OpenCompass/opencompass/datasets/natural_question.py

import csv
import os.path as osp

from datasets import Dataset, DatasetDict

from opencompass.openicl.icl_evaluator import BaseEvaluator
from opencompass.registry import ICL_EVALUATORS, LOAD_DATASET
from opencompass.utils.text_postprocessors import general_postprocess

from .base import BaseDataset


@LOAD_DATASET.register_module()
class NaturalQuestionDataset(BaseDataset):

    @staticmethod
    def load(path: str):
        dataset = DatasetDict()
        for split in ['dev', 'test']:
            filename = osp.join(path, f'nq-{split}.qa.csv')
            with open(filename) as f:
                reader = csv.reader(f, delimiter='\t')
                raw_data = []
                for row in reader:
                    assert len(row) == 2
                    question = row[0]
                    answers = eval(row[1])
                    if split == 'dev':
                        answers = answers[0]
                    raw_data.append({'question': question, 'answer': answers})
                dataset[split] = Dataset.from_list(raw_data)

        return dataset


@ICL_EVALUATORS.register_module()
class NQEvaluator(BaseEvaluator):

    def score(self, predictions, references):
        if len(predictions) != len(references):
            return {
                'error': 'predictions and references have different '
                'length'
            }
        processed_predictions = []
        for prediction in predictions:
            prediction = prediction.strip().split('\n')[0].lower()
            if 'answer is' in prediction:
                prediction = prediction.split('answer is')[-1]
            prediction = general_postprocess(prediction)
            processed_predictions.append(prediction)
        processed_answers = [[general_postprocess(j).lower() for j in i]
                             for i in references]

        cnt = 0
        for pred, cand_ans in zip(processed_predictions, processed_answers):
            cnt += int(any([cand == pred for cand in cand_ans]))
        score = cnt / len(predictions) * 100

        return {'score': score}