relations=None, ** kwargs):
inputs = tf_utils.pack_inputs(
[input_word_ids, input_mask, segment_ids, column_ids,
row_ids, prev_label_ids, column_ranks,
inv_column_ranks, numeric_relations])
return super(BertModel, self).__call__(inputs, **kwargs)
def __call__(self, input_tensor, attention_mask=None, **kwargs):
inputs = tf_utils.pack_inputs([input_tensor, attention_mask])
return super(Transformer, self).__call__(inputs=inputs, **kwargs)
def __call__(self, from_tensor, to_tensor, attention_mask=None, **kwargs):
inputs = tf_utils.pack_inputs([from_tensor, to_tensor, attention_mask])
return super(Attention, self).__call__(inputs, **kwargs)
