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WindstormLabs/
origin-Helsinki-NLP-opus-mt-tc-big-en-gmq

Name: origin-Helsinki-NLP-opus-mt-tc-big-en-gmq
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opus-mt-tc-big-en-gmq

Neural machine translation model for translating from English (en) to North Germanic languages (gmq).

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: 2022-03-17
source language(s): eng
target language(s): dan fao isl nno nob nor swe
valid target language labels: >>dan<< >>fao<< >>isl<< >>nno<< >>nob<< >>nor<< >>swe<<
model: transformer-big
data: opusTCv20210807+bt (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807+bt_transformer-big_2022-03-17.zip
more information released models: OPUS-MT eng-gmq 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. >>dan<<

Usage

A short example code:

python

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>nno<< The United States borders Canada.",
    ">>nob<< This is the biggest hotel in this city."
]

model_name = "pytorch-models/opus-mt-tc-big-en-gmq"
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:
#     USA grensar til Canada.
#     Dette er det største hotellet i denne byen.

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-gmq")
print(pipe(">>nno<< The United States borders Canada."))

# expected output: USA grensar til Canada.

Benchmarks

test set translations: opusTCv20210807+bt_transformer-big_2022-03-17.test.txt
test set scores: opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
eng-dan	tatoeba-test-v2021-08-07	0.75165	61.6	10795	79385
eng-fao	tatoeba-test-v2021-08-07	0.40395	18.3	294	1933
eng-isl	tatoeba-test-v2021-08-07	0.59731	39.9	2503	19023
eng-nno	tatoeba-test-v2021-08-07	0.61271	40.1	460	3428
eng-nob	tatoeba-test-v2021-08-07	0.72380	57.3	4539	36119
eng-swe	tatoeba-test-v2021-08-07	0.74197	60.9	10362	68067
eng-dan	flores101-devtest	0.70810	47.7	1012	24638
eng-isl	flores101-devtest	0.52076	24.1	1012	22834
eng-nob	flores101-devtest	0.62760	34.5	1012	23873
eng-swe	flores101-devtest	0.70129	46.9	1012	23121
eng-isl	newsdev2021.en-is	0.50376	22.6	2004	43721
eng-isl	newstest2021.en-is	0.50516	21.5	1000	25233

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 17:14:46 EEST 2022
port machine: LM0-400-22516.local

WindstormLabs/origin-Helsinki-NLP-opus-mt-tc-big-en-gmq