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diff --git a/Qwen2.5-Eval/evaluation/data_loader.py b/Qwen2.5-Eval/evaluation/data_loader.py
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+++ b/Qwen2.5-Eval/evaluation/data_loader.py
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+import os
+import json
+import random
+import datasets
+from datasets import load_dataset, Dataset, concatenate_datasets
+from utils import load_jsonl, lower_keys
+
+
+def load_data(data_name, split, data_dir="./data"):
+    data_file = f"{data_dir}/{data_name}/{split}.jsonl"
+    print(f"data_name: {data_name}")
+    print(f"data_file: {data_file}")
+    if os.path.exists(data_file):
+        examples = list(load_jsonl(data_file))
+    elif data_name == "deepscaler" or data_name == "deepscaler_random3p" or data_name == "deepscaler_random3p_noInstruct":
+        data_file = data_dir
+        print(data_file)
+        if os.path.exists(data_file):
+            with open(data_file, 'r', encoding='utf-8') as f:
+                examples = json.load(f)
+            # examples = list(load_jsonl(data_file))
+    # elif data_name == "deepscaler":
+    #     data_file = f"{data_dir}/{split}/{data_name}.json"
+    #     if os.path.exists(data_file):
+    #         with open(data_file, 'r', encoding='utf-8') as f:
+    #             examples = json.load(f)
+    else:
+        if data_name == "math":
+            dataset = load_dataset(
+                "competition_math",
+                split=split,
+                name="main",
+                cache_dir=f"{data_dir}/temp",
+            )
+        elif data_name == "gsm8k":
+            dataset = load_dataset(data_name, split=split)
+        elif data_name == "svamp":
+            # evaluate on training set + test set
+            dataset = load_dataset("ChilleD/SVAMP", split="train")
+            dataset = concatenate_datasets(
+                [dataset, load_dataset("ChilleD/SVAMP", split="test")]
+            )
+        elif data_name == "asdiv":
+            dataset = load_dataset("EleutherAI/asdiv", split="validation")
+            dataset = dataset.filter(
+                lambda x: ";" not in x["answer"]
+            )  # remove multi-answer examples
+        elif data_name == "mawps":
+            examples = []
+            # four sub-tasks
+            for data_name in ["singleeq", "singleop", "addsub", "multiarith"]:
+                sub_examples = list(load_jsonl(f"{data_dir}/mawps/{data_name}.jsonl"))
+                for example in sub_examples:
+                    example["type"] = data_name
+                examples.extend(sub_examples)
+            dataset = Dataset.from_list(examples)
+        elif data_name == "mmlu_stem":
+            dataset = load_dataset("hails/mmlu_no_train", "all", split="test")
+            # only keep stem subjects
+            stem_subjects = [
+                "abstract_algebra",
+                "astronomy",
+                "college_biology",
+                "college_chemistry",
+                "college_computer_science",
+                "college_mathematics",
+                "college_physics",
+                "computer_security",
+                "conceptual_physics",
+                "electrical_engineering",
+                "elementary_mathematics",
+                "high_school_biology",
+                "high_school_chemistry",
+                "high_school_computer_science",
+                "high_school_mathematics",
+                "high_school_physics",
+                "high_school_statistics",
+                "machine_learning",
+            ]
+            dataset = dataset.rename_column("subject", "type")
+            dataset = dataset.filter(lambda x: x["type"] in stem_subjects)
+        elif data_name == "carp_en":
+            dataset = load_jsonl(f"{data_dir}/carp_en/test.jsonl")
+        else:
+            raise NotImplementedError(data_name)
+
+        examples = list(dataset)
+        examples = [lower_keys(example) for example in examples]
+        dataset = Dataset.from_list(examples)
+        os.makedirs(f"{data_dir}/{data_name}", exist_ok=True)
+        dataset.to_json(data_file)
+
+    # add 'idx' in the first column
+    if "idx" not in examples[0]:
+        examples = [{"idx": i, **example} for i, example in enumerate(examples)]
+
+    # dedepulicate & sort
+    examples = sorted(examples, key=lambda x: x["idx"])
+    return examples