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.

Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (Please, cite if you use this model.)

@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",
}

Model info

Release: 2022-03-13
source language(s): eng
target language(s): spa
model: transformer-big
data: opusTCv20210807+bt (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807+bt_transformer-big_2022-03-13.zip
more information released models: OPUS-MT eng-spa README

Usage

A short example code:

python

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "A wasp stung him and he had an allergic reaction.",
    "I love nature."
]

model_name = "pytorch-models/opus-mt-tc-big-en-es"
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) )

# expected output:
#     Una avispa lo picó y tuvo una reacción alérgica.
#     Me encanta la naturaleza.

You can also use OPUS-MT models with the transformers pipelines, for example:

python

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-big-en-es")
print(pipe("A wasp stung him and he had an allergic reaction."))

# expected output: Una avispa lo picó y tuvo una reacción alérgica.

Benchmarks

test set translations: opusTCv20210807+bt_transformer-big_2022-03-13.test.txt
test set scores: opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
eng-spa	tatoeba-test-v2021-08-07	0.73863	57.2	16583	134710
eng-spa	flores101-devtest	0.56440	28.5	1012	29199
eng-spa	newssyscomb2009	0.58415	31.5	502	12503
eng-spa	news-test2008	0.56707	30.1	2051	52586
eng-spa	newstest2009	0.57836	30.2	2525	68111
eng-spa	newstest2010	0.62357	37.6	2489	65480
eng-spa	newstest2011	0.62415	38.9	3003	79476
eng-spa	newstest2012	0.63031	39.5	3003	79006
eng-spa	newstest2013	0.60354	35.9	3000	70528
eng-spa	tico19-test	0.73554	53.0	2100	66563

Acknowledgements

The work is supported by the European Language Grid as pilot project 2866, by the FoTran project, funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 771113), and the MeMAD project, funded by the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 780069. We are also grateful for the generous computational resources and IT infrastructure provided by CSC -- IT Center for Science, Finland.

Model conversion info

transformers version: 4.16.2
OPUS-MT git hash: 3405783
port time: Wed Apr 13 18:03:53 EEST 2022
port machine: LM0-400-22516.local