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train_input_ids = tokenizer(df['question'])
train_output_ids = tokenizer(df['answer'])
# Prepare decoder input and target sequences
decoder_input_ids = train_output_ids[:, :-1]
decoder_target_ids = train_output_ids[:, 1:]
# Create encoder and decoder models
encoder = encoder(vocab_size, num_layers, units, d_model, num_heads, dropout) # Your encoder model
decoder = decoder(vocab_size, num_layers, units, d_model, num_heads, dropout)# Your decoder model
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-18-2a344f922962> in <cell line: 10>()
8
9 # Create encoder and decoder models
---> 10 encoder = encoder(vocab_size, num_layers, units, d_model, num_heads, dropout) # Your encoder model
11 decoder = decoder(vocab_size, num_layers, units, d_model, num_heads, dropout)# Your decoder model
12
1 frames
/usr/local/lib/python3.10/dist-packages/keras/engine/input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name)
217
218 if len(inputs) != len(input_spec):
--> 219 raise ValueError(
220 f'Layer "{layer_name}" expects {len(input_spec)} input(s),'
221 f" but it received {len(inputs)} input tensors. "
ValueError: Layer "encoder" expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor: shape=(), dtype=int32, numpy=997>]
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