OpenCompass/opencompass/configs/datasets/nq/nq_open_1shot_gen_2e45e5.py

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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 [1]:
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)
)
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),
)
nq_eval_cfg = dict(evaluator=dict(type=NQEvaluator), pred_role='BOT')
nq_datasets.append(
dict(
type=NQOpenDataset,
abbr=f'nq_open_{k}shot',
path='opencompass/nq_open',
reader_cfg=nq_reader_cfg,
infer_cfg=nq_infer_cfg,
eval_cfg=nq_eval_cfg)
)