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/zhaominyiz/epida
공식 코드EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification
https://github.com/zhiwenyou103/SciPrompt
공식 코드SciPrompt: Knowledge-augmented Prompting for Fine-grained Categorization of Scientific Topics
https://github.com/zhjohnchan/ptunifier
공식 코드Towards Unifying Medical Vision-and-Language Pre-training via Soft Prompts
https://github.com/zhongpeixiang/SemEval2019-Task3-EmotionDetection
공식 코드ntuer at SemEval-2019 Task 3: Emotion Classification with Word and Sentence Representations in RCNN
https://github.com/zhpinkman/robust-prototype-learning
공식 코드Robust Text Classification: Analyzing Prototype-Based Networks
https://github.com/zihangdai/xlnet
공식 코드XLNet: Generalized Autoregressive Pretraining for Language Understanding
https://github.com/zihanwangki/x-tc
공식 코드https://github.com/zizi1532/BasisCustomize
공식 코드Categorical Metadata Representation for Customized Text Classification
https://github.com/zjunlp/PromptKG/tree/main/research/RetroPrompt
공식 코드Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
https://github.com/zjunlp/promptkg
공식 코드Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning
https://github.com/zl-liu/radiation-oncology-nlp-database
공식 코드https://github.com/zli12321/tenor
공식 코드Improving the TENOR of Labeling: Re-evaluating Topic Models for Content Analysis
https://github.com/zshicode/latent-variable-gnn
공식 코드Differential equation and probability inspired graph neural networks for latent variable learning
https://github.com/zyzhangustc/mellm
공식 코드MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception
https://github.com/zzz47zzz/codebase-for-incremental-learning-with-llm
공식 코드Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
https://github.com/zzz47zzz/pretrained-lm-for-incremental-learning
공식 코드Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models
https://gitlab.com/ngillis/kl-onmf
공식 코드Orthogonal Nonnegative Matrix Factorization with the Kullback-Leibler divergence
https://gitlab.com/nicolalandro/multi_optimizer
공식 코드https://gitlab.com/smontariol/multi-task-esg
공식 코드Multi-Task Learning for Features Extraction in Financial Annual Reports
https://gitlab.com/xusong19960424/global_cnn
공식 코드From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information
https://gitlab.labikb.ca/ikb-lab/nlp/contextualizer/Contextualizer_arXiv2021
공식 코드An Iterative Contextualization Algorithm with Second-Order Attention
https://huggingface.co/bdpc/src
공식 코드Beyond Document Page Classification: Design, Datasets, and Challenges
https://huggingface.co/ganchengguang/USA-7B-instruction-incontext-learning
공식 코드https://zenodo.org/record/6842883
공식 코드https://dagshub.com/Dean/BioBERT-DAGsHub
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/04RR/SOTA-Vision
https://github.com/1024er/cbert_aug
https://github.com/1075225782/GCN
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/10Exahertz/Text-ResNet-on-Sentimental-LIAR-Fake-News
https://github.com/1wy/bert
https://github.com/2023-MindSpore-1/ms-code-11
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-11/tree/main/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/2023-MindSpore-1/ms-code-12
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/2023-MindSpore-1/ms-code-132
https://github.com/2023-MindSpore-1/ms-code-14/tree/main/Pangu_alpha
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.