Neural machine translation model for translating from unknown (deu+eng+fra+por+spa) to Baltic languages (bat).
This model is part of the OPUS-MT project, an effort to make neural machine translation models widely available and accessible for many languages in the world. All models are originally trained using the amazing framework of Marian NMT, an efficient NMT implementation written in pure C++. The models have been converted to pyTorch using the transformers library by huggingface. Training data is taken from OPUS and training pipelines use the procedures of OPUS-MT-train. Model Description:
This is a multilingual translation model with multiple target languages. A sentence initial language token is required in the form of >>id<< (id = valid target language ID), e.g. >>lav<<
This model can be used for translation and text-to-text generation.
CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).
A short example code:
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>lav<< Replace this with text in an accepted source language.",
">>sgs<< This is the second sentence."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat"
tokenizer = MarianTokenizer.from_pretrained(model_name)
model = MarianMTModel.from_pretrained(model_name)
translated = model.generate(**tokenizer(src_text, return_tensors="pt", padding=True))
for t in translated:
print( tokenizer.decode(t, skip_special_tokens=True) )
You can also use OPUS-MT models with the transformers pipelines, for example:
from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-bible-big-deu_eng_fra_por_spa-bat")
print(pipe(">>lav<< Replace this with text in an accepted source language."))
| langpair | testset | chr-F | BLEU | #sent | #words |
|---|---|---|---|---|---|
| deu-lit | tatoeba-test-v2021-08-07 | 0.65379 | 39.8 | 1115 | 7091 |
| eng-lav | tatoeba-test-v2021-08-07 | 0.68823 | 46.4 | 1631 | 9932 |
| eng-lit | tatoeba-test-v2021-08-07 | 0.67792 | 39.8 | 2528 | 14942 |
| spa-lit | tatoeba-test-v2021-08-07 | 0.68133 | 43.3 | 454 | 2352 |
| deu-lav | flores101-devtest | 0.54724 | 24.4 | 1012 | 22092 |
| eng-lav | flores101-devtest | 0.59955 | 31.0 | 1012 | 22092 |
| eng-lit | flores101-devtest | 0.58961 | 27.2 | 1012 | 20695 |
| fra-lav | flores101-devtest | 0.54276 | 24.2 | 1012 | 22092 |
| fra-lit | flores101-devtest | 0.54665 | 22.4 | 1012 | 20695 |
| spa-lav | flores101-devtest | 0.50131 | 17.8 | 1012 | 22092 |
| deu-lit | flores200-devtest | 0.54957 | 22.6 | 1012 | 20695 |
| eng-lit | flores200-devtest | 0.59338 | 27.7 | 1012 | 20695 |
| fra-lit | flores200-devtest | 0.54683 | 22.3 | 1012 | 20695 |
| por-lit | flores200-devtest | 0.55033 | 22.6 | 1012 | 20695 |
| spa-lit | flores200-devtest | 0.50725 | 16.9 | 1012 | 20695 |
| eng-lav | newstest2017 | 0.53192 | 21.5 | 2001 | 39392 |
| eng-lit | newstest2019 | 0.51714 | 18.3 | 998 | 19711 |
| deu-lav | ntrex128 | 0.47980 | 16.8 | 1997 | 44709 |
| deu-lit | ntrex128 | 0.50645 | 17.6 | 1997 | 41189 |
| eng-lav | ntrex128 | 0.51026 | 20.6 | 1997 | 44709 |
| eng-lit | ntrex128 | 0.54187 | 21.5 | 1997 | 41189 |
| fra-lav | ntrex128 | 0.45346 | 15.5 | 1997 | 44709 |
| fra-lit | ntrex128 | 0.48870 | 16.2 | 1997 | 41189 |
| por-lav | ntrex128 | 0.47809 | 17.3 | 1997 | 44709 |
| por-lit | ntrex128 | 0.50653 | 17.5 | 1997 | 41189 |
| spa-lav | ntrex128 | 0.47690 | 17.1 | 1997 | 44709 |
| spa-lit | ntrex128 | 0.50412 | 17.1 | 1997 | 41189 |
@article{tiedemann2023democratizing,
title={Democratizing neural machine translation with {OPUS-MT}},
author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
journal={Language Resources and Evaluation},
number={58},
pages={713--755},
year={2023},
publisher={Springer Nature},
issn={1574-0218},
doi={10.1007/s10579-023-09704-w}
}
@inproceedings{tiedemann-thottingal-2020-opus,
title = "{OPUS}-{MT} {--} Building open translation services for the World",
author = {Tiedemann, J{\"o}rg and Thottingal, Santhosh},
booktitle = "Proceedings of the 22nd Annual Conference of the European Association for Machine Translation",
month = nov,
year = "2020",
address = "Lisboa, Portugal",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2020.eamt-1.61",
pages = "479--480",
}
@inproceedings{tiedemann-2020-tatoeba,
title = "The Tatoeba Translation Challenge {--} Realistic Data Sets for Low Resource and Multilingual {MT}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.139",
pages = "1174--1182",
}
The work is supported by the HPLT project, funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland, and the EuroHPC supercomputer LUMI.