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44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets import LongBenchF1Evaluator, LongBenchnarrativeqaDataset
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LongBench_narrativeqa_reader_cfg = dict(
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input_columns=['context', 'input'],
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output_column='answers',
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train_split='test',
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test_split='test',
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)
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LongBench_narrativeqa_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(
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role='HUMAN',
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prompt='You are given a story, which can be either a novel or a movie script, and a question. Answer the question as concisely as you can, using a single phrase if possible. Do not provide any explanation.\n\nStory: {context}\n\nNow, answer the question based on the story as concisely as you can, using a single phrase if possible. Do not provide any explanation.\n\nQuestion: {input}\n\nAnswer:',
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),
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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=128),
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)
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LongBench_narrativeqa_eval_cfg = dict(
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evaluator=dict(type=LongBenchF1Evaluator), pred_role='BOT'
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)
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LongBench_narrativeqa_datasets = [
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dict(
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type=LongBenchnarrativeqaDataset,
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abbr='LongBench_narrativeqa',
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path='opencompass/Longbench',
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name='narrativeqa',
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reader_cfg=LongBench_narrativeqa_reader_cfg,
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infer_cfg=LongBench_narrativeqa_infer_cfg,
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eval_cfg=LongBench_narrativeqa_eval_cfg,
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)
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]
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