tomtung/omikuji
An efficient implementation of Partitioned Label Trees & its variations for extreme multi-label classification
This project helps data scientists and machine learning engineers classify documents, images, or other data points into a very large number of categories efficiently. You provide a dataset with inputs and their associated multiple labels, and it produces a trained model that can quickly and accurately assign many relevant labels to new, unseen inputs. It's designed for practitioners dealing with datasets where each item can belong to hundreds or even thousands of categories.
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Use this if you need to perform multi-label classification on massive datasets with a huge number of potential labels and want faster training times without sacrificing accuracy.
Not ideal if you are working with small datasets or a limited number of categories, as its specialized efficiency for 'extreme' scenarios won't provide significant benefits.
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91
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11
Language
Rust
License
MIT
Category
Last pushed
Feb 20, 2024
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20
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0
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