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import evaluate | |
from evaluate.evaluation_suite import SubTask | |
class Suite(evaluate.EvaluationSuite): | |
def __init__(self, name): | |
super().__init__(name) | |
self.preprocessor = lambda x: {"text": x["text"].lower()} | |
self.suite = [ | |
SubTask( | |
task_type="text-classification", | |
data="glue", | |
subset="cola", | |
split="test[:10]", | |
args_for_task={ | |
"metric": "accuracy", | |
"input_column": "sentence", | |
"label_column": "label", | |
"label_mapping": { | |
"LABEL_0": 0.0, | |
"LABEL_1": 1.0 | |
} | |
} | |
), | |
SubTask( | |
task_type="text-classification", | |
data="glue", | |
subset="sst2", | |
split="validation[:10]", | |
args_for_task={ | |
"metric": "accuracy", | |
"input_column": "sentence", | |
"label_column": "label", | |
"label_mapping": { | |
"LABEL_0": 0.0, | |
"LABEL_1": 1.0 | |
} | |
} | |
), | |
SubTask( | |
task_type="text-classification", | |
data="glue", | |
subset="qqp", | |
split="validation[:10]", | |
args_for_task={ | |
"metric": "accuracy", | |
"input_column": "question1", | |
"second_input_column": "question2", | |
"label_column": "label", | |
"label_mapping": { | |
"LABEL_0": 0, | |
"LABEL_1": 1 | |
} | |
} | |
), | |
SubTask( | |
task_type="text-classification", | |
data="glue", | |
subset="mrpc", | |
split="validation[:10]", | |
args_for_task={ | |
"metric": "accuracy", | |
"input_column": "sentence1", | |
"second_input_column": "sentence2", | |
"label_column": "label", | |
"label_mapping": { | |
"LABEL_0": 0, | |
"LABEL_1": 1 | |
} | |
} | |
), | |
SubTask( | |
task_type="text-classification", | |
data="glue", | |
subset="mnli", | |
split="validation_mismatched[:10]", | |
args_for_task={ | |
"metric": "accuracy", | |
"input_column": "premise", | |
"second_input_column": "hypothesis", | |
"label_mapping": { | |
"LABEL_0": 0, | |
"LABEL_1": 1, | |
"LABEL_2": 2 | |
} | |
} | |
), | |
SubTask( | |
task_type="text-classification", | |
data="glue", | |
subset="qnli", | |
split="validation[:10]", | |
args_for_task={ | |
"metric": "accuracy", | |
"input_column": "question", | |
"second_input_column": "sentence", | |
"label_column": "label", | |
"label_mapping": { | |
"LABEL_0": 0, | |
"LABEL_1": 1 | |
} | |
} | |
), | |
SubTask( | |
task_type="text-classification", | |
data="glue", | |
subset="rte", | |
split="validation[:10]", | |
args_for_task={ | |
"metric": "accuracy", | |
"input_column": "sentence1", | |
"second_input_column": "sentence2", | |
"label_column": "label", | |
"label_mapping": { | |
"LABEL_0": 0, | |
"LABEL_1": 1 | |
} | |
} | |
), | |
SubTask( | |
task_type="text-classification", | |
data="glue", | |
subset="wnli", | |
split="validation[:10]", | |
args_for_task={ | |
"metric": "accuracy", | |
"input_column": "sentence1", | |
"second_input_column": "sentence2", | |
"label_column": "label", | |
"label_mapping": { | |
"LABEL_0": 0, | |
"LABEL_1": 1 | |
} | |
} | |
) | |
] | |