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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.

Text Classification

코드

https://github.com/opennlg/openba-v2

https://github.com/opensum/bigae

https://github.com/opringle/VDCNN-for-text-classification

https://github.com/oraby8/TextDataAug

https://github.com/ostapen/switch-and-explain

https://github.com/osu-mlb/petl_vision

https://github.com/osu-mlb/vit_peft_vision

https://github.com/overfitter/biobert_embedding

https://github.com/owainwest/uniprot_bert

https://github.com/p-karisani/FirstPHM

https://github.com/p-lambda/verified_calibration

https://github.com/paddorch/CharCNN.paddle

https://github.com/paolanu/BERT_epitope

https://github.com/paper-cat/Sentence-Classifications

https://github.com/paqs2020/paqs2020

https://github.com/parno/autobid

https://github.com/pathway/crosslang_embed

https://github.com/paulagarciamolina/qnlp_qiskit_hackathon

https://github.com/pauldevos/python-notes

https://github.com/peisuke/UnsupervisedDataAugmentation.pytorch

https://github.com/peithous/pytorch-BinClass-intro

https://github.com/peluz/VICTOR-dataset

https://github.com/peluz/kneedle-exploration

https://github.com/pengming617/bert_classification

https://github.com/pengshuyuan/Bert

https://github.com/pengsl-lab/mssl

https://github.com/pengsl-lab/mssl2drug

https://github.com/pfecht/bert-exploration

https://github.com/phanein/deepwalk

https://github.com/phanthaithuc/deep-cnn

https://github.com/phohenecker/pytorch-transformer

https://github.com/phucdev/TL_Bio_RE

https://github.com/piegu/language-models

https://github.com/pietrobarbiero/logic_explained_networks

https://github.com/pietrobarbiero/logic_explainer_networks

https://github.com/pingheng001/Cnn-Bert