No project description provided
Project description
torch-attentions
This repository contains PyTorch implementations of various attention mechanisms. The attention mechanisms are implemented as PyTorch modules and can be easily integrated into existing models.
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file torch_attentions-0.1.4.tar.gz.
File metadata
- Download URL: torch_attentions-0.1.4.tar.gz
- Upload date:
- Size: 1.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.7.1 CPython/3.10.9 Windows/10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | d8abd3a3c125bd91088add38229df67aa255e246e2c749e53f2b4ff60f6568f2 |
|
| MD5 | 87724bffe2239bec7e13bda008c9a61f |
|
| BLAKE2b-256 | 06ed489c49c5c41011f6a4c0c323d2583cc68f447ffa9c9e7fcdca3b78c29293 |
File details
Details for the file torch_attentions-0.1.4-py3-none-any.whl.
File metadata
- Download URL: torch_attentions-0.1.4-py3-none-any.whl
- Upload date:
- Size: 2.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: poetry/1.7.1 CPython/3.10.9 Windows/10
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | e48cd1eb36d517e143258c750fc604485bf2ef0198e486006e024a0fe3524f0b |
|
| MD5 | 9d7eb1034219b9be78c0cc856a929bf1 |
|
| BLAKE2b-256 | 3f6b1c2034752f8afcfe13559e5106c7b9561beebc914840cdb89b7276039431 |
