hallucination / opencompass /.github /scripts /eval_regression_chat_obj_fullbench_v6.py
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from mmengine.config import read_base
with read_base():
from opencompass.configs.datasets.aime2024.aime2024_llmjudge_gen_5e9f4f import \
aime2024_datasets # noqa: F401, E501
from opencompass.configs.datasets.aime2025.aime2025_llmjudge_gen_5e9f4f import \
aime2025_datasets # noqa: F401, E501
from opencompass.configs.datasets.ARC_Prize_Public_Evaluation.arc_prize_public_evaluation_gen_fedd04 import \
arc_prize_public_evaluation_datasets # noqa: F401, E501
from opencompass.configs.datasets.bbh.bbh_llmjudge_gen_b5bdf1 import \
bbh_datasets # noqa: F401, E501
from opencompass.configs.datasets.cmo_fib.cmo_fib_gen_2783e5 import \
cmo_fib_datasets # noqa: F401, E501
# dingo
from opencompass.configs.datasets.dingo.dingo_gen import \
datasets as dingo_datasets # noqa: F401, E501
# General Reasoning
from opencompass.configs.datasets.drop.drop_llmjudge_gen_3857b0 import \
drop_datasets # noqa: F401, E501
from opencompass.configs.datasets.GaokaoBench.GaokaoBench_no_subjective_gen_d16acb import \
GaokaoBench_datasets # noqa: F401, E501
from opencompass.configs.datasets.gpqa.gpqa_0shot_nocot_genericllmeval_gen_772ea0 import \
gpqa_datasets # noqa: F401, E501
# Math Calculation
from opencompass.configs.datasets.gsm8k.gsm8k_0shot_v2_gen_17d799 import \
gsm8k_datasets # noqa: F401, E501
from opencompass.configs.datasets.hellaswag.hellaswag_llmjudge_gen_809ef1 import \
hellaswag_datasets # noqa: F401, E501
from opencompass.configs.datasets.korbench.korbench_llmjudge_gen_56cf43 import \
korbench_0shot_single_datasets # noqa: F401, E501
from opencompass.configs.datasets.math.math_500_llmjudge_gen_6ff468 import \
math_datasets # noqa: F401, E501
from opencompass.configs.datasets.MathBench.mathbench_2024_gen_4b8f28 import \
mathbench_datasets # noqa: F401, E501
from opencompass.configs.datasets.musr.musr_llmjudge_gen_b47fd3 import \
musr_datasets # noqa: F401, E501
from opencompass.configs.datasets.supergpqa.supergpqa_llmjudge_gen_12b8bc import \
supergpqa_datasets # noqa: F401, E501
from opencompass.configs.datasets.triviaqa.triviaqa_wiki_1shot_gen_c87d61 import \
triviaqa_datasets # noqa: F401, E501
from opencompass.configs.models.hf_internlm.hf_internlm3_8b_instruct import \
models as hf_internlm3_8b_instruct_model # noqa: F401, E501
from opencompass.configs.models.hf_internlm.lmdeploy_internlm3_8b_instruct import \
models as lmdeploy_internlm3_8b_instruct_model # noqa: F401, E501
from opencompass.configs.models.qwen2_5.lmdeploy_qwen2_5_32b_instruct import \
models as lmdeploy_qwen2_5_32b_instruct # noqa: F401, E501
# Summary Groups
from opencompass.configs.summarizers.groups.bbeh import \
bbeh_summary_groups # noqa: F401, E501
from opencompass.configs.summarizers.groups.bbh import \
bbh_summary_groups # noqa: F401, E501
from opencompass.configs.summarizers.groups.cmmlu import \
cmmlu_summary_groups # noqa: F401, E501
from opencompass.configs.summarizers.groups.GaokaoBench import \
GaokaoBench_summary_groups # noqa: F401, E501
from opencompass.configs.summarizers.groups.korbench import \
korbench_summary_groups # noqa: F401, E501
from opencompass.configs.summarizers.groups.mathbench_v1_2024 import \
mathbench_2024_summary_groups # noqa: F401, E501
from opencompass.configs.summarizers.groups.mmlu import \
mmlu_summary_groups # noqa: F401, E501
from opencompass.configs.summarizers.groups.mmlu_pro import \
mmlu_pro_summary_groups # noqa: F401, E501
from opencompass.configs.summarizers.groups.musr_average import \
summarizer as musr_summarizer
from opencompass.configs.summarizers.mmmlu_lite import \
mmmlu_summary_groups # noqa: F401, E501
from ...volc import infer # noqa: F401, E501
datasets = [
v[0] for k, v in locals().items() if k.endswith('_datasets')
and 'scicode' not in k.lower() and 'dingo' not in k.lower()
and 'arc_prize' not in k.lower() and isinstance(v, list) and len(v) > 0
]
dingo_datasets[0]['abbr'] = 'qa_dingo_cn'
dingo_datasets[0]['path'] = 'data/qabench/history_prompt_case_cn.csv'
datasets.append(dingo_datasets[0])
datasets += arc_prize_public_evaluation_datasets
musr_summary_groups = musr_summarizer['summary_groups']
summary_groups = sum(
[v for k, v in locals().items() if k.endswith('_summary_groups')], [])
summary_groups.append(
{
'name': 'Mathbench',
'subsets': ['mathbench-a (average)', 'mathbench-t (average)'],
}, )
for d in datasets:
d['reader_cfg']['test_range'] = '[0:16]'
if 'dataset_cfg' in d['eval_cfg']['evaluator'] and 'reader_cfg' in d[
'eval_cfg']['evaluator']['dataset_cfg']:
d['eval_cfg']['evaluator']['dataset_cfg']['reader_cfg'][
'test_range'] = '[0:16]'
if 'llm_evaluator' in d['eval_cfg']['evaluator'] and 'dataset_cfg' in d[
'eval_cfg']['evaluator']['llm_evaluator']:
d['eval_cfg']['evaluator']['llm_evaluator']['dataset_cfg'][
'reader_cfg']['test_range'] = '[0:16]'
models = sum([v for k, v in locals().items() if k.endswith('_model')], [])
for m in models:
m['abbr'] = m['abbr'] + '_fullbench'
if 'turbomind' in m['abbr'] or 'lmdeploy' in m['abbr']:
m['engine_config']['max_batch_size'] = 1
m['batch_size'] = 1
models = sorted(models, key=lambda x: x['run_cfg']['num_gpus'])
obj_judge_model = lmdeploy_internlm3_8b_instruct_model[0]
obj_judge_model['engine_config']['max_batch_size'] = 1
obj_judge_model['engine_config']['cache_max_entry_count'] = 0.6
obj_judge_model['batch_size'] = 1
for d in datasets:
if 'judge_cfg' in d['eval_cfg']['evaluator']:
d['eval_cfg']['evaluator']['judge_cfg'] = obj_judge_model
if 'llm_evaluator' in d['eval_cfg']['evaluator'] and 'judge_cfg' in d[
'eval_cfg']['evaluator']['llm_evaluator']:
d['eval_cfg']['evaluator']['llm_evaluator'][
'judge_cfg'] = obj_judge_model