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55 lines
1.9 KiB
Python
55 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.matbench.matbench import MatbenchDataset, MatbenchEvaluator_regression, MatbenchEvaluator_classification
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matbench_reader_cfg = dict(
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input_columns=['problem'], output_column='answer')
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matbench_tasks = ['matbench_steels','matbench_expt_gap', 'matbench_expt_is_metal','matbench_glass']
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matbench_datasets = []
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for task in matbench_tasks:
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if task in ['matbench_expt_is_metal','matbench_glass']:
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matbench_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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round=[dict(role='HUMAN', prompt=f'{{problem}} Please present your answer by yes or no, do not output anything else.')])),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer))
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matbench_eval_cfg = dict(
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evaluator=dict(type=MatbenchEvaluator_classification),
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pred_role='BOT')
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elif task in ['matbench_steels','matbench_expt_gap']:
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matbench_infer_cfg = dict(
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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round=[dict(role='HUMAN', prompt=f'{{problem}} Please present your answer by one float number, do not output anything else.')])),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer))
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matbench_eval_cfg = dict(
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evaluator=dict(type=MatbenchEvaluator_regression),
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pred_role='BOT')
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matbench_datasets.append(
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dict(
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type=MatbenchDataset,
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path=f'opencompass/Matbench',
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abbr=task,
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reader_cfg=matbench_reader_cfg,
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infer_cfg=matbench_infer_cfg,
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eval_cfg=matbench_eval_cfg))
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