OpenCompass/configs/datasets/subjective/alignbench/alignbench_v1_1_judgeby_critiquellm.py

65 lines
2.0 KiB
Python

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 LMEvaluator
from opencompass.datasets import AlignmentBenchDataset
subjective_reader_cfg = dict(
input_columns=['question', 'capability', 'critiquellm_prefix'],
output_column='judge',
)
subjective_all_sets = [
'alignment_bench_v1_1',
]
data_path ='data/subjective/alignment_bench'
alignment_bench_config_path = 'data/subjective/alignment_bench/config'
alignment_bench_config_name = 'multi-dimension'
subjective_datasets = []
for _name in subjective_all_sets:
subjective_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt='{question}'
),
]),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer, max_out_len=2048),
)
subjective_eval_cfg = dict(
evaluator=dict(
type=LMEvaluator,
prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt = '{critiquellm_prefix}[助手的答案开始]\n{prediction}\n[助手的答案结束]\n'
),
]),
),
),
pred_role='BOT',
)
subjective_datasets.append(
dict(
abbr=f'{_name}',
type=AlignmentBenchDataset,
path=data_path,
name=_name,
alignment_bench_config_path=alignment_bench_config_path,
alignment_bench_config_name=alignment_bench_config_name,
reader_cfg=subjective_reader_cfg,
infer_cfg=subjective_infer_cfg,
eval_cfg=subjective_eval_cfg
))