whitepurple/HBM-loss-for-HTC
[ACL 2024 Findings] Hierarchy-aware Biased Bound Margin Loss Function for Hierarchical Text Classification
This project helps data scientists and machine learning engineers more accurately classify documents into complex, hierarchical categories. By improving how machine learning models learn from your text data, it allows you to get more precise and nuanced topic assignments. It takes raw text documents with existing category labels (which can be unevenly distributed) and outputs a better-performing classification model.
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Use this if you need to classify large volumes of text into categories that have subcategories, like legal documents, news articles, or product reviews, and are struggling with accuracy due to the hierarchical structure or imbalanced data.
Not ideal if your classification task involves flat categories with no inherent hierarchy, or if you are not working with text data.
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15
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2
Language
Python
License
MIT
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Last pushed
Oct 26, 2024
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