opus-mt-tc-base-zle-bat

Neural machine translation model for translating from East Slavic languages (zle) to Baltic languages (bat).

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: 2022-03-14
• source language(s): rus
• target language(s): lav lit
• valid target language labels: >>lav<< >>lit<<
• model: transformer-align
• data: opusTCv20210807 (source)
• tokenization: SentencePiece (spm32k,spm32k)
• original model: opusTCv20210807_transformer-align_2022-03-14.zip
• more information released models: OPUS-MT zle-bat 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. >>lav<<

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>lav<< Африка - колыбель человечества.",
    ">>lit<< Том — наш капітан."
]

model_name = "pytorch-models/opus-mt-tc-base-zle-bat"
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:
#     Āfrika ir cilvēces šūpulis.
#     Tomas yra mūsų kapitonas.

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-base-zle-bat")
print(pipe(">>lav<< Африка - колыбель человечества."))

# expected output: Āfrika ir cilvēces šūpulis.

Benchmarks

• test set translations: opusTCv20210807_transformer-align_2022-03-14.test.txt
• test set scores: opusTCv20210807_transformer-align_2022-03-14.eval.txt
• benchmark results: benchmark_results.txt
• benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
rus-lav	tatoeba-test-v2021-08-07	0.74223	55.3	274	1518
rus-lit	tatoeba-test-v2021-08-07	0.70795	47.2	3598	20662
rus-lav	flores101-devtest	0.50134	20.0	1012	22092
rus-lit	flores101-devtest	0.53732	20.6	1012	20695
ukr-lav	flores101-devtest	0.51379	21.4	1012	22092
ukr-lit	flores101-devtest	0.54085	20.5	1012	20695

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: Wed Mar 23 22:11:57 EET 2022
• port machine: LM0-400-22516.local