diff --git a/decanlp/train.py b/decanlp/train.py
index d765fc42..5b622cd6 100644
--- a/decanlp/train.py
+++ b/decanlp/train.py
@@ -188,52 +188,6 @@ def step(model, batch, opt, iteration, field, task, lr=None, grad_clip=None, wri
     return loss.item(), {}, grad_norm
 
 
-def create_mixed_set(args, train_sets, aux_sets, epoch):
-
-    assert len(aux_sets) == len(train_sets)
-    num_tasks = len(train_sets)
-    mixed_set = train_sets
-
-    for i in range(num_tasks):
-
-        train_set = train_sets[i]
-        aux_set = aux_sets[i]
-
-        assert aux_set.fields == train_set.fields
-
-        train_examples = train_set.examples
-        aux_examples = aux_set.examples
-
-        train_size = len(train_examples)
-        aux_size = len(aux_examples)
-
-        total_size = train_size + aux_size
-
-        if args.curriculum_strategy == 'linear':
-            next_fraction = args.curriculum_rate * epoch
-        elif args. curriculum_strategy == 'exp':
-            next_fraction = args.curriculum_rate * np.exp(epoch)
-
-        fraction = min(args.curriculum_max_frac, next_fraction)
-
-        train_size_target = int((1 - fraction) * total_size)
-        aux_size_target = int(fraction * total_size)
-        if aux_size_target > aux_size:
-            aux_size_target = aux_size
-            train_size_target = int(aux_size * (1 - fraction) / fraction)
-        elif train_size_target > train_size:
-            train_size_target = train_size
-            aux_size_target = int(train_size * fraction / (1 - fraction))
-
-        logging.info(f'at epoch {epoch} we have {train_size_target} examples from training set and {aux_size_target} examples from auxiliary training set')
-
-        train_set_indices = np.random.choice(range(train_size), size=train_size_target, replace=False)
-        aux_set_indices = np.random.choice(range(aux_size), size=aux_size_target, replace=False)
-
-        setattr(mixed_set[i], 'examples', [train_examples[i] for i in train_set_indices] + [aux_examples[i] for i in aux_set_indices])
-
-    return mixed_set
-
 def update_fraction(args, task_iteration):
 
     if args.curriculum_strategy == 'linear':