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

• Encoder: Bidirectional GRU
• Decoder: GRU with Attention Mechanism
• Attention: Neural Machine Translation by Jointly Learning to Align and Translate

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

• PyTorch Tutorial
• @spro/practical-pytorch
• @AuCson/PyTorch-Batch-Attention-Seq2seq