metadata

language:
  - be
  - it
  - ru
  - uk
  - zle
tags:
  - translation
  - opus-mt-tc
license: cc-by-4.0
model-index:
  - name: opus-mt-tc-big-zle-it
    results:
      - task:
          name: Translation rus-ita
          type: translation
          args: rus-ita
        dataset:
          name: flores101-devtest
          type: flores_101
          args: rus ita devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 23.7
      - task:
          name: Translation ukr-ita
          type: translation
          args: ukr-ita
        dataset:
          name: flores101-devtest
          type: flores_101
          args: ukr ita devtest
        metrics:
          - name: BLEU
            type: bleu
            value: 23.2
      - task:
          name: Translation bel-ita
          type: translation
          args: bel-ita
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: bel-ita
        metrics:
          - name: BLEU
            type: bleu
            value: 49.3
      - task:
          name: Translation rus-ita
          type: translation
          args: rus-ita
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: rus-ita
        metrics:
          - name: BLEU
            type: bleu
            value: 43.5
      - task:
          name: Translation ukr-ita
          type: translation
          args: ukr-ita
        dataset:
          name: tatoeba-test-v2021-08-07
          type: tatoeba_mt
          args: ukr-ita
        metrics:
          - name: BLEU
            type: bleu
            value: 50

opus-mt-tc-big-zle-it

Neural machine translation model for translating from East Slavic languages (zle) to Italian (it).

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.

Publications: OPUS-MT – Building open translation services for the World and The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT (Please, cite if you use this model.)

@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",
}

Model info

Release: 2022-03-19
source language(s): bel rus ukr
target language(s): ita
model: transformer-big
data: opusTCv20210807 (source)
tokenization: SentencePiece (spm32k,spm32k)
original model: opusTCv20210807_transformer-big_2022-03-19.zip
more information released models: OPUS-MT zle-ita README

Usage

A short example code:

from transformers import MarianMTModel, MarianTokenizer

src_text = [
    "Вони не ідіоти.",
    "Я не хочу идти в банк."
]

model_name = "pytorch-models/opus-mt-tc-big-zle-it"
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:
#     Non sono idioti.
#     Non voglio andare in banca.

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-zle-it")
print(pipe("Вони не ідіоти."))

# expected output: Non sono idioti.

Benchmarks

test set translations: opusTCv20210807_transformer-big_2022-03-19.test.txt
test set scores: opusTCv20210807_transformer-big_2022-03-19.eval.txt
benchmark results: benchmark_results.txt
benchmark output: benchmark_translations.zip

langpair	testset	chr-F	BLEU	#sent	#words
bel-ita	tatoeba-test-v2021-08-07	0.65945	49.3	264	1681
rus-ita	tatoeba-test-v2021-08-07	0.64037	43.5	10045	71584
ukr-ita	tatoeba-test-v2021-08-07	0.69570	50.0	5000	27846
bel-ita	flores101-devtest	0.46311	13.5	1012	27306
rus-ita	flores101-devtest	0.53054	23.7	1012	27306
ukr-ita	flores101-devtest	0.52783	23.2	1012	27306

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: 1bdabf7
port time: Wed Mar 23 23:17:47 EET 2022
port machine: LM0-400-22516.local