hierarchical-attention-networks and Hierarchical-attention-networks-pytorch
These are **competitors** — both implement the same hierarchical attention mechanism for document classification, differing only in their underlying deep learning framework (TensorFlow vs. PyTorch), so users would typically choose one based on their preferred framework rather than using both together.
About hierarchical-attention-networks
qtuantruong/hierarchical-attention-networks
TensorFlow implementation of the paper "Hierarchical Attention Networks for Document Classification"
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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