def create_and_check_electra_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFElectraModel(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
(sequence_output,) = model(inputs)
inputs = [input_ids, input_mask]
(sequence_output,) = model(inputs)
(sequence_output,) = model(input_ids)
result = {
"sequence_output": sequence_output.numpy(),
}
self.parent.assertListEqual(
list(result["sequence_output"].shape), [self.batch_size, self.seq_length, self.hidden_size]
)
def create_and_check_electra_model(self, config, input_ids, token_type_ids,
input_mask, sequence_labels,
token_labels, choice_labels):
model = TFElectraModel(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids
}
result = model(inputs)
inputs = [input_ids, input_mask]
result = model(inputs)
result = model(input_ids)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.seq_length, self.hidden_size))
def test_model_from_pretrained(self):
# for model_name in TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/electra-small-discriminator"]:
model = TFElectraModel.from_pretrained(model_name)
self.assertIsNotNone(model)
def test_model_from_pretrained(self):
# for model_name in list(TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
for model_name in ["electra-small-discriminator"]:
model = TFElectraModel.from_pretrained(model_name,
cache_dir=CACHE_DIR)
self.assertIsNotNone(model)
