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