transformers pytorch safetensors marian text2text-generation translation opus-mt-tc-bible aa am ar arc bcw byn cop daa de dsh en es fr gde gnd ha hbo he hig irk jpa kab ker kqp ktb kxc lln lme meq mfh mfi mfk mif mpg mqb mt muy oar om pbi phn pt rif sgw shi shy so sur syc syr taq thv ti tig tmc tmh tmr ttr tzm wal xed zgh license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
language:
- aa
- am
- ar
- arc
- bcw
- byn
- cop
- daa
- de
- dsh
- en
- es
- fr
- gde
- gnd
- ha
- hbo
- he
- hig
- irk
- jpa
- kab
- ker
- kqp
- ktb
- kxc
- lln
- lme
- meq
- mfh
- mfi
- mfk
- mif
- mpg
- mqb
- mt
- muy
- oar
- om
- pbi
- phn
- pt
- rif
- sgw
- shi
- shy
- so
- sur
- syc
- syr
- taq
- thv
- ti
- tig
- tmc
- tmh
- tmr
- ttr
- tzm
- wal
- xed
- zgh
library_name: transformers
license: apache-2.0
tags:
- translation
- opus-mt-tc-bible
model-index:
- name: opus-mt-tc-bible-big-afa-deu_eng_fra_por_spa
  results:
  - task:
      type: translation
      name: Translation acm-deu
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: acm-deu
    metrics:
    - type: bleu
      value: 17.6
      name: BLEU
    - type: chrf
      value: 0.48947
      name: chr-F
    - type: bleu
      value: 28.5
      name: BLEU
    - type: chrf
      value: 0.56799
      name: chr-F
    - type: bleu
      value: 26.1
      name: BLEU
    - type: chrf
      value: 0.53577
      name: chr-F
    - type: bleu
      value: 23.9
      name: BLEU
    - type: chrf
      value: 0.52441
      name: chr-F
    - type: bleu
      value: 18.2
      name: BLEU
    - type: chrf
      value: 0.46985
      name: chr-F
    - type: bleu
      value: 12.6
      name: BLEU
    - type: chrf
      value: 0.41553
      name: chr-F
    - type: bleu
      value: 22.5
      name: BLEU
    - type: chrf
      value: 0.49333
      name: chr-F
    - type: bleu
      value: 17.8
      name: BLEU
    - type: chrf
      value: 0.4489
      name: chr-F
    - type: bleu
      value: 16.5
      name: BLEU
    - type: chrf
      value: 0.43771
      name: chr-F
    - type: bleu
      value: 12.8
      name: BLEU
    - type: chrf
      value: 0.39526
      name: chr-F
    - type: bleu
      value: 16.0
      name: BLEU
    - type: chrf
      value: 0.4748
      name: chr-F
    - type: bleu
      value: 28.1
      name: BLEU
    - type: chrf
      value: 0.56075
      name: chr-F
    - type: bleu
      value: 24.6
      name: BLEU
    - type: chrf
      value: 0.52325
      name: chr-F
    - type: bleu
      value: 22.9
      name: BLEU
    - type: chrf
      value: 0.51055
      name: chr-F
    - type: bleu
      value: 17.2
      name: BLEU
    - type: chrf
      value: 0.45634
      name: chr-F
    - type: bleu
      value: 14.1
      name: BLEU
    - type: chrf
      value: 0.45844
      name: chr-F
    - type: bleu
      value: 22.7
      name: BLEU
    - type: chrf
      value: 0.52534
      name: chr-F
    - type: bleu
      value: 21.8
      name: BLEU
    - type: chrf
      value: 0.50336
      name: chr-F
    - type: bleu
      value: 20.0
      name: BLEU
    - type: chrf
      value: 0.48741
      name: chr-F
    - type: bleu
      value: 15.8
      name: BLEU
    - type: chrf
      value: 0.44516
      name: chr-F
    - type: bleu
      value: 23.4
      name: BLEU
    - type: chrf
      value: 0.48137
      name: chr-F
    - type: bleu
      value: 17.2
      name: BLEU
    - type: chrf
      value: 0.42981
      name: chr-F
    - type: bleu
      value: 15.7
      name: BLEU
    - type: chrf
      value: 0.41385
      name: chr-F
    - type: bleu
      value: 11.7
      name: BLEU
    - type: chrf
      value: 0.378
      name: chr-F
    - type: bleu
      value: 22.8
      name: BLEU
    - type: chrf
      value: 0.53482
      name: chr-F
    - type: bleu
      value: 38.0
      name: BLEU
    - type: chrf
      value: 0.63368
      name: chr-F
    - type: bleu
      value: 32.6
      name: BLEU
    - type: chrf
      value: 0.58417
      name: chr-F
    - type: bleu
      value: 30.7
      name: BLEU
    - type: chrf
      value: 0.5714
      name: chr-F
    - type: bleu
      value: 10.0
      name: BLEU
    - type: chrf
      value: 0.32121
      name: chr-F
    - type: bleu
      value: 51.1
      name: BLEU
    - type: chrf
      value: 0.73415
      name: chr-F
    - type: bleu
      value: 35.8
      name: BLEU
    - type: chrf
      value: 0.61626
      name: chr-F
    - type: bleu
      value: 21.8
      name: BLEU
    - type: chrf
      value: 0.50534
      name: chr-F
    - type: bleu
      value: 17.7
      name: BLEU
    - type: chrf
      value: 0.42764
      name: chr-F
    - type: bleu
      value: 14.4
      name: BLEU
    - type: chrf
      value: 0.39536
      name: chr-F
    - type: bleu
      value: 14.1
      name: BLEU
    - type: chrf
      value: 0.3944
      name: chr-F
    - type: bleu
      value: 10.0
      name: BLEU
    - type: chrf
      value: 0.35321
      name: chr-F
    - type: bleu
      value: 11.6
      name: BLEU
    - type: chrf
      value: 0.37119
      name: chr-F
  - task:
      type: translation
      name: Translation amh-deu
    dataset:
      name: flores101-devtest
      type: flores_101
      args: amh deu devtest
    metrics:
    - type: bleu
      value: 11.5
      name: BLEU
    - type: chrf
      value: 0.3989
      name: chr-F
    - type: bleu
      value: 21.0
      name: BLEU
    - type: chrf
      value: 0.47357
      name: chr-F
    - type: bleu
      value: 16.2
      name: BLEU
    - type: chrf
      value: 0.43155
      name: chr-F
    - type: bleu
      value: 15.1
      name: BLEU
    - type: chrf
      value: 0.42109
      name: chr-F
    - type: bleu
      value: 11.5
      name: BLEU
    - type: chrf
      value: 0.38003
      name: chr-F
    - type: bleu
      value: 20.4
      name: BLEU
    - type: chrf
      value: 0.5111
      name: chr-F
    - type: bleu
      value: 29.7
      name: BLEU
    - type: chrf
      value: 0.56934
      name: chr-F
    - type: bleu
      value: 28.2
      name: BLEU
    - type: chrf
      value: 0.55727
      name: chr-F
    - type: bleu
      value: 19.5
      name: BLEU
    - type: chrf
      value: 0.4835
      name: chr-F
    - type: bleu
      value: 21.6
      name: BLEU
    - type: chrf
      value: 0.46804
      name: chr-F
    - type: bleu
      value: 15.9
      name: BLEU
    - type: chrf
      value: 0.41827
      name: chr-F
    - type: bleu
      value: 11.5
      name: BLEU
    - type: chrf
      value: 0.37201
      name: chr-F
    - type: bleu
      value: 36.6
      name: BLEU
    - type: chrf
      value: 0.62422
      name: chr-F
    - type: bleu
      value: 49.1
      name: BLEU
    - type: chrf
      value: 0.7239
      name: chr-F
    - type: bleu
      value: 34.7
      name: BLEU
    - type: chrf
      value: 0.6084
      name: chr-F
    - type: bleu
      value: 31.8
      name: BLEU
    - type: chrf
      value: 0.59863
      name: chr-F
  - task:
      type: translation
      name: Translation amh-eng
    dataset:
      name: ntrex128
      type: ntrex128
      args: amh-eng
    metrics:
    - type: bleu
      value: 15.0
      name: BLEU
    - type: chrf
      value: 0.42042
      name: chr-F
    - type: bleu
      value: 10.8
      name: BLEU
    - type: chrf
      value: 0.37274
      name: chr-F
    - type: bleu
      value: 12.1
      name: BLEU
    - type: chrf
      value: 0.38306
      name: chr-F
    - type: bleu
      value: 26.1
      name: BLEU
    - type: chrf
      value: 0.50349
      name: chr-F
    - type: bleu
      value: 15.8
      name: BLEU
    - type: chrf
      value: 0.41837
      name: chr-F
    - type: bleu
      value: 15.3
      name: BLEU
    - type: chrf
      value: 0.40851
      name: chr-F
    - type: bleu
      value: 18.5
      name: BLEU
    - type: chrf
      value: 0.43376
      name: chr-F
    - type: bleu
      value: 17.7
      name: BLEU
    - type: chrf
      value: 0.49482
      name: chr-F
    - type: bleu
      value: 31.3
      name: BLEU
    - type: chrf
      value: 0.59241
      name: chr-F
    - type: bleu
      value: 24.0
      name: BLEU
    - type: chrf
      value: 0.5218
      name: chr-F
    - type: bleu
      value: 23.2
      name: BLEU
    - type: chrf
      value: 0.51248
      name: chr-F
    - type: bleu
      value: 30.9
      name: BLEU
    - type: chrf
      value: 0.57078
      name: chr-F
    - type: bleu
      value: 11.0
      name: BLEU
    - type: chrf
      value: 0.39394
      name: chr-F
    - type: bleu
      value: 24.3
      name: BLEU
    - type: chrf
      value: 0.49187
      name: chr-F
    - type: bleu
      value: 15.1
      name: BLEU
    - type: chrf
      value: 0.41236
      name: chr-F
    - type: bleu
      value: 15.2
      name: BLEU
    - type: chrf
      value: 0.4155
      name: chr-F
    - type: bleu
      value: 17.6
      name: BLEU
    - type: chrf
      value: 0.43278
      name: chr-F
    - type: bleu
      value: 12.5
      name: BLEU
    - type: chrf
      value: 0.37185
      name: chr-F
    - type: bleu
      value: 10.2
      name: BLEU
    - type: chrf
      value: 0.34985
      name: chr-F
  - task:
      type: translation
      name: Translation amh-eng
    dataset:
      name: tatoeba-test-v2021-03-30
      type: tatoeba_mt
      args: amh-eng
    metrics:
    - type: bleu
      value: 59.5
      name: BLEU
    - type: chrf
      value: 0.70202
      name: chr-F
    - type: bleu
      value: 41.7
      name: BLEU
    - type: chrf
      value: 0.61039
      name: chr-F
    - type: bleu
      value: 42.8
      name: BLEU
    - type: chrf
      value: 0.60134
      name: chr-F
    - type: bleu
      value: 38.8
      name: BLEU
    - type: chrf
      value: 0.5612
      name: chr-F
    - type: bleu
      value: 43.7
      name: BLEU
    - type: chrf
      value: 0.62567
      name: chr-F
    - type: bleu
      value: 42.4
      name: BLEU
    - type: chrf
      value: 0.63131
      name: chr-F
    - type: bleu
      value: 49.2
      name: BLEU
    - type: chrf
      value: 0.6496
      name: chr-F
    - type: bleu
      value: 46.3
      name: BLEU
    - type: chrf
      value: 0.64348
      name: chr-F
    - type: bleu
      value: 43.2
      name: BLEU
    - type: chrf
      value: 0.6335
      name: chr-F
    - type: bleu
      value: 27.8
      name: BLEU
    - type: chrf
      value: 0.44646
      name: chr-F
    - type: bleu
      value: 23.4
      name: BLEU
    - type: chrf
      value: 0.41526
      name: chr-F
    - type: bleu
      value: 51.0
      name: BLEU
    - type: chrf
      value: 0.66653
      name: chr-F
    - type: bleu
      value: 34.0
      name: BLEU
    - type: chrf
      value: 0.51369
      name: chr-F
  - task:
      type: translation
      name: Translation hau-eng
    dataset:
      name: newstest2021
      type: wmt-2021-news
      args: hau-eng
    metrics:
    - type: bleu
      value: 15.5
      name: BLEU
    - type: chrf
      value: 0.43744
      name: chr-F
---
# opus-mt-tc-bible-big-afa-deu_eng_fra_por_spa

## 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 Afro-Asiatic languages (afa) to unknown (deu+eng+fra+por+spa).

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**: 2024-05-29
- **License:** Apache-2.0
- **Language(s):**  
  - Source Language(s): aar acm afb amh apc ara arc arq arz bcw byn cop daa dsh gde gnd hau hbo heb hig irk jpa kab ker kqp ktb kxc lln lme meq mfh mfi mfk mif mlt mpg mqb muy oar orm pbi phn rif sgw shi shy som sur syc syr taq thv tig tir tmc tmh tmr ttr tzm wal xed zgh
  - Target Language(s): deu eng fra por spa
  - Valid Target Language Labels: >>deu<< >>eng<< >>fra<< >>por<< >>spa<< >>xxx<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.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/afa-deu%2Beng%2Bfra%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-29)
  - [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. `>>deu<<`

## 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 = [
    ">>eng<< Anta i ak-d-yennan ur yerbiḥ ara Tom?",
    ">>fra<< Iselman d aɣbalu axatar i wučči n yemdanen."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-afa-deu_eng_fra_por_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:
#     Who told you that he didn't?
#     L'eau est une source importante de nourriture pour les gens.
```

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-afa-deu_eng_fra_por_spa")
print(pipe(">>eng<< Anta i ak-d-yennan ur yerbiḥ ara Tom?"))

# expected output: Who told you that he didn't?
```

## 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_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+fra+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.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/afa-deu%2Beng%2Bfra%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-29)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/afa-deu+eng+fra+por+spa/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/afa-deu+eng+fra+por+spa/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 |
|----------|---------|-------|-------|-------|--------|
| ara-deu | tatoeba-test-v2021-08-07 | 0.61039 | 41.7 | 1209 | 8371 |
| ara-eng | tatoeba-test-v2021-08-07 | 5.430 | 0.0 | 10305 | 76975 |
| ara-fra | tatoeba-test-v2021-08-07 | 0.56120 | 38.8 | 1569 | 11066 |
| ara-spa | tatoeba-test-v2021-08-07 | 0.62567 | 43.7 | 1511 | 9708 |
| heb-deu | tatoeba-test-v2021-08-07 | 0.63131 | 42.4 | 3090 | 25101 |
| heb-eng | tatoeba-test-v2021-08-07 | 0.64960 | 49.2 | 10519 | 77427 |
| heb-fra | tatoeba-test-v2021-08-07 | 0.64348 | 46.3 | 3281 | 26123 |
| heb-por | tatoeba-test-v2021-08-07 | 0.63350 | 43.2 | 719 | 5335 |
| mlt-eng | tatoeba-test-v2021-08-07 | 0.66653 | 51.0 | 203 | 1165 |
| amh-eng | flores101-devtest | 0.47357 | 21.0 | 1012 | 24721 |
| amh-fra | flores101-devtest | 0.43155 | 16.2 | 1012 | 28343 |
| amh-por | flores101-devtest | 0.42109 | 15.1 | 1012 | 26519 |
| ara-deu | flores101-devtest | 0.51110 | 20.4 | 1012 | 25094 |
| ara-fra | flores101-devtest | 0.56934 | 29.7 | 1012 | 28343 |
| ara-por | flores101-devtest | 0.55727 | 28.2 | 1012 | 26519 |
| ara-spa | flores101-devtest | 0.48350 | 19.5 | 1012 | 29199 |
| hau-eng | flores101-devtest | 0.46804 | 21.6 | 1012 | 24721 |
| hau-fra | flores101-devtest | 0.41827 | 15.9 | 1012 | 28343 |
| heb-eng | flores101-devtest | 0.62422 | 36.6 | 1012 | 24721 |
| mlt-eng | flores101-devtest | 0.72390 | 49.1 | 1012 | 24721 |
| mlt-fra | flores101-devtest | 0.60840 | 34.7 | 1012 | 28343 |
| mlt-por | flores101-devtest | 0.59863 | 31.8 | 1012 | 26519 |
| acm-deu | flores200-devtest | 0.48947 | 17.6 | 1012 | 25094 |
| acm-eng | flores200-devtest | 0.56799 | 28.5 | 1012 | 24721 |
| acm-fra | flores200-devtest | 0.53577 | 26.1 | 1012 | 28343 |
| acm-por | flores200-devtest | 0.52441 | 23.9 | 1012 | 26519 |
| acm-spa | flores200-devtest | 0.46985 | 18.2 | 1012 | 29199 |
| amh-deu | flores200-devtest | 0.41553 | 12.6 | 1012 | 25094 |
| amh-eng | flores200-devtest | 0.49333 | 22.5 | 1012 | 24721 |
| amh-fra | flores200-devtest | 0.44890 | 17.8 | 1012 | 28343 |
| amh-por | flores200-devtest | 0.43771 | 16.5 | 1012 | 26519 |
| apc-deu | flores200-devtest | 0.47480 | 16.0 | 1012 | 25094 |
| apc-eng | flores200-devtest | 0.56075 | 28.1 | 1012 | 24721 |
| apc-fra | flores200-devtest | 0.52325 | 24.6 | 1012 | 28343 |
| apc-por | flores200-devtest | 0.51055 | 22.9 | 1012 | 26519 |
| apc-spa | flores200-devtest | 0.45634 | 17.2 | 1012 | 29199 |
| arz-deu | flores200-devtest | 0.45844 | 14.1 | 1012 | 25094 |
| arz-eng | flores200-devtest | 0.52534 | 22.7 | 1012 | 24721 |
| arz-fra | flores200-devtest | 0.50336 | 21.8 | 1012 | 28343 |
| arz-por | flores200-devtest | 0.48741 | 20.0 | 1012 | 26519 |
| arz-spa | flores200-devtest | 0.44516 | 15.8 | 1012 | 29199 |
| hau-eng | flores200-devtest | 0.48137 | 23.4 | 1012 | 24721 |
| hau-fra | flores200-devtest | 0.42981 | 17.2 | 1012 | 28343 |
| hau-por | flores200-devtest | 0.41385 | 15.7 | 1012 | 26519 |
| heb-deu | flores200-devtest | 0.53482 | 22.8 | 1012 | 25094 |
| heb-eng | flores200-devtest | 0.63368 | 38.0 | 1012 | 24721 |
| heb-fra | flores200-devtest | 0.58417 | 32.6 | 1012 | 28343 |
| heb-por | flores200-devtest | 0.57140 | 30.7 | 1012 | 26519 |
| mlt-eng | flores200-devtest | 0.73415 | 51.1 | 1012 | 24721 |
| mlt-fra | flores200-devtest | 0.61626 | 35.8 | 1012 | 28343 |
| mlt-spa | flores200-devtest | 0.50534 | 21.8 | 1012 | 29199 |
| som-eng | flores200-devtest | 0.42764 | 17.7 | 1012 | 24721 |
| tir-por | flores200-devtest | 2.931 | 0.0 | 1012 | 26519 |
| hau-eng | newstest2021 | 0.43744 | 15.5 | 997 | 27372 |
| amh-eng | ntrex128 | 0.42042 | 15.0 | 1997 | 47673 |
| hau-eng | ntrex128 | 0.50349 | 26.1 | 1997 | 47673 |
| hau-fra | ntrex128 | 0.41837 | 15.8 | 1997 | 53481 |
| hau-por | ntrex128 | 0.40851 | 15.3 | 1997 | 51631 |
| hau-spa | ntrex128 | 0.43376 | 18.5 | 1997 | 54107 |
| heb-deu | ntrex128 | 0.49482 | 17.7 | 1997 | 48761 |
| heb-eng | ntrex128 | 0.59241 | 31.3 | 1997 | 47673 |
| heb-fra | ntrex128 | 0.52180 | 24.0 | 1997 | 53481 |
| heb-por | ntrex128 | 0.51248 | 23.2 | 1997 | 51631 |
| mlt-spa | ntrex128 | 0.57078 | 30.9 | 1997 | 54107 |
| som-eng | ntrex128 | 0.49187 | 24.3 | 1997 | 47673 |
| som-fra | ntrex128 | 0.41236 | 15.1 | 1997 | 53481 |
| som-por | ntrex128 | 0.41550 | 15.2 | 1997 | 51631 |
| som-spa | ntrex128 | 0.43278 | 17.6 | 1997 | 54107 |
| tir-eng | tico19-test | 2.655 | 0.0 | 2100 | 56824 |

## 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: a0ea3b3
* port time: Mon Oct  7 17:08:30 EEST 2024
* port machine: LM0-400-22516.local
