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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>
100 lines
3.3 KiB
Python
100 lines
3.3 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 GenInferencer
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from opencompass.openicl.icl_evaluator import AccEvaluator
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from opencompass.datasets import BBHDataset, BBHEvaluator, bbh_mcq_postprocess, BBHEvaluator_mcq
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bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
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bbh_multiple_choice_sets = [
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'temporal_sequences',
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'disambiguation_qa',
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'date_understanding',
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'tracking_shuffled_objects_three_objects',
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'penguins_in_a_table',
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'geometric_shapes',
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'snarks',
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'ruin_names',
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'tracking_shuffled_objects_seven_objects',
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'tracking_shuffled_objects_five_objects',
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'logical_deduction_three_objects',
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'hyperbaton',
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'logical_deduction_five_objects',
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'logical_deduction_seven_objects',
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'movie_recommendation',
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'salient_translation_error_detection',
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'reasoning_about_colored_objects',
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]
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bbh_free_form_sets = [
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'multistep_arithmetic_two',
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'navigate',
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'dyck_languages',
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'word_sorting',
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'sports_understanding',
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'boolean_expressions',
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'object_counting',
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'formal_fallacies',
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'causal_judgement',
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'web_of_lies',
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]
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bbh_datasets = []
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for _name in bbh_multiple_choice_sets:
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with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
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_hint = f.read()
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bbh_infer_cfg = dict(
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prompt_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"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
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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=512))
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bbh_eval_cfg = dict(
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evaluator=dict(type=BBHEvaluator_mcq),
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pred_role='BOT',
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pred_postprocessor=dict(type=bbh_mcq_postprocess),
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dataset_postprocessor=dict(type=bbh_mcq_postprocess))
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bbh_datasets.append(
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dict(
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type=BBHDataset,
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path='opencompass/bbh',
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name=_name,
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abbr='bbh-' + _name,
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reader_cfg=bbh_reader_cfg,
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infer_cfg=bbh_infer_cfg.copy(),
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eval_cfg=bbh_eval_cfg.copy()))
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for _name in bbh_free_form_sets:
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with open(os.path.join(os.path.dirname(__file__), 'lib_prompt', f'{_name}.txt'), 'r') as f:
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_hint = f.read()
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bbh_infer_cfg = dict(
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prompt_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"Follow the given examples and answer the question.\n{_hint}\n\nQ: {{input}}\nA: Let's think step by step."
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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=512))
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bbh_eval_cfg = dict(evaluator=dict(type=BBHEvaluator), pred_role='BOT')
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bbh_datasets.append(
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dict(
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type=BBHDataset,
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path='opencompass/bbh',
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name=_name,
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abbr='bbh-' + _name,
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reader_cfg=bbh_reader_cfg,
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infer_cfg=bbh_infer_cfg.copy(),
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eval_cfg=bbh_eval_cfg.copy()))
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