def create_and_check_roberta_for_masked_lm(self, config, input_ids,
token_type_ids, input_mask,
sequence_labels, token_labels,
choice_labels):
model = TFRobertaForMaskedLM(config=config)
result = model([input_ids, input_mask, token_type_ids])
self.parent.assertEqual(
result.logits.shape,
(self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_roberta_for_masked_lm(self, config, input_ids, token_type_ids, input_mask, sequence_labels,
token_labels, choice_labels):
model = TFRobertaForMaskedLM(config=config)
prediction_scores = model([input_ids, input_mask, token_type_ids])[0]
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(
list(result["prediction_scores"].shape),
[self.batch_size, self.seq_length, self.vocab_size])
def test_inference_masked_lm(self):
model = TFRobertaForMaskedLM.from_pretrained("roberta-base")
input_ids = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]])
output = model(input_ids)[0]
expected_shape = [1, 11, 50265]
self.assertEqual(list(output.numpy().shape), expected_shape)
# compare the actual values for a slice.
expected_slice = tf.constant(
[[[33.8802, -4.3103, 22.7761], [4.6539, -2.8098, 13.6253], [1.8228, -3.6898, 8.8600]]]
)
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy(), expected_slice.numpy(), atol=1e-4))