def create_and_check_xlnet_token_classif(
self,
config,
input_ids_1,
input_ids_2,
input_ids_q,
perm_mask,
input_mask,
target_mapping,
segment_ids,
lm_labels,
sequence_labels,
is_impossible_labels,
token_labels,
):
model = XLNetForTokenClassification(config)
model.to(torch_device)
model.eval()
result = model(input_ids_1)
result = model(input_ids_1, labels=token_labels)
self.parent.assertEqual(result.loss.shape, ())
self.parent.assertEqual(
result.logits.shape,
(self.batch_size, self.seq_length, self.type_sequence_label_size))
self.parent.assertListEqual(
[mem.shape for mem in result.mems],
[(self.seq_length, self.batch_size, self.hidden_size)] *
self.num_hidden_layers,
)
def create_and_check_xlnet_token_classif(
self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask,
input_mask, target_mapping, segment_ids, lm_labels,
sequence_labels, is_impossible_labels, token_labels):
model = XLNetForTokenClassification(config)
model.to(torch_device)
model.eval()
logits, mems_1 = model(input_ids_1)
loss, logits, mems_1 = model(input_ids_1, labels=token_labels)
result = {
"loss": loss,
"mems_1": mems_1,
"logits": logits,
}
self.parent.assertListEqual(list(result["loss"].size()), [])
self.parent.assertListEqual(list(result["logits"].size()), [
self.batch_size, self.seq_length, self.type_sequence_label_size
])
self.parent.assertListEqual(
list(list(mem.size()) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] *
self.num_hidden_layers)