diff --git a/model_zoo/official/recommend/ncf/src/dataset.py b/model_zoo/official/recommend/ncf/src/dataset.py
index a7d22ed0b11..f710b7ec16f 100644
--- a/model_zoo/official/recommend/ncf/src/dataset.py
+++ b/model_zoo/official/recommend/ncf/src/dataset.py
@@ -579,20 +579,13 @@ def create_dataset(test_train=True, data_dir='./dataset/', dataset='ml-1m', trai
         sampler = RandomSampler(train_pos_users.shape[0], num_neg, batch_size)
         if rank_id is not None and rank_size is not None:
             sampler = DistributedSamplerOfTrain(train_pos_users.shape[0], num_neg, batch_size, rank_id, rank_size)
-        if dataset == 'ml-20m':
-            ds = GeneratorDataset(dataset,
-                                  column_names=[movielens.USER_COLUMN,
-                                                movielens.ITEM_COLUMN,
-                                                "labels",
-                                                rconst.VALID_POINT_MASK],
-                                  sampler=sampler, num_parallel_workers=32, python_multiprocessing=False)
-        else:
-            ds = GeneratorDataset(dataset,
-                                  column_names=[movielens.USER_COLUMN,
-                                                movielens.ITEM_COLUMN,
-                                                "labels",
-                                                rconst.VALID_POINT_MASK],
-                                  sampler=sampler)
+
+        ds = GeneratorDataset(dataset,
+                              column_names=[movielens.USER_COLUMN,
+                                            movielens.ITEM_COLUMN,
+                                            "labels",
+                                            rconst.VALID_POINT_MASK],
+                              sampler=sampler)
 
     else:
         eval_batch_size = parse_eval_batch_size(eval_batch_size=eval_batch_size)