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. input_ids: indices of tokens which constitute batches of text segments 0: AxisType(BatchTag) 1: AxisType(TimeTag) input_mask: bool tensor with 0s in place of tokens to be masked 0: AxisType(BatchTag) 1: AxisType(TimeTag) labels: indices of tokens which should be predicted from each of the corresponding tokens in input_ids; for left-to-right language modeling equals to input_ids shifted by 1 to the right 0: AxisType(BatchTag) 1: AxisType(TimeTag) """ return { # "input_ids": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "input_mask": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "labels": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), "input_ids": NeuralType(('B', 'T'), ChannelType()), "input_mask": NeuralType(('B', 'T'), ChannelType()), "labels": 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(('B', 'T', 'D'), LogitsType()), "labels": NeuralType(('B', 'T'), LabelsType()), "output_mask": NeuralType(('B', 'T'), MaskType(), optional=True), }
def input_ports(self): """Returns definitions of module input ports. """ return { # "logits": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag), 2: AxisType(ChannelTag)}), # "target_ids": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), "logits": NeuralType(('B', 'T', 'D'), LogitsType()), "target_ids": NeuralType(('B', 'T'), LabelsType()), }
def output_ports(self): """Returns definitions of module output ports. """ return { # "input_ids": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "input_type_ids": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "input_mask": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "loss_mask": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "subtokens_mask": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "punct_labels": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "capit_labels": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), "input_ids": NeuralType(('B', 'T'), ChannelType()), "input_type_ids": NeuralType(('B', 'T'), ChannelType()), "input_mask": NeuralType(('B', 'T'), ChannelType()), "loss_mask": NeuralType(('B', 'T'), ChannelType()), "subtokens_mask": NeuralType(('B', 'T'), ChannelType()), "punct_labels": NeuralType(('B', 'T'), LabelsType()), "capit_labels": NeuralType(('B', 'T'), LabelsType()), }
def output_ports(self): """Returns definitions of module output ports. input_ids: indices of tokens which constitute batches of masked 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 output_ids: indices of tokens which constitute batches of unmasked text segments output_mask: bool tensor with 0s in place of tokens to be masked labels: 0 or 1 for next sentence prediction classification """ return { "input_ids": NeuralType(('B', 'T'), ChannelType()), "input_type_ids": NeuralType(('B', 'T'), ChannelType()), "input_mask": NeuralType(('B', 'T'), ChannelType()), "output_ids": NeuralType(('B', 'T'), LabelsType()), "output_mask": NeuralType(('B', 'T'), MaskType()), "labels": NeuralType(tuple('B'), LabelsType()), }
def output_ports(self): """Returns definitions of module output ports. """ return { # "input_ids": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "input_type_ids": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "input_mask": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}), # "labels": NeuralType({0: AxisType(BatchTag)}), "input_ids": NeuralType(('B', 'T'), ChannelType()), "input_type_ids": NeuralType(('B', 'T'), ChannelType()), "input_mask": NeuralType(('B', 'T'), ChannelType()), "labels": NeuralType(tuple('B'), LabelsType()), }
def output_ports(self): """Returns definitions of module output ports. input_ids: indices of tokens which constitute batches of masked text segments input_mask: bool tensor with 0s in place of source tokens to be masked segment_ids: bool tensor with 0's and 1's to denote the text segment type tgt_ids: indices of target tokens which constitute batches of masked text segments labels_mask: bool tensor with 0s in place of label tokens to be masked labels: indices of tokens which should be predicted from each of the corresponding target tokens in tgt_ids loss_mask: bool tensor with 0s in place of label tokens to be masked used for CrossEntropyLossNM """ return { "input_ids": NeuralType(('B', 'T'), ChannelType()), "input_mask": NeuralType(('B', 'T'), ChannelType()), "segment_ids": NeuralType(('B', 'T'), ChannelType()), "tgt_ids": NeuralType(('B', 'T'), LabelsType()), "labels_mask": NeuralType(('B', 'T'), MaskType()), "labels": NeuralType(('B', 'T'), LabelsType()), "loss_mask": NeuralType(('B', 'T'), MaskType()), }
def output_ports(self): """Returns definitions of module output ports. input_ids: indices of tokens which constitute batches of masked 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: indices of tokens which constitute batches of unmasked text segments subtokens_mask: used to mask all but the first subtoken of the work, could be useful during inference punct_labels: punctuation label ids capit_labels: capit_labels label ids """ 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()), "punct_labels": NeuralType(('B', 'T'), LabelsType()), "capit_labels": NeuralType(('B', 'T'), LabelsType()), }
def input_ports(self): """Returns definitions of module input ports. logit_intent_status (float): Output of SGD model intent_status_labels (int): Intent labels logit_req_slot_status (float): Output of SGD model requested_slot_status (float): Takes value 1 if the corresponding slot is requested, 0 otherwise req_slot_mask (bool): Masks requested slots not used for the particular service logit_cat_slot_status (float): Output of SGD model categorical_slot_status (int): The status of each categorical slot in the service cat_slot_status_mask (bool): Masks categorical slots not used for the particular service logit_cat_slot_value (float): Output of SGD model categorical_slot_values (int): The index of the correct value for each categorical slot logit_noncat_slot_status (float): Output of SGD model noncategorical_slot_status (int): The status of each noncategorical slot in the service noncat_slot_status_mask (bool): masks noncategorical slots not used for the particular service logit_noncat_slot_start (float): Output of SGD model logit_noncat_slot_end (float): Output of SGD model noncategorical_slot_value_start (int): The index of the starting subword corresponding to the slot span for a non-categorical slot value noncategorical_slot_value_end (int): The index of the ending (inclusive) subword corresponding to the slot span for a non-categorical slot value """ return { "logit_intent_status": NeuralType(('B', 'T', 'C'), LogitsType()), "intent_status_labels": NeuralType(('B'), LabelsType()), "logit_req_slot_status": NeuralType(('B', 'T'), LogitsType()), "requested_slot_status": NeuralType(('B', 'T'), LabelsType()), "req_slot_mask": NeuralType(('B', 'T'), ChannelType()), "logit_cat_slot_status": NeuralType(('B', 'T', 'C'), LogitsType()), "categorical_slot_status": NeuralType(('B', 'T'), LabelsType()), "cat_slot_status_mask": NeuralType(('B', 'T'), ChannelType()), "logit_cat_slot_value": NeuralType(('B', 'T', 'C'), LogitsType()), "categorical_slot_values": NeuralType(('B', 'T'), LabelsType()), "logit_noncat_slot_status": NeuralType(('B', 'T', 'C'), LogitsType()), "noncategorical_slot_status": NeuralType(('B', 'T'), LabelsType()), "noncat_slot_status_mask": NeuralType(('B', 'T'), ChannelType()), "logit_noncat_slot_start": NeuralType(('B', 'T', 'C'), LogitsType()), "logit_noncat_slot_end": NeuralType(('B', 'T', 'C'), LogitsType()), "noncategorical_slot_value_start": NeuralType(('B', 'T'), LabelsType()), "noncategorical_slot_value_end": NeuralType(('B', 'T'), LabelsType()), }