OpenCompass/configs/datasets/ds1000/ds1000_gen_cbc84f.py

from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import (DS1000Dataset, ds1000_postprocess,
                                  ds1000_matplotlib_postprocess,
                                  DS1000Evaluator)

ds1000_reader_cfg = dict(
    input_columns=['prompt'],
    output_column='test_column',
    train_split='test',
    test_split='test')

ds1000_infer_cfg = dict(
    prompt_template=dict(
        type=PromptTemplate,
        template=dict(round=[
            dict(
                role='HUMAN',
                prompt='{prompt}',
            ),
        ]),
    ),
    retriever=dict(type=ZeroRetriever),
    inferencer=dict(type=GenInferencer),
)

ds1000_eval_cfg = dict(
    evaluator=dict(type=DS1000Evaluator),
    pred_role='BOT',
    pred_postprocessor=dict(type=ds1000_postprocess),
)

# The DS-1000 dataset can be downloaded from
# https://github.com/HKUNLP/DS-1000/blob/main/ds1000_data.zip
ds1000_datasets = [
    dict(
        abbr=f'ds1000_{lib}',
        type=DS1000Dataset,
        path='./data/ds1000_data/',
        libs=f'{lib}',
        reader_cfg=ds1000_reader_cfg,
        infer_cfg=ds1000_infer_cfg,
        eval_cfg=ds1000_eval_cfg,
    ) for lib in [
        'Pandas',
        'Numpy',
        'Tensorflow',
        'Scipy',
        'Sklearn',
        'Pytorch',
    ]
]
ds1000_datasets.append(
    dict(
        abbr='ds1000_Matplotlib',
        type=DS1000Dataset,
        path='./data/ds1000_data/',
        libs='Matplotlib',
        reader_cfg=ds1000_reader_cfg,
        infer_cfg=ds1000_infer_cfg,
        eval_cfg=dict(
            evaluator=dict(type=DS1000Evaluator),
            pred_role='BOT',
            pred_postprocessor=dict(type=ds1000_matplotlib_postprocess),
        ),
    ))