lucidrains/PoPE-pytorch
Efficient implementation (and explorations) into polar coordinate positional embedding (PoPE) - from Gopalakrishnan et al. under Schmidhuber
This project helps machine learning engineers improve the performance of attention mechanisms in models that process sequences, images, or videos. It takes in sequence data or multi-dimensional data like images and outputs positionally enhanced queries and keys, which leads to better context understanding. It's for researchers and practitioners who build and optimize deep learning models.
57 stars and 1,549 monthly downloads. Used by 2 other packages. Available on PyPI.
Use this if you are a machine learning engineer working with transformer models and want to enhance how your models understand the position of elements in sequences or multi-dimensional data like images or videos.
Not ideal if you are not directly involved in developing or optimizing deep learning models that use attention mechanisms.
Stars
57
Forks
2
Language
Python
License
MIT
Category
Last pushed
Mar 25, 2026
Monthly downloads
1,549
Commits (30d)
0
Dependencies
3
Reverse dependents
2
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