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/kingcong/gpu_albert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kingcong/gpu_deeptext
DeepText: A Unified Framework for Text Proposal Generation and Text Detection in Natural Images
https://github.com/kingoflolz/mesh-transformer-jax
RoFormer: Enhanced Transformer with Rotary Position Embedding
https://github.com/kinimod23/GRANT
Supervised and Unsupervised Neural Approaches to Text Readability
https://github.com/kinimod23/NMT_Project
https://github.com/kinit-sk/imgtb
https://github.com/kitnhl/partisan-tweets-classification
https://github.com/kmamykin/askamanager_salary_survey
https://github.com/kmkwon94/ainize-laser
Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond
https://github.com/kmpoon/hlta
https://github.com/knowlab/bi-weekly-paper-presentation
Unsupervised Document Embedding via Contrastive Augmentation
https://github.com/knuddj1/op_text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/knuddy/op_text
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kobowon/cs470_project_version2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kochlisGit/Stocks-Prediction
Densely Connected Bidirectional LSTM with Applications to Sentence Classification
https://github.com/kohakublueleaf/lycoris
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
https://github.com/koreyou/text-gcn-chainer
https://github.com/korymath/jann
https://github.com/kpe/bert-for-tf2
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kpot/keras-transformer
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/kpu/fastertext
https://github.com/kr900910/supreme_court_opinion
https://github.com/kramamur/sentiment-analysis
https://github.com/krantirk/BERT-Pretrained-model
https://github.com/kriesbeck/masked-language-prediction
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/krisbukovi/document_similarity
https://github.com/krypticmouse/Adapter-BERT
https://github.com/kshitij1489/review_classification
https://github.com/kuldeep7688/BioMedicalBertNer
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
https://github.com/kuleshov-group/llmtune
ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers
https://github.com/kumarnikhil936/Facial-Emotion-Recognition
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/kunde122/bilm-tf
https://github.com/kushalj001/Kaggle-Quora-Insincere-Questions-Challenge
https://github.com/kyriemao/TagGNN-SIGIR
Item Tagging for Information Retrieval: A Tripartite Graph Neural Network based Approach
https://github.com/kyubyong/bert_ner
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.