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/sucheta19/Text-Classification-Using-CNN
Character-level Convolutional Networks for Text Classification
https://github.com/sud0301/my_uda_pytorch
https://github.com/sunginmkone/AWS_Training_NLP
Character-level Convolutional Networks for Text Classification
https://github.com/sunxiaobei/mindspore_gallery/tree/main/grl/gcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/sunyilgdx/prompts4keras
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/sunzeyeah/rlhf
https://github.com/superaidou123/face_classification
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/surancy/nlp-classification-with-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/suzhiba/SimpleRecurrentUnits-SRU-
https://github.com/svakulenk0/response_eval
https://github.com/svjan5/CNN-for-text-classification
https://github.com/sydney-machine-learning/sentimentanalysis-uselections
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/syyunn/node2vec
https://github.com/tahasamavati/facial_expression_recognition_cnn
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/taishan1994/pytorch_gat
https://github.com/tamsiuhin/oppu
https://github.com/tanmaylaud/Patient_Conversation_Classifier_FastAI
Universal Language Model Fine-tuning for Text Classification
https://github.com/tanvir-ishraq/healifyai--llm-based-healthcare-system
Universal Language Model Fine-tuning for Text Classification
https://github.com/tatp22/pytorch-fast-GAT
https://github.com/tcnguyen/bert
https://github.com/tdd-ai/data-collection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tdiggelm/nn-experiments
https://github.com/teghub/TurkishNER-BERT
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tensorflow/models/tree/master/official/nlp
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tensorflow/models/tree/master/official/nlp/projects/bigbird
https://github.com/tensorflow/models/tree/master/research/adversarial_text
https://github.com/tetsu9923/scireviewgen
https://github.com/textflint/textflint
https://github.com/textvec/textvec
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
https://github.com/thaihungle/UW-DNC
https://github.com/thanhlong1997/bert_quora
https://github.com/thanhtrunghuynh93/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/the-Quert/iNLPfun
https://github.com/theQuert/inlpfun
https://github.com/thecodemasterk/BERT
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.