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a11cb45c83
* [Feat] support adv_glue dataset for adversarial robustness * reorg files * minor fix * minor fix * support prompt bench demo * minor fix * minor fix * minor fix * minor fix * minor fix * minor fix * minor fix * minor fix
49 lines
2.4 KiB
Python
49 lines
2.4 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 AttackInferencer
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from opencompass.datasets import SQuAD20Dataset, SQuAD20Evaluator
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squad20_reader_cfg = dict(
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input_columns=['context', 'question'],
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output_column='answers')
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original_prompt_list = [
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"Based on the given context, provide the best possible answer. If there's no answer available in the context, respond with 'unanswerable'.",
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"Identify the most relevant answer from the context. If it's not possible to find an answer, respond with 'unanswerable'.",
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"Find the correct answer in the context provided. If an answer cannot be found, please respond with 'unanswerable'.",
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"Please extract the most appropriate answer from the context. If an answer is not present, indicate 'unanswerable'.",
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"Using the context, determine the most suitable answer. If the context doesn't contain the answer, respond with 'unanswerable'.",
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"Locate the most accurate answer within the context. If the context doesn't provide an answer, respond with 'unanswerable'.",
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"Please derive the most fitting answer from the context. If there isn't an answer in the context, respond with 'unanswerable'.",
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"Discover the best answer based on the context. If the context doesn't include an answer, respond with 'unanswerable'.",
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"From the context, provide the most precise answer. If the answer is not in the context, respond with 'unanswerable'.",
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"Search the context for the most relevant answer. If the answer cannot be found, respond with 'unanswerable'.",
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]
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squad20_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='{adv_prompt} {context}'),
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dict(role='BOT', prompt='Answer:'),
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], )),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=AttackInferencer, max_out_len=50,
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original_prompt_list=original_prompt_list,
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adv_key='adv_prompt',
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metric_key='score'))
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squad20_eval_cfg = dict(
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evaluator=dict(type=SQuAD20Evaluator), pred_role='BOT')
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squad20_datasets = [
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dict(
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type=SQuAD20Dataset,
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abbr='squad_v2',
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path='./data/SQuAD2.0/dev-v2.0.json',
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reader_cfg=squad20_reader_cfg,
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infer_cfg=squad20_infer_cfg,
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eval_cfg=squad20_eval_cfg)
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]
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