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Model Details

Neural machine translation model for translating from Italic languages (itc) to Hebrew (he).

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 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 = [
    "La María és feminista.",
    "Contribuyan en Tatoeba."

model_name = "pytorch-models/opus-mt-tc-big-itc-he"
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:
#     מרי היא פמיניסטית.
#     תרום לטאטואבה.

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-he")
print(pipe("La María és feminista."))

# expected output: מרי היא פמיניסטית.



langpair testset chr-F BLEU #sent #words
fra-heb tatoeba-test-v2021-08-07 0.60539 39.6 3281 20655
ita-heb tatoeba-test-v2021-08-07 0.60264 40.0 1706 9796
por-heb tatoeba-test-v2021-08-07 0.63087 44.4 719 4423
spa-heb tatoeba-test-v2021-08-07 0.63883 44.5 1849 12112
cat-heb flores101-devtest 0.52457 23.0 1012 20749
fra-heb flores101-devtest 0.52953 23.2 1012 20749
glg-heb flores101-devtest 0.50918 20.8 1012 20749
ita-heb flores101-devtest 0.49007 18.3 1012 20749
por-heb flores101-devtest 0.53906 24.4 1012 20749
ron-heb flores101-devtest 0.52103 22.1 1012 20749
spa-heb flores101-devtest 0.47646 16.5 1012 20749

Citation Information

    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",

    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",


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 00:02:03 EEST 2022
  • port machine: LM0-400-22516.local
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