def create_and_check_electra_for_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = ElectraForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
self.parent.assertEqual(result.start_logits.shape,
(self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape,
(self.batch_size, self.seq_length))
def create_and_check_electra_for_question_answering(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
fake_token_labels,
):
model = ElectraForQuestionAnswering(config=config)
model.to(torch_device)
model.eval()
loss, start_logits, end_logits = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
start_positions=sequence_labels,
end_positions=sequence_labels,
)
result = {
"loss": loss,
"start_logits": start_logits,
"end_logits": end_logits,
}
self.parent.assertListEqual(list(result["start_logits"].size()),
[self.batch_size, self.seq_length])
self.parent.assertListEqual(list(result["end_logits"].size()),
[self.batch_size, self.seq_length])
self.check_loss_output(result)
for conf in configurations:
conf_id = '{}_config_{}_{}_{}'.format(j, *conf)
print(
'Run [{}] --learning rate: {}, --hidden size: {}, --hidden layer: {}'.
format(j, *conf))
# Initializing a ELECTRA electra-base-uncased style configuration
electra_conf = ElectraConfig(hidden_size=conf[1],
num_hidden_layers=conf[2])
# Initializing a model from the electra-base-uncased style configuration
model = ElectraForQuestionAnswering(electra_conf)
configuration = model.config
model.to(device)
model.train()
train_loader = DataLoader(train_dataset,
batch_size=BATCH_SIZE,
shuffle=True)
learning_rate = conf[0]
optim = AdamW(model.parameters(), lr=learning_rate)
loss_records = []
bi = 0
for epoch in tqdm(range(EPOCHS)):
for batch in train_loader:
if (bi > 3):
break
optim.zero_grad()
