Text-Classification and Hierarchical-attention-networks-pytorch
These are competitors: both implement hierarchical attention networks for document classification in PyTorch, with Tool B being a specialized single-model implementation while Tool A offers a broader suite of text classification architectures including HAN as one option.
About Text-Classification
Renovamen/Text-Classification
PyTorch implementation of some text classification models (HAN, fastText, BiLSTM-Attention, TextCNN, Transformer) | 文本分类
About Hierarchical-attention-networks-pytorch
vietnh1009/Hierarchical-attention-networks-pytorch
Hierarchical Attention Networks for document classification
Implements two-level attention mechanisms at word and sentence levels to capture document structure, with GloVe word embeddings (50-300d) initialized in the embedding layer rather than default random initialization. Built on PyTorch with early stopping regularization and TensorBoard integration for training visualization. Includes a web demo interface and pre-trained models evaluated across eight datasets (AG News, DBPedia, Yelp, Amazon, Yahoo Answers) with configurable batch size, learning rate, and embedding dimensions.
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