/seq2seq

Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch

Primary LanguagePythonMIT LicenseMIT

mini seq2seq

Minimal Seq2Seq model with attention for neural machine translation in PyTorch.

This implementation focuses on the following features:

  • Modular structure to be used in other projects
  • Minimal code for readability
  • Full utilization of batches and GPU.

This implementation relies on torchtext to minimize dataset management and preprocessing parts.

Model description

Requirements

  • GPU & CUDA
  • Python3
  • PyTorch
  • torchtext
  • Spacy
  • numpy
  • Visdom (optional)

download tokenizers by doing so:

python -m spacy download de
python -m spacy download en

References

Based on the following implementations