README.md

---
library_name: transformers
language:
- bru
- cmo
- de
- en
- es
- fr
- hoc
- jun
- kha
- km
- kxm
- mnw
- ngt
- pt
- sat
- vi
- wbm

tags:
- translation
- opus-mt-tc-bible

license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-deu_eng_fra_por_spa-aav
  results:
  - task:
      name: Translation deu-vie
      type: translation
      args: deu-vie
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: deu-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 34.0
       - name: chr-F
         type: chrf
         value: 0.53671
  - task:
      name: Translation eng-vie
      type: translation
      args: eng-vie
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: eng-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 42.4
       - name: chr-F
         type: chrf
         value: 0.59842
  - task:
      name: Translation fra-vie
      type: translation
      args: fra-vie
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: fra-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 34.6
       - name: chr-F
         type: chrf
         value: 0.54101
  - task:
      name: Translation por-vie
      type: translation
      args: por-vie
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: por-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 36.1
       - name: chr-F
         type: chrf
         value: 0.54970
  - task:
      name: Translation spa-vie
      type: translation
      args: spa-vie
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: spa-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 28.1
       - name: chr-F
         type: chrf
         value: 0.50025
  - task:
      name: Translation deu-vie
      type: translation
      args: deu-vie
    dataset:
      name: flores101-devtest
      type: flores_101
      args: deu vie devtest
    metrics:
       - name: BLEU
         type: bleu
         value: 33.8
       - name: chr-F
         type: chrf
         value: 0.53381
  - task:
      name: Translation eng-vie
      type: translation
      args: eng-vie
    dataset:
      name: flores101-devtest
      type: flores_101
      args: eng vie devtest
    metrics:
       - name: BLEU
         type: bleu
         value: 42.1
       - name: chr-F
         type: chrf
         value: 0.59621
  - task:
      name: Translation por-vie
      type: translation
      args: por-vie
    dataset:
      name: flores101-devtest
      type: flores_101
      args: por vie devtest
    metrics:
       - name: BLEU
         type: bleu
         value: 36.0
       - name: chr-F
         type: chrf
         value: 0.54919
  - task:
      name: Translation spa-vie
      type: translation
      args: spa-vie
    dataset:
      name: flores101-devtest
      type: flores_101
      args: spa vie devtest
    metrics:
       - name: BLEU
         type: bleu
         value: 27.8
       - name: chr-F
         type: chrf
         value: 0.49921
  - task:
      name: Translation deu-vie
      type: translation
      args: deu-vie
    dataset:
      name: ntrex128
      type: ntrex128
      args: deu-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 31.4
       - name: chr-F
         type: chrf
         value: 0.52124
  - task:
      name: Translation fra-vie
      type: translation
      args: fra-vie
    dataset:
      name: ntrex128
      type: ntrex128
      args: fra-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 31.8
       - name: chr-F
         type: chrf
         value: 0.52044
  - task:
      name: Translation por-vie
      type: translation
      args: por-vie
    dataset:
      name: ntrex128
      type: ntrex128
      args: por-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 33.3
       - name: chr-F
         type: chrf
         value: 0.53060
  - task:
      name: Translation spa-vie
      type: translation
      args: spa-vie
    dataset:
      name: ntrex128
      type: ntrex128
      args: spa-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 33.4
       - name: chr-F
         type: chrf
         value: 0.53293
  - task:
      name: Translation deu-vie
      type: translation
      args: deu-vie
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: deu-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 25.6
       - name: chr-F
         type: chrf
         value: 0.45795
  - task:
      name: Translation eng-vie
      type: translation
      args: eng-vie
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: eng-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 39.4
       - name: chr-F
         type: chrf
         value: 0.56461
  - task:
      name: Translation fra-vie
      type: translation
      args: fra-vie
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: fra-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 35.2
       - name: chr-F
         type: chrf
         value: 0.52806
  - task:
      name: Translation multi-multi
      type: translation
      args: multi-multi
    dataset:
      name: tatoeba-test-v2020-07-28-v2023-09-26
      type: tatoeba_mt
      args: multi-multi
    metrics:
       - name: BLEU
         type: bleu
         value: 22.9
       - name: chr-F
         type: chrf
         value: 0.40649
  - task:
      name: Translation spa-vie
      type: translation
      args: spa-vie
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: spa-vie
    metrics:
       - name: BLEU
         type: bleu
         value: 34.2
       - name: chr-F
         type: chrf
         value: 0.52131
---
# opus-mt-tc-bible-big-deu_eng_fra_por_spa-aav

## 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 Austro-Asiatic languages (aav).

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): deu eng fra por spa
  - Target Language(s): bru cmo hoc jun kha khm kxm mnw ngt sat vie wbm
  - Valid Target Language Labels: >>aem<< >>alk<< >>aml<< >>asr<< >>bbh<< >>bdq<< >>bfw<< >>bgk<< >>bgl<< >>bix<< >>biy<< >>blr<< >>brb<< >>bru<< >>brv<< >>btq<< >>caq<< >>cbn<< >>cdz<< >>cma<< >>cmo<< >>cog<< >>crv<< >>crw<< >>cua<< >>cwg<< >>dnu<< >>ekl<< >>gaq<< >>gbj<< >>hal<< >>hld<< >>hnu<< >>hoc<< >>hoc_Wara<< >>hre<< >>huo<< >>irr<< >>jah<< >>jeh<< >>jhi<< >>jun<< >>juy<< >>kdt<< >>kfp<< >>kfq<< >>kha<< >>khf<< >>khm<< >>khr<< >>kjg<< >>kjm<< >>knq<< >>kns<< >>kpm<< >>krr<< >>krv<< >>ksz<< >>kta<< >>ktv<< >>kuf<< >>kxm<< >>kxy<< >>lbn<< >>lbo<< >>lcp<< >>lnh<< >>lwl<< >>lyg<< >>mef<< >>mhe<< >>mjx<< >>mlf<< >>mmj<< >>mml<< >>mng<< >>mnn<< >>mnq<< >>mnw<< >>moo<< >>mqt<< >>mra<< >>mtq<< >>mzt<< >>ncb<< >>ncq<< >>nev<< >>ngt<< >>ngt_Latn<< >>nik<< >>nuo<< >>nyl<< >>omx<< >>oog<< >>oyb<< >>pac<< >>pbv<< >>pcb<< >>pce<< >>pcj<< >>phg<< >>pkt<< >>pll<< >>ply<< >>pnx<< >>prk<< >>prt<< >>puo<< >>rbb<< >>ren<< >>ril<< >>rka<< >>rmx<< >>sat<< >>sat_Latn<< >>sbo<< >>scb<< >>scq<< >>sct<< >>sea<< >>sed<< >>sii<< >>smu<< >>spu<< >>sqq<< >>srb<< >>ssm<< >>sss<< >>stg<< >>sti<< >>stt<< >>stu<< >>syo<< >>sza<< >>szc<< >>tdf<< >>tdr<< >>tea<< >>tef<< >>thm<< >>tkz<< >>tlq<< >>tmo<< >>tnz<< >>tou<< >>tpu<< >>trd<< >>tth<< >>tto<< >>tyh<< >>unr<< >>uuu<< >>vie<< >>vwa<< >>wbm<< >>xao<< >>xkk<< >>xnh<< >>xxx<< >>yin<< >>zng<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-aav/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/deu%2Beng%2Bfra%2Bpor%2Bspa-aav/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. `>>bru<<`

## 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 = [
    ">>khm<< Der Junge wirft einen Stein.",
    ">>vie<< ¿Y tú?"
]

model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-aav"
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:
#     ក្មេងប្រុស នោះ យក ដុំ ថ្ម គប់ ។
#     Còn anh thì sao?
```

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-aav")
print(pipe(">>khm<< Der Junge wirft einen Stein."))

# expected output: ក្មេងប្រុស នោះ យក ដុំ ថ្ម គប់ ។
```

## 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/deu+eng+fra+por+spa-aav/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/deu%2Beng%2Bfra%2Bpor%2Bspa-aav/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/deu+eng+fra+por+spa-aav/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-aav/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-vie | tatoeba-test-v2021-08-07 | 0.45795 | 25.6 | 400 | 3768 |
| eng-hoc | tatoeba-test-v2021-08-07 | 6.438 | 0.2 | 660 | 2591 |
| eng-kha | tatoeba-test-v2021-08-07 | 5.741 | 0.0 | 1314 | 9269 |
| eng-vie | tatoeba-test-v2021-08-07 | 0.56461 | 39.4 | 2500 | 24427 |
| fra-vie | tatoeba-test-v2021-08-07 | 0.52806 | 35.2 | 1299 | 13219 |
| spa-vie | tatoeba-test-v2021-08-07 | 0.52131 | 34.2 | 594 | 4740 |
| deu-vie | flores101-devtest | 0.53381 | 33.8 | 1012 | 33331 |
| eng-khm | flores101-devtest | 0.42302 | 1.3 | 1012 | 7006 |
| eng-vie | flores101-devtest | 0.59621 | 42.1 | 1012 | 33331 |
| fra-khm | flores101-devtest | 0.40042 | 2.2 | 1012 | 7006 |
| por-khm | flores101-devtest | 0.40585 | 2.1 | 1012 | 7006 |
| por-vie | flores101-devtest | 0.54919 | 36.0 | 1012 | 33331 |
| spa-vie | flores101-devtest | 0.49921 | 27.8 | 1012 | 33331 |
| deu-vie | flores200-devtest | 0.53671 | 34.0 | 1012 | 33331 |
| eng-khm | flores200-devtest | 0.42148 | 1.3 | 1012 | 7006 |
| eng-vie | flores200-devtest | 0.59842 | 42.4 | 1012 | 33331 |
| fra-vie | flores200-devtest | 0.54101 | 34.6 | 1012 | 33331 |
| por-khm | flores200-devtest | 0.40832 | 1.9 | 1012 | 7006 |
| por-vie | flores200-devtest | 0.54970 | 36.1 | 1012 | 33331 |
| spa-vie | flores200-devtest | 0.50025 | 28.1 | 1012 | 33331 |
| deu-khm | ntrex128 | 0.44903 | 3.5 | 1997 | 15866 |
| deu-vie | ntrex128 | 0.52124 | 31.4 | 1997 | 64655 |
| eng-khm | ntrex128 | 0.50494 | 1.6 | 1997 | 15866 |
| eng-vie | ntrex128 | 3.831 | 0.0 | 1997 | 64655 |
| fra-khm | ntrex128 | 0.43841 | 2.4 | 1997 | 15866 |
| fra-vie | ntrex128 | 0.52044 | 31.8 | 1997 | 64655 |
| por-khm | ntrex128 | 0.46655 | 2.5 | 1997 | 15866 |
| por-vie | ntrex128 | 0.53060 | 33.3 | 1997 | 64655 |
| spa-khm | ntrex128 | 0.46443 | 2.7 | 1997 | 15866 |
| spa-vie | ntrex128 | 0.53293 | 33.4 | 1997 | 64655 |
| eng-khm | tico19-test | 0.47806 | 2.5 | 2100 | 15810 |
| fra-khm | tico19-test | 3.268 | 1.0 | 2100 | 15810 |
| por-khm | tico19-test | 3.900 | 1.1 | 2100 | 15810 |
| spa-khm | tico19-test | 3.784 | 1.0 | 2100 | 15810 |

## 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 08:57:20 EEST 2024
* port machine: LM0-400-22516.local