OpenCompass/configs/datasets/subjective_cmp/subjective_cmp.py

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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.subjective_cmp import SubjectiveCmpDataset
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subjective_reader_cfg = dict(
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input_columns=['question', 'index', 'reference_answer', 'evaluating_guidance', 'capability', 'prompt'],
output_column=None,
train_split='test')
subjective_all_sets = [
"subjective_demo",
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]
subjective_datasets = []
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for _name in subjective_all_sets:
subjective_infer_cfg = dict(
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prompt_template=dict(
type=PromptTemplate,
template=dict(round=[
dict(
role='HUMAN',
prompt="{question}"
),
]),
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=GenInferencer),
)
subjective_eval_cfg = dict(
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evaluator=dict(
type=LMEvaluator,
cmp_order='both',
prompt_template=dict(
type=PromptTemplate,
template=dict(
begin=[
dict(
role="SYSTEM",
fallback_role="HUMAN",
prompt="{prompt}"
),
],
round=[dict(role="HUMAN",
prompt="回答 1: <回答 1 开始> {prediction} <回答 1 结束>\n回答 2: <回答 2 开始> {prediction2} <回答 2 结束>\n")]))),
pred_role="BOT",
)
subjective_datasets.append(
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dict(
abbr=f"{_name}",
type=SubjectiveCmpDataset,
path="./data/subjective/",
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name=_name,
reader_cfg=subjective_reader_cfg,
infer_cfg=subjective_infer_cfg,
eval_cfg=subjective_eval_cfg
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))