[Feature] Add GPQA Dataset (#729)

* check

* message

* add

* change prompt

* change a para nameq

* modify name of the file

* delete an useless file

configs/datasets/gpqa/gpqa_gen.py

@@ -0,0 +1,4 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .gpqa_gen_a27c4d import gpqa_datasets

configs/datasets/gpqa/gpqa_gen_a27c4d.py

@@ -0,0 +1,46 @@
+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 GPQADataset, GPQAEvaluator
+from opencompass.utils import first_option_postprocess
+
+gpqa_reader_cfg = dict(
+    input_columns=['question', 'A', 'B', 'C', 'D'],
+    output_column='answer')
+
+gpqa_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='HUMAN', prompt='{question}\nChoices:\n'
+                                          '(A){A}\n'
+                                          '(B){B}\n'
+                                          '(C){C}\n'
+                                          '(D){D}\n'
+                                          'Format your response as follows: "The correct answer is (insert answer here)"'),
+            ], )),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer))
+
+gpqa_eval_cfg = dict(evaluator=dict(type=GPQAEvaluator),
+                     pred_postprocessor=dict(type=first_option_postprocess, options='ABCD'))
+
+gpqa_datasets = []
+gpqa_subsets = {
+    'extended': 'gpqa_extended.csv',
+    'main': 'gpqa_main.csv',
+    'diamond': 'gpqa_diamond.csv'
+}
+
+for split in list(gpqa_subsets.keys()):
+    gpqa_datasets.append(
+        dict(
+            abbr='GPQA_' + split,
+            type=GPQADataset,
+            path='./data/gpqa/',
+            name=gpqa_subsets[split],
+            reader_cfg=gpqa_reader_cfg,
+            infer_cfg=gpqa_infer_cfg,
+            eval_cfg=gpqa_eval_cfg)
+    )

opencompass/datasets/__init__.py

@@ -39,6 +39,7 @@ from .flores import *  # noqa: F401, F403
 from .game24 import *  # noqa: F401, F403
 from .GaokaoBench import *  # noqa: F401, F403
 from .govrepcrs import *  # noqa: F401, F403
+from .gpqa import *  # noqa: F401, F403
 from .gsm8k import *  # noqa: F401, F403
 from .gsm_hard import *  # noqa: F401, F403
 from .hellaswag import *  # noqa: F401, F403

opencompass/datasets/gpqa.py

@@ -0,0 +1,81 @@
+import copy
+import csv
+import os
+
+from datasets import Dataset
+
+from opencompass.openicl import BaseEvaluator
+from opencompass.registry import LOAD_DATASET
+
+from .base import BaseDataset
+
+
+@LOAD_DATASET.register_module()
+class GPQADataset(BaseDataset):
+
+    @staticmethod
+    def load(path: str, name: str):
+        cnt = 0
+        data = []
+        data_new = []
+        with open(os.path.join(path, name), 'r', encoding='utf-8') as f:
+            reader = csv.reader(f, delimiter=',')
+            for row in reader:
+                if row[7] == 'Question':
+                    continue
+                cnt = cnt + 1
+                question = row[7]
+                A = row[8]
+                B = row[9]
+                C = row[10]
+                D = row[11]
+                options = [row[8], row[9], row[10], row[11]]
+                answer = 'A'
+
+                data.append({
+                    'question': question,
+                    'A': A,
+                    'B': B,
+                    'C': C,
+                    'D': D,
+                    'options': options,
+                    'answer': answer
+                })
+
+                circular_patterns = ['ABCD', 'BCDA', 'CDAB', 'DABC']  # 更新选项顺序
+                c = circular_patterns[cnt % 4]
+                line = copy.deepcopy(data[cnt - 1])
+                tmp = line['A']
+                for i in range(4):
+                    line['ABCD'[i]] = line['options'][ord(c[i]) - ord('A')]
+
+                for i in range(4):
+                    if line['ABCD'[i]] == tmp:
+                        line['answer'] = 'ABCD'[i]
+                        break
+                data_new.append(line)
+
+        dataset = Dataset.from_list(data_new)
+
+        return dataset
+
+
+class GPQAEvaluator(BaseEvaluator):
+
+    def score(self, predictions, references):
+        if len(predictions) != len(references):
+            return {
+                'error': 'predictions and references have different length'
+            }
+        correct = 0
+        count = 0
+        details = []
+        for i, j in zip(predictions, references):
+            detail = {'pred': i, 'answer': j, 'correct': False}
+            count += 1
+            if i == j:
+                correct += 1
+                detail['correct'] = True
+            details.append(detail)
+        result = {'accuracy': 100 * correct / count, 'details': details}
+        return result