e10b29f4ba212cb7e1c25a57c7e8b3a465631ccf,official/nlp/data/pretrain_dataloader.py,BertPretrainDataLoader,_decode,#BertPretrainDataLoader#,56

Before Change

  def _decode(self, record: tf.Tensor):
    Decodes a serialized tf.Example.
    name_to_features = {
        "input_ids":
            tf.io.FixedLenFeature([self._seq_length], tf.int64),
        "input_mask":
            tf.io.FixedLenFeature([self._seq_length], tf.int64),
        "segment_ids":
            tf.io.FixedLenFeature([self._seq_length], tf.int64),
        "masked_lm_positions":
            tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.int64),
        "masked_lm_ids":

After Change

  def _decode(self, record: tf.Tensor):
    Decodes a serialized tf.Example.
    name_to_features = {
        "input_mask":
            tf.io.FixedLenFeature([self._seq_length], tf.int64),
        "masked_lm_positions":
            tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.int64),
        "masked_lm_ids":
            tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.int64),
        "masked_lm_weights":
            tf.io.FixedLenFeature([self._max_predictions_per_seq], tf.float32),
    }
    if self._params.use_v2_feature_names:
      name_to_features.update({
          "input_word_ids": tf.io.FixedLenFeature([self._seq_length], tf.int64),
          "input_type_ids": tf.io.FixedLenFeature([self._seq_length], tf.int64),
      })
    else:
      name_to_features.update({
          "input_ids": tf.io.FixedLenFeature([self._seq_length], tf.int64),
          "segment_ids": tf.io.FixedLenFeature([self._seq_length], tf.int64),
      })
    if self._use_next_sentence_label:
      name_to_features["next_sentence_labels"] = tf.io.FixedLenFeature([1],
                                                                       tf.int64)
    if self._use_position_id:

Instances