Text Classification
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Text Classification is the task of assigning a sentence or document an appropriate category. The categories depend on the chosen dataset and can range from topics.
Text Classification problems include emotion classification, news classification, citation intent classification, among others. Benchmark datasets for evaluating text classification capabilities include GLUE, AGNews, among others.
In recent years, deep learning techniques like XLNet and RoBERTa have attained some of the biggest performance jumps for text classification problems.
<span style="color:grey; opacity: 0.6">( Image credit: Text Classification Algorithms: A Survey )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/deepopinion/anls-star-metric
ANLS* -- A Universal Document Processing Metric for Generative Large Language Models
https://github.com/deepset-ai/FARM
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/delldu/TextCNN
https://github.com/dengdan/seglink
https://github.com/dennybritz/cnn-text-classification-tf
https://github.com/deping-1/2019-false-news-detection-challenge
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/derekgreene/topic-stability
https://github.com/derronxu/sparsebert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/devJWSong/bert-crf-entity-recognition-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/devhemza/BERTweet_sentiment_analysis
https://github.com/dfdazac/dgi
https://github.com/dfm/arxiv-analysis
https://github.com/dhlee347/pytorchic-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/dhruvsawhney/CS152_FinalProject
Universal Language Model Fine-tuning for Text Classification
https://github.com/dhyeon/ingredient-vectors
https://github.com/diabhaque/Sixth-Sense
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/diegoschapira/CNN-Text-Classifier-using-Keras
https://github.com/dig-team/hanna-benchmark-asg
https://github.com/dispink/xpt
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/divisionai/fastText
https://github.com/dixiyao/Context-Aware-Compilation-of-DNN-Training-Pipelines-across-Edge-and-Cloud
https://github.com/dmcguire81/CS598DL4H
https://github.com/dmeoli/neuro-sat
https://github.com/dmis-lab/bern
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat
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/mxnet/sgc
https://github.com/dmlc/dgl/tree/master/examples/pytorch/gat
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/pytorch/sgc
https://github.com/dmlc/dgl/tree/master/examples/pytorch/transformer
BP-Transformer: Modelling Long-Range Context via Binary Partitioning
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gat
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/dmlc/dgl/tree/master/examples/tensorflow/sgc
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.