def test_inference_no_head(self):
model = ConvBertModel.from_pretrained("YituTech/conv-bert-base")
input_ids = torch.tensor([[1, 2, 3, 4, 5, 6]])
output = model(input_ids)[0]
expected_shape = torch.Size((1, 6, 768))
self.assertEqual(output.shape, expected_shape)
expected_slice = torch.tensor([[[-0.0864, -0.4898, -0.3677],
[0.1434, -0.2952, -0.7640],
[-0.0112, -0.4432, -0.5432]]])
self.assertTrue(
torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
def test_inference_masked_lm(self):
model = ConvBertModel.from_pretrained("YituTech/conv-bert-base")
input_ids = torch.tensor([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
print(output[:, :3, :3])
expected_shape = torch.Size((1, 6, 768))
self.assertEqual(output.shape, expected_shape)
# TODO Replace values below with what was printed above.
expected_slice = torch.tensor(
[[[-0.0348, -0.4686, -0.3064], [0.2264, -0.2699, -0.7423], [0.1032, -0.4501, -0.5828]]]
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
def test_model_from_pretrained(self):
for model_name in CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
model = ConvBertModel.from_pretrained(model_name)
self.assertIsNotNone(model)