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111 lines
3.6 KiB
Python
111 lines
3.6 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 GLMChoiceInferencer
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from opencompass.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import AGIEvalDataset
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agieval_reader_cfg = dict(
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input_columns=['problem_input'], output_column='label')
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agieval_single_choice_sets = [
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'gaokao-chinese',
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'gaokao-english',
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'gaokao-geography',
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'gaokao-history',
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'gaokao-biology',
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'gaokao-chemistry',
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'gaokao-physics',
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'gaokao-mathqa',
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'logiqa-zh',
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'lsat-ar',
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'lsat-lr',
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'lsat-rc',
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'logiqa-en',
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'sat-math',
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'sat-en',
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'sat-en-without-passage',
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'aqua-rat',
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]
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agieval_multiple_choices_sets = [
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'jec-qa-kd', # 数据需要额外处理
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'jec-qa-ca', # 数据需要额外处理
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]
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agieval_cloze_sets = ['gaokao-mathcloze', 'math']
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agieval_datasets = []
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for name in agieval_single_choice_sets:
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agieval_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template={
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label: f'{{problem_input}} {label}'
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for label in ['A', 'B', 'C', 'D']
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}),
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retriever=dict(type=ZeroRetriever
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), # retriver 不起作用，以输入参数为准 (zero-shot / few-shot)
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inferencer=dict(
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type=GLMChoiceInferencer, choices=['A', 'B', 'C', 'D']))
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agieval_eval_cfg = dict(evaluator=dict(type=AccEvaluator))
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agieval_datasets.append(
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dict(
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type=AGIEvalDataset,
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path='./data/AGIEval/data/v1/',
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name=name,
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abbr='agieval-' + name,
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setting_name='zero-shot',
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reader_cfg=agieval_reader_cfg,
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infer_cfg=agieval_infer_cfg.copy(),
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eval_cfg=agieval_eval_cfg.copy()))
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for name in agieval_multiple_choices_sets:
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_hint = '答案是： '
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agieval_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template=dict(round=[
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dict(role='HUMAN', prompt=f'{{question}}\n{{options}}\n{_hint}')
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]),
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ice_token='</E>'),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type='GenInferencer'))
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agieval_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_postprocessor=dict(type='first-capital-multi'))
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agieval_datasets.append(
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dict(
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type='AGIEvalDataset_v2',
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path='./data/AGIEval/data/v1/',
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name=name,
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abbr='agieval-' + name,
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setting_name='zero-shot',
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reader_cfg=agieval_reader_cfg,
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infer_cfg=agieval_infer_cfg.copy(),
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eval_cfg=agieval_eval_cfg.copy()))
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for name in agieval_cloze_sets:
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agieval_infer_cfg = dict(
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ice_template=dict(
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type=PromptTemplate,
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template='</E>{problem_input}',
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ice_token='</E>'),
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retriever=dict(type=ZeroRetriever
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), # retriver 不起作用，以输入参数为准 (zero-shot / few-shot)
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inferencer=dict(type='GenInferencer'))
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agieval_eval_cfg = dict(evaluator=dict(type='AGIEvalEvaluator'))
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agieval_datasets.append(
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dict(
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type=AGIEvalDataset,
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path='./data/AGIEval/data/v1/',
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name=name,
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abbr='agieval-' + name,
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setting_name='zero-shot',
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reader_cfg=agieval_reader_cfg,
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infer_cfg=agieval_infer_cfg.copy(),
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eval_cfg=agieval_eval_cfg.copy()))
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del name, agieval_infer_cfg, agieval_eval_cfg
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