Attention mechanisms and Transformers
- This goal of this repository is to host basic architecture and model traning code associated with the different attention mechanisms and transformer architecture.
- At the moment, I more interested in learning and recreating these new architectures from scratch than full-fledged training. For now, I'll just be training these models on small datasets.
Installation
- Using pip to install from pypi
pip install Attention-and-Transformers
- Using pip to install latest version from github
pip install git+https://github.com/veb-101/Attention-and-Transformers.git
- Local clone and install
git clone https://github.com/veb-101/Attention-and-Transformers.git atf
cd atf
python setup.py install
Example Use
python load_test.py
Attention Mechanisms
| # No. | Mechanism | Paper |
|---|---|---|
| 1 | Multi-head Self Attention | Attention is all you need |
| 2 | Multi-head Self Attention 2D | MobileViT V1 |
| 2 | Separable Self Attention | MobileViT V2 |
Transformer Models
1 maintainer
Metadata
1 maintainer
Release files for Attention-and-Transformers 0.0.15
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| Attention_and_Transformers-0.0.15.tar.gz | 17.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| Attention_and_Transformers-0.0.15-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 42.3 kB
Release files / Attention_and_Transformers-0.0.15.tar.gz
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| Size | 24.8 kB |
| Tags | Python 3 |
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