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Initial commit
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metadata
language:
  - bg
  - de
  - hr
  - mk
  - sh
  - sl
  - sr
language_bcp47:
  - sr_Cyrl
  - sr_Latn
tags:
  - translation
  - opus-mt-tc
license: cc-by-4.0
model-index:
  - name: opus-mt-tc-big-zls-de
    results:
      - task:
          name: Translation bul-deu
          type: translation
          args: bul-deu
        dataset:
          name: flores101-devtest
          type: flores_101
          args: bul deu devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 28.4
          - name: chr-F
            type: chrf
            value: 0.57688
      - task:
          name: Translation hrv-deu
          type: translation
          args: hrv-deu
        dataset:
          name: flores101-devtest
          type: flores_101
          args: hrv deu devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 27.4
          - name: chr-F
            type: chrf
            value: 0.56674
      - task:
          name: Translation mkd-deu
          type: translation
          args: mkd-deu
        dataset:
          name: flores101-devtest
          type: flores_101
          args: mkd deu devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 29.3
          - name: chr-F
            type: chrf
            value: 0.57688
      - task:
          name: Translation slv-deu
          type: translation
          args: slv-deu
        dataset:
          name: flores101-devtest
          type: flores_101
          args: slv deu devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 26.7
          - name: chr-F
            type: chrf
            value: 0.56258
      - task:
          name: Translation srp_Cyrl-deu
          type: translation
          args: srp_Cyrl-deu
        dataset:
          name: flores101-devtest
          type: flores_101
          args: srp_Cyrl deu devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 30.7
          - name: chr-F
            type: chrf
            value: 0.59271
      - task:
          name: Translation bul-deu
          type: translation
          args: bul-deu
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: bul-deu
        metrics:
          - name: BLEU
            type: bleu
            value: 54.5
          - name: chr-F
            type: chrf
            value: 0.7122
      - task:
          name: Translation hbs-deu
          type: translation
          args: hbs-deu
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: hbs-deu
        metrics:
          - name: BLEU
            type: bleu
            value: 54.8
          - name: chr-F
            type: chrf
            value: 0.71283
      - task:
          name: Translation hrv-deu
          type: translation
          args: hrv-deu
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: hrv-deu
        metrics:
          - name: BLEU
            type: bleu
            value: 53.1
          - name: chr-F
            type: chrf
            value: 0.69448
      - task:
          name: Translation slv-deu
          type: translation
          args: slv-deu
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: slv-deu
        metrics:
          - name: BLEU
            type: bleu
            value: 21.1
          - name: chr-F
            type: chrf
            value: 0.36339
      - task:
          name: Translation srp_Latn-deu
          type: translation
          args: srp_Latn-deu
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: srp_Latn-deu
        metrics:
          - name: BLEU
            type: bleu
            value: 56
          - name: chr-F
            type: chrf
            value: 0.72489

opus-mt-tc-big-zls-de

Table of Contents

Model Details

Neural machine translation model for translating from South Slavic languages (zls) to German (de).

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. Model Description:

Uses

This model can be used for translation and text-to-text generation.

Risks, Limitations and Biases

CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).

How to Get Started With the Model

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Jesi li ti student?",
    "Dve stvari deca treba da dobiju od svojih roditelja: korene i krila."
]

model_name = "pytorch-models/opus-mt-tc-big-zls-de"
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:
#     Sind Sie Student?
#     Zwei Dinge sollten Kinder von ihren Eltern bekommen: Wurzeln und Flügel.

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-zls-de")
print(pipe("Jesi li ti student?"))

# expected output: Sind Sie Student?

Training

Evaluation

langpair testset chr-F BLEU #sent #words
bul-deu tatoeba-test-v2021-08-07 0.71220 54.5 314 2224
hbs-deu tatoeba-test-v2021-08-07 0.71283 54.8 1959 15559
hrv-deu tatoeba-test-v2021-08-07 0.69448 53.1 782 5734
slv-deu tatoeba-test-v2021-08-07 0.36339 21.1 492 3003
srp_Latn-deu tatoeba-test-v2021-08-07 0.72489 56.0 986 8500
bul-deu flores101-devtest 0.57688 28.4 1012 25094
hrv-deu flores101-devtest 0.56674 27.4 1012 25094
mkd-deu flores101-devtest 0.57688 29.3 1012 25094
slv-deu flores101-devtest 0.56258 26.7 1012 25094
srp_Cyrl-deu flores101-devtest 0.59271 30.7 1012 25094

Citation Information

@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",
}

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: 8b9f0b0
  • port time: Sat Aug 13 00:05:30 EEST 2022
  • port machine: LM0-400-22516.local