[Feature] Support MMLU-CF Benchmark

opencompass/datasets/mmlu_cf.py

@@ -10,34 +10,28 @@ def load(path: str, name: str):
         """
         Loading HuggingFace datasets
         """
-        try:
-            # Use HuggingFace's load_dataset method to load the dataset
-            hf_dataset = load_dataset("microsoft/MMLU-CF")
-            columns_to_keep = ["Question", "A", "B", "C", "D", "Answer"]
-            hf_dataset = hf_dataset.map(lambda x: {key: x[key] for key in columns_to_keep})
-            splits = ['dev', 'val']
+        # Use HuggingFace's load_dataset method to load the dataset
+        hf_dataset = load_dataset("microsoft/MMLU-CF")
+        columns_to_keep = ["Question", "A", "B", "C", "D", "Answer"]
+        hf_dataset = hf_dataset.map(lambda x: {key: x[key] for key in columns_to_keep})
+        splits = ['dev', 'val']
 
-            for split in splits:
-                sub_set = f'{name}_{split}'
-
-                # Rename fields here if they don't match the expected names
-                hf_dataset[sub_set] = hf_dataset[sub_set].map(lambda example: {
-                    "input": example["Question"],
-                    "A": example["A"],
-                    "B": example["B"],
-                    "C": example["C"],
-                    "D": example["D"],
-                    "target": example["Answer"]
-                })
-
-            # Create a DatasetDict and return it
-            dataset = DatasetDict({
-                "dev": hf_dataset[f'{name}_{splits[0]}'],
-                "test": hf_dataset[f'{name}_{splits[1]}']  # Use 'val' as 'test'
+        for split in splits:
+            sub_set = f'{name}_{split}'
+
+            # Rename fields here if they don't match the expected names
+            hf_dataset[sub_set] = hf_dataset[sub_set].map(lambda example: {
+                "input": example["Question"],
+                "A": example["A"],
+                "B": example["B"],
+                "C": example["C"],
+                "D": example["D"],
+                "target": example["Answer"]
             })
-            return dataset
 
-        except Exception as e:
-            print(f"Failed to load the dataset from HuggingFace: {e}")
-            print("Please check if the dataset exists and if the network connection is stable.")
-            raise
+        # Create a DatasetDict and return it
+        dataset = DatasetDict({
+            "dev": hf_dataset[f'{name}_{splits[0]}'],
+            "test": hf_dataset[f'{name}_{splits[1]}']  # Use 'val' as 'test'
+        })
+        return dataset