language:

• cs
• dsb
• en
• hsb
• pl
• zlw tags:
• translation
• opus-mt-tc license: cc-by-4.0 model-index:
• name: opus-mt-tc-big-zlw-en results:
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: flores101-devtest type: flores_101 args: ces eng devtest metrics:
    • name: BLEU type: bleu value: 41.2
  • task: name: Translation pol-eng type: translation args: pol-eng dataset: name: flores101-devtest type: flores_101 args: pol eng devtest metrics:
    • name: BLEU type: bleu value: 29.6
  • task: name: Translation slk-eng type: translation args: slk-eng dataset: name: flores101-devtest type: flores_101 args: slk eng devtest metrics:
    • name: BLEU type: bleu value: 40.0
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: multi30k_test_2016_flickr type: multi30k-2016_flickr args: ces-eng metrics:
    • name: BLEU type: bleu value: 37.6
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: multi30k_test_2018_flickr type: multi30k-2018_flickr args: ces-eng metrics:
    • name: BLEU type: bleu value: 37.4
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: news-test2008 type: news-test2008 args: ces-eng metrics:
    • name: BLEU type: bleu value: 26.3
  • task: name: Translation pol-eng type: translation args: pol-eng dataset: name: newsdev2020 type: newsdev2020 args: pol-eng metrics:
    • name: BLEU type: bleu value: 32.7
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: ces-eng metrics:
    • name: BLEU type: bleu value: 57.4
  • task: name: Translation pol-eng type: translation args: pol-eng dataset: name: tatoeba-test-v2021-08-07 type: tatoeba_mt args: pol-eng metrics:
    • name: BLEU type: bleu value: 55.7
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2009 type: wmt-2009-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 29.5
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2010 type: wmt-2010-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 30.7
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2011 type: wmt-2011-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 30.9
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2012 type: wmt-2012-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 29.4
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2013 type: wmt-2013-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 32.8
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2014 type: wmt-2014-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 38.7
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2015 type: wmt-2015-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 33.4
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2016 type: wmt-2016-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 37.1
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2017 type: wmt-2017-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 32.5
  • task: name: Translation ces-eng type: translation args: ces-eng dataset: name: newstest2018 type: wmt-2018-news args: ces-eng metrics:
    • name: BLEU type: bleu value: 33.1
  • task: name: Translation pol-eng type: translation args: pol-eng dataset: name: newstest2020 type: wmt-2020-news args: pol-eng metrics:
    • name: BLEU type: bleu value: 32.6

opus-mt-tc-big-zlw-en

Neural machine translation model for translating from West Slavic languages (zlw) 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.

• 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): ces dsb hsb pol
• target language(s): eng
• 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 zlw-eng README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Aoi'ego hobby to tańczenie.",
    "Myślisz, że Tom planuje to zrobić?"
]

model_name = "pytorch-models/opus-mt-tc-big-zlw-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:
#     Aoi's hobby is dancing.
#     You think Tom's planning on doing that?

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-zlw-en")
print(pipe("Aoi'ego hobby to tańczenie."))

# expected output: Aoi's hobby is dancing.

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

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 20:19:48 EEST 2022
• port machine: LM0-400-22516.local