In this work, we implemented a Transformer architecture to realize a full attention neural network that learns to translate German to English. The best model gains a BLEU score up to 37.39, when the minimum frequency of words is selected to be 3.

1. Check required environment environment.yaml

2. Create virtual environment conda env create -f environment.yaml

1. Check required packages requirements.txt

2. Install required packages pip install -r requirements.txt

project
├── src
│   ├── __init__.py
│   ├── multi-bleu.perl
│   ├── my_transformer.py
│   └── utils.py
├── tools
│   └── tuning.xlsx
├── go_transformer.py
├── go_translate.py
├── trasn.sh
├── README.md
├── environment.yaml
├── requirements.txt
└── ...

1. Check out this repository and download our source code

   git clone [email protected]:silvery107/nmt-multi30k-pytorch.git

2. Create virtual environment

   conda env create -f environment.yaml

3. Install the required python modules

   pip install -r requirements.txt

4. Start training

   python go_transformer.py

   or

   sh train.sh

5. Evaluate model with BLEU score

   python go_translate.py --model MODEL_NAME --fre FRE

usage:  python go_transformer.py [-h]
        [--batch BATCH] [--num-enc NUM_ENC] [--num-dec NUM_DEC] 
        [--emb-dim EMB_DIM] [--ffn-dim FFN_DIM] [--head HEAD]
        [--dropout DROPOUT] [--epoch EPOCH] [--lr LR] [--fre FRE]

usage:  python go_translate.py [-h]
        [--model MODEL] [--fre FRE] [--mode MODE]