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62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever, FixKRetriever, RandomRetriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets import NQOpenDataset, NQEvaluator
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nq_datasets = []
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for k in [0, 1, 5, 25]:
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nq_reader_cfg = dict(
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input_columns=['question'], output_column='answer', train_split='train', test_split='validation')
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if k == 0:
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nq_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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round=[
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dict(role='HUMAN', prompt='Q: {question}?'),
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dict(role='BOT', prompt='A:'),
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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=50)
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)
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else:
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nq_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template=dict(
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round=[
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dict(role='HUMAN', prompt='Q: {question}?'),
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dict(role='BOT', prompt='A: {answer}.\n'),
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]
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),
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),
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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begin='</E>',
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round=[
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dict(role='HUMAN', prompt='Q: {question}?'),
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dict(role='BOT', prompt='A:'),
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]
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),
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ice_token='</E>',
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),
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retriever=dict(type=FixKRetriever, fix_id_list=list(range(k))),
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inferencer=dict(type=GenInferencer, max_out_len=50, stopping_criteria=['Q:', '\n']),
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)
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nq_eval_cfg = dict(evaluator=dict(type=NQEvaluator), pred_role='BOT')
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nq_datasets.append(
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dict(
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type=NQOpenDataset,
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abbr=f'nq_open_{k}shot',
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path='opencompass/nq_open',
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reader_cfg=nq_reader_cfg,
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infer_cfg=nq_infer_cfg,
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eval_cfg=nq_eval_cfg)
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)
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