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Document Classification

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하위 태스크: 1
평가표: 22
보존된 평가 기록: 51

Document Classification is a procedure of assigning one or more labels to a document from a predetermined set of labels.

Source: Long-length Legal Document Classification

코드

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

https://github.com/AnanyaKumar/verified_calibration

On Calibration of Modern Neural Networks

https://github.com/Andreas12321/Est-Cert-Final

On Calibration of Modern Neural Networks

https://github.com/AngusMonroe/GAT-pytorch

Graph Attention Networks

https://github.com/Anou9531/GAT

Graph Attention Networks

https://github.com/ArneDefauw/BERT_doc_classification

Phenotyping of Clinical Notes with Improved Document Classification Models Using Contextualized Neural Language Models

https://github.com/Aveek-Saha/Graph-Attention-Net

Graph Attention Networks

https://github.com/BIG-S2/keras-gnm

Graph Attention Networks

https://github.com/BingYu94860/HGNN-Node-Classification

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/BordiaS/layoutlm

Modular Multimodal Architecture for Document Classification

https://github.com/COMP6248-Reproducability-Challenge/Differentiables_FastAccurateTextClassification

Learning to Skim Text

https://github.com/ChengSashankh/trying-GAT

Graph Attention Networks

https://github.com/ChengyuSun/gat

Graph Attention Networks

https://github.com/ColdenChan/GAT_D

Graph Attention Networks

https://github.com/DeepGraphLearning/GMNN

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/Diego999/pyGAT

Graph Attention Networks

https://github.com/GraphSAINT/GraphSAINT

Graph Attention Networks

https://github.com/HamzaGbada/GCN-Numpy

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/HazyResearch/hgcn

Graph Attention Networks

https://github.com/HeapHop30/graph-attention-nets

Geometric deep learning on graphs and manifolds using mixture model CNNs

https://github.com/HeoTaksung/Document-to-Sequence-BERT

An Explainable CNN Approach for Medical Codes Prediction from Clinical Text

https://github.com/HeoTaksung/MIMIC-III_CNN_Attention

An Explainable CNN Approach for Medical Codes Prediction from Clinical Text

https://github.com/HoganZhang/pygcn_python3

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/IllinoisGraphBenchmark/IGB-Datasets

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/Kaimaoge/IGNNK

Graph Attention Networks

https://github.com/KimMeen/GCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/Kristof-Neys/GAT_Neo4j_DGL

Graph Attention Networks

https://github.com/Lackel/DNA

HDLTex: Hierarchical Deep Learning for Text Classification

https://github.com/LawrenceDuan/myLASER

Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond

https://github.com/LouisDumont/GCN---re-implementation

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/MJ-Jang/Paraphrase-Thought

Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition

https://github.com/Maysir/GRCN

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/MehmetBarutcu/Streaming-Algorithm-for-Monotone-k-Submodular-Maximization-with-Cardinality-Constraints

Graph Attention Networks

https://github.com/Messham87/COVID_GAT

Graph Attention Networks

https://github.com/Mind23-2/MindCode-4/tree/main/gat

Graph Attention Networks

https://github.com/MindSpore-scientific-2/code-4/tree/main/GR-HSTU

Graph Attention Networks

https://github.com/Negus25/GAT-Mindspore

Graph Attention Networks

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