https://github.com/mitre-attack/tram
공식 코드Cyber-Attack Technique Classification Using Two-Stage Trained Large Language Models
Papers with Code 아카이브
Text Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 16
- 평가표
- 101
- 보존된 평가 기록
- 341
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/ml-stat-sustech/local-perplexity-ranking
공식 코드On the Noise Robustness of In-Context Learning for Text Generation
https://github.com/mmoradi-iut/BioCIE
공식 코드Explaining Black-box Models for Biomedical Text Classification
https://github.com/mnaylor5/quantifying-explainability
공식 코드Quantifying Explainability in NLP and Analyzing Algorithms for Performance-Explainability Tradeoff
https://github.com/mnbucher/text-cls-llms
공식 코드https://github.com/modelscope/ms-swift
공식 코드https://github.com/modelscope/swift
공식 코드https://github.com/modeltc/mqbench
공식 코드QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization
https://github.com/modr00cka/cophe
공식 코드https://github.com/mohbattharani/GVdoc
공식 코드https://github.com/mojave-pku/TextLevelGCN
공식 코드https://github.com/moncefbenaicha/spoken-ner
공식 코드Leveraging Cross-Lingual Transfer Learning in Spoken Named Entity Recognition Systems
https://github.com/monetjoe/emelodygen
공식 코드EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature Template
https://github.com/morganlee123/deeptalkemotions
공식 코드Is Style All You Need? Dependencies Between Emotion and GST-based Speaker Recognition
https://github.com/moritzlaurer/zeroshot-classifier
공식 코드Building Efficient Universal Classifiers with Natural Language Inference
https://github.com/morning-dews/ST
공식 코드https://github.com/morningmoni/HiLAP
공식 코드Hierarchical Text Classification with Reinforced Label Assignment
https://github.com/mp2893/med2vec
공식 코드https://github.com/mpoemsl/circe
공식 코드CIRCE at SemEval-2020 Task 1: Ensembling Context-Free and Context-Dependent Word Representations
https://github.com/mrvoh/meta_learning_multilingual_doc_classification
공식 코드Multilingual and cross-lingual document classification: A meta-learning approach
https://github.com/msadat3/scihtc
공식 코드Hierarchical Multi-Label Classification of Scientific Documents
https://github.com/msadat3/ssl_for_nli
공식 코드https://github.com/muberraozmen/bncl
공식 코드https://github.com/muberraozmen/mrmp
공식 코드Multi-relation Message Passing for Multi-label Text Classification
https://github.com/muhammed-saeed/clat
공식 코드Low-Resource Cross-Lingual Adaptive Training for Nigerian Pidgin
https://github.com/muradtuk/Sets-clustering
공식 코드https://github.com/murrayds/sci-text-disagreement
공식 코드https://github.com/murtyshikhar/pushdown-layers
공식 코드Pushdown Layers: Encoding Recursive Structure in Transformer Language Models
https://github.com/muzaluisa/soft-skill-matching
공식 코드https://github.com/mvandenhi/signal-is-harder
공식 코드Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss
https://github.com/nab170130/stencil
공식 코드STENCIL: Submodular Mutual Information Based Weak Supervision for Cold-Start Active Learning
https://github.com/nadavo/mood
공식 코드https://github.com/nahmiasd/prompted-contextual-vectors-for-spear-phishing-detection
공식 코드https://github.com/namir0806/flag
공식 코드FLAG: Financial Long Document Classification via AMR-based GNN
https://github.com/naver/biobert-pretrained
공식 코드BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/nazim1021/OOD-detection-using-OECC
공식 코드Outlier Exposure with Confidence Control for Out-of-Distribution Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.