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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>
57 lines
2.1 KiB
Python
57 lines
2.1 KiB
Python
import os
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from mmengine.config import read_base
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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.datasets import BBHDataset, bbh_mcq_postprocess, BBHEvaluator, BBHEvaluator_mcq
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with read_base():
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from .bbh_subset_settings import settings
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bbh_datasets = []
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for name, test_type in settings:
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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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task_prompt, body = hint.split('\n\nQ:', 1)
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sections = ('Q:' + body).split('\n\n')
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prompt_rounds = []
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for index, section in enumerate(sections):
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question, answer = section.split('\nA:')
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answer = 'A:' + answer
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if index == 0:
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desc = task_prompt.strip() + '\n'
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else:
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desc = ''
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prompt_rounds.append(dict(role='HUMAN', prompt=f'{desc}{question.strip()}'))
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prompt_rounds.append(dict(role='BOT', prompt=answer.strip()))
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prompt_rounds.append(dict(role='HUMAN', prompt='Q: {input}'))
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bbh_reader_cfg = dict(input_columns=['input'], output_column='target')
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bbh_infer_cfg = dict(
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prompt_template=dict(type=PromptTemplate, template=dict(round=prompt_rounds)),
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retriever=dict(type=ZeroRetriever),
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inferencer=dict(type=GenInferencer, max_out_len=512))
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if test_type == 'mcq':
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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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else:
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bbh_eval_cfg = dict(
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evaluator=dict(type=BBHEvaluator),
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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.copy(),
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infer_cfg=bbh_infer_cfg.copy(),
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eval_cfg=bbh_eval_cfg.copy()))
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