SuperGLUE_RTE_ppl_66caf3.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

RTE_reader_cfg = dict(
    input_columns=['hypothesis', 'premise'],
    output_column='label',
    test_split='train')

RTE_infer_cfg = dict(
    prompt_template=dict(
        type=PromptTemplate,
        template={
            'entailment':
            dict(round=[
                dict(
                    role='HUMAN',
                    prompt=
                    '{premise}\n{hypothesis}\nIs the sentence below entailed by the sentence above?'
                ),
                dict(role='BOT', prompt='Yes'),
            ]),
            'not_entailment':
            dict(round=[
                dict(
                    role='HUMAN',
                    prompt=
                    '{premise}\n{hypothesis}\nIs the sentence below entailed by the sentence above?'
                ),
                dict(role='BOT', prompt='No'),
            ])
        },
    ),
    retriever=dict(type=ZeroRetriever),
    inferencer=dict(type=PPLInferencer),
)

RTE_eval_cfg = dict(evaluator=dict(type=AccEvaluator))

RTE_datasets = [
    dict(
        type=HFDataset,
        abbr='RTE',
        path='json',
        data_files='./data/SuperGLUE/RTE/val.jsonl',
        split='train',
        reader_cfg=RTE_reader_cfg,
        infer_cfg=RTE_infer_cfg,
        eval_cfg=RTE_eval_cfg,
    )
]