The code, training pipeline, and models that power Firefox Translations
See the codeTraining pipelines and the inference engine for Firefox Translations machine translation models.
The trained models are hosted in a public Google Cloud Storage bucket (see Model Registry UI and JSON). The model files are distributed under the MPL 2.0 license. The models are compatible with bergamot-translator and power the Firefox web page translation starting with version 118.
The pipeline was originally developed as a part of Bergamot project that focuses on improving client-side machine translation in a web browser.
The pipeline is capable of training a translation model for a language pair end to end. Translation quality depends on the chosen datasets, data cleaning procedures and hyperparameters. Some settings, especially low resource languages might require extra tuning.
We use fast translation engine Marian.
You can find more details about the pipeline steps in the documentation.
An orchestrator is responsible for workflow management and parallelization.
Public training dashboard in Weights & Biases
Marian training metrics are parsed from logs and published using a custom module within the tracking directory.
More information is available here.
Contributions are welcome! See the documentation on Contributing for more details.
Feel free to ask questions in our Matrix channel #firefoxtranslations:mozilla.org.
This project uses materials developed by:
C++
46.0%
Python
23.6%
Ruby
9.9%
Jupyter Notebook
7.4%
JavaScript
4.9%
Cuda
4.0%
CMake
2.0%
The code, training pipeline, and models that power Firefox Translations
See the codeTraining pipelines and the inference engine for Firefox Translations machine translation models.
The trained models are hosted in a public Google Cloud Storage bucket (see Model Registry UI and JSON). The model files are distributed under the MPL 2.0 license. The models are compatible with bergamot-translator and power the Firefox web page translation starting with version 118.
The pipeline was originally developed as a part of Bergamot project that focuses on improving client-side machine translation in a web browser.
The pipeline is capable of training a translation model for a language pair end to end. Translation quality depends on the chosen datasets, data cleaning procedures and hyperparameters. Some settings, especially low resource languages might require extra tuning.
We use fast translation engine Marian.
You can find more details about the pipeline steps in the documentation.
An orchestrator is responsible for workflow management and parallelization.
Public training dashboard in Weights & Biases
Marian training metrics are parsed from logs and published using a custom module within the tracking directory.
More information is available here.
Contributions are welcome! See the documentation on Contributing for more details.
Feel free to ask questions in our Matrix channel #firefoxtranslations:mozilla.org.
This project uses materials developed by:
C++
46.0%
Python
23.6%
Ruby
9.9%
Jupyter Notebook
7.4%
JavaScript
4.9%
Cuda
4.0%
CMake
2.0%