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58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
import os
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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 PPLInferencer
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from opencompass.datasets import CharmDataset
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from opencompass.openicl.icl_evaluator import AccwithDetailsEvaluator
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charm_tasks = [
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['Chinese_Anachronisms_Judgment', 'AB'],
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['Chinese_Movie_and_Music_Recommendation', 'ABCD'],
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['Chinese_Natural_Language_Inference', 'ABC'],
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['Chinese_Reading_Comprehension', 'ABCD'],
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['Chinese_Sequence_Understanding', 'ABCD'],
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['Chinese_Sport_Understanding', 'AB'],
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['Chinese_Time_Understanding', 'ABCD'],
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['Global_Anachronisms_Judgment', 'AB'],
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['Global_Movie_and_Music_Recommendation', 'ABCD'],
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['Global_Natural_Language_Inference', 'ABC'],
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['Global_Reading_Comprehension', 'ABCD'],
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['Global_Sequence_Understanding', 'ABCD'],
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['Global_Sport_Understanding', 'AB'],
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['Global_Time_Understanding', 'ABCDEF'],
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]
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charm_reason_datasets = []
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for task_name, options in charm_tasks:
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with open(os.path.join(os.path.dirname(__file__), 'few-shot-examples', f'{task_name}_Direct.txt'), 'r') as f:
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few_shot_example = f.read()
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charm_reason_reader_cfg = dict(input_columns=['input'], output_column='target')
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charm_reason_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template={
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f'({opt})': f'{few_shot_example}\n{{input}}\nA: {opt}' for opt in options
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},
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=PPLInferencer),
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)
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charm_reason_eval_cfg = dict(evaluator=dict(type=AccwithDetailsEvaluator))
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charm_reason_datasets.append(
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dict(
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type=CharmDataset,
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abbr=f'charm-reason-{task_name}_Direct',
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path=f'data/CHARM/reasoning',
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name=task_name,
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reader_cfg=charm_reason_reader_cfg,
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infer_cfg=charm_reason_infer_cfg,
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eval_cfg=charm_reason_eval_cfg,
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)
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)
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