diff --git a/examples/src/main/java/ai/djl/examples/inference/timeseries/M5ForecastingDeepAR.java b/examples/src/main/java/ai/djl/examples/inference/timeseries/M5ForecastingDeepAR.java
index 6f785a0c8f9..7a627cf2231 100644
--- a/examples/src/main/java/ai/djl/examples/inference/timeseries/M5ForecastingDeepAR.java
+++ b/examples/src/main/java/ai/djl/examples/inference/timeseries/M5ForecastingDeepAR.java
@@ -51,7 +51,6 @@
 import java.nio.FloatBuffer;
 import java.nio.charset.StandardCharsets;
 import java.nio.file.Path;
-import java.nio.file.Paths;
 import java.time.LocalDateTime;
 import java.util.ArrayList;
 import java.util.Arrays;
@@ -77,47 +76,29 @@ public static Map<String, Float> predict()
             throws IOException, TranslateException, ModelException {
         NDManager manager = NDManager.newBaseManager(null, "MXNet");
 
-        // If users want to use local repository, then the dataset can be loaded as follows
-//         Repository repository = Repository.newInstance("local_dataset",
-//         Paths.get("root/m5-forecasting-accuracy"));
-//         M5Dataset dataset = M5Dataset.builder()
-//                  .optRepository(repository)
-//                 .build();
-
+        // To use local dataset, users can load data as follows
+        // Repository repository = Repository.newInstance("local_dataset",
+        // Paths.get("rootPath/m5-forecasting-accuracy"));
+        // Then set `Builder.optRepository(repository)`
         M5Dataset dataset = M5Dataset.builder().setManager(manager).build();
 
-        // If users want to use local model, do the following:
-            Path modelPath = Paths.get("/Users/fenkexin/Downloads/m5forecast.zip");
-            int predictionLength = 4;
-            Criteria<TimeSeriesData, Forecast> criteria =
-                    Criteria.builder()
-                            .setTypes(TimeSeriesData.class, Forecast.class)
-                            .optModelPath(modelPath)
-                            .optEngine("MXNet")
-                            .optTranslatorFactory(new DeferredTranslatorFactory())
-                            .optArgument("prediction_length", predictionLength)
-                            .optArgument("freq", "D")
-                            .optArgument("use_feat_dynamic_real", "false")
-                            .optArgument("use_feat_static_cat", "false")
-                            .optArgument("use_feat_static_real", "false")
-                            .optProgress(new ProgressBar())
-                            .build();
-
-//        String modelUrl = "djl://ai.djl.mxnet/deepar/0.0.1/m5forecast";
-//        int predictionLength = 4;
-//        Criteria<TimeSeriesData, Forecast> criteria =
-//                Criteria.builder()
-//                        .setTypes(TimeSeriesData.class, Forecast.class)
-//                        .optModelUrls(modelUrl)
-//                        .optEngine("MXNet")
-//                        .optTranslatorFactory(new DeferredTranslatorFactory())
-//                        .optArgument("prediction_length", predictionLength)
-//                        .optArgument("freq", "W")
-//                        .optArgument("use_feat_dynamic_real", "false")
-//                        .optArgument("use_feat_static_cat", "false")
-//                        .optArgument("use_feat_static_real", "false")
-//                        .optProgress(new ProgressBar())
-//                        .build();
+        // The modelUrl can be replaced by local model path. E.g.,
+        // String modelUrl = "rootPath/m5forecast.zip";
+        String modelUrl = "djl://ai.djl.mxnet/deepar/0.0.1/m5forecast";
+        int predictionLength = 4;
+        Criteria<TimeSeriesData, Forecast> criteria =
+                Criteria.builder()
+                        .setTypes(TimeSeriesData.class, Forecast.class)
+                        .optModelUrls(modelUrl)
+                        .optEngine("MXNet")
+                        .optTranslatorFactory(new DeferredTranslatorFactory())
+                        .optArgument("prediction_length", predictionLength)
+                        .optArgument("freq", "W")
+                        .optArgument("use_feat_dynamic_real", "false")
+                        .optArgument("use_feat_static_cat", "false")
+                        .optArgument("use_feat_static_real", "false")
+                        .optProgress(new ProgressBar())
+                        .build();
 
         try (ZooModel<TimeSeriesData, Forecast> model = criteria.loadModel();
                 Predictor<TimeSeriesData, Forecast> predictor = model.newPredictor()) {
diff --git a/examples/src/main/java/ai/djl/examples/training/transferlearning/TransferFreshFruit.java b/examples/src/main/java/ai/djl/examples/training/transferlearning/TransferFreshFruit.java
index 1ce78ae06a7..e337dea1a52 100644
--- a/examples/src/main/java/ai/djl/examples/training/transferlearning/TransferFreshFruit.java
+++ b/examples/src/main/java/ai/djl/examples/training/transferlearning/TransferFreshFruit.java
@@ -131,12 +131,10 @@ private static RandomAccessDataset getData(Dataset.Usage usage, int batchSize)
         float[] mean = {0.485f, 0.456f, 0.406f};
         float[] std = {0.229f, 0.224f, 0.225f};
 
-        // If users want to use local repository, then the dataset can be loaded as follows
-        // Repository repository = Repository.newInstance("banana", Paths.get("local_data_root/banana/train"));
-        // FruitsFreshAndRotten dataset =
-        //        FruitsFreshAndRotten.builder()
-        //                            .optRepository(repository)
-        //                            ...
+        // To use local dataset, users can load it as follows
+        // Repository repository = Repository.newInstance("banana",
+        // Paths.get("local_data_root/banana/train"));
+        // Then set `Builder.optRepository(repository)`
         FruitsFreshAndRotten dataset =
                 FruitsFreshAndRotten.builder()
                         .optUsage(usage)