def output_ports(self): """Returns definitions of module output ports. input_ids: indices of tokens which constitute batches of text segments input_type_ids: tensor with 0's and 1's to denote the text segment type input_mask: bool tensor with 0s in place of tokens to be masked loss_mask: used to mask and ignore tokens in the loss function subtokens_mask: used to ignore the outputs of unwanted tokens in the inference and evaluation like the start and end tokens intents: intents labels slots: slots labels """ return { "input_ids": NeuralType(('B', 'T'), ChannelType()), "input_type_ids": NeuralType(('B', 'T'), ChannelType()), "input_mask": NeuralType(('B', 'T'), ChannelType()), "loss_mask": NeuralType(('B', 'T'), MaskType()), "subtokens_mask": NeuralType(('B', 'T'), ChannelType()), "intents": NeuralType(tuple('B'), LabelsType()), "slots": NeuralType(('B', 'T'), LabelsType()), }
def output_ports(self): """Returns definitions of module output ports. src_ids: indices of tokens which correspond to source sentences src_mask: bool tensor with 0s in place of source tokens to be masked tgt_ids: indices of tokens which correspond to target sentences tgt_mask: bool tensor with 0s in place of target tokens to be masked labels: indices of tokens which should be predicted from each of the corresponding target tokens in tgt_ids; for standard neural machine translation equals to tgt_ids shifted by 1 to the right sent_ids: indices of the sentences in a batch; important for evaluation with external metrics, such as SacreBLEU """ return { # "src_ids": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "src_mask": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "tgt_ids": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "tgt_mask": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "labels": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "sent_ids": NeuralType({0: AxisType(BatchTag)}), "src_ids": NeuralType(('B', 'T'), ChannelType()), "src_mask": NeuralType(('B', 'T'), ChannelType()), "tgt_ids": NeuralType(('B', 'T'), ChannelType()), "tgt_mask": NeuralType(('B', 'T'), ChannelType()), "labels": NeuralType(('B', 'T'), LabelsType()), "sent_ids": NeuralType(tuple('B'), ChannelType()), }
def input_ports(self): """Returns definitions of module input ports. """ return { # "logits": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag), 2: AxisType(ChannelTag)}), # "start_positions": NeuralType({0: AxisType(BatchTag)}), # "end_positions": NeuralType({0: AxisType(BatchTag)}), "logits": NeuralType(('B', 'T', 'D'), LogitsType()), "start_positions": NeuralType(tuple('B'), ChannelType()), "end_positions": NeuralType(tuple('B'), ChannelType()), }
def input_ports(self): """Returns definitions of module input ports. """ return { # "intent_logits": NeuralType({0: AxisType(BatchTag), 1: AxisType(ChannelTag)}), # "slot_logits": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag), 2: AxisType(ChannelTag)}), # "loss_mask": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "intents": NeuralType({0: AxisType(BatchTag)}), # "slots": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), "intent_logits": NeuralType(('B', 'D'), LogitsType()), "slot_logits": NeuralType(('B', 'T', 'D'), LogitsType()), "loss_mask": NeuralType(('B', 'T'), ChannelType()), "intents": NeuralType(tuple('B'), ChannelType()), "slots": NeuralType(('B', 'T'), ChannelType()), }