OpusCleaner and OpusTrainer, open source toolkits for training Machine Translation and Large language models
This work addresses the challenge for newcomers and researchers in machine translation by providing tools to reduce work and lower entry barriers, though it is incremental as it builds on existing data processing and training methods.
The authors tackled the labor-intensive and confusing process of developing high-quality machine translation systems by introducing OpusCleaner and OpusTrainer, open-source toolkits that simplify data handling and training, resulting in the creation of robust models such as those resistant to noisy input, multilingual, and terminology-aware.
Developing high quality machine translation systems is a labour intensive, challenging and confusing process for newcomers to the field. We present a pair of tools OpusCleaner and OpusTrainer that aim to simplify the process, reduce the amount of work and lower the entry barrier for newcomers. OpusCleaner is a data downloading, cleaning, and proprocessing toolkit. It is designed to allow researchers to quickly download, visualise and preprocess bilingual (or monolingual) data that comes from many different sources, each of them with different quality, issues, and unique filtering/preprocessing requirements. OpusTrainer is a data scheduling and data augmenting tool aimed at building large scale, robust machine translation systems and large language models. It features deterministic data mixing from many different sources, on-the-fly data augmentation and more. Using these tools, we showcase how we can use it to create high quality machine translation model robust to noisy user input; multilingual models and terminology aware models.