garfieldpigljy/HSCNN
An implementation of the paper "HSCNN: A Hybrid-Siamese Convolutional Neural Network for Extremely Imbalanced Multi-label Text Classification"
This helps data scientists and machine learning engineers classify text documents that can belong to multiple categories, especially when some categories have very few examples. You feed it text data with existing labels, and it produces a model capable of accurately assigning multiple labels to new, unseen text, even for rare categories. This is ideal for those building sophisticated text classification systems.
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Use this if you need to categorize documents into multiple, potentially overlapping categories, and you're struggling with accurately classifying categories that have very little training data.
Not ideal if your text classification problem involves only a single label per document, or if all your categories have a balanced number of training examples.
Stars
8
Forks
3
Language
Python
License
MIT
Last pushed
Feb 13, 2022
Commits (30d)
0
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