language:
- en
- fi
- multilingual
license: cc-by-4.0
tags:
- translation
- opus-mt-tc
model-index:
- name: opus-mt-tc-big-en-fi
results:
- task:
type: translation
name: Translation eng-fin
dataset:
name: flores101-devtest
type: flores_101
args: eng fin devtest
metrics:
- type: bleu
value: 27.6
name: BLEU
- task:
type: translation
name: Translation eng-fin
dataset:
name: newsdev2015
type: newsdev2015
args: eng-fin
metrics:
- type: bleu
value: 24.2
name: BLEU
- task:
type: translation
name: Translation eng-fin
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-fin
metrics:
- type: bleu
value: 39.3
name: BLEU
- task:
type: translation
name: Translation eng-fin
dataset:
name: newstest2015
type: wmt-2015-news
args: eng-fin
metrics:
- type: bleu
value: 26.4
name: BLEU
- task:
type: translation
name: Translation eng-fin
dataset:
name: newstest2016
type: wmt-2016-news
args: eng-fin
metrics:
- type: bleu
value: 28.8
name: BLEU
- task:
type: translation
name: Translation eng-fin
dataset:
name: newstest2017
type: wmt-2017-news
args: eng-fin
metrics:
- type: bleu
value: 31.3
name: BLEU
- task:
type: translation
name: Translation eng-fin
dataset:
name: newstest2019
type: wmt-2019-news
args: eng-fin
metrics:
- type: bleu
value: 26.4
name: BLEU
opus-mt-tc-big-en-fi
Neural machine translation model for translating from English (en) to Finnish (fi).
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: 2022-03-09
- source language(s): eng
- target language(s): fin
- valid target language labels: >>fin<<
- model: transformer (big)
- data: opusTCv20210807+bt (source)
- tokenization: SentencePiece (spm32k,spm32k)
- original model: opusTCv20210807+bt_transformer-big_2022-03-09.zip
- more information released models: OPUS-MT eng-fin 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. >>fin<<
Usage
A short example code:
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Russia is big.",
"Touch wood!"
]
model_name = "pytorch-models/opus-mt-tc-big-en-fi"
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:
# Ven�j� on suuri.
# Kosketa puuta!
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-fi")
print(pipe("Russia is big."))
# expected output: Ven�j� on suuri.
Benchmarks
- test set translations: opusTCv20210807+bt_transformer-big_2022-03-09.test.txt
- test set scores: opusTCv20210807+bt_transformer-big_2022-03-09.eval.txt
- benchmark results: benchmark_results.txt
- benchmark output: benchmark_translations.zip
| langpair | testset | chr-F | BLEU | #sent | #words |
|---|---|---|---|---|---|
| eng-fin | tatoeba-test-v2021-08-07 | 0.64352 | 39.3 | 10690 | 65122 |
| eng-fin | flores101-devtest | 0.61334 | 27.6 | 1012 | 18781 |
| eng-fin | newsdev2015 | 0.58367 | 24.2 | 1500 | 23091 |
| eng-fin | newstest2015 | 0.60080 | 26.4 | 1370 | 19735 |
| eng-fin | newstest2016 | 0.61636 | 28.8 | 3000 | 47678 |
| eng-fin | newstest2017 | 0.64381 | 31.3 | 3002 | 45269 |
| eng-fin | newstest2018 | 0.55626 | 19.7 | 3000 | 44836 |
| eng-fin | newstest2019 | 0.58420 | 26.4 | 1997 | 38369 |
| eng-fin | newstestB2016 | 0.57554 | 23.3 | 3000 | 45766 |
| eng-fin | newstestB2017 | 0.60212 | 26.8 | 3002 | 45506 |
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: f084bad
- port time: Tue Mar 22 14:42:32 EET 2022
- port machine: LM0-400-22516.local