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46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
from mmengine.config import read_base
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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 TheoremQADatasetV3, TheoremQA_postprocess_v3, TheoremQAEvaluatorV3
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with read_base():
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from .TheoremQA_few_shot_examples import examples
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num_shot = 5
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rounds = []
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for index, (query, response) in enumerate(examples[:num_shot]):
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if index == 0:
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desc = 'You are supposed to provide a solution to a given problem.\n\n'
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else:
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desc = ''
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rounds += [
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dict(role='HUMAN', prompt=f'{desc}Problem:\n{query}\nSolution:'),
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dict(role='BOT', prompt=f'{response}')
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]
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rounds += [dict(role='HUMAN', prompt='Problem:\n{Question}\nSolution:')]
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TheoremQA_reader_cfg = dict(input_columns=['Question', 'Answer_type'], output_column='Answer', train_split='test', test_split='test')
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TheoremQA_infer_cfg = dict(
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prompt_template=dict(type=PromptTemplate, template=dict(round=rounds)),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=1024, stopping_criteria=['Problem:', 'Problem']),
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)
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TheoremQA_eval_cfg = dict(
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evaluator=dict(type=TheoremQAEvaluatorV3),
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pred_postprocessor=dict(type=TheoremQA_postprocess_v3)
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)
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TheoremQA_datasets = [
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dict(
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abbr='TheoremQA',
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type=TheoremQADatasetV3,
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path='data/TheoremQA/theoremqa_test.json',
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reader_cfg=TheoremQA_reader_cfg,
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infer_cfg=TheoremQA_infer_cfg,
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eval_cfg=TheoremQA_eval_cfg,
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)
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]
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