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metadata
language:
  - ca
  - es
  - fr
  - gl
  - it
  - oc
  - pt
  - ro
  - tr
tags:
  - translation
  - opus-mt-tc
license: cc-by-4.0
model-index:
  - name: opus-mt-tc-big-itc-tr
    results:
      - task:
          name: Translation cat-tur
          type: translation
          args: cat-tur
        dataset:
          name: flores101-devtest
          type: flores_101
          args: cat tur devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 21.7
          - name: chr-F
            type: chrf
            value: 0.54892
      - task:
          name: Translation fra-tur
          type: translation
          args: fra-tur
        dataset:
          name: flores101-devtest
          type: flores_101
          args: fra tur devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 21.7
          - name: chr-F
            type: chrf
            value: 0.55342
      - task:
          name: Translation glg-tur
          type: translation
          args: glg-tur
        dataset:
          name: flores101-devtest
          type: flores_101
          args: glg tur devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 20.6
          - name: chr-F
            type: chrf
            value: 0.53936
      - task:
          name: Translation ita-tur
          type: translation
          args: ita-tur
        dataset:
          name: flores101-devtest
          type: flores_101
          args: ita tur devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 18.4
          - name: chr-F
            type: chrf
            value: 0.52842
      - task:
          name: Translation oci-tur
          type: translation
          args: oci-tur
        dataset:
          name: flores101-devtest
          type: flores_101
          args: oci tur devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 17.6
          - name: chr-F
            type: chrf
            value: 0.50618
      - task:
          name: Translation por-tur
          type: translation
          args: por-tur
        dataset:
          name: flores101-devtest
          type: flores_101
          args: por tur devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 23.5
          - name: chr-F
            type: chrf
            value: 0.56396
      - task:
          name: Translation ron-tur
          type: translation
          args: ron-tur
        dataset:
          name: flores101-devtest
          type: flores_101
          args: ron tur devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 21.5
          - name: chr-F
            type: chrf
            value: 0.55409
      - task:
          name: Translation spa-tur
          type: translation
          args: spa-tur
        dataset:
          name: flores101-devtest
          type: flores_101
          args: spa tur devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 16.5
          - name: chr-F
            type: chrf
            value: 0.51066
      - task:
          name: Translation fra-tur
          type: translation
          args: fra-tur
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: fra-tur
        metrics:
          - name: BLEU
            type: bleu
            value: 34.8
          - name: chr-F
            type: chrf
            value: 0.63006
      - task:
          name: Translation ita-tur
          type: translation
          args: ita-tur
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: ita-tur
        metrics:
          - name: BLEU
            type: bleu
            value: 34.9
          - name: chr-F
            type: chrf
            value: 0.59991
      - task:
          name: Translation por-tur
          type: translation
          args: por-tur
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: por-tur
        metrics:
          - name: BLEU
            type: bleu
            value: 40.1
          - name: chr-F
            type: chrf
            value: 0.67836
      - task:
          name: Translation ron-tur
          type: translation
          args: ron-tur
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: ron-tur
        metrics:
          - name: BLEU
            type: bleu
            value: 35.5
          - name: chr-F
            type: chrf
            value: 0.64031
      - task:
          name: Translation spa-tur
          type: translation
          args: spa-tur
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: spa-tur
        metrics:
          - name: BLEU
            type: bleu
            value: 45.2
          - name: chr-F
            type: chrf
            value: 0.71524

opus-mt-tc-big-itc-tr

Table of Contents

Model Details

Neural machine translation model for translating from Italic languages (itc) to Turkish (tr).

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 = [
    ""Di che nazionalità sono le tue dottoresse?" "Malese."",
    ""Di che nazionalità sono i nostri amici?" "Maltese.""
]

model_name = "pytorch-models/opus-mt-tc-big-itc-tr"
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:
#     "Doktorların hangi milletten?" "Malezyalı."
#     "Arkadaşlarımız hangi milletten?" "Maltalı."

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-itc-tr")
print(pipe(""Di che nazionalità sono le tue dottoresse?" "Malese.""))

# expected output: "Doktorların hangi milletten?" "Malezyalı."

Training

Evaluation

langpair testset chr-F BLEU #sent #words
fra-tur tatoeba-test-v2021-08-07 0.63006 34.8 2582 14307
ita-tur tatoeba-test-v2021-08-07 0.59991 34.9 10000 75807
por-tur tatoeba-test-v2021-08-07 0.67836 40.1 1794 9312
ron-tur tatoeba-test-v2021-08-07 0.64031 35.5 2460 13788
spa-tur tatoeba-test-v2021-08-07 0.71524 45.2 10615 56099
cat-tur flores101-devtest 0.54892 21.7 1012 20253
fra-tur flores101-devtest 0.55342 21.7 1012 20253
glg-tur flores101-devtest 0.53936 20.6 1012 20253
ita-tur flores101-devtest 0.52842 18.4 1012 20253
oci-tur flores101-devtest 0.50618 17.6 1012 20253
por-tur flores101-devtest 0.56396 23.5 1012 20253
ron-tur flores101-devtest 0.55409 21.5 1012 20253
spa-tur flores101-devtest 0.51066 16.5 1012 20253

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 00:03:26 EEST 2022
  • port machine: LM0-400-22516.local