[Sync] update evaluator (#1175)

configs/datasets/cmmlu/cmmlu_gen_c13365.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import FixKRetriever
 from opencompass.openicl.icl_inferencer import GenInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import CMMLUDataset
 from opencompass.utils.text_postprocessors import first_capital_postprocess
 
@@ -101,7 +101,7 @@ for _name in cmmlu_all_sets:
     )
 
     cmmlu_eval_cfg = dict(
-        evaluator=dict(type=AccEvaluator),
+        evaluator=dict(type=AccwithDetailsEvaluator),
         pred_postprocessor=dict(type=first_capital_postprocess))
 
     cmmlu_datasets.append(

configs/datasets/cmmlu/cmmlu_ppl_041cbf.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import FixKRetriever
 from opencompass.openicl.icl_inferencer import PPLInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import CMMLUDataset
 from opencompass.utils.text_postprocessors import first_capital_postprocess
 
@@ -97,7 +97,7 @@ for _name in cmmlu_all_sets:
         inferencer=dict(type=PPLInferencer),
     )
 
-    cmmlu_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
+    cmmlu_eval_cfg = dict(evaluator=dict(type=AccwithDetailsEvaluator))
 
     cmmlu_datasets.append(
         dict(

configs/datasets/hellaswag/hellaswag_10shot_gen_e42710.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import FixKRetriever
 from opencompass.openicl.icl_inferencer import GenInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import hellaswagDatasetwithICE
 from opencompass.utils.text_postprocessors import first_option_postprocess
 
@@ -41,7 +41,7 @@ hellaswag_infer_cfg = dict(
 )
 
 hellaswag_eval_cfg = dict(
-    evaluator=dict(type=AccEvaluator),
+    evaluator=dict(type=AccwithDetailsEvaluator),
     pred_role='BOT',
     pred_postprocessor=dict(type=first_option_postprocess, options='ABCD'),
 )

configs/datasets/hellaswag/hellaswag_10shot_ppl_59c85e.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import FixKRetriever
 from opencompass.openicl.icl_inferencer import PPLInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import hellaswagDatasetwithICE
 from opencompass.utils.text_postprocessors import first_capital_postprocess
 
@@ -29,7 +29,7 @@ hellaswag_infer_cfg = dict(
 )
 
 hellaswag_eval_cfg = dict(
-    evaluator=dict(type=AccEvaluator),
+    evaluator=dict(type=AccwithDetailsEvaluator),
     pred_postprocessor=dict(type=first_capital_postprocess),
 )
 

configs/datasets/mmlu/mmlu_gen_4d595a.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import FixKRetriever
 from opencompass.openicl.icl_inferencer import GenInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import MMLUDataset
 from opencompass.utils.text_postprocessors import first_option_postprocess
 
@@ -106,7 +106,7 @@ for _name in mmlu_all_sets:
     )
 
     mmlu_eval_cfg = dict(
-        evaluator=dict(type=AccEvaluator),
+        evaluator=dict(type=AccwithDetailsEvaluator),
         pred_postprocessor=dict(type=first_option_postprocess, options='ABCD'))
 
     mmlu_datasets.append(

configs/datasets/mmlu/mmlu_ppl_ac766d.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import FixKRetriever
 from opencompass.openicl.icl_inferencer import PPLInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import MMLUDataset
 
 # None of the mmlu dataset in huggingface is correctly parsed, so we use our own dataset reader
@@ -90,7 +90,7 @@ for _name in mmlu_all_sets:
         inferencer=dict(type=PPLInferencer),
     )
 
-    mmlu_eval_cfg = dict(evaluator=dict(type=AccEvaluator), )
+    mmlu_eval_cfg = dict(evaluator=dict(type=AccwithDetailsEvaluator), )
 
     mmlu_datasets.append(
         dict(

configs/datasets/race/race_gen_69ee4f.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import ZeroRetriever
 from opencompass.openicl.icl_inferencer import GenInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import RaceDataset
 from opencompass.utils.text_postprocessors import first_option_postprocess
 
@@ -26,7 +26,7 @@ race_infer_cfg = dict(
     inferencer=dict(type=GenInferencer))
 
 race_eval_cfg = dict(
-    evaluator=dict(type=AccEvaluator),
+    evaluator=dict(type=AccwithDetailsEvaluator),
     pred_postprocessor=dict(type=first_option_postprocess, options='ABCD'),
     pred_role='BOT')
 

configs/datasets/race/race_ppl_abed12.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import ZeroRetriever
 from opencompass.openicl.icl_inferencer import PPLInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import RaceDataset
 
 race_reader_cfg = dict(
@@ -20,7 +20,7 @@ race_infer_cfg = dict(
     retriever=dict(type=ZeroRetriever),
     inferencer=dict(type=PPLInferencer))
 
-race_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
+race_eval_cfg = dict(evaluator=dict(type=AccwithDetailsEvaluator))
 
 race_datasets = [
     dict(

configs/datasets/winogrande/winogrande_5shot_gen_b36770.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import FixKRetriever
 from opencompass.openicl.icl_inferencer import GenInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import winograndeDataset_V3
 from opencompass.utils.text_postprocessors import first_option_postprocess
 
@@ -29,7 +29,7 @@ winogrande_infer_cfg = dict(
 )
 
 winogrande_eval_cfg = dict(
-    evaluator=dict(type=AccEvaluator),
+    evaluator=dict(type=AccwithDetailsEvaluator),
     pred_role='BOT',
     pred_postprocessor=dict(type=first_option_postprocess, options='AB'),
 )

configs/datasets/winogrande/winogrande_5shot_ll_252f01.py

@@ -1,7 +1,7 @@
 from opencompass.openicl.icl_prompt_template import PromptTemplate
 from opencompass.openicl.icl_retriever import FixKRetriever
 from opencompass.openicl.icl_inferencer import LLInferencer
-from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
 from opencompass.datasets import winograndeDataset_V3
 
 winogrande_reader_cfg = dict(
@@ -25,7 +25,7 @@ winogrande_infer_cfg = dict(
     retriever=dict(type=FixKRetriever, fix_id_list=[0, 2, 4, 6, 8]),
     inferencer=dict(type=LLInferencer),
 )
-winogrande_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
+winogrande_eval_cfg = dict(evaluator=dict(type=AccwithDetailsEvaluator))
 
 winogrande_datasets = [
     dict(

configs/models/qwen/hf_qwen1_5_110b.py

@@ -7,6 +7,6 @@ models = [
         path='Qwen/Qwen1.5-110B',
         max_out_len=1024,
         batch_size=8,
-        run_cfg=dict(num_gpus=4),
+        run_cfg=dict(num_gpus=8),
     )
 ]

configs/models/qwen/hf_qwen1_5_110b_chat.py

@@ -7,6 +7,6 @@ models = [
         path='Qwen/Qwen1.5-110B-Chat',
         max_out_len=1024,
         batch_size=8,
-        run_cfg=dict(num_gpus=4),
+        run_cfg=dict(num_gpus=8),
     )
 ]

configs/models/qwen/hf_qwen1_5_14b.py

@@ -7,6 +7,6 @@ models = [
         path='Qwen/Qwen1.5-14B',
         max_out_len=1024,
         batch_size=8,
-        run_cfg=dict(num_gpus=1),
+        run_cfg=dict(num_gpus=2),
     )
 ]

configs/models/qwen/hf_qwen1_5_14b_chat.py

@@ -7,6 +7,6 @@ models = [
         path='Qwen/Qwen1.5-14B-Chat',
         max_out_len=1024,
         batch_size=8,
-        run_cfg=dict(num_gpus=1),
+        run_cfg=dict(num_gpus=2),
     )
 ]

configs/models/qwen/hf_qwen1_5_72b.py

@@ -7,6 +7,6 @@ models = [
         path='Qwen/Qwen1.5-72B',
         max_out_len=1024,
         batch_size=8,
-        run_cfg=dict(num_gpus=4),
+        run_cfg=dict(num_gpus=8),
     )
 ]

configs/models/qwen/hf_qwen1_5_72b_chat.py

@@ -7,6 +7,6 @@ models = [
         path='Qwen/Qwen1.5-72B-Chat',
         max_out_len=1024,
         batch_size=8,
-        run_cfg=dict(num_gpus=4),
+        run_cfg=dict(num_gpus=8),
     )
 ]

configs/models/qwen/lmdeploy_qwen1_5_series.py

@@ -8,6 +8,7 @@ settings = [
     ('qwen1.5-14b-pytorch', 'Qwen/Qwen1.5-14B', 1),
     ('qwen1.5-32b-pytorch', 'Qwen/Qwen1.5-32B', 2),
     ('qwen1.5-72b-pytorch', 'Qwen/Qwen1.5-72B', 4),
+    ('qwen1.5-110b-pytorch', 'Qwen/Qwen1.5-110B', 4),
     ('qwen1.5-moe-a2.7b-pytorch', 'Qwen/Qwen1.5-MoE-A2.7B', 1),
 ]
 

configs/summarizers/chat_OC15_multi_faceted.py

@@ -115,6 +115,14 @@ sanitized_mbpp_dataset_abbrs = [
     ['sanitized_mbpp', 'timeout'],
 ]
 
+IFEval_dataset_abbrs = [
+    ['IFEval', 'Prompt-level-strict-accuracy'],
+    ['IFEval', 'Inst-level-strict-accuracy'],
+    ['IFEval', 'Prompt-level-loose-accuracy'],
+    ['IFEval', 'Inst-level-loose-accuracy'],
+]
+
+
 summarizer = dict(
     type=MultiFacetedSummarizer,
     dataset_abbrs_list=[
@@ -124,6 +132,17 @@ summarizer = dict(
         {'name': 'bbh', 'dataset_abbrs': bbh_dataset_abbrs},
         {'name': 'GaokaoBench', 'dataset_abbrs': GaokaoBench_dataset_abbrs},
         {'name': 'sanitized_mbpp', 'dataset_abbrs': sanitized_mbpp_dataset_abbrs},
+        {'name': 'triviaqa', 'dataset_abbrs': [['triviaqa_wiki_1shot', 'score']]},
+        {'name': 'nq', 'dataset_abbrs': [['nq_open_1shot', 'score']]},
+        {'name': 'race', 'dataset_abbrs': [['race-high', 'accuracy']]},
+        {'name': 'winogrande', 'dataset_abbrs': [['winogrande', 'accuracy']]},
+        {'name': 'hellaswag', 'dataset_abbrs': [['hellaswag', 'accuracy']]},
+        {'name': 'gsm8k', 'dataset_abbrs': [['gsm8k', 'accuracy']]},
+        {'name': 'math', 'dataset_abbrs': [['math', 'accuracy']]},
+        {'name': 'TheoremQA', 'dataset_abbrs': [['TheoremQA', 'score']]},
+        {'name': 'humaneval', 'dataset_abbrs': [['openai_humaneval', 'humaneval_pass@1']]},
+        {'name': 'GPQA', 'dataset_abbrs': [['GPQA_diamond', 'accuracy']]},
+        {'name': 'IFEval', 'dataset_abbrs': IFEval_dataset_abbrs},
         {'name': 'overall', 'dataset_abbrs': overall_dataset_abbrs},
     ],
     summary_groups=sum([v for k, v in locals().items() if k.endswith('_summary_groups')], []),

opencompass/datasets/GaokaoBench.py

@@ -91,34 +91,51 @@ class GaokaoBenchEvaluator(BaseEvaluator):
         ]:
             return {'score': 0}
         elif self.question_type == 'multi_choice':
+            details = {}
             correct_score, total_score = 0, 0
-            for pred, refr in zip(predictions, references):
+            for index, (pred, refr) in enumerate(zip(predictions, references)):
                 pred = self.do_predictions_postprocess(pred)
                 pred = self.ensure_same_length(pred, refr)
+                is_corrects = []
                 for p, r in zip(pred, refr):
                     if p == r:
                         correct_score += 2
+                        is_corrects.append(True)
                     else:
                         for i in p:
                             if i not in r:
                                 break
                         else:
                             correct_score += 1
+                        is_corrects.append(False)
                     total_score += 2
-            return {'score': correct_score / total_score * 100}
+                details[str(index)] = {
+                    'pred': pred,
+                    'refr': refr,
+                    'is_correct': all(is_corrects),
+                }
+
         else:
+            details = {}
             correct_score, total_score = 0, 0
-            for pred, refr in zip(predictions, references):
+            for index, (pred, refr) in enumerate(zip(predictions, references)):
                 if self.question_type == 'multi_question_choice':
                     pred = self.do_predictions_postprocess(pred, len(refr))
                 else:
                     pred = self.do_predictions_postprocess(pred)
                 pred = self.ensure_same_length(pred, refr)
+                is_corrects = []
                 for p, r in zip(pred, refr):
-                    if p == r:
-                        correct_score += 1
+                    is_correct = p == r
+                    correct_score += is_correct
                     total_score += 1
-            return {'score': correct_score / total_score * 100}
+                    is_corrects.append(is_correct)
+                details[str(index)] = {
+                    'pred': pred,
+                    'refr': refr,
+                    'is_correct': all(is_corrects),
+                }
+        return {'score': correct_score / total_score * 100, 'details': details}
 
 
 for question_type in valid_gaokao_bench_question_types:

opencompass/datasets/IFEval/ifeval.py

@@ -26,11 +26,13 @@ class IFEvalDataset(BaseDataset):
 
 class IFEvaluator(BaseEvaluator):
 
-    def score(self, predictions, references):
-        results = dict()
-        for metric in ('strict', 'loose'):
-            results[metric] = []
-        for pred, refer in zip(predictions, references):
+    def score(self, predictions, references, origin_prompt):
+        prompt_strict_correct, prompt_strict_total = 0, 0
+        inst_strict_correct, inst_strict_total = 0, 0
+        prompt_loose_correct, prompt_loose_total = 0, 0
+        inst_loose_correct, inst_loose_total = 0, 0
+        details = {}
+        for index, (pred, refer) in enumerate(zip(predictions, references)):
             input = InputExample(
                 key=refer['key'],
                 instruction_id_list=refer['instruction_id_list'],
@@ -40,29 +42,54 @@ class IFEvaluator(BaseEvaluator):
                 for k in list(kwarg.keys()):
                     if kwarg[k] is None:
                         kwarg.pop(k, None)
-            results['strict'].append(
-                test_instruction_following_strict(input, pred))
-            results['loose'].append(
-                test_instruction_following_loose(input, pred))
-        final_scores = dict()
-        for metric in ('strict', 'loose'):
-            prompt_total = 0
-            prompt_correct = 0
-            inst_total = 0
-            inst_correct = 0
 
-            for example in results[metric]:
+            # strict
+            example = test_instruction_following_strict(input, pred)
             follow_instruction_list = example.follow_instruction_list
             instruction_id_list = example.instruction_id_list
+            prompt_strict_total += 1
+            is_strict_correct = all(follow_instruction_list)
+            prompt_strict_correct += is_strict_correct
+            inst_strict_total += len(instruction_id_list)
+            inst_strict_correct += sum(follow_instruction_list)
 
-                prompt_total += 1
-                if all(follow_instruction_list):
-                    prompt_correct += 1
+            # loose
+            example = test_instruction_following_loose(input, pred)
+            follow_instruction_list = example.follow_instruction_list
+            instruction_id_list = example.instruction_id_list
+            prompt_loose_total += 1
+            is_loose_correct = all(follow_instruction_list)
+            prompt_loose_correct += is_loose_correct
+            inst_loose_total += len(instruction_id_list)
+            inst_loose_correct += sum(follow_instruction_list)
 
-                inst_total += len(instruction_id_list)
-                inst_correct += sum(follow_instruction_list)
-            prompt_score = f'Prompt-level-{metric}-accuracy'
-            inst_score = f'Inst-level-{metric}-accuracy'
-            final_scores[prompt_score] = prompt_correct / prompt_total * 100
-            final_scores[inst_score] = inst_correct / inst_total * 100
-        return final_scores
+            if is_strict_correct:
+                grade = 'strict'
+            elif is_loose_correct:
+                grade = 'loose'
+            else:
+                grade = 'none'
+
+            details[str(index)] = {
+                'prompt': origin_prompt[index],
+                'pred': pred,
+                'refer': refer,
+                'is_strict_correct': is_strict_correct,
+                'is_loose_correct': is_loose_correct,
+                'is_correct': is_strict_correct,
+                'grade': grade
+            }
+
+        results = {
+            'Prompt-level-strict-accuracy':
+            prompt_strict_correct / prompt_strict_total * 100,
+            'Inst-level-strict-accuracy':
+            inst_strict_correct / inst_strict_total * 100,
+            'Prompt-level-loose-accuracy':
+            prompt_loose_correct / prompt_loose_total * 100,
+            'Inst-level-loose-accuracy':
+            inst_loose_correct / inst_loose_total * 100,
+            'details':
+            details
+        }
+        return results

opencompass/datasets/mbpp.py

@@ -227,9 +227,10 @@ class MBPPEvaluator(BaseEvaluator):
 
                 from tqdm import tqdm
                 for future in tqdm(as_completed(futures), total=len(futures)):
-                    index, key = future.result()
-                    result[key] += 1
-                    details[str(index)]['result'] = key
+                    index, ret = future.result()
+                    result[ret] += 1
+                    details[str(index)]['result'] = ret
+                    details[str(index)]['is_correct'] = (ret == 'pass')
 
             result['score'] = result['pass'] / len(predictions) * 100
             result['details'] = details

opencompass/models/huggingface_above_v4_33.py

@@ -59,7 +59,7 @@ def _get_possible_max_seq_len(max_seq_len, path):
     raise ValueError('max_seq_len is not provided and cannot be inferred from the model config.')
 
 
-def _convert_chat_messages(inputs):
+def _convert_chat_messages(inputs, merge_role=True):
     outputs = []
     for _input in inputs:
         messages = []
@@ -73,7 +73,18 @@ def _convert_chat_messages(inputs):
                     'SYSTEM': 'system',
                 }[item['role']]
                 messages.append({'role': role, 'content': item['prompt']})
+
+        if merge_role:
+            merged_messages = []
+            for item in messages:
+                if merged_messages and merged_messages[-1]['role'] == item['role']:
+                    merged_messages[-1]['content'] += '\n' + item['content']
+                else:
+                    merged_messages.append(item)
+            messages = merged_messages
+
         outputs.append(messages)
+        print(messages)
     return outputs
 
 
@@ -104,6 +115,8 @@ def _get_meta_template(meta_template):
     default_meta_template = dict(
         round=[
             dict(role='HUMAN', api_role='HUMAN'),
+            # XXX: all system roles are mapped to human in purpose
+            dict(role='SYSTEM', api_role='HUMAN'),
             dict(role='BOT', api_role='BOT', generate=True),
         ]
     )

opencompass/models/turbomind.py

@@ -37,6 +37,9 @@ class TurboMindModel(BaseModel):
             arguments like session_len, max_batch_size for TurboMind.
         gen_config (Dict, optional): Generation config to set
                 arguments like top_k, top_p, temperature.
+        end_str (str, optional): Whether to trim generated strings with end_str
+            if the model has special ending strings that are not handled well.
+            Defaults to None.
     """
 
     def __init__(self,
@@ -45,7 +48,8 @@ class TurboMindModel(BaseModel):
                  max_seq_len: int = 2048,
                  meta_template: Optional[Dict] = None,
                  engine_config: Dict = {},
-                 gen_config: Dict = {}):
+                 gen_config: Dict = {},
+                 end_str: Optional[str] = None):
         super().__init__(path=path,
                          max_seq_len=max_seq_len,
                          meta_template=meta_template)
@@ -64,6 +68,7 @@ class TurboMindModel(BaseModel):
         self.generator_ids = [i + 1 for i in range(concurrency)]
         self.gen_config = gen_config
         self.major_version, self.minor_version, _ = version_info
+        self.end_str = end_str
 
     def generate(self,
                  inputs: List[str],
@@ -119,6 +124,7 @@ class TurboMindModel(BaseModel):
                         batch_input,
                         [max_out_len] * len(batch_input),
                         [gen_config] * len(batch_input),
+                        [self.end_str] * len(batch_input),
                     ))
                 results += _results
         if stopping_criteria:
@@ -142,7 +148,8 @@ class TurboMindModel(BaseModel):
                   session_id,
                   prompt: PromptType,
                   max_out_len: int,
-                  gen_config=None) -> str:
+                  gen_config=None,
+                  end_str: Optional[str] = None) -> str:
         """Generate results given a list of inputs.
 
         Args:
@@ -152,6 +159,10 @@ class TurboMindModel(BaseModel):
             max_out_len (int): The maximum length of the output.
             gen_config (EngineGenerationConfig, optional): Generation
                 config to set arguments like top_k, top_p, temperature.
+            end_str (str, optional): Whether to trim generated strings
+                with end_str if the model has special ending strings
+                that are not handled well.
+                Defaults to None.
         Returns:
             str: The generated string.
         """
@@ -174,6 +185,9 @@ class TurboMindModel(BaseModel):
                 _, output_ids, _ = outputs
             response = self.tokenizer.decode(output_ids)
             response = valid_str(response)
+        # used to trim
+        if end_str:
+            response = response.split(end_str)[0]
         return response
 
     def get_ppl(self,

opencompass/openicl/icl_evaluator/icl_hf_evaluator.py

@@ -342,3 +342,29 @@ class EDAccEvaluator(AccEvaluator):
             'predictions': preds,
             'references': golds,
         }
+
+
+@ICL_EVALUATORS.register_module()
+class AccwithDetailsEvaluator(BaseEvaluator):
+
+    def score(self, predictions, references, origin_prompt) -> dict:
+
+        if len(predictions) != len(references):
+            return {'error': 'preds and refrs have different length.'}
+
+        details = {}
+        correct, total = 0, 0
+        for index, (pred, ref) in enumerate(zip(predictions, references)):
+            is_correct = pred == ref
+            correct += is_correct
+            details[str(index)] = {
+                'prompt': origin_prompt[index],
+                'pred': pred,
+                'refr': ref,
+                'is_correct': is_correct,
+            }
+            total += 1
+
+        results = {'accuracy': correct / total * 100, 'details': details}
+
+        return results