def _set_placeholders(self, target, on_export=False, **kwargs): self.placeholders = { 'input_ids': utils.get_placeholder(target, 'input_ids', [None, self.max_seq_length], tf.int32), 'input_mask': utils.get_placeholder(target, 'input_mask', [None, self.max_seq_length], tf.int32), 'segment_ids': utils.get_placeholder(target, 'segment_ids', [None, self.max_seq_length], tf.int32), 'label_ids': utils.get_placeholder(target, 'label_ids', [None], tf.int32), } if kwargs.get('is_training'): self.placeholders['aug_input_ids'] = utils.get_placeholder( target, 'aug_input_ids', [None, self.max_seq_length], tf.int32) self.placeholders['aug_input_mask'] = utils.get_placeholder( target, 'aug_input_mask', [None, self.max_seq_length], tf.int32) self.placeholders['aug_segment_ids'] = utils.get_placeholder( target, 'aug_segment_ids', [None, self.max_seq_length], tf.int32) self.placeholders['is_supervised'] = utils.get_placeholder( target, 'is_supervised', [None], tf.float32) if not on_export: self.placeholders['sample_weight'] = \ utils.get_placeholder( target, 'sample_weight', [None], tf.float32)
def _set_placeholders(self, target, on_export=False): self.placeholders = { 'input_ids': utils.get_placeholder(target, 'input_ids', [None, self.max_seq_length], tf.int32), } if not on_export: self.placeholders['sample_weight'] = \ utils.get_placeholder( target, 'sample_weight', [None], tf.float32)
def _set_placeholders(self, target, on_export=False): self.placeholders = { 'input_ids': utils.get_placeholder(target, 'input_ids', [None, self.max_seq_length], tf.int32), 'input_mask': utils.get_placeholder(target, 'input_mask', [None, self.max_seq_length], tf.int32), 'segment_ids': utils.get_placeholder(target, 'segment_ids', [None, self.max_seq_length], tf.int32), 'masked_lm_positions': utils.get_placeholder(target, 'masked_lm_positions', [None, self._max_predictions_per_seq], tf.int32), 'masked_lm_ids': utils.get_placeholder(target, 'masked_lm_ids', [None, self._max_predictions_per_seq], tf.int32), 'masked_lm_weights': utils.get_placeholder(target, 'masked_lm_weights', [None, self._max_predictions_per_seq], tf.float32), 'sentence_order_labels': utils.get_placeholder(target, 'sentence_order_labels', [None], tf.int32), } if not on_export: self.placeholders['sample_weight'] = \ utils.get_placeholder( target, 'sample_weight', [None], tf.float32)
def _set_placeholders(self, target, on_export=False, **kwargs): self.placeholders = { 'dilated_ids': utils.get_placeholder(target, 'dilated_ids', [None, self.max_seq_length * 2], tf.int32), 'label_ids': utils.get_placeholder(target, 'label_ids', [None, self.max_seq_length * 2], tf.int32), } if not on_export: self.placeholders['sample_weight'] = \ utils.get_placeholder( target, 'sample_weight', [None], tf.float32)
def _set_placeholders(self, target, on_export=False, **kwargs): self.placeholders = { 'source_ids': utils.get_placeholder(target, 'source_ids', [None, self.source_max_seq_length], tf.int32), 'target_ids': utils.get_placeholder(target, 'target_ids', [None, self.target_max_seq_length], tf.int32), } if not on_export: self.placeholders['sample_weight'] = \ utils.get_placeholder( target, 'sample_weight', [None], tf.float32)
def _set_placeholders(self, target, on_export=False): self.placeholders = { 'input_ids': utils.get_placeholder( target, 'input_ids', [None, self.max_seq_length], tf.int32), 'input_mask': utils.get_placeholder( target, 'input_mask', [None, self.max_seq_length], tf.int32), 'query_mask': utils.get_placeholder( target, 'query_mask', [None, self.max_seq_length], tf.int32), 'segment_ids': utils.get_placeholder( target, 'segment_ids', [None, self.max_seq_length], tf.int32), 'label_ids': utils.get_placeholder( target, 'label_ids', [None, 2], tf.int32), 'has_answer': utils.get_placeholder( target, 'has_answer', [None], tf.int32), } if not on_export: self.placeholders['sample_weight'] = utils.get_placeholder( target, 'sample_weight', [None], tf.float32)
def _set_placeholders(self, target, on_export=False, **kwargs): self.placeholders = { 'input_ids': utils.get_placeholder( target, 'input_ids', [None, self.max_seq_length], tf.int32), 'input_mask': utils.get_placeholder( target, 'input_mask', [None, self.max_seq_length], tf.int32), 'segment_ids': utils.get_placeholder( target, 'segment_ids', [None, self.max_seq_length], tf.int32), 'n_wide_features': utils.get_placeholder( target, 'n_wide_features', [None], tf.int32), 'wide_features': utils.get_placeholder( target, 'wide_features', [None, len(self.wide_features)], tf.int32), 'label_ids': utils.get_placeholder( target, 'label_ids', [None], tf.int32), } if not on_export: self.placeholders['sample_weight'] = \ utils.get_placeholder( target, 'sample_weight', [None], tf.float32)
def _set_placeholders(self, target, on_export=False, **kwargs): self.placeholders = { 'input_values': utils.get_placeholder( target, 'input_values', [None, self.max_seq_length - 1, self.max_unit_length], tf.float32), 'input_mask': utils.get_placeholder(target, 'input_mask', [None, self.max_seq_length], tf.int32), 'label_ids': utils.get_placeholder(target, 'label_ids', [None], tf.int32), } if not on_export: self.placeholders['sample_weight'] = \ utils.get_placeholder( target, 'sample_weight', [None], tf.float32)
def _set_placeholders(self, target, on_export=False, **kwargs): self.placeholders = { 'input': utils.get_placeholder(target, 'input', [None, self.max_seq_length], tf.int32), 'target': utils.get_placeholder(target, 'target', [None, self.max_seq_length], tf.int32), 'seg_id': utils.get_placeholder(target, 'seg_id', [None, self.max_seq_length], tf.int32), 'label': utils.get_placeholder(target, 'label', [None], tf.int32), 'is_masked': utils.get_placeholder(target, 'is_masked', [None, self.max_seq_length], tf.int32), } if not on_export: self.placeholders['sample_weight'] = \ utils.get_placeholder( target, 'sample_weight', [None], tf.float32)