Edit model card

opus-mt-tc-big-zle-en

Neural machine translation model for translating from East Slavic languages (zle) to English (en).

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.

@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

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Скільки мені слід купити пива?",
    "Я клієнтка."
]

model_name = "pytorch-models/opus-mt-tc-big-zle-en"
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:
#     How much beer should I buy?
#     I'm a client.

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-zle-en")
print(pipe("Скільки мені слід купити пива?"))

# expected output: How much beer should I buy?

Benchmarks

langpair testset chr-F BLEU #sent #words
bel-eng tatoeba-test-v2021-08-07 0.65221 48.1 2500 18571
rus-eng tatoeba-test-v2021-08-07 0.71452 57.4 19425 147872
ukr-eng tatoeba-test-v2021-08-07 0.71162 56.9 13127 88607
bel-eng flores101-devtest 0.51689 18.1 1012 24721
rus-eng flores101-devtest 0.62581 35.2 1012 24721
ukr-eng flores101-devtest 0.65001 39.2 1012 24721
rus-eng newstest2012 0.63724 39.2 3003 72812
rus-eng newstest2013 0.57641 31.3 3000 64505
rus-eng newstest2014 0.65667 40.5 3003 69190
rus-eng newstest2015 0.61747 36.1 2818 64428
rus-eng newstest2016 0.61414 35.7 2998 69278
rus-eng newstest2017 0.65365 40.8 3001 69025
rus-eng newstest2018 0.61386 35.2 3000 71291
rus-eng newstest2019 0.65476 41.6 2000 42642
rus-eng newstest2020 0.64878 36.9 991 20217
rus-eng newstestB2020 0.65685 39.3 991 20423
rus-eng tico19-test 0.63280 33.3 2100 56323

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 22:17:11 EET 2022
  • port machine: LM0-400-22516.local
Downloads last month
220
Hosted inference API
This model can be loaded on the Inference API on-demand.

Evaluation results