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/unimorph/umlabeller
공식 코드Evaluating Subword Tokenization: Alien Subword Composition and OOV Generalization Challenge
https://github.com/unslothai/unsloth
공식 코드https://github.com/upunaprosk/la-tda
공식 코드https://github.com/uristern123/united-we-stand-using-epoch-wise-agreement-of-ensembles-to-combat-overfit
공식 코드United We Stand: Using Epoch-wise Agreement of Ensembles to Combat Overfit
https://github.com/usmaann/BioALBERT
공식 코드Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT
https://github.com/usydnlp/FedNLP
공식 코드FedNLP: An interpretable NLP System to Decode Federal Reserve Communications
https://github.com/usydnlp/TextGCN_analysis
공식 코드Understanding Graph Convolutional Networks for Text Classification
https://github.com/usydnlp/inducttgcn
공식 코드InducT-GCN: Inductive Graph Convolutional Networks for Text Classification
https://github.com/usydnlp/me_gcn
공식 코드https://github.com/uva-dsa/dkec
공식 코드DKEC: Domain Knowledge Enhanced Multi-Label Classification for Diagnosis Prediction
https://github.com/uva-nlp/flat
공식 코드Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation
https://github.com/vadel/acpd
공식 코드Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions
https://github.com/vadis-project/sv-ident
공식 코드https://github.com/vadis-project/user-tests
공식 코드VADIS -- a VAriable Detection, Interlinking and Summarization system
https://github.com/valdersoul/bn-vae
공식 코드A Batch Normalized Inference Network Keeps the KL Vanishing Away
https://github.com/valuesimplex/flitext
공식 코드FLiText: A Faster and Lighter Semi-Supervised Text Classification with Convolution Networks
https://github.com/varadhbhatnagar/Hostile-Post-Detection-in-Hindi
공식 코드Divide and Conquer: An Ensemble Approach for Hostile Post Detection in Hindi
https://github.com/varsha33/lcl_loss
공식 코드Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification
https://github.com/vbeliveau/radiology-text-classification
공식 코드Classification of Radiological Text in Small and Imbalanced Datasets in a Non-English Language
https://github.com/veason-silverbullet/vitlp
공식 코드Visually Guided Generative Text-Layout Pre-training for Document Intelligence
https://github.com/vgupta123/SCDV-MS
공식 코드Improving Document Classification with Multi-Sense Embeddings
https://github.com/vicentvankor/sun-shine
공식 코드https://github.com/vijay4313/proxemo
공식 코드https://github.com/vijit-m/Adv-OLM
공식 코드https://github.com/villacu/ic_xlt
공식 코드Adaptive Cross-lingual Text Classification through In-Context One-Shot Demonstrations
https://github.com/vincent-zhq/unis-mmc
공식 코드UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learning
https://github.com/virallab/turkishbertweet
공식 코드TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis
https://github.com/visual-ds/plausible-nlp-explanations
공식 코드https://github.com/vithyayogarajan/medicaltext_embeddings
공식 코드https://github.com/voidism/L2KD
공식 코드https://github.com/vojtech-balek/llm-features
공식 코드LLM-based feature generation from text for interpretable machine learning
https://github.com/vr18ub/court_view_generation
공식 코드One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support
https://github.com/vveitch/causal-text-embeddings-tf2
공식 코드https://github.com/vyraun/dlp
공식 코드On Dimensional Linguistic Properties of the Word Embedding Space
https://github.com/waashk/atcbench
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.