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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>
62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
from opencompass.openicl.icl_prompt_template import PromptTemplate
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from opencompass.openicl.icl_retriever import ZeroRetriever, FixKRetriever
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from opencompass.openicl.icl_inferencer import GenInferencer
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from opencompass.datasets import NaturalQuestionDataset, NQEvaluator
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nq_datasets = []
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for k in [0, 1, 5]:
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nq_reader_cfg = dict(
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input_columns=['question'], output_column='answer', train_split='dev')
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if k == 0:
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nq_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='Answer these questions, your answer should be as simple as possible, start your answer with the prompt \'The answer is \'.\nQ: {question}?'),
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dict(role='BOT', prompt='A:'),
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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, max_out_len=50)
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)
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else:
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nq_infer_cfg = dict(
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ice_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='Answer the question, your answer should be as simple as possible, start your answer with the prompt \'The answer is \'.\nQ: {question}?'),
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dict(role='BOT', prompt='A: The answer is {answer}.\n'),
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]
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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(role='HUMAN', prompt='Answer the question, your answer should be as simple as possible, start your answer with the prompt \'The answer is \'.\nQ: {question}?'),
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dict(role='BOT', prompt='A:'),
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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=list(range(k))),
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inferencer=dict(type=GenInferencer, max_out_len=50),
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)
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nq_eval_cfg = dict(evaluator=dict(type=NQEvaluator), pred_role='BOT')
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nq_datasets.append(
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dict(
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type=NaturalQuestionDataset,
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abbr='nq' if k == 0 else f'nq_{k}shot',
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path='opencompass/natural_question',
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reader_cfg=nq_reader_cfg,
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infer_cfg=nq_infer_cfg,
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eval_cfg=nq_eval_cfg)
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)
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