# seq2seq

**Repository Path**: mirrors_keon/seq2seq

## Basic Information

- **Project Name**: seq2seq
- **Description**: Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-01-10
- **Last Updated**: 2026-01-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://github.com/pytorch/text) 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](https://arxiv.org/abs/1409.0473)

![](http://www.wildml.com/wp-content/uploads/2015/12/Screen-Shot-2015-12-30-at-1.16.08-PM.png)

## 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](http://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html)
* [@spro/practical-pytorch](https://github.com/spro/practical-pytorch)
* [@AuCson/PyTorch-Batch-Attention-Seq2seq](https://github.com/AuCson/PyTorch-Batch-Attention-Seq2seq)