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There's unfortunately not much to read here yet...
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A common design pattern I see in a lot of ML projects is to have some sort of experiment configuration file, and then a bunch of code that constructs the appropriate objects based on these configurations. Frequently, the resulting code blocks have a bunch of if/elif/else statements, or a manually created lookup dictionary somewhere. This can quickly get messy and inconsistent as you add new models/losses/encoders/optimizers.
There's unfortunately not much to read here yet...
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