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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/ayushbits/robust-aggregate-lfs
공식 코드Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
https://github.com/azminewasi/Drug-Classification-NLP
공식 코드https://github.com/azubiaga/tfcr
공식 코드https://github.com/bachtranxuan/GCTM
공식 코드A Graph Convolutional Topic Model for Short and Noisy Text Streams
https://github.com/bahareharandizade/keyetm
공식 코드https://github.com/baidu/Familia
공식 코드Familia: An Open-Source Toolkit for Industrial Topic Modeling
https://github.com/balikasg/topicModelling
공식 코드https://github.com/batorskq/prl
공식 코드https://github.com/batsresearch/nplm
공식 코드https://github.com/bboylyg/backdoorllm
공식 코드BackdoorLLM: A Comprehensive Benchmark for Backdoor Attacks and Defenses on Large Language Models
https://github.com/bert-nmt/bert-nmt
공식 코드https://github.com/beyondguo/STA
공식 코드Selective Text Augmentation with Word Roles for Low-Resource Text Classification
https://github.com/beyondguo/genius
공식 코드https://github.com/beyondguo/label_confusion_learning
공식 코드Label Confusion Learning to Enhance Text Classification Models
https://github.com/bgonzalezbustamante/TextClass-Benchmark
공식 코드TextClass Benchmark: A Continuous Elo Rating of LLMs in Social Sciences
https://github.com/bgshin/distill_demo
공식 코드https://github.com/bids-xu-lab/biomedical-nlp-benchmarks
공식 코드https://github.com/bigdata-ccnu/reprint
공식 코드Reprint: a randomized extrapolation based on principal components for data augmentation
https://github.com/bikash617/Aggressive-Sampling-for-Multi-class-to-BinaryReduction
공식 코드Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification
https://github.com/bkolosk1/bablfusion
공식 코드AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification
https://github.com/blei-lab/causal-text-embeddings
공식 코드https://github.com/blessu/balancedlossnlp
공식 코드Balancing Methods for Multi-label Text Classification with Long-Tailed Class Distribution
https://github.com/bloodraven66/ssl_aai
공식 코드https://github.com/bluewhalelab/dcscore
공식 코드https://github.com/bobxwu/cfdtm
공식 코드https://github.com/bobxwu/ecrtm
공식 코드Effective Neural Topic Modeling with Embedding Clustering Regularization
https://github.com/bobxwu/fastopic
공식 코드FASTopic: Pretrained Transformer is a Fast, Adaptive, Stable, and Transferable Topic Model
https://github.com/bobxwu/infoctm
공식 코드InfoCTM: A Mutual Information Maximization Perspective of Cross-Lingual Topic Modeling
https://github.com/bobxwu/paper-neural-topic-models
공식 코드A Survey on Neural Topic Models: Methods, Applications, and Challenges
https://github.com/bobxwu/topmost
공식 코드https://github.com/bochengroup/topicnet
공식 코드https://github.com/bonaventuredossou/mlm_al
공식 코드AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African Languages
https://github.com/booktrackerGirl/Sys-lit-review-Sexism
공식 코드https://github.com/boschresearch/adversarial_meta_embeddings
공식 코드FAME: Feature-Based Adversarial Meta-Embeddings for Robust Input Representations
https://github.com/boschresearch/switchprompt
공식 코드SwitchPrompt: Learning Domain-Specific Gated Soft Prompts for Classification in Low-Resource Domains
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.