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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/cuhksz-nlp/mcasp

ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram Representations

https://github.com/cybermonic/cage-4-submission

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/danielegrattarola/keras-gat

Graph Attention Networks

https://github.com/darnbi/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/davidpicard/homm

Graph Attention Networks

https://github.com/deepchem/moleculenet

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/deepopinion/anls-star-metric

ANLS* -- A Universal Document Processing Metric for Generative Large Language Models

https://github.com/dfdazac/dgi

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/dmeoli/neuro-sat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/monet

Geometric deep learning on graphs and manifolds using mixture model CNNs

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/monet

Geometric deep learning on graphs and manifolds using mixture model CNNs

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat

Graph Attention Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/ds4dm/sGat

Graph Attention Networks

https://github.com/ds4dm/sparse-gcn

Graph Attention Networks

https://github.com/dzb1998/pyGAT

Graph Attention Networks

https://github.com/emartinezs44/SparkGCN

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/emorynlp/character-mining

Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks

https://github.com/epfml/sent2vec

Robust Cross-lingual Embeddings from Parallel Sentences

https://github.com/eske/multivec

BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

https://github.com/facebookresearch/vizseq

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

https://github.com/facebookresearch/xformers/blob/main/xformers/ops/fmha/flash.py

FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

https://github.com/fanzhenliu/dagad

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/firojalam/domain-adaptation

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/fongyk/graph-attention

Graph Attention Networks

https://github.com/galkampel/HyperNetworks

Graph Attention Networks

https://github.com/gayanku/FDGATII

Graph Attention Networks

https://github.com/gcucurull/jax-gat

Graph Attention Networks

https://github.com/gen3111620/DeepWalk

DeepWalk: Online Learning of Social Representations

https://github.com/giuseppefutia/link-prediction-code

Graph Attention Networks

https://github.com/gmpoli/electramed

Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

https://github.com/gmum/umwpl2021

Graph Attention Networks

https://github.com/google-research-datasets/wiki-reading

WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia

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