metadata
language:
- be
- en
- ru
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-zle
results:
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: flores101-devtest
type: flores_101
args: eng rus devtest
metrics:
- name: BLEU
type: bleu
value: 32.7
- task:
name: Translation eng-ukr
type: translation
args: eng-ukr
dataset:
name: flores101-devtest
type: flores_101
args: eng ukr devtest
metrics:
- name: BLEU
type: bleu
value: 32.1
- task:
name: Translation eng-bel
type: translation
args: eng-bel
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-bel
metrics:
- name: BLEU
type: bleu
value: 24.9
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 45.5
- task:
name: Translation eng-ukr
type: translation
args: eng-ukr
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-ukr
metrics:
- name: BLEU
type: bleu
value: 37.7
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: tico19-test
type: tico19-test
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 33.7
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: newstest2012
type: wmt-2012-news
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 36.8
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: newstest2013
type: wmt-2013-news
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 26.9
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: newstest2014
type: wmt-2014-news
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 43.5
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: newstest2015
type: wmt-2015-news
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 34.9
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: newstest2016
type: wmt-2016-news
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 33.1
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: newstest2017
type: wmt-2017-news
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 37.3
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: newstest2018
type: wmt-2018-news
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 32.9
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: newstest2019
type: wmt-2019-news
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 31.8
- task:
name: Translation eng-rus
type: translation
args: eng-rus
dataset:
name: newstest2020
type: wmt-2020-news
args: eng-rus
metrics:
- name: BLEU
type: bleu
value: 25.5
opus-mt-tc-big-en-zle
Neural machine translation model for translating from English (en) to East Slavic languages (zle).
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: 2022-03-13
- source language(s): eng
- target language(s): bel rus ukr
- valid target language labels: >>bel<< >>rus<< >>ukr<<
- model: transformer-big
- data: opusTCv20210807+bt (source)
- tokenization: SentencePiece (spm32k,spm32k)
- original model: opusTCv20210807+bt_transformer-big_2022-03-13.zip
- more information released models: OPUS-MT eng-zle README
- more information about the model: MarianMT
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. >>bel<<
Usage
A short example code:
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>rus<< Are they coming as well?",
">>rus<< I didn't let Tom do what he wanted to do."
]
model_name = "pytorch-models/opus-mt-tc-big-en-zle"
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:
# Они тоже приедут?
# Я не позволил Тому сделать то, что он хотел.
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-zle")
print(pipe(">>rus<< Are they coming as well?"))
# expected output: Они тоже приедут?
Benchmarks
- test set translations: opusTCv20210807+bt_transformer-big_2022-03-13.test.txt
- test set scores: opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt
- benchmark results: benchmark_results.txt
- benchmark output: benchmark_translations.zip
| langpair | testset | chr-F | BLEU | #sent | #words |
|---|---|---|---|---|---|
| eng-bel | tatoeba-test-v2021-08-07 | 0.50345 | 24.9 | 2500 | 16237 |
| eng-rus | tatoeba-test-v2021-08-07 | 0.66182 | 45.5 | 19425 | 134296 |
| eng-ukr | tatoeba-test-v2021-08-07 | 0.60175 | 37.7 | 13127 | 80998 |
| eng-bel | flores101-devtest | 0.42078 | 11.2 | 1012 | 24829 |
| eng-rus | flores101-devtest | 0.59654 | 32.7 | 1012 | 23295 |
| eng-ukr | flores101-devtest | 0.60131 | 32.1 | 1012 | 22810 |
| eng-rus | newstest2012 | 0.62842 | 36.8 | 3003 | 64790 |
| eng-rus | newstest2013 | 0.54627 | 26.9 | 3000 | 58560 |
| eng-rus | newstest2014 | 0.68348 | 43.5 | 3003 | 61603 |
| eng-rus | newstest2015 | 0.62621 | 34.9 | 2818 | 55915 |
| eng-rus | newstest2016 | 0.60595 | 33.1 | 2998 | 62014 |
| eng-rus | newstest2017 | 0.64249 | 37.3 | 3001 | 60253 |
| eng-rus | newstest2018 | 0.61219 | 32.9 | 3000 | 61907 |
| eng-rus | newstest2019 | 0.57902 | 31.8 | 1997 | 48147 |
| eng-rus | newstest2020 | 0.52939 | 25.5 | 2002 | 47083 |
| eng-rus | tico19-test | 0.59314 | 33.7 | 2100 | 55843 |
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: Thu Mar 24 01:58:40 EET 2022
- port machine: LM0-400-22516.local