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/trusthlt/privacy-legal-nlp-lm
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
https://github.com/tsandefer/capstone_2
https://github.com/tsandefer/dsi_capstone_2
https://github.com/tshev/faster-FastText
https://github.com/tshi04/AspDecSSCL
A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection
https://github.com/tsujuifu/pytorch_lstm-shuttle
https://github.com/tuananh0305/GCN_ANOMALY_DETECTION
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/tugstugi/mongolian-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tuvuumass/SCoPE
Character-level Convolutional Networks for Text Classification
https://github.com/tvinith/bert
https://github.com/twisha96/Natural-Language-Processing
Not All Attention Is Needed: Gated Attention Network for Sequence Data
https://github.com/tyxr/bert
https://github.com/uabinf/nlp-fall-2019-project-shuhui_shuqu
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uchange/ulangel
Universal Language Model Fine-tuning for Text Classification
https://github.com/udlf/wsef
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/ufal/wembedding_service
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uizard-technologies/realmix
https://github.com/unik00/textCNN-pytorch
https://github.com/unik00/textCNN-tf2
https://github.com/upunaprosk/fair-pruning
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
https://github.com/upunaprosk/la-tda/blob/master/5_Head_importance.ipynb
https://github.com/usama6832/attention_model
https://github.com/utsjiyaoli/qa-attack
https://github.com/utterworks/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uva-dsa/ems-pipeline
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uygarkurt/BERT-PyTorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uzaymacar/comparatively-finetuning-bert
https://github.com/vaibhavska/Very-Deep-Convolutional-Neural-Network-for-Sentiment-Analysis
https://github.com/vailatuts/SSFG-regularization
https://github.com/valteruo/sql2circuits
QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer
https://github.com/vanboefer/nn_doc2vec_exercise
https://github.com/vanpersie32/Multigpu-Bert
https://github.com/vanslerry/long_text
https://github.com/varshinireddyt/ULMFiT
Universal Language Model Fine-tuning for Text Classification
https://github.com/varungumma/fairseq
RoFormer: Enhanced Transformer with Rotary Position Embedding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.