https://github.com/avinashok/TextCategorization
Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec
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/avinashsai/Convolutional-Neural-Networks-for-Text-Classification
https://github.com/avishek-018/Emotion-Classification-using-Transformers
Emotion Classification in a Resource Constrained Language Using Transformer-based Approach
https://github.com/avishek-018/TransEmoC
Emotion Classification in a Resource Constrained Language Using Transformer-based Approach
https://github.com/avishek-018/single_notebook_projects
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/avyavkumar/few-shot-learning-notebooks
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
https://github.com/aws-samples/amazon-sagemaker-bert-pytorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/awslabs/mlm-scoring
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/aymericdamien/TopDeepLearning
https://github.com/babelscape/wikineural
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/baichuan-inc/baichuan-13b
RoFormer: Enhanced Transformer with Rotary Position Embedding
https://github.com/baichuan-inc/baichuan-7b
RoFormer: Enhanced Transformer with Rotary Position Embedding
https://github.com/bailejor/SAMSHA_Transfer_Learning
SuperTML: Two-Dimensional Word Embedding for the Precognition on Structured Tabular Data
https://github.com/baiyang2464/chatbot-base-on-Knowledge-Graph
https://github.com/bangoc123/rope-visualization
RoFormer: Enhanced Transformer with Rotary Position Embedding
https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/basf/chemteb
https://github.com/basiralab/RG-Select
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/Reproducible-Generative-Learning
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/basiralab/reproduciblefedgnn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/batsresearch/yu-aistats22-code
https://github.com/bcaitech1/p2-klue-Heeseok-Jeong
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bcaitech1/p4-dkt-no_caffeine_no_gain
https://github.com/bcsrn/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/bencrabbe/npdependency
FlauBERT: Unsupervised Language Model Pre-training for French
https://github.com/benedekrozemberczki/karateclub
https://github.com/benjaminvdb/110kDBRD
Universal Language Model Fine-tuning for Text Classification
https://github.com/benjaminvdb/DBRD
Universal Language Model Fine-tuning for Text Classification
https://github.com/bentrevett/pytorch-sentiment-analysis
https://github.com/benywon/ChiQA
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/benywon/ChineseBert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/benywon/ComQA
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/benywon/ReCO
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/bepping/non-oversmoothing-gcns
https://github.com/bestend/tf2-bi-lstm-crf-nni
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.