83f0a576015953b30bf8a05fc0033983e0d07354,official/nlp/bert_models.py,,pretrain_model,#Any#Any#Any#Any#,260

Before Change

  masked_lm_ids = tf.keras.layers.Input(
      shape=(max_predictions_per_seq,), name="masked_lm_ids", dtype=tf.int32)

  bert_submodel_name = "bert_model"
  bert_submodel = modeling.get_bert_model(
      input_word_ids,
      input_mask,
      input_type_ids,
      name=bert_submodel_name,
      config=bert_config)
  pooled_output = bert_submodel.outputs[0]
  sequence_output = bert_submodel.outputs[1]

  pretrain_layer = BertPretrainLayer(
      bert_config,
      bert_submodel.get_layer(bert_submodel_name),

After Change

  transformer_encoder = _get_transformer_encoder(bert_config, seq_length)
  if initializer is None:
    initializer = tf.keras.initializers.TruncatedNormal(
        stddev=bert_config.initializer_range)
  pretrainer_model = bert_pretrainer.BertPretrainer(
      network=transformer_encoder,
      num_classes=2,  // The next sentence prediction label has two classes.
      num_token_predictions=max_predictions_per_seq,

Instances