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Initial commit
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---
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: 0.48930
- 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: 18.0
- 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: 26.0
- 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: 31.0
- 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: 51.0
- 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](#model-details)
- [Uses](#uses)
- [Risks, Limitations and Biases](#risks-limitations-and-biases)
- [How to Get Started With the Model](#how-to-get-started-with-the-model)
- [Training](#training)
- [Evaluation](#evaluation)
- [Citation Information](#citation-information)
- [Acknowledgements](#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](https://github.com/Helsinki-NLP/Opus-MT), 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](https://marian-nmt.github.io/), 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](https://opus.nlpl.eu/) and training pipelines use the procedures of [OPUS-MT-train](https://github.com/Helsinki-NLP/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](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.zip)
- **Resources for more information:**
- [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
- More information about released models for this language pair: [OPUS-MT gmq-itc README](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gmq-itc/README.md)
- [More information about MarianNMT models in the transformers library](https://huggingface.co/docs/transformers/model_doc/marian)
- [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)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
## How to Get Started With the Model
A short example code:
```python
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:
```python
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](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:** transformer-big
- **Original MarianNMT Model**: [opusTCv20210807_transformer-big_2022-08-09.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
## Evaluation
* test set translations: [opusTCv20210807_transformer-big_2022-08-09.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.test.txt)
* test set scores: [opusTCv20210807_transformer-big_2022-08-09.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-itc/opusTCv20210807_transformer-big_2022-08-09.eval.txt)
* benchmark results: [benchmark_results.txt](benchmark_results.txt)
* benchmark output: [benchmark_translations.zip](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-MTBuilding open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation ChallengeRealistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (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](https://www.european-language-grid.eu/) as [pilot project 2866](https://live.european-language-grid.eu/catalogue/#/resource/projects/2866), by the [FoTran project](https://www.helsinki.fi/en/researchgroups/natural-language-understanding-with-cross-lingual-grounding), funded by the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 771113), and the [MeMAD project](https://memad.eu/), funded by the European Unions 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](https://www.csc.fi/), 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