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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>
43 lines
1.5 KiB
Python
43 lines
1.5 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 GenInferencer
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from opencompass.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import MaxminDataset
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from opencompass.utils.text_postprocessors import first_capital_postprocess
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maxmin_reader_cfg = dict(
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input_columns=['nl_tokens', 'pl_tokens'],
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output_column='answer',
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)
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maxmin_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=[
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dict(role='HUMAN', prompt="Code:{pl_tokens}\nThe aim of the code: {nl_tokens}\nQuestion: Please tell me what \"<mask>\" in the code should be replaced with and you must response to me only A or B.\nA. max\nB. min\nAnswer:"),
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dict(role='BOT', prompt='{answer}'),
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]
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),
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),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer),
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)
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maxmin_eval_cfg = dict(evaluator=dict(type=AccEvaluator),
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pred_role='BOT',
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pred_postprocessor=dict(type=first_capital_postprocess))
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maxmin_datasets = [
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dict(
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type=MaxminDataset,
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abbr=f'maxmin',
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test_path='opencompass/clozeTest_maxmin',
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answer_path='opencompass/clozeTest_maxmin_answers',
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reader_cfg=maxmin_reader_cfg,
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infer_cfg=maxmin_infer_cfg,
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eval_cfg=maxmin_eval_cfg,
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)
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]
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