README.md

---
library_name: transformers
language:
- br
- cy
- de
- en
- es
- fr
- ga
- gd
- gv
- kw
- pt

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

license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-cel-deu_eng_fra_por_spa
  results:
  - task:
      name: Translation cym-deu
      type: translation
      args: cym-deu
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: cym-deu
    metrics:
       - name: BLEU
         type: bleu
         value: 22.6
       - name: chr-F
         type: chrf
         value: 0.52745
  - task:
      name: Translation cym-eng
      type: translation
      args: cym-eng
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: cym-eng
    metrics:
       - name: BLEU
         type: bleu
         value: 55.5
       - name: chr-F
         type: chrf
         value: 0.75234
  - task:
      name: Translation cym-fra
      type: translation
      args: cym-fra
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: cym-fra
    metrics:
       - name: BLEU
         type: bleu
         value: 31.4
       - name: chr-F
         type: chrf
         value: 0.58339
  - task:
      name: Translation cym-por
      type: translation
      args: cym-por
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: cym-por
    metrics:
       - name: BLEU
         type: bleu
         value: 18.3
       - name: chr-F
         type: chrf
         value: 0.47566
  - task:
      name: Translation cym-spa
      type: translation
      args: cym-spa
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: cym-spa
    metrics:
       - name: BLEU
         type: bleu
         value: 19.9
       - name: chr-F
         type: chrf
         value: 0.48834
  - task:
      name: Translation gla-deu
      type: translation
      args: gla-deu
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gla-deu
    metrics:
       - name: BLEU
         type: bleu
         value: 13.0
       - name: chr-F
         type: chrf
         value: 0.41962
  - task:
      name: Translation gla-eng
      type: translation
      args: gla-eng
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gla-eng
    metrics:
       - name: BLEU
         type: bleu
         value: 26.4
       - name: chr-F
         type: chrf
         value: 0.53374
  - task:
      name: Translation gla-fra
      type: translation
      args: gla-fra
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gla-fra
    metrics:
       - name: BLEU
         type: bleu
         value: 16.6
       - name: chr-F
         type: chrf
         value: 0.44916
  - task:
      name: Translation gla-por
      type: translation
      args: gla-por
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gla-por
    metrics:
       - name: BLEU
         type: bleu
         value: 12.1
       - name: chr-F
         type: chrf
         value: 0.39790
  - task:
      name: Translation gla-spa
      type: translation
      args: gla-spa
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gla-spa
    metrics:
       - name: BLEU
         type: bleu
         value: 12.9
       - name: chr-F
         type: chrf
         value: 0.40375
  - task:
      name: Translation gle-deu
      type: translation
      args: gle-deu
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gle-deu
    metrics:
       - name: BLEU
         type: bleu
         value: 19.2
       - name: chr-F
         type: chrf
         value: 0.49962
  - task:
      name: Translation gle-eng
      type: translation
      args: gle-eng
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gle-eng
    metrics:
       - name: BLEU
         type: bleu
         value: 38.9
       - name: chr-F
         type: chrf
         value: 0.64866
  - task:
      name: Translation gle-fra
      type: translation
      args: gle-fra
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gle-fra
    metrics:
       - name: BLEU
         type: bleu
         value: 26.7
       - name: chr-F
         type: chrf
         value: 0.54564
  - task:
      name: Translation gle-por
      type: translation
      args: gle-por
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gle-por
    metrics:
       - name: BLEU
         type: bleu
         value: 14.9
       - name: chr-F
         type: chrf
         value: 0.44768
  - task:
      name: Translation gle-spa
      type: translation
      args: gle-spa
    dataset:
      name: flores200-devtest
      type: flores200-devtest
      args: gle-spa
    metrics:
       - name: BLEU
         type: bleu
         value: 18.7
       - name: chr-F
         type: chrf
         value: 0.47347
  - task:
      name: Translation cym-deu
      type: translation
      args: cym-deu
    dataset:
      name: flores101-devtest
      type: flores_101
      args: cym deu devtest
    metrics:
       - name: BLEU
         type: bleu
         value: 22.4
       - name: chr-F
         type: chrf
         value: 0.52672
  - task:
      name: Translation cym-fra
      type: translation
      args: cym-fra
    dataset:
      name: flores101-devtest
      type: flores_101
      args: cym fra devtest
    metrics:
       - name: BLEU
         type: bleu
         value: 31.3
       - name: chr-F
         type: chrf
         value: 0.58299
  - task:
      name: Translation cym-por
      type: translation
      args: cym-por
    dataset:
      name: flores101-devtest
      type: flores_101
      args: cym por devtest
    metrics:
       - name: BLEU
         type: bleu
         value: 18.4
       - name: chr-F
         type: chrf
         value: 0.47733
  - task:
      name: Translation gle-eng
      type: translation
      args: gle-eng
    dataset:
      name: flores101-devtest
      type: flores_101
      args: gle eng devtest
    metrics:
       - name: BLEU
         type: bleu
         value: 38.6
       - name: chr-F
         type: chrf
         value: 0.64773
  - task:
      name: Translation gle-fra
      type: translation
      args: gle-fra
    dataset:
      name: flores101-devtest
      type: flores_101
      args: gle fra devtest
    metrics:
       - name: BLEU
         type: bleu
         value: 26.5
       - name: chr-F
         type: chrf
         value: 0.54559
  - task:
      name: Translation cym-deu
      type: translation
      args: cym-deu
    dataset:
      name: ntrex128
      type: ntrex128
      args: cym-deu
    metrics:
       - name: BLEU
         type: bleu
         value: 16.3
       - name: chr-F
         type: chrf
         value: 0.46627
  - task:
      name: Translation cym-eng
      type: translation
      args: cym-eng
    dataset:
      name: ntrex128
      type: ntrex128
      args: cym-eng
    metrics:
       - name: BLEU
         type: bleu
         value: 40.0
       - name: chr-F
         type: chrf
         value: 0.65343
  - task:
      name: Translation cym-fra
      type: translation
      args: cym-fra
    dataset:
      name: ntrex128
      type: ntrex128
      args: cym-fra
    metrics:
       - name: BLEU
         type: bleu
         value: 23.8
       - name: chr-F
         type: chrf
         value: 0.51183
  - task:
      name: Translation cym-por
      type: translation
      args: cym-por
    dataset:
      name: ntrex128
      type: ntrex128
      args: cym-por
    metrics:
       - name: BLEU
         type: bleu
         value: 14.4
       - name: chr-F
         type: chrf
         value: 0.42857
  - task:
      name: Translation cym-spa
      type: translation
      args: cym-spa
    dataset:
      name: ntrex128
      type: ntrex128
      args: cym-spa
    metrics:
       - name: BLEU
         type: bleu
         value: 25.0
       - name: chr-F
         type: chrf
         value: 0.51542
  - task:
      name: Translation gle-deu
      type: translation
      args: gle-deu
    dataset:
      name: ntrex128
      type: ntrex128
      args: gle-deu
    metrics:
       - name: BLEU
         type: bleu
         value: 15.5
       - name: chr-F
         type: chrf
         value: 0.46495
  - task:
      name: Translation gle-eng
      type: translation
      args: gle-eng
    dataset:
      name: ntrex128
      type: ntrex128
      args: gle-eng
    metrics:
       - name: BLEU
         type: bleu
         value: 33.5
       - name: chr-F
         type: chrf
         value: 0.60913
  - task:
      name: Translation gle-fra
      type: translation
      args: gle-fra
    dataset:
      name: ntrex128
      type: ntrex128
      args: gle-fra
    metrics:
       - name: BLEU
         type: bleu
         value: 20.7
       - name: chr-F
         type: chrf
         value: 0.49513
  - task:
      name: Translation gle-por
      type: translation
      args: gle-por
    dataset:
      name: ntrex128
      type: ntrex128
      args: gle-por
    metrics:
       - name: BLEU
         type: bleu
         value: 13.2
       - name: chr-F
         type: chrf
         value: 0.41767
  - task:
      name: Translation gle-spa
      type: translation
      args: gle-spa
    dataset:
      name: ntrex128
      type: ntrex128
      args: gle-spa
    metrics:
       - name: BLEU
         type: bleu
         value: 23.6
       - name: chr-F
         type: chrf
         value: 0.50755
  - task:
      name: Translation bre-eng
      type: translation
      args: bre-eng
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: bre-eng
    metrics:
       - name: BLEU
         type: bleu
         value: 35.0
       - name: chr-F
         type: chrf
         value: 0.53473
  - task:
      name: Translation bre-fra
      type: translation
      args: bre-fra
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: bre-fra
    metrics:
       - name: BLEU
         type: bleu
         value: 28.3
       - name: chr-F
         type: chrf
         value: 0.49013
  - task:
      name: Translation cym-eng
      type: translation
      args: cym-eng
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: cym-eng
    metrics:
       - name: BLEU
         type: bleu
         value: 52.4
       - name: chr-F
         type: chrf
         value: 0.68892
  - task:
      name: Translation gla-eng
      type: translation
      args: gla-eng
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: gla-eng
    metrics:
       - name: BLEU
         type: bleu
         value: 23.2
       - name: chr-F
         type: chrf
         value: 0.39607
  - task:
      name: Translation gla-spa
      type: translation
      args: gla-spa
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: gla-spa
    metrics:
       - name: BLEU
         type: bleu
         value: 26.1
       - name: chr-F
         type: chrf
         value: 0.51208
  - task:
      name: Translation gle-eng
      type: translation
      args: gle-eng
    dataset:
      name: tatoeba-test-v2021-08-07
      type: tatoeba_mt
      args: gle-eng
    metrics:
       - name: BLEU
         type: bleu
         value: 50.7
       - name: chr-F
         type: chrf
         value: 0.64268
  - 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: 24.9
       - name: chr-F
         type: chrf
         value: 0.42670
---
# opus-mt-tc-bible-big-cel-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 Celtic languages (cel) 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): bre cor cym gla gle glv
  - 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/cel-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/cel-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-cel-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-cel-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/cel-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/cel-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/cel-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/cel-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 |
|----------|---------|-------|-------|-------|--------|
| bre-eng | tatoeba-test-v2021-08-07 | 0.53473 | 35.0 | 383 | 2065 |
| bre-fra | tatoeba-test-v2021-08-07 | 0.49013 | 28.3 | 2494 | 13324 |
| cym-eng | tatoeba-test-v2021-08-07 | 0.68892 | 52.4 | 818 | 5563 |
| gla-eng | tatoeba-test-v2021-08-07 | 0.39607 | 23.2 | 955 | 6611 |
| gla-spa | tatoeba-test-v2021-08-07 | 0.51208 | 26.1 | 289 | 1608 |
| gle-eng | tatoeba-test-v2021-08-07 | 0.64268 | 50.7 | 1913 | 11190 |
| cym-deu | flores101-devtest | 0.52672 | 22.4 | 1012 | 25094 |
| cym-fra | flores101-devtest | 0.58299 | 31.3 | 1012 | 28343 |
| cym-por | flores101-devtest | 0.47733 | 18.4 | 1012 | 26519 |
| gle-eng | flores101-devtest | 0.64773 | 38.6 | 1012 | 24721 |
| gle-fra | flores101-devtest | 0.54559 | 26.5 | 1012 | 28343 |
| cym-deu | flores200-devtest | 0.52745 | 22.6 | 1012 | 25094 |
| cym-eng | flores200-devtest | 0.75234 | 55.5 | 1012 | 24721 |
| cym-fra | flores200-devtest | 0.58339 | 31.4 | 1012 | 28343 |
| cym-por | flores200-devtest | 0.47566 | 18.3 | 1012 | 26519 |
| cym-spa | flores200-devtest | 0.48834 | 19.9 | 1012 | 29199 |
| gla-deu | flores200-devtest | 0.41962 | 13.0 | 1012 | 25094 |
| gla-eng | flores200-devtest | 0.53374 | 26.4 | 1012 | 24721 |
| gla-fra | flores200-devtest | 0.44916 | 16.6 | 1012 | 28343 |
| gla-spa | flores200-devtest | 0.40375 | 12.9 | 1012 | 29199 |
| gle-deu | flores200-devtest | 0.49962 | 19.2 | 1012 | 25094 |
| gle-eng | flores200-devtest | 0.64866 | 38.9 | 1012 | 24721 |
| gle-fra | flores200-devtest | 0.54564 | 26.7 | 1012 | 28343 |
| gle-por | flores200-devtest | 0.44768 | 14.9 | 1012 | 26519 |
| gle-spa | flores200-devtest | 0.47347 | 18.7 | 1012 | 29199 |
| cym-deu | ntrex128 | 0.46627 | 16.3 | 1997 | 48761 |
| cym-eng | ntrex128 | 0.65343 | 40.0 | 1997 | 47673 |
| cym-fra | ntrex128 | 0.51183 | 23.8 | 1997 | 53481 |
| cym-por | ntrex128 | 0.42857 | 14.4 | 1997 | 51631 |
| cym-spa | ntrex128 | 0.51542 | 25.0 | 1997 | 54107 |
| gle-deu | ntrex128 | 0.46495 | 15.5 | 1997 | 48761 |
| gle-eng | ntrex128 | 0.60913 | 33.5 | 1997 | 47673 |
| gle-fra | ntrex128 | 0.49513 | 20.7 | 1997 | 53481 |
| gle-por | ntrex128 | 0.41767 | 13.2 | 1997 | 51631 |
| gle-spa | ntrex128 | 0.50755 | 23.6 | 1997 | 54107 |

## 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 23:09:42 EEST 2024
* port machine: LM0-400-22516.local