OpenCompass/opencompass/datasets/drop_simple_eval.py
klein 153c4fc988
[Feature] update drop dataset from openai simple eval (#1092)
* [Feature] update drop dataset from openai simple eval

* update drop template presentation

* update

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Co-authored-by: Leymore <zfz-960727@163.com>
2024-05-06 13:37:08 +08:00

81 lines
2.3 KiB
Python

import json
import re
import string
from datasets import Dataset, DatasetDict
from opencompass.openicl.icl_evaluator import BaseEvaluator
from opencompass.registry import LOAD_DATASET
from .base import BaseDataset
# Modified from https://github.com/openai/simple-evals/blob/main/drop_eval.py
ANSWER_PATTERN = r'(?i)Answer\s*:\s*([^\n]+)'
def normalize(s: str) -> str:
"""Lower text and remove punctuation, articles and extra whitespace."""
s = s.lower()
exclude = set(string.punctuation)
s = ''.join(char for char in s if char not in exclude)
s = re.sub(r'\b(a|an|the)\b', ' ', s)
s = ' '.join(s.split())
return s
def fuzzy_match(s1: str, s2: str) -> bool:
s1 = normalize(s1)
s2 = normalize(s2)
if s1 == '' or s2 == '':
return s1 == s2
return s1 in s2 or s2 in s1
@LOAD_DATASET.register_module()
class DropOpenAIDataset(BaseDataset):
@staticmethod
def load(path):
dataset_list = []
with open(path, 'r', encoding='utf-8') as f:
for line in f:
data = json.loads(line)
item = {
'prompt': data['context'],
'answers': data['ref_text'],
}
dataset_list.append(item)
dataset_list = Dataset.from_list(dataset_list)
return DatasetDict({'validation': dataset_list})
class DropOpenAIEvaluator(BaseEvaluator):
def score(self, predictions, references):
if len(predictions) != len(references):
return {'error': 'preds and refers have different length'}
num_correct = 0
count = 0
details = []
for pred, refr in zip(predictions, references):
match = re.search(ANSWER_PATTERN, pred)
extracted_answer = match.group(1) if match else pred
refrs = refr.split('|')
matches = [
fuzzy_match(extracted_answer, correct_answer)
for correct_answer in refrs
]
correct = True in matches
num_correct += correct
detail = {'pred': pred, 'answer': refr, 'correct': correct}
count += 1
details.append(detail)
result = {'accuracy': 100 * num_correct / count, 'details': details}
return result