metadata
language:
- be
- bg
- hr
- ru
- sh
- sl
- sr_Cyrl
- sr_Latn
- uk
- zle
- zls
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-zls
results:
- task:
name: Translation rus-bul
type: translation
args: rus-bul
dataset:
name: flores101-devtest
type: flores_101
args: rus bul devtest
metrics:
- name: BLEU
type: bleu
value: 28.9
- task:
name: Translation rus-hrv
type: translation
args: rus-hrv
dataset:
name: flores101-devtest
type: flores_101
args: rus hrv devtest
metrics:
- name: BLEU
type: bleu
value: 23.2
- task:
name: Translation rus-mkd
type: translation
args: rus-mkd
dataset:
name: flores101-devtest
type: flores_101
args: rus mkd devtest
metrics:
- name: BLEU
type: bleu
value: 24.3
- task:
name: Translation rus-slv
type: translation
args: rus-slv
dataset:
name: flores101-devtest
type: flores_101
args: rus slv devtest
metrics:
- name: BLEU
type: bleu
value: 23.1
- task:
name: Translation rus-srp_Cyrl
type: translation
args: rus-srp_Cyrl
dataset:
name: flores101-devtest
type: flores_101
args: rus srp_Cyrl devtest
metrics:
- name: BLEU
type: bleu
value: 24.1
- task:
name: Translation ukr-bul
type: translation
args: ukr-bul
dataset:
name: flores101-devtest
type: flores_101
args: ukr bul devtest
metrics:
- name: BLEU
type: bleu
value: 30.8
- task:
name: Translation ukr-hrv
type: translation
args: ukr-hrv
dataset:
name: flores101-devtest
type: flores_101
args: ukr hrv devtest
metrics:
- name: BLEU
type: bleu
value: 24.6
- task:
name: Translation ukr-mkd
type: translation
args: ukr-mkd
dataset:
name: flores101-devtest
type: flores_101
args: ukr mkd devtest
metrics:
- name: BLEU
type: bleu
value: 26.2
- task:
name: Translation ukr-slv
type: translation
args: ukr-slv
dataset:
name: flores101-devtest
type: flores_101
args: ukr slv devtest
metrics:
- name: BLEU
type: bleu
value: 24.2
- task:
name: Translation ukr-srp_Cyrl
type: translation
args: ukr-srp_Cyrl
dataset:
name: flores101-devtest
type: flores_101
args: ukr srp_Cyrl devtest
metrics:
- name: BLEU
type: bleu
value: 26.2
- task:
name: Translation rus-bul
type: translation
args: rus-bul
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-bul
metrics:
- name: BLEU
type: bleu
value: 53.7
- task:
name: Translation rus-hbs
type: translation
args: rus-hbs
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-hbs
metrics:
- name: BLEU
type: bleu
value: 49.4
- task:
name: Translation rus-slv
type: translation
args: rus-slv
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-slv
metrics:
- name: BLEU
type: bleu
value: 21.5
- task:
name: Translation rus-srp_Cyrl
type: translation
args: rus-srp_Cyrl
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-srp_Cyrl
metrics:
- name: BLEU
type: bleu
value: 46.1
- task:
name: Translation rus-srp_Latn
type: translation
args: rus-srp_Latn
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-srp_Latn
metrics:
- name: BLEU
type: bleu
value: 51.7
- task:
name: Translation ukr-bul
type: translation
args: ukr-bul
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-bul
metrics:
- name: BLEU
type: bleu
value: 61.3
- task:
name: Translation ukr-hbs
type: translation
args: ukr-hbs
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-hbs
metrics:
- name: BLEU
type: bleu
value: 52.1
- task:
name: Translation ukr-hrv
type: translation
args: ukr-hrv
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-hrv
metrics:
- name: BLEU
type: bleu
value: 50.1
- task:
name: Translation ukr-srp_Cyrl
type: translation
args: ukr-srp_Cyrl
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-srp_Cyrl
metrics:
- name: BLEU
type: bleu
value: 54.7
- task:
name: Translation ukr-srp_Latn
type: translation
args: ukr-srp_Latn
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: ukr-srp_Latn
metrics:
- name: BLEU
type: bleu
value: 53.4
opus-mt-tc-big-zle-zls
Neural machine translation model for translating from East Slavic languages (zle) to South Slavic languages (zls).
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: 2022-03-23
- source language(s): bel rus ukr
- target language(s): bul hbs hrv slv srp_Cyrl srp_Latn
- valid target language labels: >>bul<< >>hbs<< >>hrv<< >>slv<< >>srp_Cyrl<< >>srp_Latn<<
- model: transformer-big
- data: opusTCv20210807+bt (source)
- tokenization: SentencePiece (spm32k,spm32k)
- original model: opusTCv20210807+bt_transformer-big_2022-03-23.zip
- more information released models: OPUS-MT zle-zls 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. >>bul<<
Usage
A short example code:
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>bul<< Новы каранавірус вельмі заразны.",
">>srp_Latn<< Моє ім'я — Саллі."
]
model_name = "pytorch-models/opus-mt-tc-big-zle-zls"
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:
# Короната е силно заразна.
# Zovem se Sali.
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-zls")
print(pipe(">>bul<< Новы каранавірус вельмі заразны."))
# expected output: Короната е силно заразна.
Benchmarks
- test set translations: opusTCv20210807+bt_transformer-big_2022-03-23.test.txt
- test set scores: opusTCv20210807+bt_transformer-big_2022-03-23.eval.txt
- benchmark results: benchmark_results.txt
- benchmark output: benchmark_translations.zip
| langpair | testset | chr-F | BLEU | #sent | #words |
|---|---|---|---|---|---|
| rus-bul | tatoeba-test-v2021-08-07 | 0.71515 | 53.7 | 1247 | 8272 |
| rus-hbs | tatoeba-test-v2021-08-07 | 0.69192 | 49.4 | 2500 | 14736 |
| rus-slv | tatoeba-test-v2021-08-07 | 0.38051 | 21.5 | 657 | 3969 |
| rus-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.66622 | 46.1 | 881 | 5407 |
| rus-srp_Latn | tatoeba-test-v2021-08-07 | 0.70990 | 51.7 | 1483 | 8552 |
| ukr-bul | tatoeba-test-v2021-08-07 | 0.77283 | 61.3 | 1020 | 5181 |
| ukr-hbs | tatoeba-test-v2021-08-07 | 0.69401 | 52.1 | 942 | 5130 |
| ukr-hrv | tatoeba-test-v2021-08-07 | 0.67202 | 50.1 | 389 | 2302 |
| ukr-srp_Cyrl | tatoeba-test-v2021-08-07 | 0.70064 | 54.7 | 205 | 1112 |
| ukr-srp_Latn | tatoeba-test-v2021-08-07 | 0.72405 | 53.4 | 348 | 1716 |
| bel-bul | flores101-devtest | 0.49528 | 16.1 | 1012 | 24700 |
| bel-hrv | flores101-devtest | 0.46308 | 12.4 | 1012 | 22423 |
| bel-mkd | flores101-devtest | 0.48608 | 13.5 | 1012 | 24314 |
| bel-slv | flores101-devtest | 0.44452 | 12.2 | 1012 | 23425 |
| bel-srp_Cyrl | flores101-devtest | 0.44424 | 12.6 | 1012 | 23456 |
| rus-bul | flores101-devtest | 0.58653 | 28.9 | 1012 | 24700 |
| rus-hrv | flores101-devtest | 0.53494 | 23.2 | 1012 | 22423 |
| rus-mkd | flores101-devtest | 0.55184 | 24.3 | 1012 | 24314 |
| rus-slv | flores101-devtest | 0.52201 | 23.1 | 1012 | 23425 |
| rus-srp_Cyrl | flores101-devtest | 0.53038 | 24.1 | 1012 | 23456 |
| ukr-bul | flores101-devtest | 0.59625 | 30.8 | 1012 | 24700 |
| ukr-hrv | flores101-devtest | 0.54530 | 24.6 | 1012 | 22423 |
| ukr-mkd | flores101-devtest | 0.56822 | 26.2 | 1012 | 24314 |
| ukr-slv | flores101-devtest | 0.53092 | 24.2 | 1012 | 23425 |
| ukr-srp_Cyrl | flores101-devtest | 0.54618 | 26.2 | 1012 | 23456 |
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 00:46:26 EET 2022
- port machine: LM0-400-22516.local