MatchXML: Efficient Text-label Matching Framework for Extreme Multi-label Text Classification
MatchXML proposes a novel approach to efficiently match text samples with relevant labels in extreme multi-label text classification. The method leverages dense label embeddings and fine-tuned Transformer models to achieve state-of-the-art accuracies and training speed.
MatchXML: Efficient Text-label Matching Framework for Extreme Multi-label Text Classification
MatchXML proposes an efficient text-label matching framework for extreme multi-label text classification, achieving state-of-the-art accuracies and outperforming competing methods in training speed.
MatchXML: Efficient Text-label Matching Framework for Extreme Multi-label Text Classification
MatchXML proposes an efficient text-label matching framework for extreme multi-label text classification, achieving state-of-the-art accuracies and outperforming competing methods in training speed.
MatchXML: An Efficient Text-label Matching Framework for Extreme Multi-label Text Classification
MatchXML proposes an efficient text-label matching framework for Extreme Multi-label Text Classification, achieving state-of-the-art accuracies and outperforming competing methods in training speed.
© 2024 by Linnk AI