language:
- da
- gmq
- nb
- false
- ru
- sv
- uk
- zle
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-zle-gmq
results:
- task:
name: Translation rus-dan
type: translation
args: rus-dan
dataset:
name: flores101-devtest
type: flores_101
args: rus dan devtest
metrics:
- name: BLEU
type: bleu
value: 28
- task:
name: Translation rus-nob
type: translation
args: rus-nob
dataset:
name: flores101-devtest
type: flores_101
args: rus nob devtest
metrics:
- name: BLEU
type: bleu
value: 20.6
- task:
name: Translation rus-swe
type: translation
args: rus-swe
dataset:
name: flores101-devtest
type: flores_101
args: rus swe devtest
metrics:
- name: BLEU
type: bleu
value: 26.4
- task:
name: Translation ukr-dan
type: translation
args: ukr-dan
dataset:
name: flores101-devtest
type: flores_101
args: ukr dan devtest
metrics:
- name: BLEU
type: bleu
value: 30.3
- task:
name: Translation ukr-nob
type: translation
args: ukr-nob
dataset:
name: flores101-devtest
type: flores_101
args: ukr nob devtest
metrics:
- name: BLEU
type: bleu
value: 21.1
- task:
name: Translation ukr-swe
type: translation
args: ukr-swe
dataset:
name: flores101-devtest
type: flores_101
args: ukr swe devtest
metrics:
- name: BLEU
type: bleu
value: 28.8
- task:
name: Translation rus-dan
type: translation
args: rus-dan
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-dan
metrics:
- name: BLEU
type: bleu
value: 59.6
- task:
name: Translation rus-nob
type: translation
args: rus-nob
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-nob
metrics:
- name: BLEU
type: bleu
value: 46.1
- task:
name: Translation rus-swe
type: translation
args: rus-swe
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: rus-swe
metrics:
- name: BLEU
type: bleu
value: 53.3
opus-mt-tc-big-zle-gmq
Neural machine translation model for translating from East Slavic languages (zle) to North Germanic languages (gmq).
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: 2022-03-14
- source language(s): rus ukr
- target language(s): dan nob nor swe
- valid target language labels: >>dan<< >>nob<< >>nor<< >>swe<<
- model: transformer-big
- data: opusTCv20210807+pft (source)
- tokenization: SentencePiece (spm32k,spm32k)
- original model: opusTCv20210807+pft_transformer-big_2022-03-14.zip
- more information released models: OPUS-MT zle-gmq 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. >>dan<<
Usage
A short example code:
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>dan<< Заўтра ўжо чацвер.",
">>swe<< Том грав з Мері в кішки-мишки."
]
model_name = "pytorch-models/opus-mt-tc-big-zle-gmq"
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:
# I morgen er det torsdag.
# Tom lekte med Mary i katt-möss.
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-gmq")
print(pipe(">>dan<< Заўтра ўжо чацвер."))
# expected output: I morgen er det torsdag.
Benchmarks
- test set translations: opusTCv20210807+pft_transformer-big_2022-03-14.test.txt
- test set scores: opusTCv20210807+pft_transformer-big_2022-03-14.eval.txt
- benchmark results: benchmark_results.txt
- benchmark output: benchmark_translations.zip
| langpair | testset | chr-F | BLEU | #sent | #words |
|---|---|---|---|---|---|
| rus-dan | tatoeba-test-v2021-08-07 | 0.74307 | 59.6 | 1713 | 11746 |
| rus-nob | tatoeba-test-v2021-08-07 | 0.66376 | 46.1 | 1277 | 11672 |
| rus-swe | tatoeba-test-v2021-08-07 | 0.69608 | 53.3 | 1282 | 8449 |
| bel-dan | flores101-devtest | 0.47621 | 13.9 | 1012 | 24638 |
| bel-nob | flores101-devtest | 0.44966 | 10.8 | 1012 | 23873 |
| bel-swe | flores101-devtest | 0.47274 | 13.2 | 1012 | 23121 |
| rus-dan | flores101-devtest | 0.55917 | 28.0 | 1012 | 24638 |
| rus-nob | flores101-devtest | 0.50724 | 20.6 | 1012 | 23873 |
| rus-swe | flores101-devtest | 0.55812 | 26.4 | 1012 | 23121 |
| ukr-dan | flores101-devtest | 0.57829 | 30.3 | 1012 | 24638 |
| ukr-nob | flores101-devtest | 0.52271 | 21.1 | 1012 | 23873 |
| ukr-swe | flores101-devtest | 0.57499 | 28.8 | 1012 | 23121 |
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 23:13:54 EET 2022
- port machine: LM0-400-22516.local