metadata

language:
  - cs
  - sk
  - uk
tags:
  - translation
  - opus-mt-tc
license: cc-by-4.0
model-index:
  - name: opus-mt-tc-base-uk-ces_slk
    results:
      - task:
          name: Translation ukr-ces
          type: translation
          args: ukr-ces
        dataset:
          name: flores101-devtest
          type: flores_101
          args: ukr ces devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 23
      - task:
          name: Translation ukr-slk
          type: translation
          args: ukr-slk
        dataset:
          name: flores101-devtest
          type: flores_101
          args: ukr slk devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 22.1
      - task:
          name: Translation ukr-ces
          type: translation
          args: ukr-ces
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: ukr-ces
        metrics:
          - name: BLEU
            type: bleu
            value: 54.2

opus-mt-tc-base-uk-ces_slk

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

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): ukr
target language(s): ces
valid target language labels: >>ces<< >>slk<<
model: transformer-align
data: opusTCv20210807+pft (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807+pft_transformer-align_2022-03-17.zip
more information released models: OPUS-MT ukr-ces+slk 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. >>ces<<

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    ">>ces<< А чого так?",
    ">>ces<< Я загубив свої окуляри."
]

model_name = "pytorch-models/opus-mt-tc-base-uk-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:
#     Proč to tak je?
#     Ztratil jsem brýle.

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-uk-ces_slk")
print(pipe(">>ces<< А чого так?"))

# expected output: Proč to tak je?

Benchmarks

test set translations: opusTCv20210807+pft_transformer-align_2022-03-17.test.txt
test set scores: opusTCv20210807+pft_transformer-align_2022-03-17.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
ukr-ces	tatoeba-test-v2021-08-07	0.70661	54.2	1787	8550
ukr-ces	flores101-devtest	0.51283	23.0	1012	22101
ukr-slk	flores101-devtest	0.51043	22.1	1012	22543

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: f084bad
port time: Wed Mar 23 21:54:02 EET 2022
port machine: LM0-400-22516.local