metadata
language:
- da
- en
- fo
- gmq
- is
- nb
- nn
- false
- sv
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-en-gmq
results:
- task:
name: Translation eng-dan
type: translation
args: eng-dan
dataset:
name: flores101-devtest
type: flores_101
args: eng dan devtest
metrics:
- name: BLEU
type: bleu
value: 47.7
- task:
name: Translation eng-isl
type: translation
args: eng-isl
dataset:
name: flores101-devtest
type: flores_101
args: eng isl devtest
metrics:
- name: BLEU
type: bleu
value: 24.1
- task:
name: Translation eng-nob
type: translation
args: eng-nob
dataset:
name: flores101-devtest
type: flores_101
args: eng nob devtest
metrics:
- name: BLEU
type: bleu
value: 34.5
- task:
name: Translation eng-swe
type: translation
args: eng-swe
dataset:
name: flores101-devtest
type: flores_101
args: eng swe devtest
metrics:
- name: BLEU
type: bleu
value: 46.9
- task:
name: Translation eng-isl
type: translation
args: eng-isl
dataset:
name: newsdev2021.en-is
type: newsdev2021.en-is
args: eng-isl
metrics:
- name: BLEU
type: bleu
value: 22.6
- task:
name: Translation eng-dan
type: translation
args: eng-dan
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-dan
metrics:
- name: BLEU
type: bleu
value: 61.6
- task:
name: Translation eng-isl
type: translation
args: eng-isl
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-isl
metrics:
- name: BLEU
type: bleu
value: 39.9
- task:
name: Translation eng-nno
type: translation
args: eng-nno
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-nno
metrics:
- name: BLEU
type: bleu
value: 40.1
- task:
name: Translation eng-nob
type: translation
args: eng-nob
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-nob
metrics:
- name: BLEU
type: bleu
value: 57.3
- task:
name: Translation eng-swe
type: translation
args: eng-swe
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-swe
metrics:
- name: BLEU
type: bleu
value: 60.9
- task:
name: Translation eng-isl
type: translation
args: eng-isl
dataset:
name: newstest2021.en-is
type: wmt-2021-news
args: eng-isl
metrics:
- name: BLEU
type: bleu
value: 21.5
opus-mt-tc-big-en-gmq
Neural machine translation model for translating from English (en) 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: 2022-03-17
- source language(s): eng
- target language(s): dan fao isl nno nob nor swe
- valid target language labels: >>dan<< >>fao<< >>isl<< >>nno<< >>nob<< >>nor<< >>swe<<
- model: transformer-big
- data: opusTCv20210807+bt (source)
- tokenization: SentencePiece (spm32k,spm32k)
- original model: opusTCv20210807+bt_transformer-big_2022-03-17.zip
- more information released models: OPUS-MT eng-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 = [
">>nno<< The United States borders Canada.",
">>nob<< This is the biggest hotel in this city."
]
model_name = "pytorch-models/opus-mt-tc-big-en-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:
# USA grensar til Canada.
# Dette er det største hotellet i denne byen.
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-gmq")
print(pipe(">>nno<< The United States borders Canada."))
# expected output: USA grensar til Canada.
Benchmarks
- test set translations: opusTCv20210807+bt_transformer-big_2022-03-17.test.txt
- test set scores: opusTCv20210807+bt_transformer-big_2022-03-17.eval.txt
- benchmark results: benchmark_results.txt
- benchmark output: benchmark_translations.zip
| langpair | testset | chr-F | BLEU | #sent | #words |
|---|---|---|---|---|---|
| eng-dan | tatoeba-test-v2021-08-07 | 0.75165 | 61.6 | 10795 | 79385 |
| eng-fao | tatoeba-test-v2021-08-07 | 0.40395 | 18.3 | 294 | 1933 |
| eng-isl | tatoeba-test-v2021-08-07 | 0.59731 | 39.9 | 2503 | 19023 |
| eng-nno | tatoeba-test-v2021-08-07 | 0.61271 | 40.1 | 460 | 3428 |
| eng-nob | tatoeba-test-v2021-08-07 | 0.72380 | 57.3 | 4539 | 36119 |
| eng-swe | tatoeba-test-v2021-08-07 | 0.74197 | 60.9 | 10362 | 68067 |
| eng-dan | flores101-devtest | 0.70810 | 47.7 | 1012 | 24638 |
| eng-isl | flores101-devtest | 0.52076 | 24.1 | 1012 | 22834 |
| eng-nob | flores101-devtest | 0.62760 | 34.5 | 1012 | 23873 |
| eng-swe | flores101-devtest | 0.70129 | 46.9 | 1012 | 23121 |
| eng-isl | newsdev2021.en-is | 0.50376 | 22.6 | 2004 | 43721 |
| eng-isl | newstest2021.en-is | 0.50516 | 21.5 | 1000 | 25233 |
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: 3405783
- port time: Wed Apr 13 17:14:46 EEST 2022
- port machine: LM0-400-22516.local