update

opencompass/configs/datasets/humaneval_pro/humaneval_pro.py

@@ -1,4 +0,0 @@
-from mmengine.config import read_base
-
-with read_base():
-    from .humaneval_pro_gen_ import humanevalpro_datasets  # noqa: F401, F403

opencompass/configs/datasets/humaneval_pro/humaneval_pro_gen.py

@@ -0,0 +1,4 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .humaneval_pro_gen_3dc067 import humanevalpro_datasets  # noqa: F401, F403

opencompass/configs/datasets/humaneval_pro/humaneval_pro_gen_3dc067.py

@@ -3,16 +3,6 @@ from opencompass.openicl.icl_retriever import ZeroRetriever
 from opencompass.openicl.icl_inferencer import GenInferencer
 from opencompass.datasets import HumanevalevalProDataset, HumanevalProEvaluator, humaneval_postprocess_v2
 
-OFFICIAL_PROMPT_WRAPPER = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
-@@ Instruction
-Write a solution of python file to the following problems, the solution of the second problem requires single or multiple calls to the first solution.
-```python
-{raw_problem}
-{new_problem}
-```
-@@ Response
-Please put the two solutions to the above problems in one Python code block.
-"""
 
 PROMPT_WRAPPER = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
 Write a solution of python file to the following problems, the solution of the second problem requires single or multiple calls to the first solution.

opencompass/configs/datasets/humaneval_pro/humaneval_pro_repeat_gen_3dc067.py

@@ -0,0 +1,48 @@
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.datasets import HumanevalevalProDataset, HumanevalProEvaluator, humaneval_postprocess_v2
+
+
+PROMPT_WRAPPER = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
+Write a solution of python file to the following problems, the solution of the second problem requires single or multiple calls to the first solution.
+```python
+{raw_problem}
+{new_problem}
+```
+Please put the two solutions within the Python code block provided below, and make sure that the block contains no other unrelated content:
+```python
+```
+"""
+
+
+humanevalpro_reader_cfg = dict(
+    input_columns=['raw_problem', 'new_problem'], output_column='test_code')
+
+humanevalpro_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(round=[
+            dict(
+                role='HUMAN',
+                prompt=PROMPT_WRAPPER),
+        ])),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer))
+
+humanevalpro_eval_cfg = dict(
+    evaluator=dict(type=HumanevalProEvaluator,
+                   ip_address='https://opencompass-multiple-evaluator.hf.space')
+)
+
+humanevalpro_datasets = [
+    dict(
+        abbr='humaneval_pro',
+        type=HumanevalevalProDataset,
+        path='opencompass/humaneval_pro',
+        reader_cfg=humanevalpro_reader_cfg,
+        infer_cfg=humanevalpro_infer_cfg,
+        eval_cfg=humanevalpro_eval_cfg,
+        n=5,
+        k=3)
+]

opencompass/configs/datasets/mbpp_pro/mbpp_pro_gen.py

@@ -1,4 +1,4 @@
 from mmengine.config import read_base
 
 with read_base():
-    from .mbpp_pro_gen_ import mbpppro_datasets  # noqa: F401, F403
+    from .mbpp_pro_gen_3dc067 import mbpppro_datasets  # noqa: F401, F403

opencompass/configs/datasets/mbpp_pro/mbpp_pro_gen_3dc067.py

@@ -3,16 +3,6 @@ from opencompass.openicl.icl_retriever import ZeroRetriever
 from opencompass.openicl.icl_inferencer import GenInferencer
 from opencompass.datasets import MBPPProDataset, MBPPProEvaluator
 
-OFFICIAL_PROMPT_WRAPPER = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
-@@ Instruction
-Write a solution of python file to the following problems, the solution of the second problem requires single or multiple calls to the first solution.
-```python
-{raw_problem}
-{new_problem}
-```
-@@ Response
-Please put the two solutions to the above problems in one Python code block.
-"""
 
 PROMPT_WRAPPER = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
 Write a solution of python file to the following problems, the solution of the second problem requires single or multiple calls to the first solution.

opencompass/configs/datasets/mbpp_pro/mbpp_pro_repeat_gen_3dc067.py

@@ -0,0 +1,48 @@
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.datasets import MBPPProDataset, MBPPProEvaluator
+
+
+PROMPT_WRAPPER = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
+Write a solution of python file to the following problems, the solution of the second problem requires single or multiple calls to the first solution.
+```python
+{raw_problem}
+{new_problem}
+```
+Please put the two solutions within the Python code block provided below, and make sure that the block contains no other unrelated content:
+```python
+```
+"""
+
+
+mbpppro_reader_cfg = dict(
+    input_columns=['raw_problem', 'new_problem'], output_column='test_code')
+
+mbpppro_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(round=[
+            dict(
+                role='HUMAN',
+                prompt=PROMPT_WRAPPER),
+        ])),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer))
+
+mbpppro_eval_cfg = dict(
+    evaluator=dict(type=MBPPProEvaluator, 
+                   ip_address='https://opencompass-multiple-evaluator.hf.space'),
+)
+
+mbpppro_datasets = [
+    dict(
+        abbr='mbpp_pro',
+        type=MBPPProDataset,
+        path='opencompass/mbpp_pro',
+        reader_cfg=mbpppro_reader_cfg,
+        infer_cfg=mbpppro_infer_cfg,
+        eval_cfg=mbpppro_eval_cfg,
+        n=5,
+        k=3)
+]

opencompass/configs/datasets/multipl_e/multiple_gen.py

@@ -0,0 +1,4 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .multiple_top_ten_gen_f44aaf import multiple_datasets  # noqa: F401, F403

opencompass/configs/datasets/multipl_e/multiple_top_ten_gen_f44aaf.py

@@ -32,7 +32,6 @@ multiple_datasets = [
         type=MultiplEDataset,
         abbr=f'humaneval-multiple-{lang}',
         language=lang,
-        num_repeats=1,
         path='opencompass/multipl_e',
         tag='humaneval',
         reader_cfg=multiple_reader_cfg,
@@ -46,7 +45,6 @@ multiple_datasets += [
         type=MultiplEDataset,
         abbr=f'mbpp-multiple-{lang}',
         language=lang,
-        num_repeats=1,
         path='opencompass/multipl_e',
         tag='mbpp',
         reader_cfg=multiple_reader_cfg,

opencompass/configs/datasets/multipl_e/multiple_top_ten_repeat_gen_f44aaf.py

@@ -0,0 +1,56 @@
+# Select the 10 most popular programming languages from MultiPL-E to compose the test set.
+
+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import GenInferencer
+from opencompass.datasets import MultiplEDataset, MultiplEEvaluator
+
+
+_TOP_TEN_LANGUAGE_ = ['cpp', 'cs', 'go', 'java', 'rb', 'js', 'php', 'r', 'rs', 'sh']
+
+multiple_reader_cfg = dict(input_columns=['language', 'prompt'], output_column='tests')
+
+multiple_infer_cfg = dict(
+    prompt_template=dict(type=PromptTemplate, template='Based on the provided {language} code snippet, complete the subsequent content. The initial part of the completed code must match the provided code snippet exactly:\n{prompt}'),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer),
+)
+
+multiple_eval_cfg = {
+    lang: dict(
+        evaluator=dict(
+            type=MultiplEEvaluator,
+            language=lang,
+            ip_address='https://opencompass-multiple-evaluator.hf.space',
+        ),
+        pred_role='BOT',
+    ) for lang in _TOP_TEN_LANGUAGE_
+}
+
+multiple_datasets = [
+    dict(
+        type=MultiplEDataset,
+        abbr=f'humaneval-multiple-{lang}',
+        language=lang,
+        path='opencompass/multipl_e',
+        tag='humaneval',
+        reader_cfg=multiple_reader_cfg,
+        infer_cfg=multiple_infer_cfg,
+        eval_cfg=multiple_eval_cfg[lang],
+    ) for lang in _TOP_TEN_LANGUAGE_
+]
+
+multiple_datasets += [
+    dict(
+        type=MultiplEDataset,
+        abbr=f'mbpp-multiple-{lang}',
+        language=lang,
+        path='opencompass/multipl_e',
+        tag='mbpp',
+        reader_cfg=multiple_reader_cfg,
+        infer_cfg=multiple_infer_cfg,
+        eval_cfg=multiple_eval_cfg[lang],
+        n=5,
+        k=3
+    ) for lang in _TOP_TEN_LANGUAGE_
+]

opencompass/datasets/humaneval_pro.py

@@ -1,7 +1,8 @@
+# flake8: noqa: E501s
+
 import json
 from typing import Dict, List
 
-import numpy as np
 from datasets import Dataset
 
 from opencompass.openicl.icl_evaluator.code_evaluator import CodeEvaluator
@@ -9,29 +10,33 @@ from opencompass.utils import get_data_path
 
 from .base import BaseDataset
 
+PROMPT_WRAPPER = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
+Write a solution of python file to the following problems, the solution of the second problem requires single or multiple calls to the first solution.
+```python
+{raw_problem}
+{new_problem}
+```
+Please put the two solutions within the Python code block provided below, and make sure that the block contains no other unrelated content:
+```python
+```
+"""
+
 
 class HumanevalevalProDataset(BaseDataset):
 
     @staticmethod
-    def load(path, num_repeats=1, local_mode=False):
+    def load(path, local_mode=False):
         path = get_data_path(path, local_mode=local_mode)
         dataset = []
         with open(path, encoding='utf-8') as f:
             raw_data = json.load(f)
             for data in raw_data:
-                dataset.extend([data for _ in range(num_repeats)])
+                dataset.append(data)
         return Dataset.from_list(dataset)
 
 
 class HumanevalProEvaluator(CodeEvaluator):
 
-    def _process_completions(self, test_case: dict, completions: list) -> list:
-        processed_completions = []
-        for comp in completions:
-            post_comp = self._extract_code(comp)
-            processed_completions.append(post_comp)
-        return processed_completions
-
     def score(self, predictions: List, references: List,
               test_set: Dataset) -> Dict:
         if len(predictions) != len(references):
@@ -45,52 +50,32 @@ class HumanevalProEvaluator(CodeEvaluator):
         test_set = test_set.to_pandas()
         # Use the first column as the unique identifier
         test_set_origin = test_set.drop_duplicates(subset=test_set.columns[0])
-        num_repeats = int(len(test_set) / len(test_set_origin))
 
         # 1. Prepare data for all test cases
-        all_test_cases = []
+        all_test_cases, prompts = [], []
         for i in range(len(test_set_origin)):
             test_case = test_set_origin.iloc[i]
-            completions = predictions[i * num_repeats:(i + 1) * num_repeats]
+            completion = predictions[i]
 
             # Process code completions
-            processed_completions = self._process_completions(
-                test_case, completions)
-
+            processed_completion = self._process_completions(completion)
+            code = processed_completion + '\n' + test_case['test_code']
             sub_data_dict = {
                 'name': int(test_case['id']),
                 'language': self.language,
-                'prompt': '',
-                'tests': test_case['test_code'],
-                'processed_completions': processed_completions,
-                'completions': completions
+                'code': code,
             }
-
             all_test_cases.append(sub_data_dict)
 
+            prompt = PROMPT_WRAPPER.format(
+                raw_problem=test_case['raw_problem'],
+                new_problem=test_case['new_problem'])
+            prompts.append(prompt)
+
         # 2. Send all test cases to the evaluation service
         success, outputs, error_message = self._evaluate(all_test_cases)
         if not success:
             return {'error': error_message}
 
         # 3. Process the returned results
-        details = []
-        total, correct = [], []
-        for output in outputs:
-            passed = [m['status'] == 'OK' for m in output['meta_data']]
-            total.append(len(passed))
-            correct.append(sum(passed))
-            details.append(output)
-        total = np.array(total)
-        correct = np.array(correct)
-
-        pass_at_k = {
-            f'pass@{k}':
-            self.estimate_pass_at_k(total, correct, k).mean() * 100
-            for k in self.k if (total >= k).all()
-        }
-
-        return {
-            **pass_at_k,
-            'details': details,
-        }
+        return self._process_results(outputs, prompts, len(test_set_origin))

opencompass/datasets/mbpp_pro.py

@@ -1,7 +1,8 @@
+# flake8: noqa: E501
+
 import json
 from typing import Dict, List
 
-import numpy as np
 from datasets import Dataset
 
 from opencompass.openicl.icl_evaluator.code_evaluator import CodeEvaluator
@@ -9,30 +10,33 @@ from opencompass.utils import get_data_path
 
 from .base import BaseDataset
 
+PROMPT_WRAPPER = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
+Write a solution of python file to the following problems, the solution of the second problem requires single or multiple calls to the first solution.
+```python
+{raw_problem}
+{new_problem}
+```
+Please put the two solutions within the Python code block provided below, and make sure that the block contains no other unrelated content:
+```python
+```
+"""
+
 
 class MBPPProDataset(BaseDataset):
 
     @staticmethod
-    def load(path, num_repeats=1, local_mode=False):
+    def load(path, local_mode=False):
         path = get_data_path(path, local_mode=local_mode)
         print(path)
         dataset = []
         with open(path, encoding='utf-8') as f:
             for line in f:
-                dataset.extend(
-                    [json.loads(line.strip()) for _ in range(num_repeats)])
+                dataset.append(json.loads(line.strip()))
         return Dataset.from_list(dataset)
 
 
 class MBPPProEvaluator(CodeEvaluator):
 
-    def _process_completions(self, test_case: dict, completions: list) -> list:
-        processed_completions = []
-        for comp in completions:
-            post_comp = self._extract_code(comp)
-            processed_completions.append(post_comp)
-        return processed_completions
-
     def score(self, predictions: List, references: List,
               test_set: Dataset) -> Dict:
         if len(predictions) != len(references):
@@ -46,52 +50,32 @@ class MBPPProEvaluator(CodeEvaluator):
         test_set = test_set.to_pandas()
         # Use the first column as the unique identifier
         test_set_origin = test_set.drop_duplicates(subset=test_set.columns[0])
-        num_repeats = int(len(test_set) / len(test_set_origin))
 
         # 1. Prepare data for all test cases
-        all_test_cases = []
+        all_test_cases, prompts = [], []
         for i in range(len(test_set_origin)):
             test_case = test_set_origin.iloc[i]
-            completions = predictions[i * num_repeats:(i + 1) * num_repeats]
+            completion = predictions[i]
 
             # Process code completions
-            processed_completions = self._process_completions(
-                test_case, completions)
-
+            processed_completion = self._process_completions(completion)
+            code = processed_completion + '\n' + test_case['test_code']
             sub_data_dict = {
                 'name': int(test_case['id']),
                 'language': self.language,
-                'prompt': '',
-                'tests': test_case['test_code'],
-                'processed_completions': processed_completions,
-                'completions': completions
+                'code': code,
             }
-
             all_test_cases.append(sub_data_dict)
 
+            prompt = PROMPT_WRAPPER.format(
+                raw_problem=test_case['raw_problem'],
+                new_problem=test_case['new_problem'])
+            prompts.append(prompt)
+
         # 2. Send all test cases to the evaluation service
         success, outputs, error_message = self._evaluate(all_test_cases)
         if not success:
             return {'error': error_message}
 
         # 3. Process the returned results
-        details = []
-        total, correct = [], []
-        for output in outputs:
-            passed = [m['status'] == 'OK' for m in output['meta_data']]
-            total.append(len(passed))
-            correct.append(sum(passed))
-            details.append(output)
-        total = np.array(total)
-        correct = np.array(correct)
-
-        pass_at_k = {
-            f'pass@{k}':
-            self.estimate_pass_at_k(total, correct, k).mean() * 100
-            for k in self.k if (total >= k).all()
-        }
-
-        return {
-            **pass_at_k,
-            'details': details,
-        }
+        return self._process_results(outputs, prompts, len(test_set_origin))

opencompass/datasets/multipl_e.py

@@ -1,3 +1,4 @@
+import difflib
 import json
 import os.path as osp
 
@@ -28,7 +29,6 @@ class MultiplEDataset(BaseDataset):
     @staticmethod
     def load(path: str,
              language: str,
-             num_repeats: int = 1,
              tag: str = 'humaneval',
              local_mode: bool = False):
         """Load dataset for pass k mode.
@@ -56,8 +56,7 @@ class MultiplEDataset(BaseDataset):
         dataset = []
         with open(file_path, 'r', encoding='utf-8') as f:
             for line in f:
-                dataset.extend(
-                    [json.loads(line.strip()) for _ in range(num_repeats)])
+                dataset.append(json.loads(line.strip()))
         return Dataset.from_list(dataset)
 
 
@@ -84,20 +83,56 @@ class MultiplEEvaluator(CodeEvaluator):
                 min_stop_index = stop_index
         return decoded_string[:min_stop_index]
 
-    def _process_completions(self, test_case, completions):
+    def _remove_prefix(self,
+                       prompt: str,
+                       completion: str,
+                       threshold: float = 0.95) -> str:
+        """Determine the truncation point in the completion based on the last
+        line of the prompt, remove all content before that line in the
+        completion, and return the completion string after removing the prefix.
+        This is done to convert chatbot-style inference mode to completion
+        mode.
+
+        Args:
+            prompt (str): The prompt text.
+            completion (str): The completion text.
+            threshold (float): Line similarity threshold.
+
+        Returns:
+            str: The completion string after removing the prefix.
+        """
+        prompt_lines = prompt.splitlines()
+        completion_lines = completion.splitlines()
+
+        if not prompt_lines:
+            return completion
+
+        last_prompt_line = prompt_lines[-1]
+        cut_index = -1
+
+        for i, completion_line in enumerate(completion_lines):
+            similarity = difflib.SequenceMatcher(None, last_prompt_line,
+                                                 completion_line).ratio()
+            if similarity >= threshold:
+                cut_index = i
+                break
+
+        if cut_index != -1:
+            return '\n'.join(completion_lines[cut_index + 1:])
+        else:
+            return completion
+
+    def _process_completions(self, test_case, completion):
         """Process completions with a test case.
 
         Args:
-            test_case: A test case.
-            completions: A list of completions.
+            test_case (dict): A test case containing prompt and stop tokens.
+            completion (str): The generated code completion.
         Returns:
-            A list of processed completions.
+            str: Processed code completion.
         """
-        processed_completions = []
-        for comp in completions:
-            comp = self._extract_code(comp)
-            post_comp = self._remove_prefix(test_case['prompt'], comp)
-            post_comp = self._stop_at_stop_token(post_comp,
-                                                 test_case['stop_tokens'])
-            processed_completions.append(post_comp)
-        return processed_completions
+        post_comp = self._extract_code(completion)
+        post_comp = self._stop_at_stop_token(post_comp,
+                                             test_case['stop_tokens'])
+        post_comp = self._remove_prefix(test_case['prompt'], post_comp)
+        return post_comp

opencompass/openicl/icl_evaluator/code_evaluator.py

@@ -1,7 +1,5 @@
 # flake8: noqa: E501
 
-import difflib
-import itertools
 import os
 import re
 import tempfile
@@ -73,6 +71,7 @@ class CodeEvaluator(BaseEvaluator):
                 - output (dict/list/str): Evaluation results or error message
         """
         try:
+            import requests
             temp_file_path = None
             # Handle file path input
             if isinstance(input_data, str):
@@ -85,7 +84,15 @@ class CodeEvaluator(BaseEvaluator):
                 input_data = temp_file_path
 
             # Send to evaluation service
-            result = self.client.predict(input_data, api_name='/evaluate')
+            try:
+                result = self.client.predict(input_data, api_name='/evaluate')
+            except Exception as e:
+                # Catch timeout and other exceptions
+                if 'timed out' in str(e).lower() or 'timeout' in str(
+                        e).lower():
+                    return False, f'Request to code eval service timed out: {e}'
+                else:
+                    raise
 
             # Process the result
             if isinstance(result, (dict, list)):
@@ -109,63 +116,16 @@ class CodeEvaluator(BaseEvaluator):
                 except:  # noqa: E722
                     pass
 
-    def _remove_prefix(self,
-                       prompt: str,
-                       completion: str,
-                       threshold: float = 0.95) -> str:
-        """Determine the truncation point in the completion based on the last
-        line of the prompt, remove all content before that line in the
-        completion, and return the completion string after removing the prefix.
-        This is done to convert chatbot-style inference mode to completion
-        mode.
+    def _process_completions(self, completion: str) -> list:
+        """Process code completions to extract the relevant code.
 
         Args:
-            prompt (str): The prompt text.
-            completion (str): The completion text.
-            threshold (float): Line similarity threshold.
-
+            completion (str): Code completion string.
         Returns:
-            str: The completion string after removing the prefix.
+            list: List of processed code completions.
         """
-        prompt_lines = prompt.splitlines()
-        completion_lines = completion.splitlines()
-
-        if not prompt_lines:
-            return completion
-
-        last_prompt_line = prompt_lines[-1]
-        cut_index = -1
-
-        for i, completion_line in enumerate(completion_lines):
-            similarity = difflib.SequenceMatcher(None, last_prompt_line,
-                                                 completion_line).ratio()
-            if similarity >= threshold:
-                cut_index = i
-                break
-
-        if cut_index != -1:
-            return '\n'.join(completion_lines[cut_index + 1:])
-        else:
-            return completion
-
-    def _process_completions(self, test_case: dict, completions: list) -> list:
-        """Process code completion list, which typically involves extracting
-        code, removing repetitive prefixes caused by chatbot mode, and other
-        steps to ensure the model-generated code can be compiled successfully.
-
-        Args:
-            test_case (dict): Dictionary containing test case information including:
-            completions (list): List of code completions generated by the model.
-
-        Returns:
-            list: Processed code completion list.
-        """
-        processed_completions = []
-        for comp in completions:
-            comp = self._extract_code(comp)
-            post_comp = self._remove_prefix(test_case['prompt'], comp)
-            processed_completions.append(post_comp)
-        return processed_completions
+        post_comp = self._extract_code(completion)
+        return post_comp
 
     def _evaluate(
         self, input_data: Union[Dict, List]
@@ -197,6 +157,31 @@ class CodeEvaluator(BaseEvaluator):
 
         return True, output, None
 
+    def _process_results(self, outputs: List, prompts: List,
+                         total_count: int) -> Dict:
+        """Process the evaluation results.
+        Args:
+            outputs (list): List of evaluation results for each test case.
+            prompts (list): List of prompts used for each test case.
+            total_count (int): Total number of test cases.
+        Returns:
+            dict: Processed results including:
+                - pass@1: Percentage of test cases passed
+                - details: Detailed results for each test case
+        """
+        details = []
+        correct = 0
+        for output, prompt in zip(outputs, prompts):
+            output['prompt'] = prompt
+            if output.get('status') == 'OK':
+                output['correct'] = True
+                correct += 1
+            else:
+                output['correct'] = False
+            details.append(output)
+
+        return {f'pass@1': 100 * correct / total_count, 'details': details}
+
     def score(self, predictions: List, references: List,
               test_set: Dataset) -> Dict:
         """Score code generation predictions against references.
@@ -225,25 +210,23 @@ class CodeEvaluator(BaseEvaluator):
         test_set_origin = test_set.drop_duplicates(subset=test_set.columns[0])
 
         # 1. Prepare data for all test cases
-        all_test_cases = []
+        all_test_cases, prompts = [], []
         for i in range(len(test_set_origin)):
             test_case = test_set_origin.iloc[i]
-            completions = predictions[i]
+            completion = predictions[i]
 
             # Process code completions
-            processed_completions = self._process_completions(
-                test_case, completions)
-
+            processed_completion = self._process_completions(
+                test_case, completion)
+            code = test_case[
+                'prompt'] + processed_completion + '\n' + test_case['tests']
             sub_data_dict = {
                 'name': test_case['name'],
                 'language': test_case['language'],
-                'prompt': test_case['prompt'],
-                'tests': test_case['tests'],
-                'processed_completions': processed_completions,
-                'completions': completions
+                'code': code
             }
-
             all_test_cases.append(sub_data_dict)
+            prompts.append(test_case['prompt'])
 
         # 2. Send all test cases to the evaluation service
         success, outputs, error_message = self._evaluate(all_test_cases)
@@ -251,18 +234,4 @@ class CodeEvaluator(BaseEvaluator):
             return {'error': error_message}
 
         # 3. Process the returned results
-        details = []
-        correct = 0
-        for output in outputs:
-            if output.get('status') == 'OK':
-                output['correct'] = True
-                correct += 1
-            else:
-                output['correct'] = False
-
-            details.append(output)
-
-        return {
-            f'pass@1': 100 * correct / len(test_set_origin),
-            'details': details
-        }
+        return self._process_results(outputs, prompts, len(test_set_origin))