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/xinzhel/kaggle-toxicity-2021
Convolutional Neural Networks for Toxic Comment Classification
https://github.com/xitianxiaofeixue/BERT
https://github.com/xiuyu0000/new_papers_codes/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xiuyu0000/tutorials/tree/main/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/xuhaiming1996/Densely-Connected-Bidirectional-LSTM
Densely Connected Bidirectional LSTM with Applications to Sentence Classification
https://github.com/xuzhezhaozhao/bert_reading
https://github.com/xuzhezhaozhao/fastText_reading
https://github.com/xyfJASON/Comment-Analysis
https://github.com/yCobanoglu/infinite-width-gnns
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yangheng95/LC-ABSA
PyABSA: A Modularized Framework for Reproducible Aspect-based Sentiment Analysis
https://github.com/yangjun1994/CAGCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yangrui123/Hidden
https://github.com/yangyucheng000/University/tree/main/model-2/fnet
https://github.com/yangyucheng000/University/tree/main/model-3/roberta
https://github.com/yangyucheng000/University/tree/main/model-3/roc_bert
BERT for Sentiment Analysis: Pre-trained and Fine-Tuned Alternatives
https://github.com/yangyucheng000/University/tree/main/model-3/squeezebert
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
https://github.com/yangyucheng000/ascend_albert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/ascend_deeptext
DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
https://github.com/yangyucheng000/bert_Ascend
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/bert_GPU
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yangyucheng000/fasttext
https://github.com/yangyucheng000/textcnn
https://github.com/yangzonglin1994/bilm-tf-extended
https://github.com/yanhuchen/quantum-graph-convolutional-network
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/yannvgn/laserembeddings
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/yanzhitech/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yasufumy/pytorch_lstm_jump
https://github.com/ydzhang-stormstout/HAT
https://github.com/yeahshow/word2vec_medical_record
https://github.com/yeranosyanvahan/SuperTML
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
https://github.com/yifding/hetseq
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/yikangshen/Ordered-Memory
https://github.com/yinghao1019/NLP_and_DL_practice
https://github.com/yinghao1019/NLP_and_DL_practice/blob/master/Convolution_Neural_Netowrks_for_sentence_classification_Practice.ipynb
https://github.com/yinghao1019/imdb_prac
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.