https://github.com/adam-mehdi/MuarAugment
On the Generalization Effects of Linear Transformations in Data Augmentation
Papers with Code 아카이브
Text Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 16
- 평가표
- 101
- 보존된 평가 기록
- 341
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/adamlin120/Zero-shot_Classification_of_News_Title
Train Once, Test Anywhere: Zero-Shot Learning for Text Classification
https://github.com/adamx97/Data-Science-Advanced-Capstone
https://github.com/adi2103/AML-CoVe
https://github.com/adlnlp/3mhealth
A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning
https://github.com/adlnlp/Chulo
ChuLo: Chunk-Level Key Information Representation for Long Document Processing
https://github.com/adlnlp/mm_emog
A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning
https://github.com/adobe-research/Cross-lingual-Test-Dataset-XTD10
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
https://github.com/adrinta/MAGNET
Multi-Label Text Classification using Attention-based Graph Neural Network
https://github.com/advaithsrao/TwitterSentimentAnalysis_using_UniversalSentenceEncoder
https://github.com/aengusl/latent-adversarial-training
Defending Against Unforeseen Failure Modes with Latent Adversarial Training
https://github.com/af-ai-center/bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/af-ai-center/nerblackbox
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aflah02/Easy-Data-Augmentation-Implementation
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks
https://github.com/afrozloya/charrec
https://github.com/agiresearch/InstructGLM
https://github.com/ahmadelsallab/READ
Universal Language Model Fine-tuning for Text Classification
https://github.com/ahmedbahaaeldin/Papers-from-Scratch
https://github.com/ahmedbesbes/character-based-cnn
Character-level Convolutional Networks for Text Classification
https://github.com/ahmedssabir/textual-visual-semantic-dataset
https://github.com/aigen/df-posthoc-calibration
https://github.com/airi-institute/al_toolbox
https://github.com/airi-institute/gena_lm
RoFormer: Enhanced Transformer with Rotary Position Embedding
https://github.com/airsplay/vimpac
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aistairc/kirt_bert_on_abci
https://github.com/ajbisberg/gcn
https://github.com/ajenningsfrankston/Dynamic-Memory-Network-Plus-master
Ask Me Anything: Dynamic Memory Networks for Natural Language Processing
https://github.com/ajinkyabedekar/Face-to-Emoji
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/ak24watch/Graph-Deeplearning-GAT
https://github.com/akari0216/Paddle-awdlstm
Universal Language Model Fine-tuning for Text Classification
https://github.com/akash18tripathi/MAGNET-Multi-Label-Text-Classi-cation-using-Attention-based-Graph-Neural-Network
https://github.com/akashgit/autoencoding_vi_for_topic_models
https://github.com/alankarj/robust_nlp
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
https://github.com/alantancr/Hotel-Recommender
Universal Language Model Fine-tuning for Text Classification
https://github.com/aleksac99/SQuAD-SR
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/alexOarga/haiku-geometric
Semi-Supervised Classification with Graph Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.