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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>
126 lines
4.5 KiB
Python
126 lines
4.5 KiB
Python
import json
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import os.path as osp
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from os import environ
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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 ICL_EVALUATORS, 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 .math_equivalence import is_equiv
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from .post_process import parse_math_answer
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@LOAD_DATASET.register_module()
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class AGIEvalDataset(BaseDataset):
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@staticmethod
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def load(path: str, name: str, setting_name: str):
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path = get_data_path(path)
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from .dataset_loader import load_dataset, load_dataset_as_result_schema
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assert setting_name in 'zero-shot', 'only support zero-shot setting'
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dataset_wo_label = load_dataset(name, setting_name, path)
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dataset_with_label = load_dataset_as_result_schema(name, path)
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dataset = []
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for d1, d2 in zip(dataset_wo_label, dataset_with_label):
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dataset.append({
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'id': d2.index,
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'problem_input': d1['context'],
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'label': d2.label,
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})
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dataset = Dataset.from_list(dataset)
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return dataset
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@LOAD_DATASET.register_module()
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class AGIEvalDataset_v2(BaseDataset):
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@staticmethod
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def load(path: str, name: str, setting_name: str):
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path = get_data_path(path)
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assert setting_name in 'zero-shot', 'only support zero-shot setting'
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if environ.get('DATASET_SOURCE') == 'ModelScope':
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from modelscope import MsDataset
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ms_dataset = MsDataset.load(path, subset_name=name, split='test')
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dataset = []
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for item in ms_dataset:
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passage = item['passage'] if item['passage'] else ''
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question = passage + item['question']
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options = '\n'.join(item['options']) if item['options'] else ''
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if item['label']:
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try:
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label = eval(item['label'])
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except Exception:
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label = item['label']
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if isinstance(label, list):
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label = ''.join(label)
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else:
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label = item['answer']
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d = {'question': question, 'options': options, 'label': label}
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dataset.append(d)
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dataset = Dataset.from_list(dataset)
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else:
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filename = osp.join(path, name + '.jsonl')
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with open(filename, encoding='utf-8') as f:
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data = [json.loads(line.strip()) for line in f]
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dataset = []
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for item in data:
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passage = item['passage'] if item['passage'] else ''
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question = passage + item['question']
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options = '\n'.join(item['options']) if item['options'] else ''
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if item['label']:
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if isinstance(item['label'], list):
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label = ''.join(item['label'])
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else:
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label = item['label']
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else:
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label = item['answer']
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d = {'question': question, 'options': options, 'label': label}
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dataset.append(d)
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dataset = Dataset.from_list(dataset)
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return dataset
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@ICL_EVALUATORS.register_module()
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class AGIEvalEvaluator(BaseEvaluator):
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def score(self, predictions, references):
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predictions = [parse_math_answer('', pred) for pred in predictions]
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details = []
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cnt = 0
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for pred, ref in zip(predictions, references):
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detail = {'pred': pred, 'answer': ref, 'correct': False}
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if is_equiv(pred, ref):
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cnt += 1
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detail['correct'] = True
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details.append(detail)
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score = cnt / len(predictions) * 100
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return {'score': score, 'details': details}
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@ICL_EVALUATORS.register_module()
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class AGIEvalEvaluator_mcq(BaseEvaluator):
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def score(self, predictions, references):
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if len(predictions) != len(references):
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return {
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'error': 'predictions and references have different '
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'length'
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}
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details = []
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cnt = 0
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for pred, ref in zip(predictions, references):
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detail = {'pred': pred, 'answer': ref, 'correct': False}
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if pred == ref:
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cnt += 1
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detail['correct'] = True
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details.append(detail)
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score = cnt / len(predictions) * 100
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return {'score': score, 'details': details}
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