This repo is a pytorch implementation of Sequence to Sequence Learning with Neural Networks on Multi30k dataset using De->En pair language, the algorithm achieved 17.50 Belu-score.
Code is developed under following library dependencies
python 3.7
torch 1.10.1
torchtext 0.11.1
Start with creating a virtual environment then open your terminal and follow the following steps:
git clone "https://github.com/zaghlol94/Machine-Translation-Seq2Seq"
cd Machine-Translation-Seq2Seq
pip install -r requirements.txt
bash download_assets.sh
cd src
python inference.py -s "your sentence"
Multi30k Dataset of paired with sentences in English and German. the data-set folder contains:
├── test_2016_flickr.de
├── test_2016_flickr.en
├── train.de
├── train.en
├── val.de
└── val.en
There are two files of training, validation, and testing data. For each German line in train.de there is a corresponding line in train.en that represents the English translation.
To train the model on custom data-set of different language pair, you could add your data files in the same structure in the data-set folder and change the train/valid files' path in config.py then follow the following steps in the command line:
cd src
python train.py
After these steps you will find model.pt in src folder, which is the weights of the seq2sq model.
To test your model and print out the Belu-score, loss on the testing data, change the path of model_path, src_test
and trg_test in the config.py, Then follow the following steps
cd src
python test.py
@misc{https://doi.org/10.48550/arxiv.1409.3215,
doi = {10.48550/ARXIV.1409.3215},
url = {https://arxiv.org/abs/1409.3215},
author = {Sutskever, Ilya and Vinyals, Oriol and Le, Quoc V.},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Sequence to Sequence Learning with Neural Networks},
publisher = {arXiv},
year = {2014},
copyright = {arXiv.org perpetual, non-exclusive license}
}