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This way, end-to-end learning avoids introducing expert knowledge or preprocessing, usually relying only on huge amounts of data. … So it is certainly possible to skip data preprocessing completely. …

Again, with Transformers and BPE usually there is no need for much preprocessing. If any, I would ensure there is no garbage in your data. …

In tf.keras.preprocessing.text (docs) you have utilities to process discrete token sequences, normally used to represent text. In tf.keras.preprocessing.sequence (docs) you have utilities to process b …

You can first write the bottleneck features into a tfrecords file, and then load them as a dataset for the training phase. In the tensorflow documentation you can find complete examples of how to do b …

Embeddings: A --> [vector_A] B --> [vector_B] ... They are frequently used in neural networks to represent categorical values that can have a lot of different discrete values, like text. The vectors a …

Imagine that after the split there is no "good" in the training data. If you had done the encoding after the split, then you would have no idea that there can be a "good". There you have your leakage. …

I suggest that you input the sketch data as text. There is no problem encoding these as just text. Also, have the model generate text as well. With that in mind, you should use a model that is meant f …

UMAP is a stochastic algorithm. In order to reproduce their results, it's needed that they used the random_state and you need to set the same value as they did. Given that Unfortunately, there isn't …

Instead of the approaches you mentioned, I suggest a completely different approach: a retrieval-augmented generation (RAG) system. I am doing this because what you described is a typical use case for …