hplt-project/HPLT-MT-Models

This contains the configuration and scripts for HPLT MT model releases.

Python

7

55 commits

updated Aug 4, 2025

See the code

README

HPLT MT Models

This contains the configuration and scripts for HPLT MT model releases.

Latest release

Mar 2025 - v2.0

Second batch of hPLT translation models, trained on the parallel data from HPLT v2. Please check our data pipeline and model results. Visit our Hugging Face v2.0 collection page for model weights.

Feb 2024 - v1.0/v1.2

First batch of HPLT translation models, trained on the parallel data in HPLT v1.0/1.2. Please Read our deliverable report for technical details. Visit our Hugging Face v1.2 collection page for model weights. Check respective release folders in this repository for training, inference, and evaluation instructions.

Acknowledgements

This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101070350 and from UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee [grant number 10052546]

Brought to you by researchers from the University of Edinburgh, Charles University in Prague, and the whole HPLT consortium.

hplt-project/HPLT-MT-Models

This contains the configuration and scripts for HPLT MT model releases.

Python

7

55 commits

updated Aug 4, 2025

See the code

README

HPLT MT Models

This contains the configuration and scripts for HPLT MT model releases.

Latest release

Mar 2025 - v2.0

Second batch of hPLT translation models, trained on the parallel data from HPLT v2. Please check our data pipeline and model results. Visit our Hugging Face v2.0 collection page for model weights.

Feb 2024 - v1.0/v1.2

First batch of HPLT translation models, trained on the parallel data in HPLT v1.0/1.2. Please Read our deliverable report for technical details. Visit our Hugging Face v1.2 collection page for model weights. Check respective release folders in this repository for training, inference, and evaluation instructions.

Acknowledgements

This project has received funding from the European Union's Horizon Europe research and innovation programme under grant agreement No 101070350 and from UK Research and Innovation (UKRI) under the UK government's Horizon Europe funding guarantee [grant number 10052546]

Brought to you by researchers from the University of Edinburgh, Charles University in Prague, and the whole HPLT consortium.

Languages

Python

83.6%

Shell

16.4%