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import json
import os.path as osp
import re
from datasets import Dataset
from opencompass.openicl.icl_evaluator import BaseEvaluator
from opencompass.registry import (ICL_EVALUATORS, LOAD_DATASET,
TEXT_POSTPROCESSORS)
from .base import BaseDataset
@LOAD_DATASET.register_module()
class BBHDataset(BaseDataset):
@staticmethod
def load(path: str, name: str):
with open(osp.join(path, f'{name}.json'), 'r') as f:
data = json.load(f)['examples']
dataset = Dataset.from_list(data)
return dataset
@TEXT_POSTPROCESSORS.register_module('bbh-mcq')
def bbh_mcq_postprocess(text: str) -> str:
ans = text
ans_line = ans.split('answer is ')
if len(ans_line) != 1:
ans = ans_line[1].strip()
match = re.search(r'\(([A-Z])\)*', ans)
if match:
return match.group(1)
match = re.search(r'([A-Z])', ans)
if match:
return match.group(1)
return ans
@TEXT_POSTPROCESSORS.register_module('bbh-freeform')
def bbh_freeform_postprocess(text: str) -> str:
ans = text
ans_line = ans.split('answer is ')
if len(ans_line) != 1:
ans = ans_line[1].strip()
ans = ans.split('\n')[0]
if ans.endswith('.'):
ans = ans[:-1]
return ans
@ICL_EVALUATORS.register_module()
class BBHEvaluator(BaseEvaluator):
def score(self, predictions, references):
if len(predictions) != len(references):
return {
'error': 'predictions and references have different '
'length'
}
predictions = [bbh_freeform_postprocess(pred) for pred in predictions]
details = []
cnt = 0
for pred, ref in zip(predictions, references):
detail = {'pred': pred, 'answer': ref, 'correct': False}
if pred == ref:
cnt += 1
detail['correct'] = True
details.append(detail)
score = cnt / len(predictions) * 100
return {'score': score, 'details': details}
@ICL_EVALUATORS.register_module()
class BBHEvaluator_mcq(BaseEvaluator):
def score(self, predictions, references):
if len(predictions) != len(references):
return {
'error': 'predictions and references have different '
'length'
}
details = []
cnt = 0
for pred, ref in zip(predictions, references):
detail = {'pred': pred, 'answer': ref, 'correct': False}
if pred == ref:
cnt += 1
detail['correct'] = True
details.append(detail)
score = cnt / len(predictions) * 100
return {'score': score, 'details': details}
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