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/jpablou/Matching-The-Blanks-Ths
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/jsantoso2/yelp-clone-ml-project
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/julian-risch/ICADL2018
https://github.com/jungomi/swiss-language-model
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/junhahyung/bert_finetune
https://github.com/junhahyung/bert_transfer
https://github.com/jxz542189/dmn_plus
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/jyp1111/sentiment_analysis
https://github.com/kafura-kafiri/tf2-elmo
https://github.com/kaist-dmlab/BioNER
https://github.com/kajyuuen/daaja
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
https://github.com/kakaobrain/kogpt
RoFormer: Enhanced Transformer with Rotary Position Embedding
https://github.com/kamalkraj/BERT-SQuAD
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kamalkraj/BERT_NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kapilnchauhan77/Disfluency-Detection
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
https://github.com/karenacorn99/explore-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/karlmoritz/bicvm
Multilingual Distributed Representations without Word Alignment
https://github.com/karta282950/bert-multiclass
https://github.com/karumugamio/SMAIProject-Team31
https://github.com/kaushaltrivedi/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kbsriharsha/FacialEmotionAnalysis
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/keithRebello/ULMFiT_sentiment_analysis
Universal Language Model Fine-tuning for Text Classification
https://github.com/keiyamashita111/aaproject
https://github.com/kekmodel/UDA-pytorch
https://github.com/kelly2016/multi-label-bert
https://github.com/keonlee9420/Comprehensive-Transformer-TTS
https://github.com/kevindeangeli/capsuleNetwork
Investigating Capsule Networks with Dynamic Routing for Text Classification
https://github.com/kevinwong2013/COMS4995_Team_4_Zero_Shot_Classifier
Integrating Semantic Knowledge to Tackle Zero-shot Text Classification
https://github.com/kh-kim/simple-ntc
https://github.com/khalidsaifullaah/bertify
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/khumbuai/keras_wiki_lm
Universal Language Model Fine-tuning for Text Classification
https://github.com/kiharalab/gnn_pocket
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/kihohan/NLP_Reference
https://github.com/kiko441500/google_bert
https://github.com/kimiyoung/planetoid
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