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Helsinki-NLP/
opus-mt-tc-base-ces_slk-uk

Name: opus-mt-tc-base-ces_slk-uk
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opus-mt-tc-base-ces_slk-uk

Neural machine translation model for translating from Czech and Slovak (cs+sk) to Ukrainian (uk).

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: 2022-03-08
source language(s): ces
target language(s): ukr
model: transformer-align
data: opusTCv20210807+pbt (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807+pbt_transformer-align_2022-03-08.zip
more information released models: OPUS-MT ces+slk-ukr README

Usage

A short example code:

python

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Replace this with text in an accepted source language.",
    "This is the second sentence."
]

model_name = "pytorch-models/opus-mt-tc-base-ces_slk-uk"
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:

python

from transformers import pipeline
pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-tc-base-ces_slk-uk")
print(pipe("Replace this with text in an accepted source language."))

Benchmarks

test set translations: opusTCv20210807+pbt_transformer-align_2022-03-08.test.txt
test set scores: opusTCv20210807+pbt_transformer-align_2022-03-08.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
ces-ukr	tatoeba-test-v2021-08-07	0.66867	48.6	1787	8891
ces-ukr	flores101-devtest	0.51387	21.8	1012	22810
slk-ukr	flores101-devtest	0.51418	21.4	1012	22810

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: 1bdabf7
port time: Thu Mar 24 01:01:20 EET 2022
port machine: LM0-400-22516.local