https://github.com/lejon/PartiallyCollapsedLDA
공식 코드Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
Papers with Code 아카이브
Text Classification
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- 평가표
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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/letenothibaud/wasserstein_fair_classification
공식 코드https://github.com/levelfour/pumil
공식 코드Convex Formulation of Multiple Instance Learning from Positive and Unlabeled Bags
https://github.com/lgalke/text-clf-baselines
공식 코드https://github.com/lhf-labs/finance-news-analysis-bert
공식 코드https://github.com/liam0949/llm-ood
공식 코드VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection
https://github.com/liamdm/flowtransformer
공식 코드FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems
https://github.com/lijfrank-open/GraphCFC
공식 코드https://github.com/lijiazheng99/aera
공식 코드Distilling ChatGPT for Explainable Automated Student Answer Assessment
https://github.com/lijiazheng99/cue
공식 코드https://github.com/likich/tm_graph
공식 코드Automating the Information Extraction from Semi-Structured Interview Transcripts
https://github.com/lilyli2004/satire
공식 코드A Multi-Modal Method for Satire Detection using Textual and Visual Cues
https://github.com/lingyugao/labeldesctraining
공식 코드The Benefits of Label-Description Training for Zero-Shot Text Classification
https://github.com/linyanglee/knn-bert
공식 코드KNN-BERT: Fine-Tuning Pre-Trained Models with KNN Classifier
https://github.com/litesslhub/disco
공식 코드DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining
https://github.com/little1tow/DELE_pytorch
공식 코드Description-Enhanced Label Embedding Contrastive Learning for Text Classification
https://github.com/liwb1219/zhfeat
공식 코드https://github.com/liyongqi67/Data-Distillation-for-Text-Classification
공식 코드https://github.com/lizeyujack/oceanship
공식 코드Oceanship: A Large-Scale Dataset for Underwater Audio Target Recognition
https://github.com/ljw9510/SDL
공식 코드https://github.com/lorenzoscottb/Dream_Reports_Annotation
공식 코드Automatic Scoring of Dream Reports' Emotional Content with Large Language Models
https://github.com/lrjconan/RBP
공식 코드https://github.com/lsy3/clinical-notes-diagnosis-dl-nlp
공식 코드An Empirical Evaluation of Deep Learning for ICD-9 Code Assignment using MIMIC-III Clinical Notes
https://github.com/ltian678/metatroll-code
공식 코드MetaTroll: Few-shot Detection of State-Sponsored Trolls with Transformer Adapters
https://github.com/lttam/generalized-sobolev-transport
공식 코드Generalized Sobolev Transport for Probability Measures on a Graph
https://github.com/lttam/robustot-noisytreemetric
공식 코드https://github.com/lttam/sobolevtransport
공식 코드Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
https://github.com/luca-parisi/hyper-sinh_TensorFlow_Keras
공식 코드hyper-sinh: An Accurate and Reliable Function from Shallow to Deep Learning in TensorFlow and Keras
https://github.com/lucaoffice/publications
공식 코드https://github.com/lucastheis/logistic_lda
공식 코드https://github.com/luck1y/beamattack
공식 코드Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack
https://github.com/ludwig-ai/ludwig
공식 코드Personalized Benchmarking with the Ludwig Benchmarking Toolkit
https://github.com/luffy06/refusion
공식 코드https://github.com/luisfredgs/cnn-hierarchical-network-for-document-classification
공식 코드Hierarchical Attentional Hybrid Neural Networks for Document Classification
https://github.com/lujiaying/gt-doc2graph
공식 코드Weakly Supervised Concept Map Generation through Task-Guided Graph Translation
https://github.com/luka-group/odda-data-augmentation
공식 코드On-the-fly Denoising for Data Augmentation in Natural Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.