https://github.com/rc19/CS221Project
공식 코드Can We Achieve More with Less? Exploring Data Augmentation for Toxic Comment Classification
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/react-nlp/tcab_generation
공식 코드https://github.com/recorderhou/contrastive_bootstrapping_label_refinement
공식 코드https://github.com/reglab/casehold
공식 코드When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
https://github.com/remyhuang/pop-music-highlighter
공식 코드https://github.com/rendanim/mphayaner
공식 코드https://github.com/reza321/T-Miner
공식 코드T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification
https://github.com/rezacsedu/BengFastText
공식 코드https://github.com/rhtm02/Bootstrap-Your-Emotion-Latent
공식 코드https://github.com/rickltt/evs
공식 코드Evolutionary Verbalizer Search for Prompt-based Few Shot Text Classification
https://github.com/riflezhang/genco
공식 코드https://github.com/rishabhmaheshwary/query-attack
공식 코드A Strong Baseline for Query Efficient Attacks in a Black Box Setting
https://github.com/ritamargherita/llms-topic-classification
공식 코드Zero-Shot Topic Classification of Column Headers: Leveraging LLMs for Metadata Enrichment
https://github.com/ritaranx/wander
공식 코드Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training
https://github.com/rjagerman/glint
공식 코드Computing Web-scale Topic Models using an Asynchronous Parameter Server
https://github.com/rjagerman/tois2019-safe-exploration-algorithm
공식 코드https://github.com/rjavadi/social-signal-project
공식 코드The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)
https://github.com/rktamplayo/MCFA
공식 코드Translations as Additional Contexts for Sentence Classification
https://github.com/rktamplayo/MicroASM
공식 코드https://github.com/robert-giaquinto/dynamic-author-persona-topic-model
공식 코드Topic Modeling on Health Journals with Regularized Variational Inference
https://github.com/romanplaud/revisitinghtc
공식 코드Revisiting Hierarchical Text Classification: Inference and Metrics
https://github.com/rooooyy/hill
공식 코드HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification
https://github.com/rooooyy/hitin
공식 코드HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification
https://github.com/rotmanguy/amoc
공식 코드Model Compression for Domain Adaptation through Causal Effect Estimation
https://github.com/rrmenon10/DISCERN
공식 코드DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers
https://github.com/rsinghlab/qzero
공식 코드https://github.com/rturrisige/textclassification
공식 코드https://github.com/rub-syssec/adversarial-papers
공식 코드No more Reviewer #2: Subverting Automatic Paper-Reviewer Assignment using Adversarial Learning
https://github.com/ruslankhalitov/chordmixer
공식 코드ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths
https://github.com/rxlian/la-scl
공식 코드Learning Label Hierarchy with Supervised Contrastive Learning
https://github.com/ryersonmultimedialab/emotionrecognitionuda
공식 코드https://github.com/s-nlp/formality
공식 코드Detecting Text Formality: A Study of Text Classification Approaches
https://github.com/s1998/progressivetraincodeswitch
공식 코드https://github.com/sachertort/petkaz-semeval-ecac
공식 코드https://github.com/saeednajafi/riff
공식 코드RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language Models
https://github.com/sagorbrur/bangla-bert
공식 코드Emotion Classification in a Resource Constrained Language Using Transformer-based Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.