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Text Classification is a deep learning repository focused on text classification models for NLP. It provides a broad set of baseline architectures that can be used to study, train, compare, and adapt classification approaches. The project supports both single-label and multi-label classification, making it useful for sentence-level and document-level tasks. It includes classic and advanced models such as fastText, TextCNN, BERT, TextRNN, RCNN, hierarchical attention networks, seq2seq attention, Transformers, dynamic memory networks, entity networks, ensembles, and boosting methods. The repository also includes training, prediction, testing, preprocessing, sample data, cached data guidance, and performance comparison notes. Overall, it is a hands-on reference for developers and researchers who want to experiment with deep learning methods for text classification.
Features
- Deep learning models for text classification
- Single-label and multi-label classification support
- fastText, TextCNN, BERT, TextRNN, RCNN, and Transformer models
- Memory network, entity network, ensemble, and boosting implementations
- Training, prediction, testing, and preprocessing workflows
- Sample data and cached data guidance for faster experimentation
Project Samples
