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edab1c07ba
* add ceval, gsm8k modelscope surpport * update race, mmlu, arc, cmmlu, commonsenseqa, humaneval and unittest * update bbh, flores, obqa, siqa, storycloze, summedits, winogrande, xsum datasets * format file * format file * update dataset format * support ms_dataset * udpate dataset for modelscope support * merge myl_dev and update test_ms_dataset * udpate dataset for modelscope support * update readme * update eval_api_zhipu_v2 * remove unused code * add get_data_path function * update readme * remove tydiqa japanese subset * add ceval, gsm8k modelscope surpport * update race, mmlu, arc, cmmlu, commonsenseqa, humaneval and unittest * update bbh, flores, obqa, siqa, storycloze, summedits, winogrande, xsum datasets * format file * format file * update dataset format * support ms_dataset * udpate dataset for modelscope support * merge myl_dev and update test_ms_dataset * update readme * udpate dataset for modelscope support * update eval_api_zhipu_v2 * remove unused code * add get_data_path function * remove tydiqa japanese subset * update util * remove .DS_Store * fix md format * move util into package * update docs/get_started.md * restore eval_api_zhipu_v2.py, add environment setting * Update dataset * Update * Update * Update * Update --------- Co-authored-by: Yun lin <yunlin@U-Q9X2K4QV-1904.local> Co-authored-by: Yunnglin <mao.looper@qq.com> Co-authored-by: Yun lin <yunlin@laptop.local> Co-authored-by: Yunnglin <maoyl@smail.nju.edu.cn> Co-authored-by: zhangsongyang <zhangsongyang@pjlab.org.cn>
78 lines
2.5 KiB
Python
78 lines
2.5 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import FixKRetriever
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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 ChemBenchDataset
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from opencompass.utils.text_postprocessors import first_capital_postprocess
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chembench_reader_cfg = dict(
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input_columns=['input', 'A', 'B', 'C', 'D'],
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output_column='target',
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train_split='dev')
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chembench_all_sets = [
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'Name_Conversion',
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'Property_Prediction',
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'Mol2caption',
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'Caption2mol',
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'Product_Prediction',
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'Retrosynthesis',
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'Yield_Prediction',
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'Temperature_Prediction',
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'Solvent_Prediction'
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]
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chembench_datasets = []
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for _name in chembench_all_sets:
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# _hint = f'There is a single choice question about {_name.replace("_", " ")}. Answer the question by replying A, B, C or D.'
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_hint = f'There is a single choice question about chemistry. Answer the question by replying A, B, C or D.'
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chembench_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(
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role='HUMAN',
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prompt=
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f'{_hint}\nQuestion: {{input}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nAnswer: '
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),
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dict(role='BOT', prompt='{target}\n')
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]),
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),
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prompt_template=dict(
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type=PromptTemplate,
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template=dict(
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begin='</E>',
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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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f'{_hint}\nQuestion: {{input}}\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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ice_token='</E>',
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),
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retriever=dict(type=FixKRetriever, fix_id_list=[0, 1, 2, 3, 4]),
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inferencer=dict(type=GenInferencer),
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)
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chembench_eval_cfg = dict(
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evaluator=dict(type=AccEvaluator),
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pred_postprocessor=dict(type=first_capital_postprocess))
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chembench_datasets.append(
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dict(
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abbr=f'ChemBench_{_name}',
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type=ChemBenchDataset,
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path='opencompass/ChemBench',
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name=_name,
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reader_cfg=chembench_reader_cfg,
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infer_cfg=chembench_infer_cfg,
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eval_cfg=chembench_eval_cfg,
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))
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del _name, _hint
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