Update README.md

README.md

@@ -15,16 +15,16 @@ Partial data from SimXRD (the original dataset is too large to be shared on Hugg
     import mlcroissant as mlc
     url = "https://huggingface.co/datasets/caobin/SimXRDreview/raw/main/simxrd_croissant.json"
 
-    # 2. Inspect metadata
-    dataset_info = mlc.Dataset(url).metadata.to_json
-    print(dataset_info)
+# 2. Inspect metadata
+  dataset_info = mlc.Dataset(url).metadata.to_json
+  print(dataset_info)
 
-    from dataset.parse import load_dataset,bar_progress # defined in our github : https://github.com/compasszzn/XRDBench/blob/main/dataset/parse.py
-    for file_info in dataset_info['distribution']:
-        wget.download(file_info['contentUrl'], './', bar=bar_progress)
+  from dataset.parse import load_dataset,bar_progress # defined in our github : https://github.com/compasszzn/XRDBench/blob/main/dataset/parse.py
+  for file_info in dataset_info['distribution']:
+      wget.download(file_info['contentUrl'], './', bar=bar_progress)
 
-    # 3. Use Croissant dataset in your ML workload
-    train_loader = DataLoader(load_dataset(name='train.tfrecord'), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)
-    val_loader = DataLoader(load_dataset(name='val.tfrecord'), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers,drop_last=False)
-    test_loader = DataLoader(load_dataset(name='test.tfrecord'), batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers,drop_last=False)
+# 3. Use Croissant dataset in your ML workload
+  train_loader = DataLoader(load_dataset(name='train.tfrecord'), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers)
+  val_loader = DataLoader(load_dataset(name='val.tfrecord'), batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers,drop_last=False)
+  test_loader = DataLoader(load_dataset(name='test.tfrecord'), batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers,drop_last=False)
 ```