thomasahle/cce
Clustered Compositional Embeddings
When building recommendation systems, this helps manage extremely large categorical data like user IDs or product categories, which can otherwise overwhelm memory during training. It takes your raw categorical features and outputs highly compressed, efficient embedding tables that your machine learning model can learn from more effectively. This is for machine learning engineers or researchers working on large-scale recommendation engines.
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Use this if you are building machine learning models, especially recommendation systems, that use large categorical features and you are struggling with memory constraints during model training.
Not ideal if your datasets are small, or if your machine learning models do not heavily rely on categorical features and embeddings.
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Python
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Last pushed
Oct 25, 2023
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