Update README.md

README.md

@@ -85,23 +85,35 @@ Each sample contains:
 | `test_section3` | 2300 – 3449 | 1,150 |
 ## Usage
 ```python
-from datasets import load_dataset
-ds = load_dataset("fansel/nordland")
-# Access a specific split
-train = ds["train"]
-test = ds["test"]
-# Filter by season
-winter_test = ds["test"].filter(lambda x: x["season"] == "winter")
-# Filter train section 1 (all seasons)
-train_section1 = ds["train"].filter(lambda x: x["section"] == "train_section1")
-# Filter test section 2 for a specific season
-test_s2_fall = ds["test"].filter(lambda x: x["section"] == "test_section2" and x["season"] == "fall")
-# Cross-season matching: get the same location in all four seasons
-frame = 42
-same_location = ds["test"].filter(lambda x: x["frame_id"] == frame)
-for sample in same_location:
-    print(sample["season"], sample["section"])  # e.g. "fall", "test_section1"
-```
+from datasets import load_dataset, Value
+
+  ds = load_dataset("fansel/nordland")
+                                                                                                                                                               
+  # season and section are stored as ClassLabel 
+  # Cast them to plain strings once for natural filtering:                                                                                                     
+  ds = (ds                                                                                                                                                     
+        .cast_column("season",  Value("string"))                                                                                                               
+        .cast_column("section", Value("string")))                                                                                                              
+                                                                                                                                                               
+  # Access splits
+  train, test = ds["train"], ds["test"]                                                                                                                        
+                                                            
+  # Filter by season
+  winter_test = test.filter(lambda x: x["season"] == "winter")                                                                                                 
+                                                                                                                                                               
+  # Filter train section 1, all seasons                                                                                                                        
+  train_section1 = train.filter(lambda x: x["section"] == "train_section1")                                                                                    
+                                                                                                                                                               
+  # Filter test section 2, season fall
+  test_s2_fall = test.filter(                                                                                                                                  
+      lambda x: x["section"] == "test_section2" and x["season"] == "fall"
+  )                                                                                                                                                            
+                                                            
+  # Cross-season matching: same frame_id across seasons
+  same_location = test.filter(lambda x: x["frame_id"] == 42)                                                                                                   
+  for sample in same_location:                                                                                                                                 
+      print(sample["season"], sample["section"])                                                                                                               
+  ```                                   
 ## Citation
 Please cite this publication if you use this partitioned version of the dataset.
 ```bibtex