https://github.com/songruiecho/tacit
공식 코드TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification
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/sonlam1102/job-prediction-multilabel-vietnamese
공식 코드Predicting Job Titles from Job Descriptions with Multi-label Text Classification
https://github.com/sonlam1102/text_augmentation_vietnamese
공식 코드Empirical Study of Text Augmentation on Social Media Text in Vietnamese
https://github.com/soumyac1999/hyperbolic-label-emb-for-hmc
공식 코드Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
https://github.com/sprinter1999/fnbench
공식 코드FNBench: Benchmarking Robust Federated Learning against Noisy Labels
https://github.com/sprocketlab/otter
공식 코드OTTER: Effortless Label Distribution Adaptation of Zero-shot Models
https://github.com/srebuffi/lsd-clusters
공식 코드https://github.com/srishtigupta253/tone
공식 코드https://github.com/sruthisudheer/comment-classification-of-c-code
공식 코드Identification of the Relevance of Comments in Codes Using Bag of Words and Transformer Based Models
https://github.com/ssharoff/robust
공식 코드Know thy corpus! Robust methods for digital curation of Web corpora
https://github.com/stareru/csqa_debertav3
공식 코드https://github.com/stat-ml/nuq
공식 코드Nonparametric Uncertainty Quantification for Single Deterministic Neural Network
https://github.com/stefanolocci/LLMClassification
공식 코드https://github.com/stepantita/space-model
공식 코드https://github.com/stephenslab/fastTopics
공식 코드Non-negative matrix factorization algorithms greatly improve topic model fits
https://github.com/stern5497/doc2docbeirir
공식 코드One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
https://github.com/studio-ousia/textent
공식 코드Representation Learning of Entities and Documents from Knowledge Base Descriptions
https://github.com/successar/FRESH
공식 코드https://github.com/sumilergao/sungen
공식 코드Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning
https://github.com/sungjoonpark/emotiondetection
공식 코드https://github.com/sungraepark/AdvDrop-sMNIST
공식 코드https://github.com/sungraepark/adversarial_dropout_text_classification
공식 코드https://github.com/superyue72/OptLLM
공식 코드OptLLM: Optimal Assignment of Queries to Large Language Models
https://github.com/swj0419/knn_prompt
공식 코드https://github.com/sy-wada/blue_benchmark_with_transformers
공식 코드Pre-training technique to localize medical BERT and enhance biomedical BERT
https://github.com/sycny/chatgraph
공식 코드ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs
https://github.com/symanto-research/active-few-shot-learning
공식 코드https://github.com/symanto-research/few-shot-learning-label-tuning
공식 코드https://github.com/taddylab/deepir
공식 코드Document Classification by Inversion of Distributed Language Representations
https://github.com/tagoyal/snac
공식 코드https://github.com/tal-ai/Robust-learning-MSSHEM
공식 코드Robust Learning for Text Classification with Multi-source Noise Simulation and Hard Example Mining
https://github.com/tanganke/fusion_bench
공식 코드https://github.com/tanyuqian/learning-data-manipulation
공식 코드https://github.com/tapilab/aaai-2021-counterfactuals
공식 코드https://github.com/tapilab/emnlp-2020-spurious
공식 코드Identifying Spurious Correlations for Robust Text Classification
https://github.com/tatHi/optok
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.