CLUE_afqmc_ppl_6507d7.py

from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import PPLInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import HFDataset

afqmc_reader_cfg = dict(
    input_columns=['sentence1', 'sentence2'],
    output_column='label',
    test_split='train')

afqmc_infer_cfg = dict(
    prompt_template=dict(
        type=PromptTemplate,
        template={
            0:
            dict(round=[
                dict(
                    role='HUMAN',
                    prompt=
                    '语句一：“{sentence1}”\n语句二：“{sentence2}”\n语句一与语句二是关于蚂蚁金融产品的疑问，两者所询问的内容是否完全一致？'
                ),
                dict(role='BOT', prompt='不完全一致')
            ]),
            1:
            dict(round=[
                dict(
                    role='HUMAN',
                    prompt=
                    '语句一：“{sentence1}”\n语句二：“{sentence2}”\n语句一与语句二是关于蚂蚁金融产品的疑问，两者所询问的内容是否完全一致？'
                ),
                dict(role='BOT', prompt='完全一致')
            ]),
        }),
    retriever=dict(type=ZeroRetriever),
    inferencer=dict(type=PPLInferencer))

afqmc_eval_cfg = dict(evaluator=dict(type=AccEvaluator))

afqmc_datasets = [
    dict(
        type=HFDataset,
        abbr='afqmc-dev',
        path='json',
        data_files='./data/CLUE/AFQMC/dev.json',
        split='train',
        reader_cfg=afqmc_reader_cfg,
        infer_cfg=afqmc_infer_cfg,
        eval_cfg=afqmc_eval_cfg),
]