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63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.openicl.icl_evaluator import LMEvaluator
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from opencompass.datasets import FlamesDataset
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subjective_reader_cfg = dict(
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input_columns=['prompt','instruction'],
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output_column='judge',
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)
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subjective_all_sets = [
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'data_protection', 'legality', 'morality_non_environmental_friendly', 'morality_disobey_social_norm', 'morality_chinese_values', 'safety_non_anthropomorphism', 'safety_physical_harm', 'safety_mental_harm', 'safety_property_safety', 'fairness'
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]
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#this is the path to flames dataset
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data_path ='./data/flames'
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flames_datasets = []
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for _name in subjective_all_sets:
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subjective_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(
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role='HUMAN',
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prompt='{prompt}'
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),
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]),
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=2048),
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)
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subjective_eval_cfg = dict(
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evaluator=dict(
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type=LMEvaluator,
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(
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role='HUMAN',
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prompt = '{instruction}{prediction}',
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),
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]),
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),
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),
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pred_role='BOT',
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)
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flames_datasets.append(
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dict(
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abbr=f'{_name}',
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type=FlamesDataset,
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path=data_path,
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name=_name,
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reader_cfg=subjective_reader_cfg,
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infer_cfg=subjective_infer_cfg,
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eval_cfg=subjective_eval_cfg
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))
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