def test_inference_nezha_model(self):
model = NezhaModel.from_pretrained("sijunhe/nezha-cn-base")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1]])
with torch.no_grad():
output = model(input_ids, attention_mask=attention_mask)[0]
expected_shape = torch.Size((1, 6, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[[0.0685, 0.2441, 0.1102],
[0.0600, 0.1906, 0.1349],
[0.0221, 0.0819, 0.0586]]])
self.assertTrue(
torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))
def create_and_check_model(self, config, input_ids, token_type_ids,
input_mask, sequence_labels, token_labels,
choice_labels):
model = NezhaModel(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids)
result = model(input_ids, token_type_ids=token_type_ids)
result = model(input_ids)
self.parent.assertEqual(
result.last_hidden_state.shape,
(self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape,
(self.batch_size, self.hidden_size))
def test_model_from_pretrained(self):
for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = NezhaModel.from_pretrained(model_name)
self.assertIsNotNone(model)