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Product Categorization

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평가표

1

보존된 평가 기록

2

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/CIOL-SUST/SCG

공식 코드

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

https://github.com/OctoberChang/X-Transformer

공식 코드

Taming Pretrained Transformers for Extreme Multi-label Text Classification

https://github.com/amazon-science/synthesizrr

공식 코드

SynthesizRR: Generating Diverse Datasets with Retrieval Augmentation

https://github.com/vumaasha/atlas

공식 코드

Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization

https://github.com/yourh/AttentionXML

공식 코드

AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

https://github.com/zhen-tan-dmml/tlp-fsnc

공식 코드

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification

https://github.com/AemikaChow/DATASOURCE

Atlas: A Dataset and Benchmark for E-commerce Clothing Product Categorization

https://github.com/JerryKwon/melon_plylst_continuation

Taming Pretrained Transformers for Extreme Multi-label Text Classification

https://github.com/NeuromorphicComputationResearchProgram/Learning-with-Holographic-Reduced-Representations

AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

https://github.com/ciol-researchlab/SupplyGraph

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

https://github.com/ciol-sust/supplygraph

Graph Neural Networks in Supply Chain Analytics and Optimization: Concepts, Perspectives, Dataset and Benchmarks

https://github.com/xmc-aalto/adv-xmtc

AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.