Comments (2)
Your understanding about sparse features and embedding tables is correct.
In this dataset by default the number of rows in an embedding is based on the unique indices that a sparse feature can attain, so that each unique index maps to a single embedding vector. However, if the index range is too large, we can limit it with --max-ind-range parameter, which is typically chosen empirically.
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Got it. Thanks!
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