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from evaluate.evaluation_suite import SubTask
from evaluate.visualization import radar_plot
from intel_evaluate_extension.evaluation_suite.model_card_suite import ModelCardSuiteResults
_HEADER = "GLUE/AdvGlue Evaluation Results"
_DESCRIPTION = """
The suite compares the GLUE results with Adversarial GLUE (AdvGLUE), a
multi-task benchmark that tests the vulnerability of modern large-scale
language models againstvarious adversarial attacks."""
class Suite(ModelCardSuiteResults):
def __init__(self, name):
super().__init__(name)
self.result_keys = ["accuracy", "f1"]
self.preprocessor = lambda x: {"text": x["text"].lower()}
self.suite = [
SubTask(
task_type="text-classification",
data="glue",
subset="sst2",
split="validation[:5]",
args_for_task={
"metric": "glue",
"input_column": "sentence",
"label_column": "label",
"config_name": "sst2",
"label_mapping": {
"LABEL_0": 0.0,
"LABEL_1": 1.0
}
}
),
SubTask(
task_type="text-classification",
data="adv_glue",
subset="adv_sst2",
split="validation[:5]",
args_for_task={
"metric": "glue",
"input_column": "sentence",
"label_column": "label",
"config_name": "sst2",
"label_mapping": {
"LABEL_0": 0.0,
"LABEL_1": 1.0
}
}
),
SubTask(
task_type="text-classification",
data="glue",
subset="qqp",
split="validation[:5]",
args_for_task={
"metric": "glue",
"input_column": "question1",
"second_input_column": "question2",
"label_column": "label",
"config_name": "qqp",
"label_mapping": {
"LABEL_0": 0,
"LABEL_1": 1
}
}
),
SubTask(
task_type="text-classification",
data="adv_glue",
subset="adv_qqp",
split="validation[:5]",
args_for_task={
"metric": "glue",
"input_column": "question1",
"second_input_column": "question2",
"label_column": "label",
"config_name": "qqp",
"label_mapping": {
"LABEL_0": 0,
"LABEL_1": 1
}
}
),
SubTask(
task_type="text-classification",
data="glue",
subset="qnli",
split="validation[:5]",
args_for_task={
"metric": "glue",
"input_column": "question",
"second_input_column": "sentence",
"label_column": "label",
"config_name": "qnli",
"label_mapping": {
"LABEL_0": 0,
"LABEL_1": 1
}
}
),
SubTask(
task_type="text-classification",
data="adv_glue",
subset="adv_qnli",
split="validation[:5]",
args_for_task={
"metric": "glue",
"input_column": "question",
"second_input_column": "sentence",
"label_column": "label",
"config_name": "qnli",
"label_mapping": {
"LABEL_0": 0,
"LABEL_1": 1
}
}
),
SubTask(
task_type="text-classification",
data="glue",
subset="rte",
split="validation[:5]",
args_for_task={
"metric": "glue",
"input_column": "sentence1",
"second_input_column": "sentence2",
"label_column": "label",
"config_name": "rte",
"label_mapping": {
"LABEL_0": 0,
"LABEL_1": 1
}
}
),
SubTask(
task_type="text-classification",
data="adv_glue",
subset="adv_rte",
split="validation[:5]",
args_for_task={
"metric": "glue",
"input_column": "sentence1",
"second_input_column": "sentence2",
"label_column": "label",
"config_name": "rte",
"label_mapping": {
"LABEL_0": 0,
"LABEL_1": 1
}
}
),
SubTask(
task_type="text-classification",
data="glue",
subset="mnli",
split="validation_mismatched[:5]",
args_for_task={
"metric": "glue",
"input_column": "premise",
"second_input_column": "hypothesis",
"config_name": "mnli",
"label_mapping": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
}
}
),
SubTask(
task_type="text-classification",
data="adv_glue",
subset="adv_mnli",
split="validation[:5]",
args_for_task={
"metric": "glue",
"input_column": "premise",
"second_input_column": "hypothesis",
"config_name": "mnli",
"label_mapping": {
"LABEL_0": 0,
"LABEL_1": 1,
"LABEL_2": 2
}
}
),
]
def process_results(self, results):
radar_data = [
{"accuracy " + result["task_name"].split("/")[-1]:
result["accuracy"] for result in results[::2]},
{"accuracy " + result["task_name"].replace("adv_", "").split("/")[-1]:
result["accuracy"] for result in results[1::2]}]
return radar_plot(radar_data, ['GLUE', 'AdvGLUE'])
def plot_results(self, results, model_or_pipeline):
radar_data = self.process_results(results)
graphic = radar_plot(radar_data, ['GLUE ' + model_or_pipeline, 'AdvGLUE ' + model_or_pipeline])
return graphic
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