akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network
This GitHub repository provides an implementation of the paper "MAGNET: Multi-Label Text Classification using Attention-based Graph Neural Network" . MAGNET is a state-of-the-art approach for multi-label text classification, leveraging the power of graph neural networks (GNNs) and attention mechanisms.
This project helps you automatically categorize text documents or online comments that might belong to several different categories simultaneously. You input text, and it outputs all relevant labels, like classifying a news article as both 'politics' and 'economy,' or flagging an online comment for 'toxicity' and 'insult.' It's designed for data analysts, content moderators, or anyone managing large volumes of multi-topic text.
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Use this if you need to assign multiple predefined categories to a single piece of text and want to leverage advanced AI that understands relationships between those categories.
Not ideal if your text documents only ever fit into one category, or if you don't have existing labeled examples to train the system.
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Nov 02, 2023
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