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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/glassroom/heinsen_routing
공식 코드https://github.com/gonzalezf/multimodal_neural_topic_modeling
공식 코드Neural Multimodal Topic Modeling: A Comprehensive Evaluation
https://github.com/google-research-datasets/disfl-qa
공식 코드Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
https://github.com/google-research/adapter-bert
공식 코드https://github.com/google-research/bert
공식 코드BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/google-research/bigbird
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/uda
공식 코드https://github.com/gordon-lim/tobacco3482-mistakes
공식 코드https://github.com/goru001/inltk
공식 코드https://github.com/gospelnnadi/text-summarization-sota-experiment
공식 코드Survey on Abstractive Text Summarization: Dataset, Models, and Metrics
https://github.com/gouwsmeister/bilbowa
공식 코드BilBOWA: Fast Bilingual Distributed Representations without Word Alignments
https://github.com/goyalanil/PB-MVBoost
공식 코드Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
https://github.com/gpleiss/temperature_scaling
공식 코드https://github.com/grecosalvatore/nlpguard
공식 코드NLPGuard: A Framework for Mitigating the Use of Protected Attributes by NLP Classifiers
https://github.com/grig-guz/coherence-rst
공식 코드https://github.com/griko/vanpy
공식 코드https://github.com/gsarti/ETC-NLG
공식 코드ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation
https://github.com/gtshs2/BetaLSTM
공식 코드https://github.com/guangyizhangbci/parse
공식 코드PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition
https://github.com/gugarosa/mh_fine_tuning
공식 코드Improving Pre-Trained Weights Through Meta-Heuristics Fine-Tuning
https://github.com/guotong1988/Automatic-Label-Error-Correction
공식 코드https://github.com/guoyinwang/LEAM
공식 코드https://github.com/gurdaspuriya/cawa
공식 코드https://github.com/gxlarson/rvl-cdip-ood
공식 코드Evaluating Out-of-Distribution Performance on Document Image Classifiers
https://github.com/h2oai/h2o-llmstudio
공식 코드https://github.com/halaa901/qnlp-thesis
공식 코드https://github.com/hamedr96/antm
공식 코드ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics
https://github.com/hamedr96/ctc
공식 코드https://github.com/hamidm21/Revisit_FSA
공식 코드Market-Derived Financial Sentiment Analysis: Context-Aware Language Models for Crypto Forecasting
https://github.com/hannight/amulap
공식 코드Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
https://github.com/hanqi-qi/sine
공식 코드https://github.com/haoyelyu/On_the_Dynamics_of_Training_Attention_Models
공식 코드https://github.com/haroonshakeel/bilingual_sms_classification
공식 코드A Multi-cascaded Deep Model for Bilingual SMS Classification
https://github.com/harshp1802/text-dataset-distillation
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.