Papers with Code 아카이브
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/theneuralbeing/bert-finetuning-webinar
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/theswgong/MoNet
Geometric deep learning on graphs and manifolds using mixture model CNNs
https://github.com/thethirdwheel/malumagraph
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/thirdairesearch/bolt_benchmarks
https://github.com/thisisiron/kaggle-toxic-comment
https://github.com/thisiskhan/tensorflow-developer-certificate-machine-learning-kit
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
https://github.com/threelittlemonkeys/cnn-text-classification-pytorch
https://github.com/threelittlemonkeys/text-cnn-pytorch
https://github.com/threelittlemonkeys/vdcnn-pytorch
https://github.com/thu-keg/omnievent
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/thuiar/textoir
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
https://github.com/thuiar/textoir-demo
An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction
https://github.com/thumlp/tensorgcn
https://github.com/thunlp/ReInfoSelect
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tighu20/Kaggle-Tweet-Sentiment-Extraction
https://github.com/timerstime/SDG4DA
https://github.com/tk-rusch/graphcon
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tkipf/gcn
https://github.com/tkipf/ica
https://github.com/tkipf/keras-gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tlmakinen/GAT-noise
https://github.com/tmalbonph/tcbert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tobyatgithub/bert_tutorial
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/toinesayan/node-classification-and-label-dependencies
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tomas-gajarsky/facetorch
https://github.com/tomgoter/nlp_finalproject
https://github.com/tomwalczak/PubMed-Abstract-Analyzer
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
https://github.com/tongjingqi/code2logic
https://github.com/toriving/KoEDA
AEDA: An Easier Data Augmentation Technique for Text Classification
https://github.com/tornermarton/bert-keras
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/toru34/kim_emnlp_2014
https://github.com/transducens/LASERtrain
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/traviscoan/cards
https://github.com/tree-park/bert-pretrainer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/trietnm2/sent2vec4j
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.