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nq_open_gen_e93f8a.py 2.14 KB
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Fengzhe Zhou's avatar
[Sync] Sync with internal codes 2023.01.08 (#777)  
Fengzhe Zhou committed
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from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever, FixKRetriever,  RandomRetriever
from opencompass.openicl.icl_inferencer import GenInferencer
from opencompass.datasets import NQOpenDataset, NQEvaluator

nq_datasets = []
for k in [0, 1, 5, 25]:
    nq_reader_cfg = dict(
        input_columns=['question'], output_column='answer', train_split='train', test_split='validation')

    if k == 0:
        nq_infer_cfg = dict(
            prompt_template=dict(
                type=PromptTemplate,
                template=dict(
                    round=[
                        dict(role='HUMAN', prompt='Q: {question}?'),
                        dict(role='BOT', prompt='A:'),
                    ]
                )
            ),
            retriever=dict(type=ZeroRetriever),
            inferencer=dict(type=GenInferencer, max_out_len=50)
        )
    else:
        nq_infer_cfg = dict(
            ice_template=dict(
                type=PromptTemplate,
                template=dict(
                    round=[
                        dict(role='HUMAN', prompt='Q: {question}?'),
                        dict(role='BOT', prompt='A: {answer}.\n'),
                    ]
                ),
            ),
            prompt_template=dict(
                type=PromptTemplate,
                template=dict(
                    begin="</E>",
                    round=[
                        dict(role='HUMAN', prompt='Q: {question}?'),
                        dict(role='BOT', prompt='A:'),
                    ]
                ),
                ice_token="</E>",
            ),
            retriever=dict(type=FixKRetriever, fix_id_list=list(range(k))),
            inferencer=dict(type=GenInferencer, max_out_len=50, stopping_criteria=["Q:", "\n"]),
        )

    nq_eval_cfg = dict(evaluator=dict(type=NQEvaluator), pred_role="BOT")

    nq_datasets.append(
        dict(
            type=NQOpenDataset,
            abbr=f'nq_open_{k}shot',
            path='./data/nq-open/',
            reader_cfg=nq_reader_cfg,
            infer_cfg=nq_infer_cfg,
            eval_cfg=nq_eval_cfg)
        )