Document-Classifier-LSTM and self-attention-classification
These are competitors offering alternative implementations of the same architectural pattern—both apply LSTM with attention mechanisms for document classification—so you would typically choose one based on factors like code quality, documentation, or specific attention variant rather than using both together.
About Document-Classifier-LSTM
AlexGidiotis/Document-Classifier-LSTM
A bidirectional LSTM with attention for multiclass/multilabel text classification.
This project helps classify short texts, like paper abstracts, by assigning them to one or more predefined categories or tags. You provide a collection of text documents, and it outputs predictions about what each document is about, along with the trained model. Researchers, librarians, or information managers dealing with large text archives would find this useful for organizing and retrieving information.
About self-attention-classification
nn116003/self-attention-classification
document classification using LSTM + self attention
This tool helps you automatically sort and categorize written documents like movie reviews or customer feedback. You feed it a collection of text documents, and it tells you what each document is about or how it should be grouped. Anyone who needs to quickly understand the sentiment or topic of many texts, such as a market researcher or content moderator, would find this useful.
Scores updated daily from GitHub, PyPI, and npm data. How scores work