https://github.com/kyzhouhzau/BERT-NER
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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/labixiaoK/lda
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/lackel/hierarchical_weighted_scl
https://github.com/lamp-benchmark/lamp
https://github.com/lantgabor/Unsupervised-Data-Augmentation-PyTorch
https://github.com/laohur/LearnToCompareText
https://github.com/laohur/RelationNet
https://github.com/lashoun/hanna-benchmark-asg
https://github.com/latkins/trifast
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
https://github.com/lazarotm/SVDCNN
Squeezed Very Deep Convolutional Neural Networks for Text Classification
https://github.com/lbneon/research
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
https://github.com/lcs2-iiitd/emotion-flip-reasoning
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/leblancdaniel/paraphraser
https://github.com/leemsaebom/attention-guided-cam-visual-explanations-of-vision-transformer-guided-by-self-attention
https://github.com/lehoanganh298/BERT-Question-Answering
https://github.com/leihuayi/NetworkEmbedding
https://github.com/lemonhu/ner-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lennonzurich/lalala
https://github.com/leolya/Audio-Super-Resolution-Tensorflow2.0-TFiLM
Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations
https://github.com/leuchine/S-LSTM
https://github.com/lffloyd/embedded-topic-model
https://github.com/lfmatosm/embedded-topic-model
https://github.com/lgalke/vec4ir
All-but-the-Top: Simple and Effective Postprocessing for Word Representations
https://github.com/linguishi/chinese_sentiment
https://github.com/linkfluence/fastText4j
https://github.com/linlei1214/SITS-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/linyanglee/token-aware-vat
TAVAT: Token-Aware Virtual Adversarial Training for Language Understanding
https://github.com/lipingcoding/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/lishenghui/blades
Byzantine-robust Federated Learning through Collaborative Malicious Gradient Filtering
https://github.com/listenviolet/XLNet
XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/liu6zijian/simplified-gcn-model
https://github.com/liupengyuan/EmotionAction_EmotionInference
Emotion Action Detection and Emotion Inference: the Task and Dataset
https://github.com/liuqiangict/lamb_optimizer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/lizhaoliu-Lec/CNLE
https://github.com/lkfo415579/MT-Readling-List
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.