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from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import FixKRetriever
from opencompass.openicl.icl_inferencer import PPLInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import HFDataset
_hint = "The following are semantic matching questions. \n" \
"Please determine whether the following two sentences are semantically duplicate: " \
"0 means not duplicate, 1 means duplicate.\n"
QQP_infer_cfg = dict(
ice_template=dict(
type=PromptTemplate,
template="Sentence one: {question1}\nSentence two: {question2}\nResult: {label}",
),
prompt_template=dict(
type=PromptTemplate,
template={
answer:
f"{_hint}</E>Sentence one: {{question1}}\nSentence two: {{question2}}\nResult: {answer}"
for answer in [0, 1]
},
ice_token='</E>',
),
retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]),
inferencer=dict(type=PPLInferencer))
QQP_eval_cfg = dict(evaluator=dict(type=AccEvaluator), )
QQP_datasets = []
for _split in ["validation", "test"]:
QQP_reader_cfg = dict(
input_columns=['question1', 'question2'],
output_column='label',
train_split="train",
test_split=_split
)
QQP_datasets.append(
dict(
abbr=f'QQP-{_split}',
type=HFDataset,
path='glue',
name='qqp',
reader_cfg=QQP_reader_cfg,
infer_cfg=QQP_infer_cfg,
eval_cfg=QQP_eval_cfg
)
)
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