Product Categorization
6 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Product Categorization
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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Atlas
SCG
Most implemented papers
AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification
yourh/AttentionXML
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NeurIPS 2019
We propose a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: 1) a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label; and 2) a shallow and wide probabilistic label tree (PLT), which allows to handle millions of labels, especially for "tail labels".
Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks
Graph Neural Networks (GNNs) have recently gained traction in transportation, bioinformatics, language and image processing, but research on their application to supply chain management remains limited.
Taming Pretrained Transformers for Extreme Multi-label Text Classification
OctoberChang/X-Transformer
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However, naively applying deep transformer models to the XMC problem leads to sub-optimal performance due to the large output space and the label sparsity issue.
Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization
vumaasha/atlas
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In E-commerce, it is a common practice to organize the product catalog using product taxonomy.
Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
More recently, inspired by the development of graph self-supervised learning, transferring pretrained node embeddings for few-shot node classification could be a promising alternative to meta-learning but remains unexposed.
SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation
amazon-science/synthesizrr
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It is often desirable to distill the capabilities of large language models (LLMs) into smaller student models due to compute and memory constraints.
