transformers pytorch safetensors marian text2text-generation translation opus-mt-tc-bible abi acd ade adj ak akp ann anv atg avn bas bav bba beh bem bfd bfo bim biv bkv blh bmq bmv bom bov box bqj bss btt bud bwu cce cjk cko cme csk cwe cwt dag de dga dgi dig dop dug dyi dyo ee efi en es fal ff fon fr gej gkn gng gog gud gur guw gux gwr hag hay heh hz ife ig iri izr jbu jmc kam kbp kdc kdl kdn ken keu kez kg ki kia kj kki kkj kma kmb ksb ktj kub kus kyf las lee lef lem lg lia lip ln lob lon lua luy maw mcp mcu mda mfq mgo mnf mnh mor mos muh myk myx mzk mzm mzw ncu nd ndz nfr ng nhu nim nin nmz nnb nnh nnw nr nso ntm ntr nuj nwb ny nyf nyn nyo nyy nzi oku old ozm pai pbl pkb pt rim rn rw seh sg sig sil sld sn snw soy spp ss st suk sw sxb tbz tem thk tik tlj tn toh toi tpm ts tsw tum tw umb vag ve vmw vun wmw wo wob xh xog xon xrb xsm xuo yam yaz yo zu license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
language:
- abi
- acd
- ade
- adj
- ak
- akp
- ann
- anv
- atg
- avn
- bas
- bav
- bba
- beh
- bem
- bfd
- bfo
- bim
- biv
- bkv
- blh
- bmq
- bmv
- bom
- bov
- box
- bqj
- bss
- btt
- bud
- bwu
- cce
- cjk
- cko
- cme
- csk
- cwe
- cwt
- dag
- de
- dga
- dgi
- dig
- dop
- dug
- dyi
- dyo
- ee
- efi
- en
- es
- fal
- ff
- fon
- fr
- gej
- gkn
- gng
- gog
- gud
- gur
- guw
- gux
- gwr
- hag
- hay
- heh
- hz
- ife
- ig
- iri
- izr
- jbu
- jmc
- kam
- kbp
- kdc
- kdl
- kdn
- ken
- keu
- kez
- kg
- ki
- kia
- kj
- kki
- kkj
- kma
- kmb
- ksb
- ktj
- kub
- kus
- kyf
- las
- lee
- lef
- lem
- lg
- lia
- lip
- ln
- lob
- lon
- lua
- luy
- maw
- mcp
- mcu
- mda
- mfq
- mgo
- mnf
- mnh
- mor
- mos
- muh
- myk
- myx
- mzk
- mzm
- mzw
- ncu
- nd
- ndz
- nfr
- ng
- nhu
- nim
- nin
- nmz
- nnb
- nnh
- nnw
- nr
- nso
- ntm
- ntr
- nuj
- nwb
- ny
- nyf
- nyn
- nyo
- nyy
- nzi
- oku
- old
- ozm
- pai
- pbl
- pkb
- pt
- rim
- rn
- rw
- seh
- sg
- sig
- sil
- sld
- sn
- snw
- soy
- spp
- ss
- st
- suk
- sw
- sxb
- tbz
- tem
- thk
- tik
- tlj
- tn
- toh
- toi
- tpm
- ts
- tsw
- tum
- tw
- umb
- vag
- ve
- vmw
- vun
- wmw
- wo
- wob
- xh
- xog
- xon
- xrb
- xsm
- xuo
- yam
- yaz
- yo
- zu
library_name: transformers
license: apache-2.0
tags:
- translation
- opus-mt-tc-bible
model-index:
- name: opus-mt-tc-bible-big-alv-deu_eng_fra_por_spa
  results:
  - task:
      type: translation
      name: Translation bem-eng
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: bem-eng
    metrics:
    - type: bleu
      value: 13.1
      name: BLEU
    - type: chrf
      value: 0.37071
      name: chr-F
    - type: bleu
      value: 14.6
      name: BLEU
    - type: chrf
      value: 0.38994
      name: chr-F
    - type: bleu
      value: 18.1
      name: BLEU
    - type: chrf
      value: 0.41964
      name: chr-F
    - type: bleu
      value: 10.7
      name: BLEU
    - type: chrf
      value: 0.34887
      name: chr-F
    - type: bleu
      value: 11.3
      name: BLEU
    - type: chrf
      value: 0.34262
      name: chr-F
    - type: bleu
      value: 14.2
      name: BLEU
    - type: chrf
      value: 0.37728
      name: chr-F
    - type: bleu
      value: 11.0
      name: BLEU
    - type: chrf
      value: 0.35052
      name: chr-F
    - type: bleu
      value: 10.6
      name: BLEU
    - type: chrf
      value: 0.31805
      name: chr-F
    - type: bleu
      value: 22.3
      name: BLEU
    - type: chrf
      value: 0.45662
      name: chr-F
    - type: bleu
      value: 10.5
      name: BLEU
    - type: chrf
      value: 0.33732
      name: chr-F
    - type: bleu
      value: 15.8
      name: BLEU
    - type: chrf
      value: 0.39887
      name: chr-F
    - type: bleu
      value: 15.5
      name: BLEU
    - type: chrf
      value: 0.39846
      name: chr-F
    - type: bleu
      value: 10.8
      name: BLEU
    - type: chrf
      value: 0.34845
      name: chr-F
    - type: bleu
      value: 10.2
      name: BLEU
    - type: chrf
      value: 0.33836
      name: chr-F
    - type: bleu
      value: 17.2
      name: BLEU
    - type: chrf
      value: 0.41974
      name: chr-F
    - type: bleu
      value: 11.3
      name: BLEU
    - type: chrf
      value: 0.36443
      name: chr-F
    - type: bleu
      value: 10.2
      name: BLEU
    - type: chrf
      value: 0.3426
      name: chr-F
    - type: bleu
      value: 20.7
      name: BLEU
    - type: chrf
      value: 0.45415
      name: chr-F
    - type: bleu
      value: 10.7
      name: BLEU
    - type: chrf
      value: 0.34608
      name: chr-F
    - type: bleu
      value: 16.1
      name: BLEU
    - type: chrf
      value: 0.39768
      name: chr-F
    - type: bleu
      value: 10.9
      name: BLEU
    - type: chrf
      value: 0.38892
      name: chr-F
    - type: bleu
      value: 29.1
      name: BLEU
    - type: chrf
      value: 0.54048
      name: chr-F
    - type: bleu
      value: 18.2
      name: BLEU
    - type: chrf
      value: 0.44837
      name: chr-F
    - type: bleu
      value: 17.6
      name: BLEU
    - type: chrf
      value: 0.44062
      name: chr-F
    - type: bleu
      value: 11.6
      name: BLEU
    - type: chrf
      value: 0.38855
      name: chr-F
    - type: bleu
      value: 15.3
      name: BLEU
    - type: chrf
      value: 0.4041
      name: chr-F
    - type: bleu
      value: 10.3
      name: BLEU
    - type: chrf
      value: 0.34284
      name: chr-F
    - type: bleu
      value: 17.6
      name: BLEU
    - type: chrf
      value: 0.41504
      name: chr-F
    - type: bleu
      value: 10.1
      name: BLEU
    - type: chrf
      value: 0.33502
      name: chr-F
    - type: bleu
      value: 23.7
      name: BLEU
    - type: chrf
      value: 0.47667
      name: chr-F
    - type: bleu
      value: 14.1
      name: BLEU
    - type: chrf
      value: 0.39392
      name: chr-F
    - type: bleu
      value: 12.3
      name: BLEU
    - type: chrf
      value: 0.37032
      name: chr-F
    - type: bleu
      value: 23.4
      name: BLEU
    - type: chrf
      value: 0.47798
      name: chr-F
    - type: bleu
      value: 13.9
      name: BLEU
    - type: chrf
      value: 0.39504
      name: chr-F
    - type: bleu
      value: 12.3
      name: BLEU
    - type: chrf
      value: 0.36947
      name: chr-F
  - task:
      type: translation
      name: Translation ibo-eng
    dataset:
      name: flores101-devtest
      type: flores_101
      args: ibo eng devtest
    metrics:
    - type: bleu
      value: 12.5
      name: BLEU
    - type: chrf
      value: 0.3632
      name: chr-F
    - type: bleu
      value: 13.2
      name: BLEU
    - type: chrf
      value: 0.36765
      name: chr-F
    - type: bleu
      value: 14.7
      name: BLEU
    - type: chrf
      value: 0.40847
      name: chr-F
    - type: bleu
      value: 10.1
      name: BLEU
    - type: chrf
      value: 0.33906
      name: chr-F
    - type: bleu
      value: 11.2
      name: BLEU
    - type: chrf
      value: 0.35968
      name: chr-F
  - task:
      type: translation
      name: Translation bem-eng
    dataset:
      name: ntrex128
      type: ntrex128
      args: bem-eng
    metrics:
    - type: bleu
      value: 14.9
      name: BLEU
    - type: chrf
      value: 0.38068
      name: chr-F
    - type: bleu
      value: 10.2
      name: BLEU
    - type: chrf
      value: 0.33394
      name: chr-F
    - type: bleu
      value: 17.4
      name: BLEU
    - type: chrf
      value: 0.42002
      name: chr-F
    - type: bleu
      value: 10.3
      name: BLEU
    - type: chrf
      value: 0.34993
      name: chr-F
    - type: bleu
      value: 11.7
      name: BLEU
    - type: chrf
      value: 0.36108
      name: chr-F
    - type: bleu
      value: 16.9
      name: BLEU
    - type: chrf
      value: 0.42892
      name: chr-F
    - type: bleu
      value: 10.7
      name: BLEU
    - type: chrf
      value: 0.35842
      name: chr-F
    - type: bleu
      value: 10.0
      name: BLEU
    - type: chrf
      value: 0.34399
      name: chr-F
    - type: bleu
      value: 12.5
      name: BLEU
    - type: chrf
      value: 0.37224
      name: chr-F
    - type: bleu
      value: 13.8
      name: BLEU
    - type: chrf
      value: 0.3964
      name: chr-F
    - type: bleu
      value: 10.0
      name: BLEU
    - type: chrf
      value: 0.34638
      name: chr-F
    - type: bleu
      value: 17.0
      name: BLEU
    - type: chrf
      value: 0.42278
      name: chr-F
    - type: bleu
      value: 11.3
      name: BLEU
    - type: chrf
      value: 0.35027
      name: chr-F
    - type: bleu
      value: 19.2
      name: BLEU
    - type: chrf
      value: 0.42702
      name: chr-F
    - type: bleu
      value: 10.7
      name: BLEU
    - type: chrf
      value: 0.35503
      name: chr-F
    - type: bleu
      value: 10.4
      name: BLEU
    - type: chrf
      value: 0.33912
      name: chr-F
    - type: bleu
      value: 12.5
      name: BLEU
    - type: chrf
      value: 0.36355
      name: chr-F
    - type: bleu
      value: 18.0
      name: BLEU
    - type: chrf
      value: 0.43041
      name: chr-F
    - type: bleu
      value: 11.4
      name: BLEU
    - type: chrf
      value: 0.35392
      name: chr-F
    - type: bleu
      value: 11.2
      name: BLEU
    - type: chrf
      value: 0.39475
      name: chr-F
    - type: bleu
      value: 30.4
      name: BLEU
    - type: chrf
      value: 0.54492
      name: chr-F
    - type: bleu
      value: 15.6
      name: BLEU
    - type: chrf
      value: 0.43008
      name: chr-F
    - type: bleu
      value: 15.4
      name: BLEU
    - type: chrf
      value: 0.42343
      name: chr-F
    - type: bleu
      value: 18.9
      name: BLEU
    - type: chrf
      value: 0.44892
      name: chr-F
    - type: bleu
      value: 20.1
      name: BLEU
    - type: chrf
      value: 0.44944
      name: chr-F
    - type: bleu
      value: 11.5
      name: BLEU
    - type: chrf
      value: 0.36584
      name: chr-F
    - type: bleu
      value: 11.1
      name: BLEU
    - type: chrf
      value: 0.35774
      name: chr-F
    - type: bleu
      value: 13.8
      name: BLEU
    - type: chrf
      value: 0.38149
      name: chr-F
    - type: bleu
      value: 12.7
      name: BLEU
    - type: chrf
      value: 0.36848
      name: chr-F
    - type: bleu
      value: 21.8
      name: BLEU
    - type: chrf
      value: 0.46636
      name: chr-F
    - type: bleu
      value: 11.6
      name: BLEU
    - type: chrf
      value: 0.36905
      name: chr-F
    - type: bleu
      value: 10.6
      name: BLEU
    - type: chrf
      value: 0.35687
      name: chr-F
    - type: bleu
      value: 13.3
      name: BLEU
    - type: chrf
      value: 0.38176
      name: chr-F
    - type: bleu
      value: 11.2
      name: BLEU
    - type: chrf
      value: 0.34615
      name: chr-F
    - type: bleu
      value: 21.9
      name: BLEU
    - type: chrf
      value: 0.45848
      name: chr-F
    - type: bleu
      value: 11.4
      name: BLEU
    - type: chrf
      value: 0.36203
      name: chr-F
    - type: bleu
      value: 10.8
      name: BLEU
    - type: chrf
      value: 0.35081
      name: chr-F
    - type: bleu
      value: 13.0
      name: BLEU
    - type: chrf
      value: 0.3727
      name: chr-F
  - task:
      type: translation
      name: Translation multi-multi
    dataset:
      name: tatoeba-test-v2020-07-28-v2023-09-26
      type: tatoeba_mt
      args: multi-multi
    metrics:
    - type: bleu
      value: 27.3
      name: BLEU
    - type: chrf
      value: 0.44751
      name: chr-F
    - type: bleu
      value: 34.9
      name: BLEU
    - type: chrf
      value: 0.49949
      name: chr-F
    - type: bleu
      value: 22.4
      name: BLEU
    - type: chrf
      value: 0.41431
      name: chr-F
    - type: bleu
      value: 41.5
      name: BLEU
    - type: chrf
      value: 0.57031
      name: chr-F
    - type: bleu
      value: 36.4
      name: BLEU
    - type: chrf
      value: 0.5125
      name: chr-F
  - task:
      type: translation
      name: Translation zul-eng
    dataset:
      name: tico19-test
      type: tico19-test
      args: zul-eng
    metrics:
    - type: bleu
      value: 25.2
      name: BLEU
    - type: chrf
      value: 0.48762
      name: chr-F
    - type: bleu
      value: 13.7
      name: BLEU
    - type: chrf
      value: 0.37823
      name: chr-F
    - type: bleu
      value: 14.0
      name: BLEU
    - type: chrf
      value: 0.38586
      name: chr-F
    - type: bleu
      value: 15.9
      name: BLEU
    - type: chrf
      value: 0.40041
      name: chr-F
---
# opus-mt-tc-bible-big-alv-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 Atlantic-Congo languages (alv) 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**: 2024-05-30
- **License:** Apache-2.0
- **Language(s):**  
  - Source Language(s): abi acd ade adj aka akp ann anv atg avn bas bav bba beh bem bfd bfo bim biv bkv blh bmq bmv bom bov box bqj bss btt bud bwu cce cjk cko cme csk cwe cwt dag dga dgi dig dop dug dyi dyo efi ewe fal fon fuc ful gej gkn gng gog gud gur guw gux gwr hag hay heh her ibo ife iri izr jbu jmc kam kbp kdc kdl kdn ken keu kez kia kik kin kki kkj kma kmb kon ksb ktj kua kub kus kyf las lee lef lem lia lin lip lob lon lua lug luy maw mcp mcu mda mfq mgo mnf mnh mor mos muh myk myx mzk mzm mzw nbl ncu nde ndo ndz nfr nhu nim nin nmz nnb nnh nnw nso ntm ntr nuj nwb nya nyf nyn nyo nyy nzi oku old ozm pai pbl pkb rim run sag seh sig sil sld sna snw sot soy spp ssw suk swa swc swh sxb tbz tem thk tik tlj toh toi tpm tsn tso tsw tum twi umb vag ven vmw vun wmw wob wol xho xog xon xrb xsm xuo yam yaz yor zul
  - 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_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/alv-deu+eng+fra+por+spa/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/alv-deu%2Beng%2Bfra%2Bpor%2Bspa/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. `>>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 = [
    ">>deu<< Replace this with text in an accepted source language.",
    ">>spa<< This is the second sentence."
]

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

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-alv-deu_eng_fra_por_spa")
print(pipe(">>deu<< 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/alv-deu+eng+fra+por+spa/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/alv-deu%2Beng%2Bfra%2Bpor%2Bspa/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/alv-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/alv-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 |
|----------|---------|-------|-------|-------|--------|
| run-eng | tatoeba-test-v2021-08-07 | 0.49949 | 34.9 | 1703 | 10041 |
| run-fra | tatoeba-test-v2021-08-07 | 0.41431 | 22.4 | 1274 | 7479 |
| swa-eng | tatoeba-test-v2021-08-07 | 0.57031 | 41.5 | 387 | 2508 |
| swh-por | flores101-devtest | 0.40847 | 14.7 | 1012 | 26519 |
| kin-eng | flores200-devtest | 0.41964 | 18.1 | 1012 | 24721 |
| nso-eng | flores200-devtest | 0.45662 | 22.3 | 1012 | 24721 |
| sna-eng | flores200-devtest | 0.41974 | 17.2 | 1012 | 24721 |
| sot-eng | flores200-devtest | 0.45415 | 20.7 | 1012 | 24721 |
| swh-eng | flores200-devtest | 0.54048 | 29.1 | 1012 | 24721 |
| swh-fra | flores200-devtest | 0.44837 | 18.2 | 1012 | 28343 |
| swh-por | flores200-devtest | 0.44062 | 17.6 | 1012 | 26519 |
| tsn-eng | flores200-devtest | 0.40410 | 15.3 | 1012 | 24721 |
| tso-eng | flores200-devtest | 0.41504 | 17.6 | 1012 | 24721 |
| xho-eng | flores200-devtest | 0.47667 | 23.7 | 1012 | 24721 |
| zul-eng | flores200-devtest | 0.47798 | 23.4 | 1012 | 24721 |
| ibo-eng | ntrex128 | 0.42002 | 17.4 | 1997 | 47673 |
| kin-eng | ntrex128 | 0.42892 | 16.9 | 1997 | 47673 |
| nso-eng | ntrex128 | 0.42278 | 17.0 | 1997 | 47673 |
| nya-eng | ntrex128 | 0.42702 | 19.2 | 1997 | 47673 |
| ssw-eng | ntrex128 | 0.43041 | 18.0 | 1997 | 47673 |
| swa-eng | ntrex128 | 0.54492 | 30.4 | 1997 | 47673 |
| swa-fra | ntrex128 | 0.43008 | 15.6 | 1997 | 53481 |
| swa-por | ntrex128 | 0.42343 | 15.4 | 1997 | 51631 |
| swa-spa | ntrex128 | 0.44892 | 18.9 | 1997 | 54107 |
| tsn-eng | ntrex128 | 0.44944 | 20.1 | 1997 | 47673 |
| xho-eng | ntrex128 | 0.46636 | 21.8 | 1997 | 47673 |
| zul-eng | ntrex128 | 0.45848 | 21.9 | 1997 | 47673 |
| zul-eng | tico19-test | 0.48762 | 25.2 | 2100 | 56804 |
| zul-spa | tico19-test | 0.40041 | 15.9 | 2100 | 66563 |

## 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 17:13:22 EEST 2024
* port machine: LM0-400-22516.local
