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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>
98 lines
3.6 KiB
Python
98 lines
3.6 KiB
Python
import json
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from datasets import Dataset
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from opencompass.openicl.icl_evaluator import BaseEvaluator
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from opencompass.registry import LOAD_DATASET
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from opencompass.utils import get_data_path
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from ..base import BaseDataset
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from .evaluation_main import (InputExample, test_instruction_following_loose,
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test_instruction_following_strict)
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@LOAD_DATASET.register_module()
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class IFEvalDataset(BaseDataset):
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@staticmethod
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def load(path):
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path = get_data_path(path, local_mode=True)
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datasets = []
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with open(path, 'r', encoding='utf-8') as file:
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for line in file:
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tmp = json.loads(line.strip())
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dataset = dict(prompt=tmp['prompt'], reference=tmp)
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datasets.append(dataset)
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return Dataset.from_list(datasets)
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class IFEvaluator(BaseEvaluator):
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def score(self, predictions, references, origin_prompt):
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prompt_strict_correct, prompt_strict_total = 0, 0
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inst_strict_correct, inst_strict_total = 0, 0
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prompt_loose_correct, prompt_loose_total = 0, 0
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inst_loose_correct, inst_loose_total = 0, 0
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details = {}
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for index, (pred, refer) in enumerate(zip(predictions, references)):
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input = InputExample(
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key=refer['key'],
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instruction_id_list=refer['instruction_id_list'],
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prompt=refer['prompt'],
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kwargs=refer['kwargs'])
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for kwarg in input.kwargs:
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for k in list(kwarg.keys()):
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if kwarg[k] is None:
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kwarg.pop(k, None)
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# strict
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example = test_instruction_following_strict(input, pred)
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follow_instruction_list = example.follow_instruction_list
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instruction_id_list = example.instruction_id_list
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prompt_strict_total += 1
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is_strict_correct = all(follow_instruction_list)
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prompt_strict_correct += is_strict_correct
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inst_strict_total += len(instruction_id_list)
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inst_strict_correct += sum(follow_instruction_list)
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# loose
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example = test_instruction_following_loose(input, pred)
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follow_instruction_list = example.follow_instruction_list
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instruction_id_list = example.instruction_id_list
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prompt_loose_total += 1
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is_loose_correct = all(follow_instruction_list)
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prompt_loose_correct += is_loose_correct
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inst_loose_total += len(instruction_id_list)
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inst_loose_correct += sum(follow_instruction_list)
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if is_strict_correct:
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grade = 'strict'
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elif is_loose_correct:
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grade = 'loose'
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else:
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grade = 'none'
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details[str(index)] = {
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'prompt': origin_prompt[index],
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'pred': pred,
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'refer': refer,
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'is_strict_correct': is_strict_correct,
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'is_loose_correct': is_loose_correct,
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'is_correct': is_strict_correct,
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'grade': grade
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}
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results = {
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'Prompt-level-strict-accuracy':
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prompt_strict_correct / prompt_strict_total * 100,
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'Inst-level-strict-accuracy':
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inst_strict_correct / inst_strict_total * 100,
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'Prompt-level-loose-accuracy':
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prompt_loose_correct / prompt_loose_total * 100,
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'Inst-level-loose-accuracy':
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inst_loose_correct / inst_loose_total * 100,
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'details':
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details
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}
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return results
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