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69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
import json
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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.registry import LOAD_DATASET
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from ..base import BaseDataset
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from .evaluation_main import (InputExample, test_instruction_following_loose,
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test_instruction_following_strict)
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@LOAD_DATASET.register_module()
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class IFEvalDataset(BaseDataset):
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@staticmethod
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def load(path):
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datasets = []
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with open(path, 'r', encoding='utf-8') as file:
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for line in file:
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tmp = json.loads(line.strip())
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dataset = dict(prompt=tmp['prompt'], reference=tmp)
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datasets.append(dataset)
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return Dataset.from_list(datasets)
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class IFEvaluator(BaseEvaluator):
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def score(self, predictions, references):
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results = dict()
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for metric in ('strict', 'loose'):
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results[metric] = []
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for pred, refer in zip(predictions, references):
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input = InputExample(
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key=refer['key'],
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instruction_id_list=refer['instruction_id_list'],
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prompt=refer['prompt'],
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kwargs=refer['kwargs'])
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for kwarg in input.kwargs:
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for k in list(kwarg.keys()):
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if kwarg[k] is None:
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kwarg.pop(k, None)
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results['strict'].append(
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test_instruction_following_strict(input, pred))
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results['loose'].append(
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test_instruction_following_loose(input, pred))
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final_scores = dict()
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for metric in ('strict', 'loose'):
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prompt_total = 0
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prompt_correct = 0
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inst_total = 0
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inst_correct = 0
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for example in results[metric]:
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follow_instruction_list = example.follow_instruction_list
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instruction_id_list = example.instruction_id_list
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prompt_total += 1
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if all(follow_instruction_list):
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prompt_correct += 1
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inst_total += len(instruction_id_list)
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inst_correct += sum(follow_instruction_list)
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prompt_score = f'Prompt-level-{metric}-accuracy'
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inst_score = f'Inst-level-{metric}-accuracy'
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final_scores[prompt_score] = prompt_correct / prompt_total * 100
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final_scores[inst_score] = inst_correct / inst_total * 100
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return final_scores
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