timbmg/Structured-Self-Attentive-Sentence-Embedding
Re-Implementation of "A Structured Self-Attentive Sentence Embedding" by Lin et al., 2017
This project helps data scientists and machine learning engineers analyze text data, specifically sentence embeddings. It takes raw text inputs, like customer reviews, and processes them to output a structured sentence embedding. The end user can then visualize attention patterns within sentences and evaluate classification performance with a confusion matrix.
Use this if you are a data scientist working with text data and need to understand how attention mechanisms contribute to sentence embeddings for classification tasks.
Not ideal if you are looking for a pre-trained, production-ready model for immediate use without needing to train or visualize attention patterns.
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Language
Python
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Last pushed
Mar 18, 2026
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0
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