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)
