comments upadate

opencompass/configs/datasets/multipl_e/multiple_top_ten_gen.py

@@ -13,7 +13,7 @@ multiple_reader_cfg = dict(input_columns=['language', 'prompt'], output_column='
 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, max_out_len=2048),
+    inferencer=dict(type=GenInferencer),
 )
 
 multiple_eval_cfg = {

opencompass/datasets/multipl_e.py

@@ -31,20 +31,17 @@ class MultiplEDataset(BaseDataset):
              num_repeats: int = 1,
              tag: str = 'humaneval',
              local_mode: bool = False):
-        """Load humaneval dataset for pass k mode.
-
-        Note that you can use num_repeats > 1 when your model does not support
-        `num_return_sequence` in generation, otherwise use the raw
-        humaneval dataset and set `num_return_sequence` in model config to
-        generate multiple responses for testing pass@k>1.
-
-        It better to change your dataset abbr correspondingly if you want to
-        change num_repeats>1, otherwise the number in
-        `.cache/dataset_size.json` might be inconsistent.
+        """Load dataset for pass k mode.
 
         Args:
-            num_repeats(int): Number of repetition for this dataset to get
-        multiple responses in special cases.
+            path(str): The path to the dataset.
+            language(str): The language of the dataset.
+            num_repeats(int): Number of repetition for this dataset to get.
+            tag(str): The tag of the dataset.
+            local_mode(bool): Whether to load the dataset in local mode.
+
+        Returns:
+            Dataset: A PyTorch dataset.
         """
         path = get_data_path(path, local_mode=local_mode)
         assert tag in ['humaneval',
@@ -72,6 +69,13 @@ class MultiplEEvaluator(CodeEvaluator):
 
         WARNING: the decoded_string *must not* include the prompt,
         which may have stop tokens itself.
+
+        Args:
+            decoded_string: A string generated by the model.
+            stop_tokens: A list of strings, where each string is a stop token.
+        Returns:
+            The decoded_string, truncated at the first occurrence of a stop
+            token.
         """
         min_stop_index = len(decoded_string)
         for stop_token in stop_tokens:
@@ -81,6 +85,14 @@ class MultiplEEvaluator(CodeEvaluator):
         return decoded_string[:min_stop_index]
 
     def _process_completions(self, test_case, completions):
+        """Process completions with a test case.
+
+        Args:
+            test_case: A test case.
+            completions: A list of completions.
+        Returns:
+            A list of processed completions.
+        """
         processed_completions = []
         for comp in completions:
             comp = self._extract_code(comp)