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): cat spa
valid target language labels: >>cat<< >>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-cat+oci+spa README
more information about the model: MarianMT

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. >>cat<<

Usage

A short example code:

python

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>spa<< Why do you want Tom to go there with me?",
    ">>spa<< She forced him to eat spinach."
]

model_name = "pytorch-models/opus-mt-tc-big-en-cat_oci_spa"
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:
#     ¿Por qué quieres que Tom vaya conmigo?
#     Ella lo obligó a comer espinacas.

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-cat_oci_spa")
print(pipe(">>spa<< Why do you want Tom to go there with me?"))

# expected output: ¿Por qué quieres que Tom vaya conmigo?

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-cat	tatoeba-test-v2021-08-07	0.66414	47.8	1631	12344
eng-spa	tatoeba-test-v2021-08-07	0.73725	57.0	16583	134710
eng-cat	flores101-devtest	0.66071	41.5	1012	27304
eng-oci	flores101-devtest	0.56192	25.4	1012	27305
eng-spa	flores101-devtest	0.56288	28.1	1012	29199
eng-spa	newssyscomb2009	0.58431	31.4	502	12503
eng-spa	news-test2008	0.56622	30.0	2051	52586
eng-spa	newstest2009	0.57988	30.5	2525	68111
eng-spa	newstest2010	0.62343	37.4	2489	65480
eng-spa	newstest2011	0.62424	39.1	3003	79476
eng-spa	newstest2012	0.63006	39.6	3003	79006
eng-spa	newstest2013	0.60291	35.8	3000	70528
eng-spa	tico19-test	0.73224	52.5	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 16:40:45 EEST 2022
port machine: LM0-400-22516.local