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))