Update EMT.py

EMT.py

@@ -253,15 +253,10 @@ class EMT(datasets.GeneratorBasedBuilder):
     
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
-            name="train",
+            name="emt",
             description="Training split of the EMT dataset",
             version=datasets.Version("1.0.0"),
         ),
-        datasets.BuilderConfig(
-            name="test",
-            description="Test split of the EMT dataset",
-            version=datasets.Version("1.0.0"),
-        ),
     ]
 
 
@@ -326,11 +321,9 @@ class EMT(datasets.GeneratorBasedBuilder):
     #         ),
     #     ]
     def _split_generators(self, dl_manager):
-        """Download (if not cached) and prepare only the requested dataset split."""
-    
-        # Get the requested split
-        requested_split = self.config.name
-    
+        """Download (if not cached) and prepare dataset splits."""
+        
+        # Define dataset URLs
         image_urls = {
             "train": _TRAIN_IMAGE_ARCHIVE_URL,
             "test": _TEST_IMAGE_ARCHIVE_URL,
@@ -340,28 +333,36 @@ class EMT(datasets.GeneratorBasedBuilder):
             "train": _TRAIN_ANNOTATION_ARCHIVE_URL,
             "test": _TEST_ANNOTATION_ARCHIVE_URL,
         }
+        
+        # Extract all data (both splits)
+        extracted_images = dl_manager.download_and_extract(image_urls)
+        extracted_annotations = dl_manager.download_and_extract(annotation_urls)
+        
+        # Define paths
+        train_annotation_path = os.path.join(extracted_annotations["train"], "annotations", "train")
+        test_annotation_path = os.path.join(extracted_annotations["test"], "annotations", "test")
     
-        # Validate split name
-        if requested_split not in image_urls:
-            raise ValueError(f"Invalid split '{requested_split}'. Available splits: 'train', 'test'.")
-    
-        # Extract only the requested split
-        extracted_images = dl_manager.download_and_extract({requested_split: image_urls[requested_split]})
-        extracted_annotations = dl_manager.download_and_extract({requested_split: annotation_urls[requested_split]})
-    
-        # Define paths based on the requested split
-        annotation_path = os.path.join(extracted_annotations[requested_split], "annotations", requested_split)
-        image_path = extracted_images[requested_split]
+        train_image_path = extracted_images["train"]
+        test_image_path = extracted_images["test"]
     
+        # Return available splits (Hugging Face will filter based on user request)
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN if requested_split == "train" else datasets.Split.TEST,
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "image_dir": train_image_path,
+                    "annotation_path": train_annotation_path,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
                 gen_kwargs={
-                    "image_dir": image_path,
-                    "annotation_path": annotation_path,
+                    "image_dir": test_image_path,
+                    "annotation_path": test_annotation_path,
                 },
-            )
+            ),
         ]
+
     
 
     def _generate_examples(self, images, annotation_path):