def create_and_check_xxx_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels):
config.num_labels = self.num_labels
model = XxxForSequenceClassification(config)
model.eval()
loss, logits = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
labels=sequence_labels)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()),
[self.batch_size, self.num_labels])
self.check_loss_output(result)
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
model = XxxForSequenceClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))