https://github.com/zer0sh0t/artificial_intelligence/tree/master/language_models/bidirectional_encoder_representations_from_transformers
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Semi-Supervised Classification with Graph Convolutional Networks
Categorical Metadata Representation for Customized Text Classification
Generating Natural Language Attacks in a Hard Label Black Box Setting
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.