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https://github.com/open-compass/opencompass.git
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689ffe5b63
* update commonsenseqa * update drop * update flores_first100 * update gsm8k * update humaneval * update lambda * update obqa * update piqa * update race * update siqa * update story_cloze * update strategyqa * update tydiqa * update winogrande * update doc * update hellaswag * fix obqa * update collections * update .zip name
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
import json
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import os
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import re
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from collections import Counter
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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.utils.text_postprocessors import general_postprocess
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from .base import BaseDataset
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class TydiQADataset(BaseDataset):
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@staticmethod
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def load(path, lang):
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path = os.path.join(path, 'dev', f'{lang}-dev.jsonl')
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dataset_list = []
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with open(path, 'r', encoding='utf-8') as f:
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for line in f:
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line = json.loads(line)
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answer = list(set([i['text'] for i in line['answers']]))
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line['answer'] = answer
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dataset_list.append(line)
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return Dataset.from_list(dataset_list)
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class TydiQAEvaluator(BaseEvaluator):
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# This evaluation class is edited from:
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# https://github.com/allenai/bi-att-flow/blob/master/squad/evaluate-v1.1.py
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def f1_score(self, prediction, ground_truth):
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prediction_tokens = general_postprocess(prediction).split()
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ground_truth_tokens = general_postprocess(ground_truth).split()
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common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
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num_same = sum(common.values())
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if num_same == 0:
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return 0
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precision = 1.0 * num_same / len(prediction_tokens)
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recall = 1.0 * num_same / len(ground_truth_tokens)
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f1 = (2 * precision * recall) / (precision + recall)
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return f1
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def exact_match_score(self, prediction, ground_truth):
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return (general_postprocess(prediction) == general_postprocess(
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ground_truth))
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def metric_max_over_ground_truths(self, metric_fn, prediction,
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ground_truths):
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scores_for_ground_truths = []
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for ground_truth in ground_truths:
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score = metric_fn(prediction, ground_truth)
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scores_for_ground_truths.append(score)
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return max(scores_for_ground_truths)
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def score(self, predictions, references):
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f1 = exact_match = total = 0
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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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for prediction, reference in zip(predictions, references):
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prediction = re.split(r'[\n]', prediction, 1)[0].lower()
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exact_match += self.metric_max_over_ground_truths(
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self.exact_match_score, prediction, reference)
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f1 += self.metric_max_over_ground_truths(self.f1_score, prediction,
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reference)
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total += 1
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exact_match = 100.0 * exact_match / total
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f1 = 100.0 * f1 / total
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return {'exact_match': exact_match, 'f1': f1}
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