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| from typing import Optional, Union, Callable | |
| from dataclasses import dataclass | |
| from datasets import Dataset | |
| from evaluate.evaluation_suite import EvaluationSuite | |
| class SubTask: | |
| model_or_pipeline: Optional[Union[str, "Pipeline", Callable, "PreTrainedModel", "TFPreTrainedModel"]] = None | |
| data: Optional[Union[str, Dataset]] = None | |
| subset: Optional[str] = None | |
| split: Optional[str] = None | |
| data_preprocessor: Optional[Callable] = None, | |
| args_for_task: Optional[dict] = None | |
| class EvaluationSuite: | |
| def __init__(self): | |
| self.preprocessor = None #lambda x: x["text"].lower() | |
| self.suite = [ | |
| SubTask( | |
| data="imdb", | |
| split="test", | |
| data_preprocessor=self.preprocessor, | |
| args_for_task={ | |
| "metric": "accuracy", | |
| "input_column": "text", | |
| "label_column": "label", | |
| "label_mapping": { | |
| "LABEL_0": 0.0, | |
| "LABEL_1": 1.0 | |
| } | |
| } | |
| ), | |
| SubTask( | |
| data="sst2", | |
| split="test[:10]", | |
| data_preprocessor=self.preprocessor, | |
| args_for_task={ | |
| "metric": "accuracy", | |
| "input_column": "sentence", | |
| "label_column": "label", | |
| "label_mapping": { | |
| "LABEL_0": 0.0, | |
| "LABEL_1": 1.0 | |
| } | |
| } | |
| ) | |
| ] | |