opus-mt-tc-big-en-ces_slk

Neural machine translation model for translating from English (en) to Czech and Slovak (ces+slk).

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): ces
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-ces+slk README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>ces<< We were enemies.",
    ">>ces<< Do you think Tom knows what's going on?"
]

model_name = "pytorch-models/opus-mt-tc-big-en-ces_slk"
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:
#     Byli jsme nepřátelé.
#     Myslíš, že Tom ví, co se děje?

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-big-en-ces_slk")
print(pipe(">>ces<< We were enemies."))

# expected output: Byli jsme nepřátelé.

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-ces	tatoeba-test-v2021-08-07	0.66128	47.5	13824	91332
eng-ces	flores101-devtest	0.60411	34.1	1012	22101
eng-slk	flores101-devtest	0.62415	35.9	1012	22543
eng-ces	multi30k_test_2016_flickr	0.58547	33.4	1000	10503
eng-ces	multi30k_test_2018_flickr	0.59236	33.4	1071	11631
eng-ces	newssyscomb2009	0.52702	25.3	502	10032
eng-ces	news-test2008	0.50286	22.8	2051	42484
eng-ces	newstest2009	0.52152	24.3	2525	55533
eng-ces	newstest2010	0.52527	24.4	2489	52955
eng-ces	newstest2011	0.52721	25.5	3003	65653
eng-ces	newstest2012	0.50007	22.6	3003	65456
eng-ces	newstest2013	0.53643	27.4	3000	57250
eng-ces	newstest2014	0.58944	31.4	3003	59902
eng-ces	newstest2015	0.55094	27.0	2656	45858
eng-ces	newstest2016	0.56864	29.9	2999	56998
eng-ces	newstest2017	0.52504	24.9	3005	54361
eng-ces	newstest2018	0.52490	24.6	2983	54652
eng-ces	newstest2019	0.53994	26.4	1997	43113

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 16:46:48 EEST 2022
port machine: LM0-400-22516.local

Helsinki-NLP
/

opus-mt-tc-big-en-ces_slk