diff --git a/opencompass/configs/datasets/SciKnowEval/SciKnowEval_gen.py b/opencompass/configs/datasets/SciKnowEval/SciKnowEval_gen.py
index 5e9c2cab..f697077d 100644
--- a/opencompass/configs/datasets/SciKnowEval/SciKnowEval_gen.py
+++ b/opencompass/configs/datasets/SciKnowEval/SciKnowEval_gen.py
@@ -47,7 +47,7 @@ eval_cfg = dict(
 sciknoweval_dataset_biology = dict(
     type=SciKnowEvalDataset,
     abbr='sciknoweval_biology',
-    path='/root/workspace/opencompass/opencompass/configs/datasets/SciKnowEval/SciKnowEval/data/Biology',
+    path='hicai-zju/SciKnowEval',
     prompt_mode='zero-shot',
     reader_cfg=reader_cfg,
     infer_cfg=infer_cfg,
@@ -57,7 +57,7 @@ sciknoweval_dataset_biology = dict(
 sciknoweval_dataset_chemistry = dict(
     type=SciKnowEvalDataset,
     abbr='sciknoweval_chemistry',
-    path='/root/workspace/opencompass/opencompass/configs/datasets/SciKnowEval/SciKnowEval/data/Chemistry',
+    path='hicai-zju/SciKnowEval',
     prompt_mode='zero-shot',
     reader_cfg=reader_cfg,
     infer_cfg=infer_cfg,
@@ -66,7 +66,7 @@ sciknoweval_dataset_chemistry = dict(
 sciknoweval_dataset_material = dict(
     type=SciKnowEvalDataset,
     abbr='sciknoweval_material',
-    path='/root/workspace/opencompass/opencompass/configs/datasets/SciKnowEval/SciKnowEval/data/Material',
+    path='hicai-zju/SciKnowEval',
     prompt_mode='zero-shot',
     reader_cfg=reader_cfg,
     infer_cfg=infer_cfg,
@@ -76,7 +76,7 @@ sciknoweval_dataset_material = dict(
 sciknoweval_dataset_physics = dict(
     type=SciKnowEvalDataset,
     abbr='sciknoweval_physics',
-    path='/root/workspace/opencompass/opencompass/configs/datasets/SciKnowEval/SciKnowEval/data/Physics',
+    path='hicai-zju/SciKnowEval',
     prompt_mode='zero-shot',
     reader_cfg=reader_cfg,
     infer_cfg=infer_cfg,
diff --git a/opencompass/datasets/SciKnowEval.py b/opencompass/datasets/SciKnowEval.py
index 984dee5a..3f848723 100644
--- a/opencompass/datasets/SciKnowEval.py
+++ b/opencompass/datasets/SciKnowEval.py
@@ -108,124 +108,3 @@ def answer_cleansing(
 
     return prediction[0]
 
-
-def _generic_llmjudge_postprocess(judgement: str):
-    match = re.search(r'(A|B)', judgement)
-    grade_letter = (match.group(0) if match else 'B'
-                    )  # Default to "INCORRECT" if no match
-    return grade_letter
-
-
-def SciKnowEval_llmjudge_postprocess(
-    output: dict,
-    output_path: str,
-    dataset: Dataset,
-) -> dict:
-    # Get the original dataset
-    original_dataset = dataset.reader.dataset['test']
-
-    judged_answers = []
-    original_responses = []
-    references = []
-    details = []
-
-    # Initialize statistics dictionaries
-    stats = {'medical_task': {}, 'body_system': {}, 'question_type': {}}
-
-    total_correct = 0
-    total_count = 0
-
-    # Process each sample
-    for k, v in output.items():
-        idx = int(k)  # Convert key to integer for indexing
-        original_responses.append(v['prediction'])
-        processed_judge = _generic_llmjudge_postprocess(v['prediction'])
-
-        # Get category information from the dataset
-        sample = original_dataset[idx]
-        medical_task = sample.get('medical_task', 'unknown')
-        body_system = sample.get('body_system', 'unknown')
-        question_type = sample.get('question_type', 'unknown')
-
-        # Initialize category stats if not exists
-        for level, key in [
-            ('medical_task', medical_task),
-            ('body_system', body_system),
-            ('question_type', question_type),
-        ]:
-            if key not in stats[level]:
-                stats[level][key] = {'correct': 0, 'total': 0}
-
-        # Record the judgment
-        if processed_judge is not None:
-            judged_answers.append(processed_judge)
-            try:
-                gold = v['gold']
-                references.append(gold)
-            except KeyError:
-                get_logger().warning(
-                    f'No gold answer for {k}, use empty string as reference!')
-                gold = ''
-                references.append('')
-
-            # Check if the answer is correct (A means correct)
-            is_correct = processed_judge == 'A'
-            total_count += 1
-
-            if is_correct:
-                total_correct += 1
-                # Update category stats
-                for level, key in [
-                    ('medical_task', medical_task),
-                    ('body_system', body_system),
-                    ('question_type', question_type),
-                ]:
-                    stats[level][key]['correct'] += 1
-
-            # Update category totals
-            for level, key in [
-                ('medical_task', medical_task),
-                ('body_system', body_system),
-                ('question_type', question_type),
-            ]:
-                stats[level][key]['total'] += 1
-            # Add to details
-            details.append({
-                'id': k,
-                'question': sample['question'],
-                'options': sample['options'],
-                'origin_prompt': v['origin_prompt'],
-                'llm_judge': processed_judge,
-                'gold': gold,
-                'is_correct': is_correct,
-                'medical_task': medical_task,
-                'body_system': body_system,
-                'question_type': question_type,
-            })
-
-    # Calculate overall accuracy with two decimal places
-    overall_accuracy = (round(
-        (total_correct / total_count * 100), 2) if total_count > 0 else 0.00)
-
-    # Initialize results dictionary
-    results = {
-        'accuracy': overall_accuracy,
-        'total_correct': total_correct,
-        'total_count': total_count,
-        'details': details,
-    }
-
-    # Calculate accuracy for each category and flatten into results
-    for level in stats:
-        for key, value in stats[level].items():
-            if value['total'] > 0:
-                # Calculate accuracy with two decimal places
-                accuracy = round((value['correct'] / value['total'] * 100), 2)
-
-                # Create a flattened key for the category
-                flat_key = f'MedXpertQA-{key}'
-
-                # Add to results
-                results[flat_key] = accuracy
-
-    return results