OpenCompass/configs/datasets/SuperGLUE_WSC/SuperGLUE_WSC_ppl_1c4a90.py

50 lines
1.4 KiB
Python
Raw Normal View History

from opencompass.openicl.icl_prompt_template import PromptTemplate
from opencompass.openicl.icl_retriever import ZeroRetriever
from opencompass.openicl.icl_inferencer import PPLInferencer
from opencompass.openicl.icl_evaluator import AccEvaluator
from opencompass.datasets import WSCDataset_V3
WSC_reader_cfg = dict(
input_columns=["span1", "span2", "text"],
output_column="label",
)
WSC_infer_cfg = dict(
prompt_template=dict(
type=PromptTemplate,
template={
'A':
dict(round=[
dict(
role="HUMAN",
prompt="Passage: {text}\nDoes the pronoun # {span2} # refer to * {span1} *?\nA. Yes\nB. No\nAnswer: "
),
dict(role='BOT', prompt='A'),
]),
'B':
dict(round=[
dict(
role="HUMAN",
prompt="Passage: {text}\nDoes the pronoun # {span2} # refer to * {span1} *?\nA. Yes\nB. No\nAnswer: "
),
dict(role='BOT', prompt='B'),
]),
},
),
retriever=dict(type=ZeroRetriever),
inferencer=dict(type=PPLInferencer),
)
WSC_eval_cfg = dict(evaluator=dict(type=AccEvaluator), )
WSC_datasets = [
dict(
abbr="WSC",
type=WSCDataset_V3,
path="./data/SuperGLUE/WSC/val.jsonl",
reader_cfg=WSC_reader_cfg,
infer_cfg=WSC_infer_cfg,
eval_cfg=WSC_eval_cfg,
)
]