https://github.com/vchudinov/dynamic_memory_networks_with_keras
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
Papers with Code 아카이브
Text Classification
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- 보존된 평가 기록
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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/vdefont/readnet
ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis
https://github.com/venkyr91193/Sentiment-Analysis-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/venkyr91193/Sentiment_Analysis_with_BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/venomouscyanide/S3GRL_OGB
https://github.com/venomouscyanide/s3grl
https://github.com/venomouscyanide/s3grl_ogb
https://github.com/vfcarida/Semantic-Sensitive-TF-IDF-to-Determine-Word-Relevance-in-Documents
Semantic Sensitive TF-IDF to Determine Word Relevance in Documents
https://github.com/vgaraujov/lll-nlp
https://github.com/vicksam/fer-model
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/viethoang1512/kpa
https://github.com/vijay120/bert
https://github.com/vincent861223/Train_BERT_With_TextFooler
https://github.com/vineet54/fnet-google-pytorch
https://github.com/vinhkhuc/JFastText
https://github.com/viniciusoliveirasd/bert-applications
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vishalrk1/SkimLit
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
https://github.com/vishnuanil122/Bert-Binary-Text-Classifier
https://github.com/vithyayogarajan/Medical-Domain-Specific-Language-Models
https://github.com/vithyayogarajan/covid-19-patient-shielding
https://github.com/vivekgohel56/Neural-machine-translation-english-to-polish
https://github.com/vkiani/ShortPersianEmo
https://github.com/vlukiyanov/pt-avitm
https://github.com/vmware-labs/bert-pretraining
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/voidism/invrat_debias
Challenges in Automated Debiasing for Toxic Language Detection
https://github.com/volcengine/vegiantmodel
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/vyraun/Half-Size
On Dimensional Linguistic Properties of the Word Embedding Space
https://github.com/wac81/textda
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
https://github.com/wadoodabdul/clinical_ner_benchmark
https://github.com/wan2000/mc-mot
https://github.com/wangcongcong123/ttt
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/wapping/FaceClassification
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/warnikchow/coaudiotext
Speech Emotion Recognition Using Multi-hop Attention Mechanism
https://github.com/wayalhruhi/gogle_bert
https://github.com/wchh127/yykf
https://github.com/webis-de/acl-22
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.