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105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
import csv
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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, DatasetDict
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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 opencompass.utils.text_postprocessors import general_postprocess
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from .base import BaseDataset
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@LOAD_DATASET.register_module()
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class NaturalQuestionDataset(BaseDataset):
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@staticmethod
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def load(path: str):
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path = get_data_path(path)
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dataset = DatasetDict()
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for split in ['dev', 'test']:
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if environ.get('DATASET_SOURCE') == 'ModelScope':
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from modelscope.msdatasets import MsDataset
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ms_dataset = MsDataset.load(path, split=split)
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raw_data = []
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for row in ms_dataset:
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question = row['question']
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answers = eval(row['answer'])
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if split == 'dev':
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answers = answers[0]
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raw_data.append({'question': question, 'answer': answers})
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else:
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filename = osp.join(path, f'nq-{split}.qa.csv')
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with open(filename, 'r', encoding='utf-8') as f:
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reader = csv.reader(f, delimiter='\t')
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raw_data = []
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for row in reader:
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assert len(row) == 2
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question = row[0]
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answers = eval(row[1])
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if split == 'dev':
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answers = answers[0]
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raw_data.append({
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'question': question,
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'answer': answers
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})
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dataset[split] = Dataset.from_list(raw_data)
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return dataset
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@LOAD_DATASET.register_module()
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class NQOpenDataset(BaseDataset):
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@staticmethod
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def load(path: str):
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path = get_data_path(path)
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dataset = DatasetDict()
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for split in ['validation', 'train']:
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filename = osp.join(path, f'nq-open-{split}.jsonl')
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raw_data = []
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with open(filename, 'r', encoding='utf-8') as f:
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for doc in f:
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doc = json.loads(doc)
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if split == 'train':
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doc['answer'] = doc['answer'][0]
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raw_data.append(doc)
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dataset[split] = Dataset.from_list(raw_data)
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return dataset
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@ICL_EVALUATORS.register_module()
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class NQEvaluator(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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processed_predictions = []
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for prediction in predictions:
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prediction = prediction.strip().split('\n')[0].lower()
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if 'answer is' in prediction:
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prediction = prediction.split('answer is')[-1]
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prediction = general_postprocess(prediction)
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processed_predictions.append(prediction)
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processed_answers = [[general_postprocess(j).lower() for j in i]
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for i in references]
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details = []
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cnt = 0
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for pred, cand_ans in zip(processed_predictions, processed_answers):
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detail = {'pred': pred, 'answer': cand_ans, 'correct': False}
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# is_correct = any([cand == pred for cand in cand_ans])
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is_correct = any([cand in pred for cand in cand_ans])
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cnt += int(is_correct)
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detail['correct'] = is_correct
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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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