MarianCG: a code generation transformer model inspired by machine translation
This model is to improve the solving of the code generation problem and implement a transformer model that can work with high accurate results. We implemented MarianCG transformer model which is a code generation model that can be able to generate code from natural language. This work declares the impact of using Marian machine translation model for solving the problem of code generation. In our implementation, we prove that a machine translation model can be operated and working as a code generation model. Finally, we set the new contributors and state-of-the-art on CoNaLa reaching a BLEU score of 30.92 and Exact Match Accuracy of 6.2 in the code generation problem with CoNaLa dataset.
We now have a
paper
for this work and you can cite:
@article{soliman2022mariancg,
title={MarianCG: a code generation transformer model inspired by machine translation},
author={Soliman, Ahmed S and Hadhoud, Mayada M and Shaheen, Samir I},
journal={Journal of Engineering and Applied Science},
volume={69},
number={1},
pages={1--23},
year={2022},
publisher={SpringerOpen}
url={https://doi.org/10.1186/s44147-022-00159-4}
}
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Total runs
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24-hour runs
5
3-day runs
7
7-day runs
17
30-day runs
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