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import re |
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from typing import List |
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from datasets import load_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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@LOAD_DATASET.register_module() |
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class crowspairsDataset(BaseDataset): |
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@staticmethod |
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def load(**kwargs): |
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dataset = load_dataset(**kwargs) |
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def preprocess(example): |
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example['label'] = 0 |
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return example |
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return dataset.map(preprocess) |
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@LOAD_DATASET.register_module() |
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class crowspairsDataset_V2(BaseDataset): |
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@staticmethod |
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def load(**kwargs): |
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dataset = load_dataset(**kwargs) |
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def preprocess(example): |
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example['label'] = 'A' |
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return example |
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return dataset.map(preprocess) |
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def crowspairs_postprocess(text: str) -> str: |
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"""Cannot cover all the cases, try to be as accurate as possible.""" |
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if re.search('Neither', text) or re.search('Both', text): |
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return 'invalid' |
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if text != '': |
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first_option = text[0] |
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if first_option.isupper() and first_option in 'AB': |
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return first_option |
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if re.search(' A ', text) or re.search('A.', text): |
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return 'A' |
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if re.search(' B ', text) or re.search('B.', text): |
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return 'B' |
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return 'invalid' |
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class CrowspairsEvaluator(BaseEvaluator): |
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"""Calculate accuracy and valid accuracy according the prediction for |
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crows-pairs dataset.""" |
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def __init__(self) -> None: |
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super().__init__() |
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def score(self, predictions: List, references: List) -> dict: |
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"""Calculate scores and accuracy. |
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Args: |
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predictions (List): List of probabilities for each class of each |
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sample. |
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references (List): List of target labels for each sample. |
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Returns: |
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dict: calculated scores. |
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""" |
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if len(predictions) != len(references): |
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return { |
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'error': 'predictions and references have different length.' |
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} |
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all_match = 0 |
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for i, j in zip(predictions, references): |
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all_match += i == j |
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valid_match = 0 |
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valid_length = 0 |
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for i, j in zip(predictions, references): |
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if i != 'invalid': |
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valid_length += 1 |
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valid_match += i == j |
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accuracy = round(all_match / len(predictions), 4) * 100 |
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valid_accuracy = round(valid_match / valid_length, 4) * 100 |
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valid_frac = round(valid_length / len(predictions), 4) * 100 |
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return dict(accuracy=accuracy, |
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valid_accuracy=valid_accuracy, |
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valid_frac=valid_frac) |
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