def create_and_check_mpnet_model( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = TFMPNetModel(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_inference_masked_lm(self): model = TFMPNetModel.from_pretrained("microsoft/mpnet-base") 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([[ [-0.1067172, 0.08216473, 0.0024543], [-0.03465879, 0.8354118, -0.03252288], [-0.06569476, -0.12424111, -0.0494436], ]]) tf.debugging.assert_near(output[:, :3, :3], expected_slice, atol=1e-4)
def test_model_from_pretrained(self): for model_name in ["microsoft/mpnet-base"]: model = TFMPNetModel.from_pretrained(model_name) self.assertIsNotNone(model)