Skip default filtering if dataset transformations are configured
@@ -621,6 +621,7 @@ def training_pipeline(
if transform_fns is not None:
for transform_fn in transform_fns:
dataset = dataset.apply(transform_fn)
+ else:
dataset = dataset.apply(
filter_examples_by_length(
maximum_features_length=maximum_features_length,
