ValueError: A KerasTensor is symbolic: it's a placeholder for a shape an a dtype. It doesn't have any actual numerical value. You cannot convert it to a NumPy array.

[accioharshita]

Hey, so I've downloaded the preprocessing & encoder layer of BERT in order to build a simple email classification model. When I'm finally building my model to pass the training data it throws this error. Can someone tell me what's wrong?

[LeviBen]

I have the same problem. With the URL was working fine, but with the model working locally, for some reason it crashes

[SoumyaCodes2020]

@accioharshita Hi, I was having the same problem. In fact, I was using the exact same code as you. I managed to solve my problem by importing Bert through the keras_nlp library. Here is the code I ended up with:

text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
preprocessor = keras_nlp.models.BertPreprocessor.from_preset("bert_base_en_uncased",trainable=True)
encoder_inputs = preprocessor(text_input)
encoder = keras_nlp.models.BertBackbone.from_preset("bert_base_en_uncased")
outputs = encoder(encoder_inputs)
pooled_output = outputs["pooled_output"]      # [batch_size, 768].
sequence_output = outputs["sequence_output"]  # [batch_size, seq_length, 768].

[BismaAyaz]

@SoumyaCodes2020 can you please let me know how you saved model with this approach. I'm using this approach model3.save("model3.keras")
model3 = keras.models.load_model("model3.keras") but getting error

No vocabulary has been set for WordPieceTokenizer. Make sure to pass a `vocabulary` argument when creating the layer.