Replace None by null in configuration reference
@@ -34,7 +34,7 @@ params:
# (optional) Global parameter initialization [-param_init, param_init].
param_init: 0.1
- # (optional) Maximum gradients norm (default: None).
+ # (optional) Maximum gradients norm (default: null).
clip_gradients: 5.0
# (optional) 1 training step will process this many batches and accumulates
# their gradients (default: 1).
@@ -57,7 +57,7 @@ params:
# (optional) Average loss in the time dimension in addition to the batch dimension (default: False).
average_loss_in_time: false
- # (optional) The type of learning rate decay (default: None). See:
+ # (optional) The type of learning rate decay (default: null). See:
# * https://www.tensorflow.org/versions/master/api_guides/python/train#Decaying_the_learning_rate
# * opennmt/utils/decay.py
# This value may change the semantics of other decay options. See the documentation or the code.
@@ -119,7 +119,7 @@ train:
# (optional) Batch size is the number of "examples" or "tokens" (default: "examples").
batch_type: examples
# (optional) Tune gradient accumulation to train with at least this effective batch size
- # (default: None).
+ # (default: null).
effective_batch_size: 25000
# (optional) Save a checkpoint every this many steps.
@@ -135,9 +135,9 @@ train:
# (optional) If true, makes a single pass over the training data (default: false).
single_pass: false
- # (optional) The maximum length of feature sequences during training (default: None).
+ # (optional) The maximum length of feature sequences during training (default: null).
maximum_features_length: 70
- # (optional) The maximum length of label sequences during training (default: None).
+ # (optional) The maximum length of label sequences during training (default: null).
maximum_labels_length: 70
# (optional) The width of the length buckets to select batch candidates from (default: 5).
