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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
45 lines
1.7 KiB
Python
45 lines
1.7 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 MATHDataset, MATHEvaluator, math_postprocess
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math_reader_cfg = dict(input_columns=['problem'], output_column='solution')
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original_prompt_list = [
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"Solve the following math question about",
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"Determine the solution to this mathematical problem related to",
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"Calculate the answer to the following math query about",
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"Find the solution for this mathematical challenge with",
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"Compute the result of this math task concerning",
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"Resolve the following mathematical question associated with",
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"Work out the answer to this math problem featuring",
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"Figure out the solution for the following mathematical task with",
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"Obtain the result for this math question regarding",
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"Evaluate the following mathematical problem that includes",
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]
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math_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(
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role="HUMAN",
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prompt="{adv_prompt} {problem}:"),
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]),
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=AttackInferencer, original_prompt_list=original_prompt_list,max_out_len=512, adv_key='adv_prompt'))
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math_eval_cfg = dict(
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evaluator=dict(type=MATHEvaluator), pred_postprocessor=dict(type=math_postprocess))
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math_datasets = [
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dict(
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type=MATHDataset,
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abbr='math',
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path='./data/math/math.json',
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reader_cfg=math_reader_cfg,
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infer_cfg=math_infer_cfg,
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eval_cfg=math_eval_cfg)
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]
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