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63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
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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.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import OBQADataset
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from opencompass.utils.text_postprocessors import first_option_postprocess
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_input_columns = [
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['question_stem', 'A', 'B', 'C', 'D'],
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['question_stem', 'A', 'B', 'C', 'D', 'fact1'],
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]
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_template = [
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dict(
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round=[
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dict(
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role='HUMAN',
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prompt=
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'Question: {question_stem}\nA. {A}\nB. {B}\nC. {C}\nD. {D}\nAnswer:'
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),
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], ),
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dict(
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round=[
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dict(
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role='HUMAN',
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prompt=
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'Given the fact: {fact1}\nQuestion: {question_stem}\nA. {A}\nB. {B}\nC. {C}\nD. {D}\nAnswer:',
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),
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], ),
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]
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obqa_datasets = [
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dict(
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abbr='openbookqa',
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type=OBQADataset,
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path='opencompass/openbookqa_test',
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name='main',
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),
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dict(
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abbr='openbookqa_fact',
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type=OBQADataset,
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path='opencompass/openbookqa_fact',
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name='additional',
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),
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]
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for _i in range(2):
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obqa_reader_cfg = dict(
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input_columns=_input_columns[_i], output_column='answerKey')
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obqa_infer_cfg = dict(
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prompt_template=dict(type=PromptTemplate, template=_template[_i]),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer),
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)
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obqa_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_role='BOT',
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pred_postprocessor=dict(type=first_option_postprocess, options='ABCD'),
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)
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obqa_datasets[_i]['reader_cfg'] = obqa_reader_cfg
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obqa_datasets[_i]['infer_cfg'] = obqa_infer_cfg
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obqa_datasets[_i]['eval_cfg'] = obqa_eval_cfg
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