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Add `opus-mt-tc` tag (#1)
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metadata
language:
  - bat
  - lt
  - lv
  - ru
  - zle
tags:
  - translation
  - opus-mt-tc
license: cc-by-4.0
model-index:
  - name: opus-mt-tc-base-zle-bat
    results:
      - task:
          name: Translation rus-lav
          type: translation
          args: rus-lav
        dataset:
          name: flores101-devtest
          type: flores_101
          args: rus lav devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 20
      - task:
          name: Translation rus-lit
          type: translation
          args: rus-lit
        dataset:
          name: flores101-devtest
          type: flores_101
          args: rus lit devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 20.6
      - task:
          name: Translation ukr-lav
          type: translation
          args: ukr-lav
        dataset:
          name: flores101-devtest
          type: flores_101
          args: ukr lav devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 21.4
      - task:
          name: Translation ukr-lit
          type: translation
          args: ukr-lit
        dataset:
          name: flores101-devtest
          type: flores_101
          args: ukr lit devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 20.5
      - task:
          name: Translation rus-lav
          type: translation
          args: rus-lav
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: rus-lav
        metrics:
          - name: BLEU
            type: bleu
            value: 55.3
      - task:
          name: Translation rus-lit
          type: translation
          args: rus-lit
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: rus-lit
        metrics:
          - name: BLEU
            type: bleu
            value: 47.2

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.

@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

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

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