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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/wuch15/Fastformer
공식 코드https://github.com/wxitxy/complementary_intra-class_and_inter-class_mixup
공식 코드https://github.com/wxitxy/synermix
공식 코드https://github.com/wzh9969/contrastive-htc
공식 코드https://github.com/wzh9969/hiadv
공식 코드Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification
https://github.com/wzh9969/hpt
공식 코드HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
https://github.com/wzy232303/fpt
공식 코드FPT: Feature Prompt Tuning for Few-shot Readability Assessment
https://github.com/x-tabdeveloping/turftopic
공식 코드https://github.com/xianjun-yang/pcmsp
공식 코드https://github.com/xiaohao-yang/topic_model_evaluation
공식 코드LLM Reading Tea Leaves: Automatically Evaluating Topic Models with Large Language Models
https://github.com/xiaohao-yang/topic_model_generalisation
공식 코드https://github.com/xiaoleihuang/Multilingual_Fairness_LREC
공식 코드Multilingual Twitter Corpus and Baselines for Evaluating Demographic Bias in Hate Speech Recognition
https://github.com/xiaoleihuang/UserEmbedding
공식 코드User Factor Adaptation for User Embedding via Multitask Learning
https://github.com/xiaoleihuang/domainfairness
공식 코드Easy Adaptation to Mitigate Gender Bias in Multilingual Text Classification
https://github.com/xiaolin1207/HTTN-master
공식 코드Does Head Label Help for Long-Tailed Multi-Label Text Classification
https://github.com/xiaoqian19940510/text-classification-surveys
공식 코드A Survey on Text Classification: From Shallow to Deep Learning
https://github.com/xiatingyu/fastclass
공식 코드FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification
https://github.com/xienliu/tensor-gcn-text-classification-tensorflow
공식 코드https://github.com/ximingwen/gaprotonet
공식 코드https://github.com/xinleihe/mgtbench
공식 코드https://github.com/xinzhel/unlearnable_texts
공식 코드Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data
https://github.com/xiyuan68/dddm
공식 코드https://github.com/xlhex/extract_and_transfer
공식 코드Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!
https://github.com/xmc-aalto/adv-xmtc
공식 코드Adversarial Examples for Extreme Multilabel Text Classification
https://github.com/xmc-aalto/inceptionxml
공식 코드https://github.com/xo28/efficient-nlp-multistage-training
공식 코드Efficient NLP Model Finetuning via Multistage Data Filtering
https://github.com/xrc10/cross-distill
공식 코드https://github.com/xrc10/unsup-cross-lingual-embedding-transfer
공식 코드Unsupervised Cross-lingual Transfer of Word Embedding Spaces
https://github.com/xuandongzhao/drw
공식 코드Distillation-Resistant Watermarking for Model Protection in NLP
https://github.com/xuandongzhao/npprompt
공식 코드https://github.com/xuczhang/UncertainDC
공식 코드https://github.com/xuyige/BERT4doc-Classification
공식 코드https://github.com/xxxxxxxxy/memotion3-SEFusion
공식 코드https://github.com/xyzacademic/mlp01example
공식 코드https://github.com/y3nk0/OMP-for-Text-Classification
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.