transformers pytorch safetensors marian text2text-generation translation opus-mt-tc-bible chm de en es et fi fkv fr hu izh krl kv liv mdf mrj myv pt se sma smn udm vep vot license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
language:
- chm
- de
- en
- es
- et
- fi
- fkv
- fr
- hu
- izh
- krl
- kv
- liv
- mdf
- mrj
- myv
- pt
- se
- sma
- smn
- udm
- vep
- vot
library_name: transformers
license: apache-2.0
tags:
- translation
- opus-mt-tc-bible
model-index:
- name: opus-mt-tc-bible-big-deu_eng_fra_por_spa-fiu
  results:
  - task:
      type: translation
      name: Translation deu-est
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: deu-est
    metrics:
    - type: bleu
      value: 21.2
      name: BLEU
    - type: chrf
      value: 0.55333
      name: chr-F
    - type: bleu
      value: 18.3
      name: BLEU
    - type: chrf
      value: 0.5402
      name: chr-F
    - type: bleu
      value: 22.0
      name: BLEU
    - type: chrf
      value: 0.53579
      name: chr-F
    - type: bleu
      value: 26.1
      name: BLEU
    - type: chrf
      value: 0.59496
      name: chr-F
    - type: bleu
      value: 23.1
      name: BLEU
    - type: chrf
      value: 0.57811
      name: chr-F
    - type: bleu
      value: 26.7
      name: BLEU
    - type: chrf
      value: 0.5767
      name: chr-F
    - type: bleu
      value: 21.2
      name: BLEU
    - type: chrf
      value: 0.54442
      name: chr-F
    - type: bleu
      value: 18.5
      name: BLEU
    - type: chrf
      value: 0.53768
      name: chr-F
    - type: bleu
      value: 21.2
      name: BLEU
    - type: chrf
      value: 0.52691
      name: chr-F
    - type: bleu
      value: 15.6
      name: BLEU
    - type: chrf
      value: 0.48227
      name: chr-F
    - type: bleu
      value: 18.6
      name: BLEU
    - type: chrf
      value: 0.53772
      name: chr-F
    - type: bleu
      value: 21.8
      name: BLEU
    - type: chrf
      value: 0.53275
      name: chr-F
    - type: bleu
      value: 15.2
      name: BLEU
    - type: chrf
      value: 0.50142
      name: chr-F
    - type: bleu
      value: 13.7
      name: BLEU
    - type: chrf
      value: 0.50401
      name: chr-F
    - type: bleu
      value: 16.4
      name: BLEU
    - type: chrf
      value: 0.49444
      name: chr-F
  - task:
      type: translation
      name: Translation eng-fin
    dataset:
      name: flores101-devtest
      type: flores_101
      args: eng fin devtest
    metrics:
    - type: bleu
      value: 21.9
      name: BLEU
    - type: chrf
      value: 0.57265
      name: chr-F
    - type: bleu
      value: 21.2
      name: BLEU
    - type: chrf
      value: 0.52691
      name: chr-F
    - type: bleu
      value: 18.6
      name: BLEU
    - type: chrf
      value: 0.53772
      name: chr-F
    - type: bleu
      value: 21.8
      name: BLEU
    - type: chrf
      value: 0.53275
      name: chr-F
    - type: bleu
      value: 15.2
      name: BLEU
    - type: chrf
      value: 0.50142
      name: chr-F
    - type: bleu
      value: 13.7
      name: BLEU
    - type: chrf
      value: 0.50401
      name: chr-F
  - task:
      type: translation
      name: Translation eng-fin
    dataset:
      name: newstestALL2016
      type: newstestALL2016
      args: eng-fin
    metrics:
    - type: bleu
      value: 24.3
      name: BLEU
    - type: chrf
      value: 0.57934
      name: chr-F
  - task:
      type: translation
      name: Translation eng-fin
    dataset:
      name: newstestALL2017
      type: newstestALL2017
      args: eng-fin
    metrics:
    - type: bleu
      value: 26.5
      name: BLEU
    - type: chrf
      value: 0.60204
      name: chr-F
  - task:
      type: translation
      name: Translation deu-est
    dataset:
      name: ntrex128
      type: ntrex128
      args: deu-est
    metrics:
    - type: bleu
      value: 18.6
      name: BLEU
    - type: chrf
      value: 0.51761
      name: chr-F
    - type: bleu
      value: 15.5
      name: BLEU
    - type: chrf
      value: 0.50759
      name: chr-F
    - type: bleu
      value: 15.6
      name: BLEU
    - type: chrf
      value: 0.46171
      name: chr-F
    - type: bleu
      value: 24.4
      name: BLEU
    - type: chrf
      value: 0.57099
      name: chr-F
    - type: bleu
      value: 18.5
      name: BLEU
    - type: chrf
      value: 0.53413
      name: chr-F
    - type: bleu
      value: 16.6
      name: BLEU
    - type: chrf
      value: 0.47342
      name: chr-F
    - type: bleu
      value: 17.7
      name: BLEU
    - type: chrf
      value: 0.50712
      name: chr-F
    - type: bleu
      value: 14.2
      name: BLEU
    - type: chrf
      value: 0.49215
      name: chr-F
    - type: bleu
      value: 14.9
      name: BLEU
    - type: chrf
      value: 0.44873
      name: chr-F
    - type: bleu
      value: 15.1
      name: BLEU
    - type: chrf
      value: 0.48098
      name: chr-F
    - type: bleu
      value: 15.0
      name: BLEU
    - type: chrf
      value: 0.50875
      name: chr-F
    - type: bleu
      value: 15.5
      name: BLEU
    - type: chrf
      value: 0.45817
      name: chr-F
    - type: bleu
      value: 18.5
      name: BLEU
    - type: chrf
      value: 0.52158
      name: chr-F
    - type: bleu
      value: 15.2
      name: BLEU
    - type: chrf
      value: 0.50947
      name: chr-F
    - type: bleu
      value: 16.1
      name: BLEU
    - type: chrf
      value: 0.46051
      name: chr-F
  - task:
      type: translation
      name: Translation deu-est
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: deu-est
    metrics:
    - type: bleu
      value: 57.8
      name: BLEU
    - type: chrf
      value: 0.76586
      name: chr-F
    - type: bleu
      value: 40.7
      name: BLEU
    - type: chrf
      value: 0.64286
      name: chr-F
    - type: bleu
      value: 31.2
      name: BLEU
    - type: chrf
      value: 0.57007
      name: chr-F
    - type: bleu
      value: 50.6
      name: BLEU
    - type: chrf
      value: 0.69134
      name: chr-F
    - type: bleu
      value: 37.6
      name: BLEU
    - type: chrf
      value: 0.62482
      name: chr-F
    - type: bleu
      value: 35.9
      name: BLEU
    - type: chrf
      value: 0.5975
      name: chr-F
    - type: bleu
      value: 45.0
      name: BLEU
    - type: chrf
      value: 0.65723
      name: chr-F
    - type: bleu
      value: 40.6
      name: BLEU
    - type: chrf
      value: 0.63096
      name: chr-F
    - type: bleu
      value: 32.8
      name: BLEU
    - type: chrf
      value: 0.58505
      name: chr-F
    - type: bleu
      value: 58.1
      name: BLEU
    - type: chrf
      value: 0.76811
      name: chr-F
    - type: bleu
      value: 42.5
      name: BLEU
    - type: chrf
      value: 0.6493
      name: chr-F
    - type: bleu
      value: 43.4
      name: BLEU
    - type: chrf
      value: 0.6622
      name: chr-F
    - type: bleu
      value: 42.0
      name: BLEU
    - type: chrf
      value: 0.63596
      name: chr-F
  - task:
      type: translation
      name: Translation deu-hun
    dataset:
      name: newstest2008
      type: wmt-2008-news
      args: deu-hun
    metrics:
    - type: bleu
      value: 17.2
      name: BLEU
    - type: chrf
      value: 0.48855
      name: chr-F
    - type: bleu
      value: 15.9
      name: BLEU
    - type: chrf
      value: 0.47636
      name: chr-F
    - type: bleu
      value: 17.7
      name: BLEU
    - type: chrf
      value: 0.48598
      name: chr-F
    - type: bleu
      value: 17.1
      name: BLEU
    - type: chrf
      value: 0.47888
      name: chr-F
  - task:
      type: translation
      name: Translation deu-hun
    dataset:
      name: newstest2009
      type: wmt-2009-news
      args: deu-hun
    metrics:
    - type: bleu
      value: 18.1
      name: BLEU
    - type: chrf
      value: 0.48692
      name: chr-F
    - type: bleu
      value: 18.4
      name: BLEU
    - type: chrf
      value: 0.49507
      name: chr-F
    - type: bleu
      value: 18.6
      name: BLEU
    - type: chrf
      value: 0.48961
      name: chr-F
    - type: bleu
      value: 18.1
      name: BLEU
    - type: chrf
      value: 0.48496
      name: chr-F
  - task:
      type: translation
      name: Translation eng-fin
    dataset:
      name: newstest2015
      type: wmt-2015-news
      args: eng-fin
    metrics:
    - type: bleu
      value: 22.8
      name: BLEU
    - type: chrf
      value: 0.56896
      name: chr-F
  - task:
      type: translation
      name: Translation eng-fin
    dataset:
      name: newstest2016
      type: wmt-2016-news
      args: eng-fin
    metrics:
    - type: bleu
      value: 24.3
      name: BLEU
    - type: chrf
      value: 0.57934
      name: chr-F
  - task:
      type: translation
      name: Translation eng-fin
    dataset:
      name: newstest2017
      type: wmt-2017-news
      args: eng-fin
    metrics:
    - type: bleu
      value: 26.5
      name: BLEU
    - type: chrf
      value: 0.60204
      name: chr-F
  - task:
      type: translation
      name: Translation eng-est
    dataset:
      name: newstest2018
      type: wmt-2018-news
      args: eng-est
    metrics:
    - type: bleu
      value: 23.8
      name: BLEU
    - type: chrf
      value: 0.56276
      name: chr-F
    - type: bleu
      value: 17.4
      name: BLEU
    - type: chrf
      value: 0.52953
      name: chr-F
  - task:
      type: translation
      name: Translation eng-fin
    dataset:
      name: newstest2019
      type: wmt-2019-news
      args: eng-fin
    metrics:
    - type: bleu
      value: 24.2
      name: BLEU
    - type: chrf
      value: 0.55882
      name: chr-F
---
# opus-mt-tc-bible-big-deu_eng_fra_por_spa-fiu

## 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 unknown (deu+eng+fra+por+spa) to Finno-Ugrian languages (fiu).

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**: 2024-05-30
- **License:** Apache-2.0
- **Language(s):**  
  - Source Language(s): deu eng fra por spa
  - Target Language(s): chm est fin fkv hun izh koi kom kpv krl liv mdf mrj myv sma sme smn udm vep vot vro
  - Valid Target Language Labels: >>chm<< >>est<< >>fin<< >>fit<< >>fkv<< >>fkv_Latn<< >>hun<< >>izh<< >>kca<< >>koi<< >>kom<< >>kpv<< >>krl<< >>liv<< >>liv_Latn<< >>mdf<< >>mns<< >>mrj<< >>myv<< >>olo<< >>sia<< >>sjd<< >>sje<< >>sjk<< >>sjt<< >>sju<< >>sma<< >>sme<< >>smj<< >>smn<< >>sms<< >>udm<< >>vep<< >>vot<< >>vot_Latn<< >>vro<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-fiu/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip)
- **Resources for more information:**
  -  [OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/deu%2Beng%2Bfra%2Bpor%2Bspa-fiu/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30)
  - [OPUS-MT-train GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)
  - [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/)
  - [HPLT bilingual data v1 (as part of the Tatoeba Translation Challenge dataset)](https://hplt-project.org/datasets/v1)
  - [A massively parallel Bible corpus](https://aclanthology.org/L14-1215/)

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. `>>chm<<`

## 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 = [
    ">>chm<< Replace this with text in an accepted source language.",
    ">>vro<< This is the second sentence."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-fiu"
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) )
```

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-bible-big-deu_eng_fra_por_spa-fiu")
print(pipe(">>chm<< Replace this with text in an accepted source language."))
```

## Training

- **Data**: opusTCv20230926max50+bt+jhubc ([source](https://github.com/Helsinki-NLP/Tatoeba-Challenge))
- **Pre-processing**: SentencePiece (spm32k,spm32k)
- **Model Type:**  transformer-big
- **Original MarianNMT Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-fiu/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip)
- **Training Scripts**: [GitHub Repo](https://github.com/Helsinki-NLP/OPUS-MT-train)

## Evaluation

* [Model scores at the OPUS-MT dashboard](https://opus.nlpl.eu/dashboard/index.php?pkg=opusmt&test=all&scoreslang=all&chart=standard&model=Tatoeba-MT-models/deu%2Beng%2Bfra%2Bpor%2Bspa-fiu/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-30)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-fiu/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt)
* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-fiu/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.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 |
|----------|---------|-------|-------|-------|--------|
| deu-est | tatoeba-test-v2021-08-07 | 0.76586 | 57.8 | 244 | 1413 |
| deu-fin | tatoeba-test-v2021-08-07 | 0.64286 | 40.7 | 2647 | 15024 |
| deu-hun | tatoeba-test-v2021-08-07 | 0.57007 | 31.2 | 15342 | 105152 |
| eng-est | tatoeba-test-v2021-08-07 | 0.69134 | 50.6 | 1359 | 7992 |
| eng-fin | tatoeba-test-v2021-08-07 | 0.62482 | 37.6 | 10690 | 65122 |
| eng-hun | tatoeba-test-v2021-08-07 | 0.59750 | 35.9 | 13037 | 79562 |
| fra-fin | tatoeba-test-v2021-08-07 | 0.65723 | 45.0 | 1920 | 9730 |
| fra-hun | tatoeba-test-v2021-08-07 | 0.63096 | 40.6 | 2494 | 13753 |
| por-fin | tatoeba-test-v2021-08-07 | 0.76811 | 58.1 | 477 | 2379 |
| por-hun | tatoeba-test-v2021-08-07 | 0.64930 | 42.5 | 2500 | 14063 |
| spa-fin | tatoeba-test-v2021-08-07 | 0.66220 | 43.4 | 2513 | 14131 |
| spa-hun | tatoeba-test-v2021-08-07 | 0.63596 | 42.0 | 2500 | 14599 |
| eng-fin | flores101-devtest | 0.57265 | 21.9 | 1012 | 18781 |
| fra-hun | flores101-devtest | 0.52691 | 21.2 | 1012 | 22183 |
| por-fin | flores101-devtest | 0.53772 | 18.6 | 1012 | 18781 |
| por-hun | flores101-devtest | 0.53275 | 21.8 | 1012 | 22183 |
| spa-est | flores101-devtest | 0.50142 | 15.2 | 1012 | 19788 |
| spa-fin | flores101-devtest | 0.50401 | 13.7 | 1012 | 18781 |
| deu-est | flores200-devtest | 0.55333 | 21.2 | 1012 | 19788 |
| deu-fin | flores200-devtest | 0.54020 | 18.3 | 1012 | 18781 |
| deu-hun | flores200-devtest | 0.53579 | 22.0 | 1012 | 22183 |
| eng-est | flores200-devtest | 0.59496 | 26.1 | 1012 | 19788 |
| eng-fin | flores200-devtest | 0.57811 | 23.1 | 1012 | 18781 |
| eng-hun | flores200-devtest | 0.57670 | 26.7 | 1012 | 22183 |
| fra-est | flores200-devtest | 0.54442 | 21.2 | 1012 | 19788 |
| fra-fin | flores200-devtest | 0.53768 | 18.5 | 1012 | 18781 |
| fra-hun | flores200-devtest | 0.52691 | 21.2 | 1012 | 22183 |
| por-est | flores200-devtest | 0.48227 | 15.6 | 1012 | 19788 |
| por-fin | flores200-devtest | 0.53772 | 18.6 | 1012 | 18781 |
| por-hun | flores200-devtest | 0.53275 | 21.8 | 1012 | 22183 |
| spa-est | flores200-devtest | 0.50142 | 15.2 | 1012 | 19788 |
| spa-fin | flores200-devtest | 0.50401 | 13.7 | 1012 | 18781 |
| spa-hun | flores200-devtest | 0.49444 | 16.4 | 1012 | 22183 |
| deu-hun | newssyscomb2009 | 0.49607 | 18.1 | 502 | 9733 |
| eng-hun | newssyscomb2009 | 0.50580 | 18.3 | 502 | 9733 |
| fra-hun | newssyscomb2009 | 0.49415 | 17.8 | 502 | 9733 |
| spa-hun | newssyscomb2009 | 0.48559 | 16.9 | 502 | 9733 |
| deu-hun | newstest2008 | 0.48855 | 17.2 | 2051 | 41875 |
| eng-hun | newstest2008 | 0.47636 | 15.9 | 2051 | 41875 |
| fra-hun | newstest2008 | 0.48598 | 17.7 | 2051 | 41875 |
| spa-hun | newstest2008 | 0.47888 | 17.1 | 2051 | 41875 |
| deu-hun | newstest2009 | 0.48692 | 18.1 | 2525 | 54965 |
| eng-hun | newstest2009 | 0.49507 | 18.4 | 2525 | 54965 |
| fra-hun | newstest2009 | 0.48961 | 18.6 | 2525 | 54965 |
| spa-hun | newstest2009 | 0.48496 | 18.1 | 2525 | 54965 |
| eng-fin | newstest2015 | 0.56896 | 22.8 | 1370 | 19735 |
| eng-fin | newstest2016 | 0.57934 | 24.3 | 3000 | 47678 |
| eng-fin | newstest2017 | 0.60204 | 26.5 | 3002 | 45269 |
| eng-est | newstest2018 | 0.56276 | 23.8 | 2000 | 36269 |
| eng-fin | newstest2018 | 0.52953 | 17.4 | 3000 | 44836 |
| eng-fin | newstest2019 | 0.55882 | 24.2 | 1997 | 38369 |
| eng-fin | newstestALL2016 | 0.57934 | 24.3 | 3000 | 47678 |
| eng-fin | newstestALL2017 | 0.60204 | 26.5 | 3002 | 45269 |
| eng-fin | newstestB2016 | 0.54388 | 19.9 | 3000 | 45766 |
| eng-fin | newstestB2017 | 0.56369 | 22.6 | 3002 | 45506 |
| deu-est | ntrex128 | 0.51761 | 18.6 | 1997 | 38420 |
| deu-fin | ntrex128 | 0.50759 | 15.5 | 1997 | 35701 |
| deu-hun | ntrex128 | 0.46171 | 15.6 | 1997 | 44462 |
| eng-est | ntrex128 | 0.57099 | 24.4 | 1997 | 38420 |
| eng-fin | ntrex128 | 0.53413 | 18.5 | 1997 | 35701 |
| eng-hun | ntrex128 | 0.47342 | 16.6 | 1997 | 44462 |
| fra-est | ntrex128 | 0.50712 | 17.7 | 1997 | 38420 |
| fra-fin | ntrex128 | 0.49215 | 14.2 | 1997 | 35701 |
| fra-hun | ntrex128 | 0.44873 | 14.9 | 1997 | 44462 |
| por-est | ntrex128 | 0.48098 | 15.1 | 1997 | 38420 |
| por-fin | ntrex128 | 0.50875 | 15.0 | 1997 | 35701 |
| por-hun | ntrex128 | 0.45817 | 15.5 | 1997 | 44462 |
| spa-est | ntrex128 | 0.52158 | 18.5 | 1997 | 38420 |
| spa-fin | ntrex128 | 0.50947 | 15.2 | 1997 | 35701 |
| spa-hun | ntrex128 | 0.46051 | 16.1 | 1997 | 44462 |

## Citation Information

* Publications: [Democratizing neural machine translation with OPUS-MT](https://doi.org/10.1007/s10579-023-09704-w) and [OPUS-MT – Building open translation services for the World](https://aclanthology.org/2020.eamt-1.61/) and [The Tatoeba Translation Challenge – Realistic Data Sets for Low Resource and Multilingual MT](https://aclanthology.org/2020.wmt-1.139/) (Please, cite if you use this model.)

```bibtex
@article{tiedemann2023democratizing,
  title={Democratizing neural machine translation with {OPUS-MT}},
  author={Tiedemann, J{\"o}rg and Aulamo, Mikko and Bakshandaeva, Daria and Boggia, Michele and Gr{\"o}nroos, Stig-Arne and Nieminen, Tommi and Raganato, Alessandro and Scherrer, Yves and Vazquez, Raul and Virpioja, Sami},
  journal={Language Resources and Evaluation},
  number={58},
  pages={713--755},
  year={2023},
  publisher={Springer Nature},
  issn={1574-0218},
  doi={10.1007/s10579-023-09704-w}
}

@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 [HPLT project](https://hplt-project.org/), funded by the European Union’s Horizon Europe research and innovation programme under grant agreement No 101070350. We are also grateful for the generous computational resources and IT infrastructure provided by [CSC -- IT Center for Science](https://www.csc.fi/), Finland, and the [EuroHPC supercomputer LUMI](https://www.lumi-supercomputer.eu/).

## Model conversion info

* transformers version: 4.45.1
* OPUS-MT git hash: 0882077
* port time: Tue Oct  8 09:01:19 EEST 2024
* port machine: LM0-400-22516.local
