Edit model card


Neural machine translation model for translating from English (en) 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.

    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",

Model info


A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "I know Tom didn't want to eat that.",
    "On Sundays, we would get up early and go fishing."

model_name = "pytorch-models/opus-mt-tc-big-en-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:
#     Tom'un bunu yemek istemediğini biliyorum.
#     Pazar günleri erkenden kalkıp balık tutmaya giderdik.

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-en-tr")
print(pipe("I know Tom didn't want to eat that."))

# expected output: Tom'un bunu yemek istemediğini biliyorum.


langpair testset chr-F BLEU #sent #words
eng-tur tatoeba-test-v2021-08-07 0.68726 42.3 13907 84364
eng-tur flores101-devtest 0.62829 31.4 1012 20253
eng-tur newsdev2016 0.58947 21.9 1001 15958
eng-tur newstest2016 0.57624 23.4 3000 50782
eng-tur newstest2017 0.58858 25.4 3007 51977
eng-tur newstest2018 0.57848 22.6 3000 53731


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: 3405783
  • port time: Wed Apr 13 18:11:39 EEST 2022
  • port machine: LM0-400-22516.local
Downloads last month
Hosted inference API
This model can be loaded on the Inference API on-demand.

Evaluation results