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)