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* first * first * first * first * SciKnowEval * fix hash * fix dataset-index & use official llm_judge_postprocess * fix dataset-index.yml * use official llmjudge_postprocess * fix lint * fix lint * fix lint * fix lint * fix lint * merge with main --------- Co-authored-by: Linchen Xiao <xxllcc1993@gmail.com>
108 lines
3.6 KiB
Python
108 lines
3.6 KiB
Python
import re
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from datasets import load_dataset
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from opencompass.openicl import BaseEvaluator
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from opencompass.registry import LOAD_DATASET, TEXT_POSTPROCESSORS
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from .base import BaseDataset
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def _parse(item, prompt_mode, discipline):
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choices = item['choices']
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item['q4'] = f'You are an expert in {discipline}.\n'
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item['q4'] += item['prompt']['default'] + '\n' + item['question'] + '\n'
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label_texts = []
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for label_meta, text_meta in zip(choices['label'], choices['text']):
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label_texts.append(f'{label_meta}. {text_meta}')
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item['q4'] += '\n'.join(label_texts) # noqa: E501, E741, E741
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item['prompt_mode'] = prompt_mode
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return item
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@LOAD_DATASET.register_module()
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class SciKnowEvalDataset(BaseDataset):
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@staticmethod
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def load(path: str, prompt_mode: str, **kwargs):
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def capitalize_first_letter(s):
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if not s: # 检查字符串是否为空
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return s
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return s[0].upper() + s[1:]
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subset = kwargs['subset']
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data_files = {}
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test_file = f'data/{capitalize_first_letter(subset)}/'
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test_file += f'sciknoweval_{subset}_test.jsonl'
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data_files['test'] = test_file
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dataset = load_dataset(path, data_files=data_files, split='test')
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# dataset = dataset.select(range(20))
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if prompt_mode == 'zero-shot':
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dataset = dataset.map(
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lambda item: _parse(item, prompt_mode, subset),
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load_from_cache_file=False)
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elif prompt_mode == 'few-shot':
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pass # TODO: Implement few-shot prompt
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return dataset
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class SciKnowEvalEvaluator(BaseEvaluator):
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def score(self, predictions, references, test_set):
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method = test_set['prompt_mode'][0]
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if len(predictions) != len(references):
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return {'error': 'preds and refrs have different length'}
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correct = 0
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count = 0
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details = []
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for idx, (i, j) in enumerate(zip(predictions, references)):
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i = answer_cleansing(method, i, test_set['choices'][idx]['label'],
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test_set['answerKey'][idx])
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detail = {'pred': i, 'answer': j, 'correct': False}
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count += 1
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if i == j:
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correct += 1
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detail['correct'] = True
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details.append(detail)
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result = {'accuracy': 100 * correct / count, 'details': details}
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return result
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@TEXT_POSTPROCESSORS.register_module()
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def answer_cleansing(
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method: str,
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prediction: str,
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options: list,
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label: str,
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) -> str:
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options_str = r'\b(' + '|'.join(options) + r')\b'
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prediction = re.findall(options_str, prediction)
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if len(prediction) == 0:
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prediction = []
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else:
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# If there is a "label" and its length is 1,
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# process prediction accordingly
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if len(label) == 1:
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if method == 'few-shot':
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answer_flag = True if len(prediction) > 1 else False
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# choose the first or last element based on the answer_flag
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if answer_flag:
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prediction = [prediction[0]]
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else:
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prediction = [prediction[-1]]
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elif method == 'zero-shot':
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# choose the first element in list
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prediction = [prediction[0]]
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else:
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raise ValueError('Method is not properly defined ...')
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# Remove trailing period if it exists
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if prediction[0] and prediction[0].endswith('.'):
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prediction[0] = prediction[0][:-1]
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return prediction[0]
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