language:
- ca
- en
- es
- oc
- multilingual
license: cc-by-4.0
tags:
- translation
- opus-mt-tc
model-index:
- name: opus-mt-tc-big-en-cat_oci_spa
results:
- task:
type: translation
name: Translation eng-cat
dataset:
name: flores101-devtest
type: flores_101
args: eng cat devtest
metrics:
- type: bleu
value: 41.5
name: BLEU
- type: bleu
value: 25.4
name: BLEU
- type: bleu
value: 28.1
name: BLEU
- task:
type: translation
name: Translation eng-spa
dataset:
name: news-test2008
type: news-test2008
args: eng-spa
metrics:
- type: bleu
value: 30
name: BLEU
- task:
type: translation
name: Translation eng-cat
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-cat
metrics:
- type: bleu
value: 47.8
name: BLEU
- type: bleu
value: 57
name: BLEU
- task:
type: translation
name: Translation eng-spa
dataset:
name: tico19-test
type: tico19-test
args: eng-spa
metrics:
- type: bleu
value: 52.5
name: BLEU
- task:
type: translation
name: Translation eng-spa
dataset:
name: newstest2009
type: wmt-2009-news
args: eng-spa
metrics:
- type: bleu
value: 30.5
name: BLEU
- task:
type: translation
name: Translation eng-spa
dataset:
name: newstest2010
type: wmt-2010-news
args: eng-spa
metrics:
- type: bleu
value: 37.4
name: BLEU
- task:
type: translation
name: Translation eng-spa
dataset:
name: newstest2011
type: wmt-2011-news
args: eng-spa
metrics:
- type: bleu
value: 39.1
name: BLEU
- task:
type: translation
name: Translation eng-spa
dataset:
name: newstest2012
type: wmt-2012-news
args: eng-spa
metrics:
- type: bleu
value: 39.6
name: BLEU
- task:
type: translation
name: Translation eng-spa
dataset:
name: newstest2013
type: wmt-2013-news
args: eng-spa
metrics:
- type: bleu
value: 35.8
name: BLEU
opus-mt-tc-big-en-cat_oci_spa
Neural machine translation model for translating from English (en) to Catalan, Occitan and Spanish (cat+oci+spa).
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: 2022-03-13
- source language(s): eng
- target language(s): cat spa
- valid target language labels: >>cat<< >>spa<<
- model: transformer-big
- data: opusTCv20210807+bt (source)
- tokenization: SentencePiece (spm32k,spm32k)
- original model: opusTCv20210807+bt_transformer-big_2022-03-13.zip
- more information released models: OPUS-MT eng-cat+oci+spa 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. >>cat<<
Usage
A short example code:
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>spa<< Why do you want Tom to go there with me?",
">>spa<< She forced him to eat spinach."
]
model_name = "pytorch-models/opus-mt-tc-big-en-cat_oci_spa"
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:
# �Por qu� quieres que Tom vaya conmigo?
# Ella lo oblig� a comer espinacas.
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-cat_oci_spa")
print(pipe(">>spa<< Why do you want Tom to go there with me?"))
# expected output: �Por qu� quieres que Tom vaya conmigo?
Benchmarks
- test set translations: opusTCv20210807+bt_transformer-big_2022-03-13.test.txt
- test set scores: opusTCv20210807+bt_transformer-big_2022-03-13.eval.txt
- benchmark results: benchmark_results.txt
- benchmark output: benchmark_translations.zip
| langpair | testset | chr-F | BLEU | #sent | #words |
|---|---|---|---|---|---|
| eng-cat | tatoeba-test-v2021-08-07 | 0.66414 | 47.8 | 1631 | 12344 |
| eng-spa | tatoeba-test-v2021-08-07 | 0.73725 | 57.0 | 16583 | 134710 |
| eng-cat | flores101-devtest | 0.66071 | 41.5 | 1012 | 27304 |
| eng-oci | flores101-devtest | 0.56192 | 25.4 | 1012 | 27305 |
| eng-spa | flores101-devtest | 0.56288 | 28.1 | 1012 | 29199 |
| eng-spa | newssyscomb2009 | 0.58431 | 31.4 | 502 | 12503 |
| eng-spa | news-test2008 | 0.56622 | 30.0 | 2051 | 52586 |
| eng-spa | newstest2009 | 0.57988 | 30.5 | 2525 | 68111 |
| eng-spa | newstest2010 | 0.62343 | 37.4 | 2489 | 65480 |
| eng-spa | newstest2011 | 0.62424 | 39.1 | 3003 | 79476 |
| eng-spa | newstest2012 | 0.63006 | 39.6 | 3003 | 79006 |
| eng-spa | newstest2013 | 0.60291 | 35.8 | 3000 | 70528 |
| eng-spa | tico19-test | 0.73224 | 52.5 | 2100 | 66563 |
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 16:40:45 EEST 2022
- port machine: LM0-400-22516.local