a9edf4728b1ce8a9d48e4b01a7f8e0eecee657f6,official/nlp/tasks/sentence_prediction_test.py,SentencePredictionTaskTest,_export_bert_tfhub,#SentencePredictionTaskTest#,216

Before Change

    self.assertEqual(outputs["sentence_prediction"].shape.as_list(), [8, 1])

  def _export_bert_tfhub(self):
    bert_config = configs.BertConfig(
        vocab_size=30522,
        hidden_size=16,
        intermediate_size=32,
        max_position_embeddings=128,
        num_attention_heads=2,
        num_hidden_layers=1)
    _, encoder = export_tfhub.create_bert_model(bert_config)
    model_checkpoint_dir = os.path.join(self.get_temp_dir(), "checkpoint")
    checkpoint = tf.train.Checkpoint(model=encoder)
    checkpoint.save(os.path.join(model_checkpoint_dir, "test"))
    model_checkpoint_path = tf.train.latest_checkpoint(model_checkpoint_dir)

    vocab_file = os.path.join(self.get_temp_dir(), "uncased_vocab.txt")
    with tf.io.gfile.GFile(vocab_file, "w") as f:
      f.write("dummy content")

    hub_destination = os.path.join(self.get_temp_dir(), "hub")
    export_tfhub.export_bert_tfhub(bert_config, model_checkpoint_path,
                                   hub_destination, vocab_file)
    return hub_destination

  def test_task_with_hub(self):
    hub_module_url = self._export_bert_tfhub()

After Change

    encoder = encoders.build_encoder(
        encoders.EncoderConfig(
            bert=encoders.BertEncoderConfig(vocab_size=30522, num_layers=1)))
    encoder_inputs_dict = {x.name: x for x in encoder.inputs}
    encoder_output_dict = encoder(encoder_inputs_dict)
    core_model = tf.keras.Model(
        inputs=encoder_inputs_dict, outputs=encoder_output_dict)

Instances