def test_inference_no_head(self):
model = TFDebertaV2Model.from_pretrained("kamalkraj/deberta-v2-xlarge")
input_ids = tf.constant(
[[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
attention_mask = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
output = model(input_ids, attention_mask=attention_mask)[0]
expected_slice = tf.constant([[[0.2356, 0.1948, 0.0369],
[-0.1063, 0.3586, -0.5152],
[-0.6399, -0.0259, -0.2525]]])
tf.debugging.assert_near(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 = TFDebertaV2Model(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids
}
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):
model = TFDebertaV2Model.from_pretrained("kamalkraj/deberta-v2-xlarge")
self.assertIsNotNone(model)