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metadata
language:
  - bs
  - en
  - hr
  - sh
  - sr
language_bcp47:
  - bs_Latn
  - sr_Cyrl
  - sr_Latn
tags:
  - translation
  - opus-mt-tc
license: cc-by-4.0
model-index:
  - name: opus-mt-tc-base-en-sh
    results:
      - task:
          name: Translation eng-hrv
          type: translation
          args: eng-hrv
        dataset:
          name: flores200-dev
          type: flores200-dev
          args: eng-hrv
        metrics:
          - name: BLEU
            type: bleu
            value: 28.1
          - name: chr-F
            type: chrf
            value: 0.57963
      - task:
          name: Translation eng-srp_Cyrl
          type: translation
          args: eng-srp_Cyrl
        dataset:
          name: flores200-dev
          type: flores200-dev
          args: eng-srp_Cyrl
        metrics:
          - name: BLEU
            type: bleu
            value: 32.2
          - name: chr-F
            type: chrf
            value: 0.60096
      - task:
          name: Translation eng-hrv
          type: translation
          args: eng-hrv
        dataset:
          name: flores200-devtest
          type: flores200-devtest
          args: eng-hrv
        metrics:
          - name: BLEU
            type: bleu
            value: 28.9
          - name: chr-F
            type: chrf
            value: 0.58652
      - task:
          name: Translation eng-srp_Cyrl
          type: translation
          args: eng-srp_Cyrl
        dataset:
          name: flores200-devtest
          type: flores200-devtest
          args: eng-srp_Cyrl
        metrics:
          - name: BLEU
            type: bleu
            value: 31.7
          - name: chr-F
            type: chrf
            value: 0.59874
      - task:
          name: Translation eng-hrv
          type: translation
          args: eng-hrv
        dataset:
          name: flores101-devtest
          type: flores_101
          args: eng hrv devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 28.7
          - name: chr-F
            type: chrf
            value: 0.586
      - task:
          name: Translation eng-srp_Cyrl
          type: translation
          args: eng-srp_Cyrl
        dataset:
          name: flores101-devtest
          type: flores_101
          args: eng srp_Cyrl devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 31.7
          - name: chr-F
            type: chrf
            value: 0.59874
      - task:
          name: Translation eng-bos_Latn
          type: translation
          args: eng-bos_Latn
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: eng-bos_Latn
        metrics:
          - name: BLEU
            type: bleu
            value: 46.3
          - name: chr-F
            type: chrf
            value: 0.666
      - task:
          name: Translation eng-hbs
          type: translation
          args: eng-hbs
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: eng-hbs
        metrics:
          - name: BLEU
            type: bleu
            value: 42.1
          - name: chr-F
            type: chrf
            value: 0.631
      - task:
          name: Translation eng-hrv
          type: translation
          args: eng-hrv
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: eng-hrv
        metrics:
          - name: BLEU
            type: bleu
            value: 49.7
          - name: chr-F
            type: chrf
            value: 0.691
      - task:
          name: Translation eng-srp_Cyrl
          type: translation
          args: eng-srp_Cyrl
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: eng-srp_Cyrl
        metrics:
          - name: BLEU
            type: bleu
            value: 45.1
          - name: chr-F
            type: chrf
            value: 0.645
      - task:
          name: Translation eng-srp_Latn
          type: translation
          args: eng-srp_Latn
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: eng-srp_Latn
        metrics:
          - name: BLEU
            type: bleu
            value: 39.8
          - name: chr-F
            type: chrf
            value: 0.613

opus-mt-tc-base-en-sh

Table of Contents

Model Details

Neural machine translation model for translating from English (en) to Serbo-Croatian (sh).

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:

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. >>bos_Latn<<

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 = [
    ">>hrv<< You're about to make a very serious mistake.",
    ">>hbs<< I've just been too busy."
]

model_name = "pytorch-models/opus-mt-tc-base-en-sh"
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:
#     Ti si o tome napraviti vrlo ozbiljnu pogrešku.
#     [4]

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-en-sh")
print(pipe(">>hrv<< You're about to make a very serious mistake."))

# expected output: Ti si o tome napraviti vrlo ozbiljnu pogrešku.

Training

Evaluation

langpair testset chr-F BLEU #sent #words
eng-bos_Latn tatoeba-test-v2021-08-07 0.666 46.3 301 1650
eng-hbs tatoeba-test-v2021-08-07 0.631 42.1 10017 63927
eng-hrv tatoeba-test-v2021-08-07 0.691 49.7 1480 9396
eng-srp_Cyrl tatoeba-test-v2021-08-07 0.645 45.1 1580 9152
eng-srp_Latn tatoeba-test-v2021-08-07 0.613 39.8 6656 43729
eng-hrv flores101-devtest 0.586 28.7 1012 22423
eng-hrv flores200-dev 0.57963 28.1 997 21567
eng-hrv flores200-devtest 0.58652 28.9 1012 22423
eng-srp_Cyrl flores101-devtest 0.59874 31.7 1012 23456
eng-srp_Cyrl flores200-dev 0.60096 32.2 997 22384
eng-srp_Cyrl flores200-devtest 0.59874 31.7 1012 23456

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: e2a6299
  • port time: Tue Oct 11 10:14:32 CEST 2022
  • port machine: LM0-400-22516.local