def create_and_check_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_bert_for_next_sequence_prediction(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
model = BertForNextSentencePrediction(config=config)
model.eval()
loss, seq_relationship_score = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels)
result = {
"loss": loss,
"seq_relationship_score": seq_relationship_score,
}
self.parent.assertListEqual(
list(result["seq_relationship_score"].size()),
[self.batch_size, 2])
self.check_loss_output(result)
def create_and_check_bert_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels):
model = BertForNextSentencePrediction(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
next_sentence_label=sequence_labels,
)
self.parent.assertListEqual(list(result["logits"].size()),
[self.batch_size, 2])
self.check_loss_output(result)