Simplify registering "built-in" presets #1818
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Instead of registering them with every class a preset should work with, we just register them with the associated backbone. We can use that to build `cls.preset` accessors for all library classes. E.g. ```python keras_nlp.models.PaliGemmaTokenizer.presets keras_nlp.models.Gpt2Backbone.presets keras_nlp.models.TextClassifier.presets ```
SamanehSaadat
approved these changes
Sep 10, 2024
What was the reason for registering the tokenizer before this change? |
Previously we didn't have the tokenizers associated with a backbone class. But not a particularly good reason, just hadn't thought of this approach? |
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Instead of registering classes with backbone and tokenizer, we just register them with the associated backbone. This will keep the accessors working for models without tokenizer classes. We can build the correct list for any class in the library by checking the associated backbone class.