kyegomez/SparseAttention
Pytorch Implementation of the sparse attention from the paper: "Generating Long Sequences with Sparse Transformers"
When developing deep learning models for Natural Language Processing (NLP) that process very long texts, traditional attention mechanisms can become too slow and computationally expensive. This tool helps by making attention calculations more efficient, allowing your models to handle longer sequences of text like entire documents or extended conversations. It takes your text data and applies a smarter, 'sparse' attention, resulting in faster training and more manageable models. This is for AI/ML engineers and researchers building advanced NLP systems.
Use this if your NLP models struggle with processing long sequences of text efficiently due to the quadratic computational cost of standard attention mechanisms.
Not ideal if you are working with short text sequences or do not have computational performance issues with your current attention implementation.
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94
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5
Language
Python
License
MIT
Category
Last pushed
Jan 31, 2026
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