A variety of sequence model architectures from scratch in PyTorch
Sequence Models
This repository implements a variety of sequence model architectures from scratch in PyTorch. Effort has been put to make the code well structured so that it can serve as learning material. The training loop implements the learner design pattern from fast.ai in pure PyTorch, with access to the loop provided through callbacks. Detailed logging and graphs are also provided with python logging and wandb. Additional implementations will be added.
Setup
Using Miniconda/Anaconda:
cd path_to_repoconda create --name--file requirements.txt conda activate
Usage
Global configuration for training/inference is found in src/config.py. To train a model customize the configuration by selecting everything from the model (For the list of available models see src/model_dispatcher.py) to learning rate and run:
python src/train.py
Training saves the model