## Challenges and Technical Difficulties
We faced challenges at every step of the way, despite having some example scripts and models ready by the 🤗 team in Flax.

- The dataset we used - Conceptual 12M took 2-3 days to translate using MBart (since we didn't have Marian at the time). The major bottleneck was implementing the translation efficiently. We tried using `mtranslate` first but it turned out to be too slow, even with multiprocessing.

- The translations with deep learning models aren't as "perfect" as translation APIs like Google and Yandex. This could lead to poor performance.

- We prepared the model and config classes for our model from scratch, basing it on `CLIP Vision` and `Marian` implementations in Flax.