language:
- en
- tr
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-tr-en
results:
- task:
name: Translation tur-eng
type: translation
args: tur-eng
dataset:
name: flores101-devtest
type: flores_101
args: tur eng devtest
metrics:
- name: BLEU
type: bleu
value: 37.6
- task:
name: Translation tur-eng
type: translation
args: tur-eng
dataset:
name: newsdev2016
type: newsdev2016
args: tur-eng
metrics:
- name: BLEU
type: bleu
value: 32.1
- task:
name: Translation tur-eng
type: translation
args: tur-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: tur-eng
metrics:
- name: BLEU
type: bleu
value: 57.6
- task:
name: Translation tur-eng
type: translation
args: tur-eng
dataset:
name: newstest2016
type: wmt-2016-news
args: tur-eng
metrics:
- name: BLEU
type: bleu
value: 29.3
- task:
name: Translation tur-eng
type: translation
args: tur-eng
dataset:
name: newstest2017
type: wmt-2017-news
args: tur-eng
metrics:
- name: BLEU
type: bleu
value: 29.7
- task:
name: Translation tur-eng
type: translation
args: tur-eng
dataset:
name: newstest2018
type: wmt-2018-news
args: tur-eng
metrics:
- name: BLEU
type: bleu
value: 30.7
opus-mt-tc-big-tr-en
Neural machine translation model for translating from Turkish (tr) to English (en).
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): tur
- target language(s): eng
- 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 tur-eng README
Usage
A short example code:
from transformers import MarianMTModel, MarianTokenizer
src_text = [
"Allahsızlığı Yayma Kürsüsü başkanıydı.",
"Tom'a ne olduğunu öğrenin."
]
model_name = "pytorch-models/opus-mt-tc-big-tr-en"
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:
# He was the president of the Curse of Spreading Godlessness.
# Find out what happened to Tom.
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-tr-en")
print(pipe("Allahsızlığı Yayma Kürsüsü başkanıydı."))
# expected output: He was the president of the Curse of Spreading Godlessness.
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 |
|---|---|---|---|---|---|
| tur-eng | tatoeba-test-v2021-08-07 | 0.71895 | 57.6 | 13907 | 109231 |
| tur-eng | flores101-devtest | 0.64152 | 37.6 | 1012 | 24721 |
| tur-eng | newsdev2016 | 0.58658 | 32.1 | 1001 | 21988 |
| tur-eng | newstest2016 | 0.56960 | 29.3 | 3000 | 66175 |
| tur-eng | newstest2017 | 0.57455 | 29.7 | 3007 | 67703 |
| tur-eng | newstest2018 | 0.58488 | 30.7 | 3000 | 68725 |
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 20:02:48 EEST 2022
- port machine: LM0-400-22516.local