def test_inference_masked_lm(self): model = TFDistilBertModel.from_pretrained("distilbert-base-uncased") input_ids = tf.constant([[0, 1, 2, 3, 4, 5]]) output = model(input_ids)[0] expected_shape = [1, 6, 768] self.assertEqual(output.shape, expected_shape) expected_slice = tf.constant([[ [0.19261885, -0.13732955, 0.4119799], [0.22150156, -0.07422661, 0.39037204], [0.22756018, -0.0896414, 0.3701467], ]]) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
def create_and_check_distilbert_model(self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels): model = TFDistilBertModel(config=config) inputs = {"input_ids": input_ids, "attention_mask": input_mask} result = model(inputs) inputs = [input_ids, input_mask] result = model(inputs) 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 list( TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]): model = TFDistilBertModel.from_pretrained(model_name) self.assertIsNotNone(model)