GitHub - keon/seq2seq: Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch (original) (raw)
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Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch
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| Name | Name | Last commit message | Last commit date |
|---|---|---|---|
| Latest commitHistory37 Commits | |||
| .gitignore | .gitignore | ||
| LICENSE | LICENSE | ||
| README.md | README.md | ||
| model.py | model.py | ||
| train.py | train.py | ||
| utils.py | utils.py |
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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
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Minimal Seq2Seq model with Attention for Neural Machine Translation in PyTorch
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deep-learning machine-translation seq2seq
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