textClassifier and Hierarchical-attention-networks-pytorch
These are independent implementations of the same paper (Hierarchical Attention Networks for Document Classification) that compete as alternative PyTorch codebases for the same task, with richliao/textClassifier offering a more feature-complete package while vietnh1009's version provides a simpler reference implementation.
About textClassifier
richliao/textClassifier
Text classifier for Hierarchical Attention Networks for Document Classification
This tool helps you automatically sort documents or pieces of text into categories. You provide a collection of text data, and it identifies the core topics or sentiments within each piece, assigning it to a specific label. It's designed for data analysts or researchers who need to categorize large volumes of textual information efficiently.
About Hierarchical-attention-networks-pytorch
vietnh1009/Hierarchical-attention-networks-pytorch
Hierarchical Attention Networks for document classification
This project helps classify large volumes of text documents into predefined categories, such as news topics, product review sentiment, or answer types. You provide a dataset of documents along with their correct categories, and the system learns to automatically assign categories to new, unseen documents. This is useful for data analysts, content managers, or anyone needing to sort or filter large collections of text efficiently.
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