def create_and_check_electra_for_pretraining(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = TFElectraForPreTraining(config=config)
inputs = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
(prediction_scores,) = model(inputs)
result = {
"prediction_scores": prediction_scores.numpy(),
}
self.parent.assertListEqual(list(result["prediction_scores"].shape), [self.batch_size, self.seq_length])
def test_inference_masked_lm(self):
model = TFElectraForPreTraining.from_pretrained(
"lysandre/tiny-electra-random")
input_ids = tf.constant([[0, 1, 2, 3, 4, 5]])
output = model(input_ids)[0]
expected_shape = [1, 6]
self.assertEqual(output.shape, expected_shape)
print(output[:, :3])
expected_slice = tf.constant([[-0.24651965, 0.8835437, 1.823782]])
tf.debugging.assert_near(output[:, :3], expected_slice, atol=1e-4)
def create_and_check_electra_for_pretraining(self, config, input_ids,
token_type_ids, input_mask,
sequence_labels, token_labels,
choice_labels):
model = TFElectraForPreTraining(config=config)
inputs = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids
}
result = model(inputs)
self.parent.assertEqual(result.logits.shape,
(self.batch_size, self.seq_length))