def create_and_check_electra_for_sequence_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = ElectraForSequenceClassification(config)
model.to(torch_device)
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_electra_for_sequence_classification(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
config.num_labels = self.num_labels
model = ElectraForSequenceClassification(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))
def __init__(self,
config: ElectraConfig,
embeddings,
discriminator=None,
embed_layer=None):
super().__init__()
self.embed_layer = nn.Embedding(num_embeddings=config.vocab_size,
embedding_dim=config.embedding_size,
padding_idx=config.vocab_size - 1)
if embed_layer:
self.embed_layer.load_state_dict(torch.load(embed_layer))
else:
self.embed_layer.weight = nn.Parameter(embeddings)
if discriminator:
self.discriminator = ElectraForSequenceClassification.from_pretrained(
discriminator, config=config)
else:
self.discriminator = ElectraForSequenceClassification(config)
self.softmax = nn.Softmax(1)