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from mmengine.config import read_base
from opencompass.datasets.circular import (CircularCEvalDataset, CircularMMLUDataset, CircularCMMLUDataset, CircularCSQADataset,
CircularARCDataset, CircularHSWAGDataset, CircularOBQADataset, CircularRaceDataset, CircularEvaluator)
from opencompass.summarizers import CircularSummarizer
with read_base():
from .datasets.ceval.ceval_gen_5f30c7 import ceval_datasets
from .datasets.mmlu.mmlu_gen_a484b3 import mmlu_datasets
from .datasets.cmmlu.cmmlu_gen_c13365 import cmmlu_datasets
from .datasets.hellaswag.hellaswag_gen_6faab5 import hellaswag_datasets
from .datasets.ARC_e.ARC_e_gen_1e0de5 import ARC_e_datasets
from .datasets.ARC_c.ARC_c_gen_1e0de5 import ARC_c_datasets
from .datasets.commonsenseqa.commonsenseqa_gen_1da2d0 import commonsenseqa_datasets
from .datasets.obqa.obqa_gen_9069e4 import obqa_datasets
from .datasets.race.race_gen_69ee4f import race_datasets
from .models.hf_internlm.hf_internlm_chat_7b import models as hf_internlm_chat_7b_model
from .models.hf_internlm.hf_internlm_chat_20b import models as hf_internlm_chat_20b_model
from .models.qwen.hf_qwen_7b_chat import models as hf_qwen_7b_chat_model
from .models.qwen.hf_qwen_14b_chat import models as hf_qwen_14b_chat_model
from .summarizers.groups.mmlu import mmlu_summary_groups
from .summarizers.groups.cmmlu import cmmlu_summary_groups
from .summarizers.groups.ceval import ceval_summary_groups
for ds, t in [
(ceval_datasets, CircularCEvalDataset),
(mmlu_datasets, CircularMMLUDataset),
(cmmlu_datasets, CircularCMMLUDataset),
(hellaswag_datasets, CircularHSWAGDataset),
(ARC_e_datasets, CircularARCDataset),
(ARC_c_datasets, CircularARCDataset),
(commonsenseqa_datasets, CircularCSQADataset),
(obqa_datasets, CircularOBQADataset),
(race_datasets, CircularRaceDataset),
]:
for d in ds:
d['type'] = t
d['abbr'] = d['abbr'] + '-circular-4'
d['eval_cfg']['evaluator'] = {'type': CircularEvaluator, 'circular_pattern': 'circular'}
d['circular_patterns'] = 'circular'
datasets = sum([v for k, v in locals().items() if k.endswith("_datasets") or k == 'datasets'], [])
models = sum([v for k, v in locals().items() if k.endswith("_model")], [])
# config summarizer
other_summary_groups = [
{'name': 'average',
'subsets': ['ceval', 'mmlu', 'cmmlu', 'hellaswag', 'ARC-e', 'ARC-c', 'commonsense_qa', 'openbookqa_fact', 'race-middle', 'race-high']},
]
origin_summary_groups = sum([v for k, v in locals().items() if k.endswith("_summary_groups")], [])
new_summary_groups = []
for item in origin_summary_groups:
new_summary_groups.append(
{
'name': item['name'] + '-circular-4',
'subsets': [i + '-circular-4' for i in item['subsets']],
}
)
summarizer = dict(
type=CircularSummarizer,
metric_types=['acc_origin', 'perf_circular'],
dataset_abbrs = [
'average-circular-4',
'ceval-circular-4',
'mmlu-circular-4',
'cmmlu-circular-4',
'hellaswag-circular-4',
'ARC-e-circular-4',
'ARC-c-circular-4',
'commonsense_qa-circular-4',
'openbookqa_fact-circular-4',
'race-middle-circular-4',
'race-high-circular-4',
'ceval-humanities-circular-4',
'ceval-stem-circular-4',
'ceval-social-science-circular-4',
'ceval-other-circular-4',
'mmlu-humanities-circular-4',
'mmlu-stem-circular-4',
'mmlu-social-science-circular-4',
'mmlu-other-circular-4',
'cmmlu-humanities-circular-4',
'cmmlu-stem-circular-4',
'cmmlu-social-science-circular-4',
'cmmlu-other-circular-4',
'cmmlu-china-specific-circular-4',
],
summary_groups=new_summary_groups,
)
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