metadata
language:
- ca
- da
- es
- fr
- gl
- is
- it
- nb
- pt
- ro
- sv
tags:
- translation
- opus-mt-tc
license: cc-by-4.0
model-index:
- name: opus-mt-tc-big-gmq-itc
results:
- task:
name: Translation dan-cat
type: translation
args: dan-cat
dataset:
name: flores101-devtest
type: flores_101
args: dan cat devtest
metrics:
- name: BLEU
type: bleu
value: 33.4
- name: chr-F
type: chrf
value: 0.59224
- task:
name: Translation dan-fra
type: translation
args: dan-fra
dataset:
name: flores101-devtest
type: flores_101
args: dan fra devtest
metrics:
- name: BLEU
type: bleu
value: 38.3
- name: chr-F
type: chrf
value: 0.63387
- task:
name: Translation dan-glg
type: translation
args: dan-glg
dataset:
name: flores101-devtest
type: flores_101
args: dan glg devtest
metrics:
- name: BLEU
type: bleu
value: 26.4
- name: chr-F
type: chrf
value: 0.54446
- task:
name: Translation dan-ita
type: translation
args: dan-ita
dataset:
name: flores101-devtest
type: flores_101
args: dan ita devtest
metrics:
- name: BLEU
type: bleu
value: 25.7
- name: chr-F
type: chrf
value: 0.55237
- task:
name: Translation dan-por
type: translation
args: dan-por
dataset:
name: flores101-devtest
type: flores_101
args: dan por devtest
metrics:
- name: BLEU
type: bleu
value: 36.9
- name: chr-F
type: chrf
value: 0.62233
- task:
name: Translation dan-ron
type: translation
args: dan-ron
dataset:
name: flores101-devtest
type: flores_101
args: dan ron devtest
metrics:
- name: BLEU
type: bleu
value: 31.8
- name: chr-F
type: chrf
value: 0.58235
- task:
name: Translation dan-spa
type: translation
args: dan-spa
dataset:
name: flores101-devtest
type: flores_101
args: dan spa devtest
metrics:
- name: BLEU
type: bleu
value: 24.3
- name: chr-F
type: chrf
value: 0.52453
- task:
name: Translation isl-cat
type: translation
args: isl-cat
dataset:
name: flores101-devtest
type: flores_101
args: isl cat devtest
metrics:
- name: BLEU
type: bleu
value: 22.7
- name: chr-F
type: chrf
value: 0.4893
- task:
name: Translation isl-fra
type: translation
args: isl-fra
dataset:
name: flores101-devtest
type: flores_101
args: isl fra devtest
metrics:
- name: BLEU
type: bleu
value: 26.2
- name: chr-F
type: chrf
value: 0.52704
- task:
name: Translation isl-glg
type: translation
args: isl-glg
dataset:
name: flores101-devtest
type: flores_101
args: isl glg devtest
metrics:
- name: BLEU
type: bleu
value: 18
- name: chr-F
type: chrf
value: 0.45387
- task:
name: Translation isl-ita
type: translation
args: isl-ita
dataset:
name: flores101-devtest
type: flores_101
args: isl ita devtest
metrics:
- name: BLEU
type: bleu
value: 18.6
- name: chr-F
type: chrf
value: 0.47303
- task:
name: Translation isl-por
type: translation
args: isl-por
dataset:
name: flores101-devtest
type: flores_101
args: isl por devtest
metrics:
- name: BLEU
type: bleu
value: 24.9
- name: chr-F
type: chrf
value: 0.51381
- task:
name: Translation isl-ron
type: translation
args: isl-ron
dataset:
name: flores101-devtest
type: flores_101
args: isl ron devtest
metrics:
- name: BLEU
type: bleu
value: 21.6
- name: chr-F
type: chrf
value: 0.48224
- task:
name: Translation isl-spa
type: translation
args: isl-spa
dataset:
name: flores101-devtest
type: flores_101
args: isl spa devtest
metrics:
- name: BLEU
type: bleu
value: 18.1
- name: chr-F
type: chrf
value: 0.45786
- task:
name: Translation nob-cat
type: translation
args: nob-cat
dataset:
name: flores101-devtest
type: flores_101
args: nob cat devtest
metrics:
- name: BLEU
type: bleu
value: 28.9
- name: chr-F
type: chrf
value: 0.55984
- task:
name: Translation nob-fra
type: translation
args: nob-fra
dataset:
name: flores101-devtest
type: flores_101
args: nob fra devtest
metrics:
- name: BLEU
type: bleu
value: 33.8
- name: chr-F
type: chrf
value: 0.60102
- task:
name: Translation nob-glg
type: translation
args: nob-glg
dataset:
name: flores101-devtest
type: flores_101
args: nob glg devtest
metrics:
- name: BLEU
type: bleu
value: 23.4
- name: chr-F
type: chrf
value: 0.52145
- task:
name: Translation nob-ita
type: translation
args: nob-ita
dataset:
name: flores101-devtest
type: flores_101
args: nob ita devtest
metrics:
- name: BLEU
type: bleu
value: 22.2
- name: chr-F
type: chrf
value: 0.52619
- task:
name: Translation nob-por
type: translation
args: nob-por
dataset:
name: flores101-devtest
type: flores_101
args: nob por devtest
metrics:
- name: BLEU
type: bleu
value: 32.2
- name: chr-F
type: chrf
value: 0.58836
- task:
name: Translation nob-ron
type: translation
args: nob-ron
dataset:
name: flores101-devtest
type: flores_101
args: nob ron devtest
metrics:
- name: BLEU
type: bleu
value: 27.6
- name: chr-F
type: chrf
value: 0.54845
- task:
name: Translation nob-spa
type: translation
args: nob-spa
dataset:
name: flores101-devtest
type: flores_101
args: nob spa devtest
metrics:
- name: BLEU
type: bleu
value: 21.8
- name: chr-F
type: chrf
value: 0.50661
- task:
name: Translation swe-cat
type: translation
args: swe-cat
dataset:
name: flores101-devtest
type: flores_101
args: swe cat devtest
metrics:
- name: BLEU
type: bleu
value: 32.4
- name: chr-F
type: chrf
value: 0.58542
- task:
name: Translation swe-fra
type: translation
args: swe-fra
dataset:
name: flores101-devtest
type: flores_101
args: swe fra devtest
metrics:
- name: BLEU
type: bleu
value: 39.3
- name: chr-F
type: chrf
value: 0.63688
- task:
name: Translation swe-glg
type: translation
args: swe-glg
dataset:
name: flores101-devtest
type: flores_101
args: swe glg devtest
metrics:
- name: BLEU
type: bleu
value: 26
- name: chr-F
type: chrf
value: 0.53989
- task:
name: Translation swe-ita
type: translation
args: swe-ita
dataset:
name: flores101-devtest
type: flores_101
args: swe ita devtest
metrics:
- name: BLEU
type: bleu
value: 25.9
- name: chr-F
type: chrf
value: 0.55232
- task:
name: Translation swe-por
type: translation
args: swe-por
dataset:
name: flores101-devtest
type: flores_101
args: swe por devtest
metrics:
- name: BLEU
type: bleu
value: 36.5
- name: chr-F
type: chrf
value: 0.61882
- task:
name: Translation swe-ron
type: translation
args: swe-ron
dataset:
name: flores101-devtest
type: flores_101
args: swe ron devtest
metrics:
- name: BLEU
type: bleu
value: 31
- name: chr-F
type: chrf
value: 0.57419
- task:
name: Translation swe-spa
type: translation
args: swe-spa
dataset:
name: flores101-devtest
type: flores_101
args: swe spa devtest
metrics:
- name: BLEU
type: bleu
value: 23.8
- name: chr-F
type: chrf
value: 0.52175
- task:
name: Translation dan-fra
type: translation
args: dan-fra
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: dan-fra
metrics:
- name: BLEU
type: bleu
value: 63.8
- name: chr-F
type: chrf
value: 0.76671
- task:
name: Translation dan-ita
type: translation
args: dan-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: dan-ita
metrics:
- name: BLEU
type: bleu
value: 56.2
- name: chr-F
type: chrf
value: 0.74658
- task:
name: Translation dan-por
type: translation
args: dan-por
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: dan-por
metrics:
- name: BLEU
type: bleu
value: 57.8
- name: chr-F
type: chrf
value: 0.74944
- task:
name: Translation dan-spa
type: translation
args: dan-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: dan-spa
metrics:
- name: BLEU
type: bleu
value: 54.8
- name: chr-F
type: chrf
value: 0.72328
- task:
name: Translation isl-ita
type: translation
args: isl-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: isl-ita
metrics:
- name: BLEU
type: bleu
value: 51
- name: chr-F
type: chrf
value: 0.69354
- task:
name: Translation isl-spa
type: translation
args: isl-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: isl-spa
metrics:
- name: BLEU
type: bleu
value: 49.2
- name: chr-F
type: chrf
value: 0.66008
- task:
name: Translation nob-fra
type: translation
args: nob-fra
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: nob-fra
metrics:
- name: BLEU
type: bleu
value: 54.4
- name: chr-F
type: chrf
value: 0.70854
- task:
name: Translation nob-spa
type: translation
args: nob-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: nob-spa
metrics:
- name: BLEU
type: bleu
value: 55.9
- name: chr-F
type: chrf
value: 0.73672
- task:
name: Translation swe-fra
type: translation
args: swe-fra
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: swe-fra
metrics:
- name: BLEU
type: bleu
value: 59.2
- name: chr-F
type: chrf
value: 0.73014
- task:
name: Translation swe-ita
type: translation
args: swe-ita
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: swe-ita
metrics:
- name: BLEU
type: bleu
value: 56.6
- name: chr-F
type: chrf
value: 0.73211
- task:
name: Translation swe-por
type: translation
args: swe-por
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: swe-por
metrics:
- name: BLEU
type: bleu
value: 48.7
- name: chr-F
type: chrf
value: 0.68146
- task:
name: Translation swe-spa
type: translation
args: swe-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: swe-spa
metrics:
- name: BLEU
type: bleu
value: 55.3
- name: chr-F
type: chrf
value: 0.71373
opus-mt-tc-big-gmq-itc
Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- How to Get Started With the Model
- Training
- Evaluation
- Citation Information
- Acknowledgements
Model Details
Neural machine translation model for translating from North Germanic languages (gmq) to Italic languages (itc).
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. Model Description:
- Developed by: Language Technology Research Group at the University of Helsinki
- Model Type: Translation (transformer-big)
- Release: 2022-08-09
- License: CC-BY-4.0
- Language(s):
- Source Language(s): dan isl nno nob nor swe
- Target Language(s): cat fra glg ita lat por ron spa
- Language Pair(s): dan-cat dan-fra dan-glg dan-ita dan-por dan-ron dan-spa isl-cat isl-fra isl-ita isl-por isl-ron isl-spa nob-cat nob-fra nob-glg nob-ita nob-por nob-ron nob-spa swe-cat swe-fra swe-glg swe-ita swe-por swe-ron swe-spa
- Valid Target Language Labels: >>acf<< >>aoa<< >>arg<< >>ast<< >>cat<< >>cbk<< >>ccd<< >>cks<< >>cos<< >>cri<< >>crs<< >>dlm<< >>drc<< >>egl<< >>ext<< >>fab<< >>fax<< >>fra<< >>frc<< >>frm<< >>fro<< >>frp<< >>fur<< >>gcf<< >>gcr<< >>glg<< >>hat<< >>idb<< >>ist<< >>ita<< >>itk<< >>kea<< >>kmv<< >>lad<< >>lad_Latn<< >>lat<< >>lat_Latn<< >>lij<< >>lld<< >>lmo<< >>lou<< >>mcm<< >>mfe<< >>mol<< >>mwl<< >>mxi<< >>mzs<< >>nap<< >>nrf<< >>oci<< >>osc<< >>osp<< >>osp_Latn<< >>pap<< >>pcd<< >>pln<< >>pms<< >>pob<< >>por<< >>pov<< >>pre<< >>pro<< >>qbb<< >>qhr<< >>rcf<< >>rgn<< >>roh<< >>ron<< >>ruo<< >>rup<< >>ruq<< >>scf<< >>scn<< >>sdc<< >>sdn<< >>spa<< >>spq<< >>spx<< >>src<< >>srd<< >>sro<< >>tmg<< >>tvy<< >>vec<< >>vkp<< >>wln<< >>xfa<< >>xum<<
- Original Model: opusTCv20210807_transformer-big_2022-08-09.zip
- Resources for more information:
- OPUS-MT-train GitHub Repo
- More information about released models for this language pair: OPUS-MT gmq-itc README
- More information about MarianNMT models in the transformers library
- [Tatoeba Translation Challenge](https://github.com/Helsinki-NLP/Tatoeba-Challenge/
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. >>fra<<
Uses
This model can be used for translation and text-to-text generation.
Risks, Limitations and Biases
CONTENT WARNING: Readers should be aware that the model is trained on various public data sets that may contain content that is disturbing, offensive, and can propagate historical and current stereotypes.
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).
How to Get Started With the Model
A short example code:
from transformers import MarianMTModel, MarianTokenizer
src_text = [
">>spa<< Jag är inte religiös.",
">>por<< Livet er for kort til å lære seg tysk."
]
model_name = "pytorch-models/opus-mt-tc-big-gmq-itc"
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:
# No soy religioso.
# A vida é muito curta para aprender alemão.
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-gmq-itc")
print(pipe(">>spa<< Jag är inte religiös."))
# expected output: No soy religioso.
Training
- Data: opusTCv20210807 (source)
- Pre-processing: SentencePiece (spm32k,spm32k)
- Model Type: transformer-big
- Original MarianNMT Model: opusTCv20210807_transformer-big_2022-08-09.zip
- Training Scripts: GitHub Repo
Evaluation
- test set translations: opusTCv20210807_transformer-big_2022-08-09.test.txt
- test set scores: opusTCv20210807_transformer-big_2022-08-09.eval.txt
- benchmark results: benchmark_results.txt
- benchmark output: benchmark_translations.zip
| langpair | testset | chr-F | BLEU | #sent | #words |
|---|---|---|---|---|---|
| dan-fra | tatoeba-test-v2021-08-07 | 0.76671 | 63.8 | 1731 | 11882 |
| dan-ita | tatoeba-test-v2021-08-07 | 0.74658 | 56.2 | 284 | 2226 |
| dan-por | tatoeba-test-v2021-08-07 | 0.74944 | 57.8 | 873 | 5360 |
| dan-spa | tatoeba-test-v2021-08-07 | 0.72328 | 54.8 | 5000 | 35528 |
| isl-ita | tatoeba-test-v2021-08-07 | 0.69354 | 51.0 | 236 | 1450 |
| isl-spa | tatoeba-test-v2021-08-07 | 0.66008 | 49.2 | 238 | 1229 |
| nob-fra | tatoeba-test-v2021-08-07 | 0.70854 | 54.4 | 323 | 2269 |
| nob-spa | tatoeba-test-v2021-08-07 | 0.73672 | 55.9 | 885 | 6866 |
| swe-fra | tatoeba-test-v2021-08-07 | 0.73014 | 59.2 | 1407 | 9580 |
| swe-ita | tatoeba-test-v2021-08-07 | 0.73211 | 56.6 | 715 | 4711 |
| swe-por | tatoeba-test-v2021-08-07 | 0.68146 | 48.7 | 320 | 2032 |
| swe-spa | tatoeba-test-v2021-08-07 | 0.71373 | 55.3 | 1351 | 8235 |
| dan-cat | flores101-devtest | 0.59224 | 33.4 | 1012 | 27304 |
| dan-fra | flores101-devtest | 0.63387 | 38.3 | 1012 | 28343 |
| dan-glg | flores101-devtest | 0.54446 | 26.4 | 1012 | 26582 |
| dan-ita | flores101-devtest | 0.55237 | 25.7 | 1012 | 27306 |
| dan-por | flores101-devtest | 0.62233 | 36.9 | 1012 | 26519 |
| dan-ron | flores101-devtest | 0.58235 | 31.8 | 1012 | 26799 |
| dan-spa | flores101-devtest | 0.52453 | 24.3 | 1012 | 29199 |
| isl-cat | flores101-devtest | 0.48930 | 22.7 | 1012 | 27304 |
| isl-fra | flores101-devtest | 0.52704 | 26.2 | 1012 | 28343 |
| isl-glg | flores101-devtest | 0.45387 | 18.0 | 1012 | 26582 |
| isl-ita | flores101-devtest | 0.47303 | 18.6 | 1012 | 27306 |
| isl-por | flores101-devtest | 0.51381 | 24.9 | 1012 | 26519 |
| isl-ron | flores101-devtest | 0.48224 | 21.6 | 1012 | 26799 |
| isl-spa | flores101-devtest | 0.45786 | 18.1 | 1012 | 29199 |
| nob-cat | flores101-devtest | 0.55984 | 28.9 | 1012 | 27304 |
| nob-fra | flores101-devtest | 0.60102 | 33.8 | 1012 | 28343 |
| nob-glg | flores101-devtest | 0.52145 | 23.4 | 1012 | 26582 |
| nob-ita | flores101-devtest | 0.52619 | 22.2 | 1012 | 27306 |
| nob-por | flores101-devtest | 0.58836 | 32.2 | 1012 | 26519 |
| nob-ron | flores101-devtest | 0.54845 | 27.6 | 1012 | 26799 |
| nob-spa | flores101-devtest | 0.50661 | 21.8 | 1012 | 29199 |
| swe-cat | flores101-devtest | 0.58542 | 32.4 | 1012 | 27304 |
| swe-fra | flores101-devtest | 0.63688 | 39.3 | 1012 | 28343 |
| swe-glg | flores101-devtest | 0.53989 | 26.0 | 1012 | 26582 |
| swe-ita | flores101-devtest | 0.55232 | 25.9 | 1012 | 27306 |
| swe-por | flores101-devtest | 0.61882 | 36.5 | 1012 | 26519 |
| swe-ron | flores101-devtest | 0.57419 | 31.0 | 1012 | 26799 |
| swe-spa | flores101-devtest | 0.52175 | 23.8 | 1012 | 29199 |
Citation Information
- 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",
}
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: 8b9f0b0
- port time: Sat Aug 13 00:00:00 EEST 2022
- port machine: LM0-400-22516.local