def setUp(self):
handler_config = DocClassificationDataHandler.Config()
handler_config.columns_to_read.append(ModelInput.DENSE_FEAT)
self.data_handler = DocClassificationDataHandler.from_config(
DocClassificationDataHandler.Config(),
ModelInputConfig(),
[],
featurizer=SimpleFeaturizer.from_config(SimpleFeaturizer.Config(),
FeatureConfig()),
)
class Config(Task.Config):
model: DocModel.Config = DocModel.Config()
trainer: Trainer.Config = Trainer.Config()
features: DocClassification.ModelInputConfig = (
DocClassification.ModelInputConfig())
labels: DocClassification.TargetConfig = DocClassification.TargetConfig(
)
data_handler: DocClassificationDataHandler.Config = (
DocClassificationDataHandler.Config())
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
class Config(Task_Deprecated.Config):
model: DocModel_Deprecated.Config = DocModel_Deprecated.Config()
trainer: Trainer.Config = Trainer.Config()
features: DocClassification.ModelInputConfig = (
DocClassification.ModelInputConfig())
labels: DocClassification.TargetConfig = DocClassification.TargetConfig(
)
data_handler: DocClassificationDataHandler.Config = (
DocClassificationDataHandler.Config())
metric_reporter: ClassificationMetricReporter.Config = (
ClassificationMetricReporter.Config())
exporter: Optional[DenseFeatureExporter.Config] = None
def setUp(self):
file_name = tests_module.test_file(
"knowledge_distillation_test_tiny.tsv")
label_config_dict = {"target_prob": True}
data_handler_dict = {
"columns_to_read": [
"text",
"target_probs",
"target_logits",
"target_labels",
"doc_label",
]
}
self.data_handler = DocClassificationDataHandler.from_config(
DocClassificationDataHandler.Config(**data_handler_dict),
ModelInputConfig(),
TargetConfig(**label_config_dict),
featurizer=SimpleFeaturizer.from_config(SimpleFeaturizer.Config(),
FeatureConfig()),
)
self.data = list(
self.data_handler.read_from_file(file_name,
self.data_handler.raw_columns))
