Initial commit

opencompass/configs/datasets/adv_glue/adv_glue_qnli/adv_glue_qnli_gen.py

+from mmengine.config import read_base
+
+with read_base():
+    from .adv_glue_qnli_gen_0b7326 import adv_qnli_datasets  # noqa: F401, F403

opencompass/configs/datasets/adv_glue/adv_glue_qnli/adv_glue_qnli_gen_0b7326.py

+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 AdvQnliDataset, AccDropEvaluator
+from opencompass.utils.text_postprocessors import first_option_postprocess
+
+adv_qnli_reader_cfg = dict(
+    input_columns=['question', 'sentence'], output_column='label_option')
+
+adv_qnli_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(round=[
+            dict(
+                role='HUMAN',
+                prompt=
+                """Please identify whether the sentence answers the question. The answer should be exactly 'A. yes' or 'B. no'.
+question: {question}
+sentence: {sentence}
+Answer:"""),
+        ]),
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer),
+)
+
+adv_qnli_eval_cfg = dict(
+    evaluator=dict(type=AccDropEvaluator),
+    pred_role='BOT',
+    pred_postprocessor=dict(type=first_option_postprocess, options='AB'),
+)
+
+adv_qnli_datasets = [
+    dict(
+        abbr='adv_qnli',
+        type=AdvQnliDataset,
+        path='opencompass/advglue-dev',
+        reader_cfg=adv_qnli_reader_cfg,
+        infer_cfg=adv_qnli_infer_cfg,
+        eval_cfg=adv_qnli_eval_cfg,
+    )
+]

opencompass/configs/datasets/adv_glue/adv_glue_qqp/adv_glue_qqp_gen.py

+from mmengine.config import read_base
+
+with read_base():
+    from .adv_glue_qqp_gen_cdc277 import adv_qqp_datasets  # noqa: F401, F403

opencompass/configs/datasets/adv_glue/adv_glue_qqp/adv_glue_qqp_gen_cdc277.py

+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 AdvQqpDataset, AccDropEvaluator
+from opencompass.utils.text_postprocessors import first_option_postprocess
+
+adv_qqp_reader_cfg = dict(
+    input_columns=['question1', 'question2'], output_column='label_option')
+
+adv_qqp_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(round=[
+            dict(
+                role='HUMAN',
+                prompt=
+                """Please identify whether Question 1 has the same meaning as Question 2. The answer should be exactly 'A. no' or 'B. yes'.
+Question 1: {question1}
+Question 2: {question2}
+Answer:"""),
+        ]),
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer),
+)
+
+adv_qqp_eval_cfg = dict(
+    evaluator=dict(type=AccDropEvaluator),
+    pred_role='BOT',
+    pred_postprocessor=dict(type=first_option_postprocess, options='AB'),
+)
+
+adv_qqp_datasets = [
+    dict(
+        abbr='adv_qqp',
+        type=AdvQqpDataset,
+        path='opencompass/advglue-dev',
+        reader_cfg=adv_qqp_reader_cfg,
+        infer_cfg=adv_qqp_infer_cfg,
+        eval_cfg=adv_qqp_eval_cfg,
+    )
+]

opencompass/configs/datasets/adv_glue/adv_glue_rte/adv_glue_rte_gen.py

+from mmengine.config import read_base
+
+with read_base():
+    from .adv_glue_rte_gen_8cc547 import adv_rte_datasets  # noqa: F401, F403

opencompass/configs/datasets/adv_glue/adv_glue_rte/adv_glue_rte_gen_8cc547.py

+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 AdvRteDataset, AccDropEvaluator
+from opencompass.utils.text_postprocessors import first_option_postprocess
+
+adv_rte_reader_cfg = dict(
+    input_columns=['sentence1', 'sentence2'], output_column='label_option')
+
+adv_rte_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(round=[
+            dict(
+                role='HUMAN',
+                prompt=
+                """Please identify whether the premise entails the hypothesis. The answer should be exactly 'A. yes' or 'B. no'.
+hypothesis: {sentence1}
+premise: {sentence2}
+Answer:"""),
+        ]),
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer),
+)
+
+adv_rte_eval_cfg = dict(
+    evaluator=dict(type=AccDropEvaluator),
+    pred_role='BOT',
+    pred_postprocessor=dict(type=first_option_postprocess, options='AB'),
+)
+
+adv_rte_datasets = [
+    dict(
+        abbr='adv_rte',
+        type=AdvRteDataset,
+        path='opencompass/advglue-dev',
+        reader_cfg=adv_rte_reader_cfg,
+        infer_cfg=adv_rte_infer_cfg,
+        eval_cfg=adv_rte_eval_cfg,
+    )
+]

opencompass/configs/datasets/adv_glue/adv_glue_sst2/adv_glue_sst2_gen.py

+from mmengine.config import read_base
+
+with read_base():
+    from .adv_glue_sst2_gen_ee8d3b import adv_sst2_datasets  # noqa: F401, F403

opencompass/configs/datasets/adv_glue/adv_glue_sst2/adv_glue_sst2_gen_ee8d3b.py

+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 AdvSst2Dataset, AccDropEvaluator
+from opencompass.utils.text_postprocessors import first_option_postprocess
+
+adv_sst2_reader_cfg = dict(
+    input_columns=['sentence'], output_column='label_option')
+
+adv_sst2_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(round=[
+            dict(
+                role='HUMAN',
+                prompt=
+                """For the given sentence, label the sentiment of the sentence as positive or negative. The answer should be exactly 'A. negative' or 'B. positive'.
+sentence: {sentence}
+Answer:"""),
+        ]),
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer),
+)
+
+adv_sst2_eval_cfg = dict(
+    evaluator=dict(type=AccDropEvaluator),
+    pred_role='BOT',
+    pred_postprocessor=dict(type=first_option_postprocess, options='AB'),
+)
+
+adv_sst2_datasets = [
+    dict(
+        abbr='adv_sst2',
+        type=AdvSst2Dataset,
+        path='opencompass/advglue-dev',
+        reader_cfg=adv_sst2_reader_cfg,
+        infer_cfg=adv_sst2_infer_cfg,
+        eval_cfg=adv_sst2_eval_cfg,
+    )
+]

opencompass/configs/datasets/agieval/agieval_gen.py

+from mmengine.config import read_base
+
+with read_base():
+    from .agieval_gen_617738 import agieval_datasets  # noqa: F401, F403

opencompass/configs/datasets/agieval/agieval_gen_397d81.py

+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.datasets import AGIEvalDataset_v2, AGIEvalEvaluator
+from opencompass.utils.text_postprocessors import first_capital_postprocess, first_capital_postprocess_multi
+
+agieval_reader_cfg = dict(
+    input_columns=['question', 'options'], output_column='label')
+
+agieval_single_choice_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-physics',
+    'gaokao-mathqa',
+    'logiqa-zh',
+    'lsat-ar',
+    'lsat-lr',
+    'lsat-rc',
+    'logiqa-en',
+    'sat-math',
+    'sat-en',
+    'sat-en-without-passage',
+    'aqua-rat',
+]
+agieval_multiple_choices_sets = [
+    'jec-qa-kd',
+    'jec-qa-ca',
+]
+agieval_cloze_sets = ['gaokao-mathcloze', 'math']
+agieval_chinese_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-physics',
+    'gaokao-mathqa',
+    'logiqa-zh',
+    'gaokao-mathcloze',
+]
+agieval_english_sets = [
+    'lsat-ar',
+    'lsat-lr',
+    'lsat-rc',
+    'logiqa-en',
+    'sat-math',
+    'sat-en',
+    'sat-en-without-passage',
+    'aqua-rat',
+    'math',
+]
+agieval_gaokao_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-physics',
+    'gaokao-mathqa',
+]
+
+agieval_datasets = []
+for _name in agieval_single_choice_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(round=[
+                dict(role='HUMAN', prompt=f'{{question}}\n{{options}}\n{_hint}')
+            ])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(
+        evaluator=dict(type=AccEvaluator),
+        pred_postprocessor=dict(type=first_capital_postprocess))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _name in agieval_multiple_choices_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(round=[
+                dict(role='HUMAN', prompt=f'{{question}}\n{{options}}\n{_hint}')
+            ])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(
+        evaluator=dict(type=AccEvaluator),
+        pred_postprocessor=dict(type=first_capital_postprocess_multi))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _name in agieval_cloze_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                round=[dict(role='HUMAN', prompt=f'{{question}}\n{_hint}')])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(evaluator=dict(type=AGIEvalEvaluator))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _item in agieval_datasets:
+    _name = _item['name']
+    _intro = {
+        'gaokao-chinese':
+        '以下是一道中国高考语文选择题，请选择正确的答案。',
+        'gaokao-english':
+        '以下是一道中国高考英语选择题，请选择正确的答案。',
+        'gaokao-geography':
+        '以下是一道中国高考地理选择题，请选择正确的答案。',
+        'gaokao-history':
+        '以下是一道中国高考历史选择题，请选择正确的答案。',
+        'gaokao-biology':
+        '以下是一道中国高考生物选择题，请选择正确的答案。',
+        'gaokao-chemistry':
+        '以下是一道中国高考化学选择题，请选择正确的答案。',
+        'gaokao-physics':
+        '以下是一道中国高考物理选择题，请选择正确的答案。',
+        'gaokao-mathqa':
+        '以下是一道中国高考数学选择题，请选择正确的答案。',
+        'logiqa-zh':
+        '以下是一道中国公务员考试题，请选择正确的答案。',
+        'lsat-ar':
+        'The following is a LSAT Analytical Reasoning question. Please select the correct answer.',
+        'lsat-lr':
+        'The following is a LSAT Logical Reasoning question. Please select the correct answer.',
+        'lsat-rc':
+        'The following is a LSAT Reading Comprehension question. Please select the correct answer.',
+        'logiqa-en':
+        'The following is a Logic Reasoning question. Please select the correct answer.',
+        'sat-math':
+        'The following is a SAT Math question. Please select the correct answer.',
+        'sat-en':
+        'The following is a SAT English question. Please select the correct answer.',
+        'sat-en-without-passage':
+        'The following is a SAT English question. Please select the correct answer.',
+        'aqua-rat':
+        'The following is a AQUA-RAT question. Please select the correct answer.',
+        'jec-qa-kd':
+        '以下是一道中国司法考试基础知识题，请选择正确的答案。',
+        'jec-qa-ca':
+        '以下是一道中国司法考试案例分析题，请选择正确的答案。',
+        'gaokao-mathcloze':
+        '以下是一道中国高考数学填空题，请填入正确的答案。',
+        'math':
+        'The following is a Math question. Please select the correct answer.',
+    }[_name]
+    _templates = _item['infer_cfg']['prompt_template']['template']
+    _templates['round'][0][
+        'prompt'] = _intro + '\n' + _templates['round'][0]['prompt']
+
+del _item, _intro, _templates, _name, _hint, agieval_infer_cfg, agieval_eval_cfg

opencompass/configs/datasets/agieval/agieval_gen_617738.py

+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.datasets import AGIEvalDataset_v2, AGIEvalEvaluator
+from opencompass.utils.text_postprocessors import first_option_postprocess, first_capital_postprocess_multi
+
+agieval_reader_cfg = dict(
+    input_columns=['question', 'options'], output_column='label')
+
+agieval_single_choice_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-mathqa',
+    'logiqa-zh',
+    'lsat-ar',
+    'lsat-lr',
+    'lsat-rc',
+    'logiqa-en',
+    'sat-math',
+    'sat-en',
+    'sat-en-without-passage',
+    'aqua-rat',
+]
+agieval_multiple_choices_sets = [
+    'gaokao-physics',
+    'jec-qa-kd',
+    'jec-qa-ca',
+]
+agieval_cloze_sets = ['gaokao-mathcloze', 'math']
+agieval_chinese_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-physics',
+    'gaokao-mathqa',
+    'logiqa-zh',
+    'gaokao-mathcloze',
+    'jec-qa-kd',
+    'jec-qa-ca',
+]
+agieval_english_sets = [
+    'lsat-ar',
+    'lsat-lr',
+    'lsat-rc',
+    'logiqa-en',
+    'sat-math',
+    'sat-en',
+    'sat-en-without-passage',
+    'aqua-rat',
+    'math',
+]
+agieval_gaokao_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-physics',
+    'gaokao-mathqa',
+]
+
+agieval_datasets = []
+for _name in agieval_single_choice_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(round=[
+                dict(
+                    role='HUMAN', prompt=f'{{question}}\n{{options}}\n{_hint}')
+            ])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(
+        evaluator=dict(type=AccEvaluator),
+        pred_postprocessor=dict(
+            type=first_option_postprocess, options='ABCDE'))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _name in agieval_multiple_choices_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(round=[
+                dict(
+                    role='HUMAN', prompt=f'{{question}}\n{{options}}\n{_hint}')
+            ])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(
+        evaluator=dict(type=AccEvaluator),
+        pred_postprocessor=dict(type=first_capital_postprocess_multi))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _name in agieval_cloze_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                round=[dict(role='HUMAN', prompt=f'{{question}}\n{_hint}')])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(evaluator=dict(type=AGIEvalEvaluator))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _item in agieval_datasets:
+    _name = _item['name']
+    _intro = {
+        'gaokao-chinese':
+        '以下是一道中国高考语文选择题，请选择正确的答案。',
+        'gaokao-english':
+        '以下是一道中国高考英语选择题，请选择正确的答案。',
+        'gaokao-geography':
+        '以下是一道中国高考地理选择题，请选择正确的答案。',
+        'gaokao-history':
+        '以下是一道中国高考历史选择题，请选择正确的答案。',
+        'gaokao-biology':
+        '以下是一道中国高考生物选择题，请选择正确的答案。',
+        'gaokao-chemistry':
+        '以下是一道中国高考化学选择题，请选择正确的答案。',
+        'gaokao-physics':
+        '以下是一道中国高考物理选择题，请选择正确的答案。',
+        'gaokao-mathqa':
+        '以下是一道中国高考数学选择题，请选择正确的答案。',
+        'logiqa-zh':
+        '以下是一道中国公务员考试题，请选择正确的答案。',
+        'lsat-ar':
+        'The following is a LSAT Analytical Reasoning question. Please select the correct answer.',
+        'lsat-lr':
+        'The following is a LSAT Logical Reasoning question. Please select the correct answer.',
+        'lsat-rc':
+        'The following is a LSAT Reading Comprehension question. Please select the correct answer.',
+        'logiqa-en':
+        'The following is a Logic Reasoning question. Please select the correct answer.',
+        'sat-math':
+        'The following is a SAT Math question. Please select the correct answer.',
+        'sat-en':
+        'The following is a SAT English question. Please select the correct answer.',
+        'sat-en-without-passage':
+        'The following is a SAT English question. Please select the correct answer.',
+        'aqua-rat':
+        'The following is a AQUA-RAT question. Please select the correct answer.',
+        'jec-qa-kd':
+        '以下是一道中国司法考试基础知识题，请选择正确的答案。',
+        'jec-qa-ca':
+        '以下是一道中国司法考试案例分析题，请选择正确的答案。',
+        'gaokao-mathcloze':
+        '以下是一道中国高考数学填空题，请填入正确的答案。',
+        'math':
+        'The following is a Math question. Please select the correct answer.',
+    }[_name]
+    _templates = _item['infer_cfg']['prompt_template']['template']
+    _templates['round'][0][
+        'prompt'] = _intro + '\n' + _templates['round'][0]['prompt']
+
+del _item, _intro, _templates, _name, _hint, agieval_infer_cfg, agieval_eval_cfg

opencompass/configs/datasets/agieval/agieval_gen_64afd3.py

+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.datasets import AGIEvalDataset_v2, AGIEvalEvaluator
+from opencompass.utils.text_postprocessors import first_option_postprocess, first_capital_postprocess_multi
+
+agieval_reader_cfg = dict(
+    input_columns=['question', 'options'], output_column='label')
+
+agieval_single_choice_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-mathqa',
+    'logiqa-zh',
+    'lsat-ar',
+    'lsat-lr',
+    'lsat-rc',
+    'logiqa-en',
+    'sat-math',
+    'sat-en',
+    'sat-en-without-passage',
+    'aqua-rat',
+]
+agieval_multiple_choices_sets = [
+    'gaokao-physics',
+    'jec-qa-kd',
+    'jec-qa-ca',
+]
+agieval_cloze_sets = ['gaokao-mathcloze', 'math']
+agieval_chinese_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-physics',
+    'gaokao-mathqa',
+    'logiqa-zh',
+    'gaokao-mathcloze',
+]
+agieval_english_sets = [
+    'lsat-ar',
+    'lsat-lr',
+    'lsat-rc',
+    'logiqa-en',
+    'sat-math',
+    'sat-en',
+    'sat-en-without-passage',
+    'aqua-rat',
+    'math',
+]
+agieval_gaokao_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-physics',
+    'gaokao-mathqa',
+]
+
+agieval_datasets = []
+for _name in agieval_single_choice_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(round=[
+                dict(
+                    role='HUMAN', prompt=f'{{question}}\n{{options}}\n{_hint}')
+            ])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(
+        evaluator=dict(type=AccEvaluator),
+        pred_postprocessor=dict(
+            type=first_option_postprocess, options='ABCDE'))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _name in agieval_multiple_choices_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(round=[
+                dict(
+                    role='HUMAN', prompt=f'{{question}}\n{{options}}\n{_hint}')
+            ])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(
+        evaluator=dict(type=AccEvaluator),
+        pred_postprocessor=dict(type=first_capital_postprocess_multi))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _name in agieval_cloze_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                round=[dict(role='HUMAN', prompt=f'{{question}}\n{_hint}')])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(evaluator=dict(type=AGIEvalEvaluator))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _item in agieval_datasets:
+    _name = _item['name']
+    _intro = {
+        'gaokao-chinese':
+        '以下是一道中国高考语文选择题，请选择正确的答案。',
+        'gaokao-english':
+        '以下是一道中国高考英语选择题，请选择正确的答案。',
+        'gaokao-geography':
+        '以下是一道中国高考地理选择题，请选择正确的答案。',
+        'gaokao-history':
+        '以下是一道中国高考历史选择题，请选择正确的答案。',
+        'gaokao-biology':
+        '以下是一道中国高考生物选择题，请选择正确的答案。',
+        'gaokao-chemistry':
+        '以下是一道中国高考化学选择题，请选择正确的答案。',
+        'gaokao-physics':
+        '以下是一道中国高考物理选择题，请选择正确的答案。',
+        'gaokao-mathqa':
+        '以下是一道中国高考数学选择题，请选择正确的答案。',
+        'logiqa-zh':
+        '以下是一道中国公务员考试题，请选择正确的答案。',
+        'lsat-ar':
+        'The following is a LSAT Analytical Reasoning question. Please select the correct answer.',
+        'lsat-lr':
+        'The following is a LSAT Logical Reasoning question. Please select the correct answer.',
+        'lsat-rc':
+        'The following is a LSAT Reading Comprehension question. Please select the correct answer.',
+        'logiqa-en':
+        'The following is a Logic Reasoning question. Please select the correct answer.',
+        'sat-math':
+        'The following is a SAT Math question. Please select the correct answer.',
+        'sat-en':
+        'The following is a SAT English question. Please select the correct answer.',
+        'sat-en-without-passage':
+        'The following is a SAT English question. Please select the correct answer.',
+        'aqua-rat':
+        'The following is a AQUA-RAT question. Please select the correct answer.',
+        'jec-qa-kd':
+        '以下是一道中国司法考试基础知识题，请选择正确的答案。',
+        'jec-qa-ca':
+        '以下是一道中国司法考试案例分析题，请选择正确的答案。',
+        'gaokao-mathcloze':
+        '以下是一道中国高考数学填空题，请填入正确的答案。',
+        'math':
+        'The following is a Math question. Please select the correct answer.',
+    }[_name]
+    _templates = _item['infer_cfg']['prompt_template']['template']
+    _templates['round'][0][
+        'prompt'] = _intro + '\n' + _templates['round'][0]['prompt']
+
+del _item, _intro, _templates, _name, _hint, agieval_infer_cfg, agieval_eval_cfg

opencompass/configs/datasets/agieval/agieval_gen_a0c741.py

+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.datasets import AGIEvalDataset, AGIEvalEvaluator
+from opencompass.utils.text_postprocessors import first_capital_postprocess
+
+agieval_reader_cfg = dict(
+    input_columns=['problem_input'], output_column='label')
+
+agieval_single_choice_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-mathqa',
+    'logiqa-zh',
+    'lsat-ar',
+    'lsat-lr',
+    'lsat-rc',
+    'logiqa-en',
+    'sat-math',
+    'sat-en',
+    'sat-en-without-passage',
+    'aqua-rat',
+]
+agieval_multiple_choices_sets = [
+    'gaokao-physics',
+    'jec-qa-kd',
+    'jec-qa-ca',
+]
+agieval_cloze_sets = ['gaokao-mathcloze', 'math']
+
+agieval_datasets = []
+for name in agieval_single_choice_sets:
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                round=[dict(role='HUMAN', prompt='{problem_input}')])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, labels=['A', 'B', 'C', 'D']))
+
+    agieval_eval_cfg = dict(
+        evaluator=dict(type=AccEvaluator),
+        pred_postprocessor=dict(type=first_capital_postprocess))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset,
+            path='opencompass/agieval',
+            name=name,
+            abbr='agieval-' + name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for name in agieval_multiple_choices_sets + agieval_cloze_sets:
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                round=[dict(role='HUMAN', prompt='{problem_input}')])),
+        retriever=dict(type=ZeroRetriever
+                       ),  # retriver 不起作用，以输入参数为准 (zero-shot / few-shot)
+        inferencer=dict(type=GenInferencer))
+
+    agieval_eval_cfg = dict(
+        evaluator=dict(type=AGIEvalEvaluator), pred_role='BOT')
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset,
+            path='opencompass/agieval',
+            name=name,
+            abbr='agieval-' + name,
+            setting_name='zero-shot',
+            reader_cfg=agieval_reader_cfg,
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+del name, agieval_infer_cfg, agieval_eval_cfg

opencompass/configs/datasets/agieval/agieval_mixed.py

+from mmengine.config import read_base
+
+with read_base():
+    from .agieval_mixed_0fa998 import agieval_datasets  # noqa: F401, F403

opencompass/configs/datasets/agieval/agieval_mixed_0fa998.py

+from opencompass.openicl.icl_prompt_template import PromptTemplate
+from opencompass.openicl.icl_retriever import ZeroRetriever
+from opencompass.openicl.icl_inferencer import PPLInferencer, GenInferencer
+from opencompass.openicl.icl_evaluator import AccEvaluator
+from opencompass.datasets import AGIEvalDataset_v2, AGIEvalEvaluator, AGIEvalEvaluator_mcq
+from opencompass.utils.text_postprocessors import first_capital_postprocess_multi
+
+agieval_single_choice_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-mathqa',
+    'logiqa-zh',
+    'lsat-ar',
+    'lsat-lr',
+    'lsat-rc',
+    'logiqa-en',
+    'sat-math',
+    'sat-en',
+    'sat-en-without-passage',
+    'aqua-rat',
+]
+agieval_multiple_choices_sets = [
+    'gaokao-physics',
+    'jec-qa-kd',
+    'jec-qa-ca',
+]
+agieval_cloze_sets = ['gaokao-mathcloze', 'math']
+agieval_chinese_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-physics',
+    'gaokao-mathqa',
+    'logiqa-zh',
+    'gaokao-mathcloze',
+    'jec-qa-kd',
+    'jec-qa-ca',
+]
+agieval_english_sets = [
+    'lsat-ar',
+    'lsat-lr',
+    'lsat-rc',
+    'logiqa-en',
+    'sat-math',
+    'sat-en',
+    'sat-en-without-passage',
+    'aqua-rat',
+    'math',
+]
+agieval_gaokao_sets = [
+    'gaokao-chinese',
+    'gaokao-english',
+    'gaokao-geography',
+    'gaokao-history',
+    'gaokao-biology',
+    'gaokao-chemistry',
+    'gaokao-physics',
+    'gaokao-mathqa',
+]
+
+agieval_datasets = []
+for _name in agieval_single_choice_sets:
+    if _name in ['lsat-ar', 'lsat-lr', 'lsat-rc', 'aqua-rat']:
+        _options = ['A', 'B', 'C', 'D', 'E']
+    else:
+        _options = ['A', 'B', 'C', 'D']
+    if _name in agieval_chinese_sets:
+        _hint = '答案是：'
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template={
+                label: dict(round=[
+                    dict(role='HUMAN', prompt='{question}\n{options}'),
+                    dict(role='BOT', prompt=f'{_hint}{label}')
+                ])
+                for label in _options
+            }),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=PPLInferencer, labels=_options))
+
+    agieval_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=dict(
+                input_columns=['question', 'options'] + _options,
+                output_column='label'),
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _name in agieval_multiple_choices_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是： '
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(round=[
+                dict(role='HUMAN', prompt=f'{{question}}\n{{options}}\n{_hint}')
+            ])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(
+        evaluator=dict(type=AGIEvalEvaluator_mcq),
+        pred_postprocessor=dict(type=first_capital_postprocess_multi))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=dict(
+                input_columns=['question', 'options'], output_column='label'),
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _name in agieval_cloze_sets:
+    if _name in agieval_chinese_sets:
+        _hint = '答案是：'
+    else:
+        _hint = 'The answer is '
+    agieval_infer_cfg = dict(
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                round=[dict(role='HUMAN', prompt=f'{{question}}{_hint}')])),
+        retriever=dict(type=ZeroRetriever),
+        inferencer=dict(type=GenInferencer, max_out_len=1024))
+
+    agieval_eval_cfg = dict(evaluator=dict(type=AGIEvalEvaluator))
+
+    agieval_datasets.append(
+        dict(
+            type=AGIEvalDataset_v2,
+            path='opencompass/agieval',
+            name=_name,
+            abbr='agieval-' + _name,
+            setting_name='zero-shot',
+            reader_cfg=dict(
+                input_columns=['question', 'options'], output_column='label'),
+            infer_cfg=agieval_infer_cfg.copy(),
+            eval_cfg=agieval_eval_cfg.copy()))
+
+for _item in agieval_datasets:
+    _name = _item['name']
+    _intro = {
+        'gaokao-chinese':
+        '以下是一道中国高考语文选择题，请选择正确的答案。',
+        'gaokao-english':
+        '以下是一道中国高考英语选择题，请选择正确的答案。',
+        'gaokao-geography':
+        '以下是一道中国高考地理选择题，请选择正确的答案。',
+        'gaokao-history':
+        '以下是一道中国高考历史选择题，请选择正确的答案。',
+        'gaokao-biology':
+        '以下是一道中国高考生物选择题，请选择正确的答案。',
+        'gaokao-chemistry':
+        '以下是一道中国高考化学选择题，请选择正确的答案。',
+        'gaokao-physics':
+        '以下是一道中国高考物理选择题，请选择正确的答案。',
+        'gaokao-mathqa':
+        '以下是一道中国高考数学选择题，请选择正确的答案。',
+        'logiqa-zh':
+        '以下是一道中国公务员考试题，请选择正确的答案。',
+        'lsat-ar':
+        'The following is a LSAT Analytical Reasoning question. Please select the correct answer.',
+        'lsat-lr':
+        'The following is a LSAT Logical Reasoning question. Please select the correct answer.',
+        'lsat-rc':
+        'The following is a LSAT Reading Comprehension question. Please select the correct answer.',
+        'logiqa-en':
+        'The following is a Logic Reasoning question. Please select the correct answer.',
+        'sat-math':
+        'The following is a SAT Math question. Please select the correct answer.',
+        'sat-en':
+        'The following is a SAT English question. Please select the correct answer.',
+        'sat-en-without-passage':
+        'The following is a SAT English question. Please select the correct answer.',
+        'aqua-rat':
+        'The following is a AQUA-RAT question. Please select the correct answer.',
+        'jec-qa-kd':
+        '以下是一道中国司法考试基础知识题，请选择正确的答案。',
+        'jec-qa-ca':
+        '以下是一道中国司法考试案例分析题，请选择正确的答案。',
+        'gaokao-mathcloze':
+        '以下是一道中国高考数学填空题，请填入正确的答案。',
+        'math':
+        'The following is a Math question. Please select the correct answer.',
+    }[_name]
+    _templates = _item['infer_cfg']['prompt_template']['template']
+
+    if _item['infer_cfg']['inferencer']['type'] == PPLInferencer:
+        for _label in _templates:
+            _templates[_label]['round'][0][
+                'prompt'] = _intro + '\n' + _templates[_label]['round'][0][
+                    'prompt']
+    else:
+        _templates['round'][0][
+            'prompt'] = _intro + '\n' + _templates['round'][0]['prompt']
+
+del _item, _intro, _templates, _label, _name, _options, _hint, agieval_infer_cfg, agieval_eval_cfg

opencompass/configs/datasets/aime2024/README.md

+### Description
+
+Math dataset composed of problems from AIME2024 (American Invitational Mathematics Examination 2024). 
+
+### Performance
+
+| Qwen2.5-Math-72B-Instruct | Qwen2.5-Math-7B-Instruct | Qwen2-Math-7B-Instruct | Qwen2-Math-1.5B-Instruct | internlm2-math-7b |
+| ----------- | ----------- | ----------- | ----------- | ----------- |
+| 20.00 | 16.67 | 16.67 | 13.33 | 3.33 |
+
+| Qwen2.5-72B-Instruct | Qwen2.5-7B-Instruct | internlm2_5-7b-chat |
+| ----------- | ----------- |  ----------- |
+| 31.25 | 26.44 | 9.13 |
\ No newline at end of file

opencompass/configs/datasets/aime2024/aime2024_0shot_nocot_gen_2b9dc2.py

+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 Aime2024Dataset, MATHEvaluator, math_postprocess_v2
+
+
+aime2024_reader_cfg = dict(
+    input_columns=['question'], 
+    output_column='answer'
+)
+
+
+aime2024_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='HUMAN', prompt='{question}\nRemember to put your final answer within \\boxed{}.'),
+            ],
+        )
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer, max_out_len=2048)
+)
+
+aime2024_eval_cfg = dict(
+    evaluator=dict(type=MATHEvaluator, version='v2'), pred_postprocessor=dict(type=math_postprocess_v2)
+)
+
+aime2024_datasets = [
+    dict(
+        abbr='aime2024',
+        type=Aime2024Dataset,
+        path='opencompass/aime2024',
+        reader_cfg=aime2024_reader_cfg,
+        infer_cfg=aime2024_infer_cfg,
+        eval_cfg=aime2024_eval_cfg
+    )
+]
\ No newline at end of file

opencompass/configs/datasets/aime2024/aime2024_0shot_nocot_genericllmeval_academic_gen.py

+# flake8: noqa
+
+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 Aime2024Dataset
+from opencompass.evaluator import GenericLLMEvaluator
+from opencompass.datasets.generic import generic_llmjudge_academic_postprocess
+
+
+aime2024_reader_cfg = dict(
+    input_columns=['question'],
+    output_column='answer'
+)
+
+
+aime2024_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='HUMAN',
+                     prompt='{question}\nRemember to put your final answer within \\boxed{}.'),
+            ],
+        )
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer, max_out_len=2048)
+)
+
+
+GRADER_TEMPLATE = """
+    Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly.
+
+    Here are some evaluation criteria:
+    1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct.
+    2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question.
+    3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct.
+    4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct.
+    5. If the prediction is given with \\boxed{}, please ignore the \\boxed{} and only judge whether the candidate's answer is consistent with the standard answer.
+
+    Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of:
+    A: CORRECT
+    B: INCORRECT
+    Just return the letters "A" or "B", with no text around it.
+
+    Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer.
+
+
+    <Original Question Begin>: \n{question}\n<Original Question End>\n\n
+    <Gold Target Begin>: \n{answer}\n<Gold Target End>\n\n
+    <Predicted Answer Begin>: \n{prediction}\n<Predicted End>\n\n
+
+    Judging the correctness of candidates' answers:
+""".strip()
+
+
+aime2024_eval_cfg = dict(
+    evaluator=dict(
+        type=GenericLLMEvaluator,
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+                begin=[
+                    dict(
+                        role='SYSTEM',
+                        fallback_role='HUMAN',
+                        prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.")
+                ],
+                round=[
+                    dict(
+                        role='HUMAN',
+                        prompt=GRADER_TEMPLATE),
+                ]),
+        ),
+        dataset_cfg=dict(
+            type=Aime2024Dataset,
+            path='opencompass/aime2024',
+            reader_cfg=aime2024_reader_cfg,
+        ),
+        judge_cfg=dict(),
+        dict_postprocessor=dict(type=generic_llmjudge_academic_postprocess,
+                                metric_name='accuracy'),
+    ),
+    pred_role='BOT',
+)
+
+aime2024_datasets = [
+    dict(
+        abbr='aime2024',
+        type=Aime2024Dataset,
+        path='opencompass/aime2024',
+        reader_cfg=aime2024_reader_cfg,
+        infer_cfg=aime2024_infer_cfg,
+        eval_cfg=aime2024_eval_cfg,
+        mode='singlescore',
+    )
+]

opencompass/configs/datasets/aime2024/aime2024_0shot_nocot_genericllmeval_gen_2b9dc2.py

+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 Aime2024Dataset, MATHEvaluator, math_postprocess_v2
+from opencompass.evaluator import GenericLLMEvaluator
+from opencompass.datasets import generic_llmjudge_postprocess
+
+aime2024_reader_cfg = dict(
+    input_columns=['question'], 
+    output_column='answer'
+)
+
+
+aime2024_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='HUMAN', prompt='{question}\nRemember to put your final answer within \\boxed{}.'),
+            ],
+        )
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer, max_out_len=2048)
+)
+
+
+GRADER_TEMPLATE = """
+    Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. 
+    
+    Here are some evaluation criteria:
+    1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct.
+    2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question.
+    3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct.
+    4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct.
+    5. If the prediction is given with \\boxed{}, please ignore the \\boxed{} and only judge whether the candidate's answer is consistent with the standard answer.
+
+    Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of:
+    A: CORRECT 
+    B: INCORRECT
+    Just return the letters "A" or "B", with no text around it.
+
+    Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer.
+
+
+    <Original Question Begin>: \n{question}\n<Original Question End>\n\n
+    <Gold Target Begin>: \n{answer}\n<Gold Target End>\n\n
+    <Predicted Answer Begin>: \n{prediction}\n<Predicted End>\n\n
+    
+    Judging the correctness of candidates' answers:
+""".strip()
+
+aime2024_eval_cfg = dict(
+    evaluator=dict(
+        type=GenericLLMEvaluator,
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+            begin=[
+                dict(
+                    role='SYSTEM',
+                    fallback_role='HUMAN',
+                    prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.")
+            ],
+                round=[
+                dict(
+                    role='HUMAN',
+                    prompt = GRADER_TEMPLATE
+                ),
+            ]),
+        ),
+        dataset_cfg=dict(
+            type=Aime2024Dataset,
+            path='opencompass/aime2024',
+            reader_cfg=aime2024_reader_cfg,
+        ),
+        judge_cfg=dict(),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
+    ),
+    pred_role='BOT',
+)
+
+aime2024_datasets = [
+    dict(
+        abbr='aime2024',
+        type=Aime2024Dataset,
+        path='opencompass/aime2024',
+        reader_cfg=aime2024_reader_cfg,
+        infer_cfg=aime2024_infer_cfg,
+        eval_cfg=aime2024_eval_cfg,
+        mode='singlescore',
+    )
+]
\ No newline at end of file

opencompass/configs/datasets/aime2024/aime2024_0shot_nocot_genericllmeval_xml_gen_2b9dc2.py

+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 Aime2024Dataset, MATHEvaluator, math_postprocess_v2
+from opencompass.evaluator import GenericLLMEvaluator
+from opencompass.datasets import generic_llmjudge_postprocess
+from opencompass.utils import xml_tag_postprocessor
+
+aime2024_reader_cfg = dict(
+    input_columns=['question'], 
+    output_column='answer'
+)
+
+
+aime2024_infer_cfg = dict(
+    prompt_template=dict(
+        type=PromptTemplate,
+        template=dict(
+            round=[
+                dict(role='HUMAN', prompt='{question}\nRemember to put your final answer within \\boxed{}.'),
+            ],
+        )
+    ),
+    retriever=dict(type=ZeroRetriever),
+    inferencer=dict(type=GenInferencer, max_out_len=2048)
+)
+
+
+GRADER_TEMPLATE = """
+    Please as a grading expert, judge whether the final answers given by the candidates below are consistent with the standard answers, that is, whether the candidates answered correctly. 
+    
+    Here are some evaluation criteria:
+    1. Please refer to the given standard answer. You don't need to re-generate the answer to the question because the standard answer has been given. You only need to judge whether the candidate's answer is consistent with the standard answer according to the form of the question. Don't try to answer the original question. You can assume that the standard answer is definitely correct.
+    2. Because the candidate's answer may be different from the standard answer in the form of expression, before making a judgment, please understand the question and the standard answer first, and then judge whether the candidate's answer is correct, but be careful not to try to answer the original question.
+    3. Some answers may contain multiple items, such as multiple-choice questions, multiple-select questions, fill-in-the-blank questions, etc. As long as the answer is the same as the standard answer, it is enough. For multiple-select questions and multiple-blank fill-in-the-blank questions, the candidate needs to answer all the corresponding options or blanks correctly to be considered correct.
+    4. Some answers may be expressed in different ways, such as some answers may be a mathematical expression, some answers may be a textual description, as long as the meaning expressed is the same. And some formulas are expressed in different ways, but they are equivalent and correct.
+    5. If the prediction is given with \\boxed{}, please ignore the \\boxed{} and only judge whether the candidate's answer is consistent with the standard answer.
+
+    Please judge whether the following answers are consistent with the standard answer based on the above criteria. Grade the predicted answer of this new question as one of:
+    A: CORRECT 
+    B: INCORRECT
+    Just return the letters "A" or "B", with no text around it.
+
+    Here is your task. Simply reply with either CORRECT, INCORRECT. Don't apologize or correct yourself if there was a mistake; we are just trying to grade the answer.
+
+
+    <Original Question Begin>: \n{question}\n<Original Question End>\n\n
+    <Gold Target Begin>: \n{answer}\n<Gold Target End>\n\n
+    <Predicted Answer Begin>: \n{prediction}\n<Predicted End>\n\n
+    
+    Judging the correctness of candidates' answers:
+""".strip()
+
+aime2024_eval_cfg = dict(
+    evaluator=dict(
+        type=GenericLLMEvaluator,
+        prompt_template=dict(
+            type=PromptTemplate,
+            template=dict(
+            begin=[
+                dict(
+                    role='SYSTEM',
+                    fallback_role='HUMAN',
+                    prompt="You are a helpful assistant who evaluates the correctness and quality of models' outputs.")
+            ],
+                round=[
+                dict(
+                    role='HUMAN',
+                    prompt = GRADER_TEMPLATE
+                ),
+            ]),
+        ),
+        dataset_cfg=dict(
+            type=Aime2024Dataset,
+            path='opencompass/aime2024',
+            reader_cfg=aime2024_reader_cfg,
+        ),
+        judge_cfg=dict(),
+        dict_postprocessor=dict(type=generic_llmjudge_postprocess),
+        pred_postprocessor=dict(type=xml_tag_postprocessor, tag='<conclude>'),
+    ),
+    pred_role='BOT',
+)
+
+aime2024_datasets = [
+    dict(
+        abbr='aime2024',
+        type=Aime2024Dataset,
+        path='opencompass/aime2024',
+        reader_cfg=aime2024_reader_cfg,
+        infer_cfg=aime2024_infer_cfg,
+        eval_cfg=aime2024_eval_cfg,
+        mode='singlescore',
+    )
+]
\ No newline at end of file