fix dataset-index & use official llm_judge_postprocess

dataset-index.yml

@@ -1023,3 +1023,9 @@
     paper: https://arxiv.org/pdf/2402.09391
     configpath: opencompass/configs/datasets/SmolInstruct/smolinstruct_gen.py
     configpath_llmjudge: ''
+- SciKnowEval:
+    name: SciKnowEval
+    category: Science
+    paper: https://arxiv.org/abs/2406.09098
+    configpath: opencompass/configs/datasets/SciKnowEval/SciKnowEval_gen_ebe47d.py
+    configpath_llmjudge: opencompass/configs/datasets/SciKnowEval/SciKnowEval_llmjudge_gen_ebe47d.py

opencompass/configs/datasets/SciKnowEval/SciKnowEval_llmjudge_gen_ebe47d.py

@@ -1,5 +1,6 @@
-from opencompass.datasets import SciKnowEvalDataset, SciKnowEvalEvaluator, SciKnowEval_llmjudge_postprocess
+from opencompass.datasets import SciKnowEvalDataset, SciKnowEvalEvaluator
 from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.datasets import generic_llmjudge_postprocess
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import ZeroRetriever
 from opencompass.evaluator import GenericLLMEvaluator
@@ -90,7 +91,7 @@ eval_cfg_biology = dict(
             reader_cfg=reader_cfg,
         ),
         judge_cfg=dict(),
-        dict_postprocessor=dict(type=SciKnowEval_llmjudge_postprocess),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
     ),
 )
 
@@ -120,7 +121,7 @@ eval_cfg_chemistry = dict(
             subset='chemistry',
         ),
         judge_cfg=dict(),
-        dict_postprocessor=dict(type=SciKnowEval_llmjudge_postprocess),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
     ),
 )
 
@@ -150,7 +151,7 @@ eval_cfg_material = dict(
             subset='material',
         ),
         judge_cfg=dict(),
-        dict_postprocessor=dict(type=SciKnowEval_llmjudge_postprocess),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
     ),
 )
 
@@ -180,7 +181,7 @@ eval_cfg_physics = dict(
             subset='physics',
         ),
         judge_cfg=dict(),
-        dict_postprocessor=dict(type=SciKnowEval_llmjudge_postprocess),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
     ),
 )
 

opencompass/datasets/SciKnowEval.py

@@ -27,12 +27,17 @@ class SciKnowEvalDataset(BaseDataset):
 
     @staticmethod
     def load(path: str, prompt_mode: str, **kwargs):
+
         def capitalize_first_letter(s):
             if not s:  # 检查字符串是否为空
                 return s
             return s[0].upper() + s[1:]
+
         subset = kwargs['subset']
-        data_files = {'test': f'data/{capitalize_first_letter(subset)}/sciknoweval_{subset}_test.jsonl'}
+        data_files = {
+            'test':
+            f'data/{capitalize_first_letter(subset)}/sciknoweval_{subset}_test.jsonl'
+        }
         dataset = load_dataset(path, data_files=data_files, split='test')
         # dataset = dataset.select(range(20))
         if prompt_mode == 'zero-shot':
@@ -101,75 +106,3 @@ def answer_cleansing(
                 prediction[0] = prediction[0][:-1]
 
     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 = []
-
-    total_correct = 0
-    total_count = 0
-
-    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'])
-
-        sample = original_dataset[idx]
-        # 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
-           
-            # Add to details
-            details.append({
-                'id': k,
-                'question': sample['question'],
-                'origin_prompt': v['origin_prompt'],
-                'llm_judge': processed_judge,
-                'gold': gold,
-                'is_correct': is_correct,
-            })
-
-    # 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,
-    }
-    return results