README.md

---
library_name: transformers
language:
- bru
- cmo
- es
- fr
- hoc
- it
- jun
- kha
- km
- kxm
- pt
- vi
- wbm

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

license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-aav-fra_ita_por_spa
 results:
 - 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: 29.3
 - name: chr-F
 type: chrf
 value: 0.48590
---
# opus-mt-tc-bible-big-aav-fra_ita_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 Austro-Asiatic languages (aav) to unknown (fra+ita+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**: 2024-08-17
- **License:** Apache-2.0
- **Language(s):** 
 - Source Language(s): bru cmo hoc jun kha khm kxm vie wbm
 - Target Language(s): fra ita por spa
 - Valid Target Language Labels: >>fra<< >>ita<< >>por<< >>spa<< >>xxx<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/aav-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.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/aav-fra%2Bita%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
 - [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. `>>fra<<`

## 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 = [
 ">>spa<< Ani jur lyer bha shane kynsan-kynsan.",
 ">>spa<< Con bận à?"
]

model_name = "pytorch-models/opus-mt-tc-bible-big-aav-fra_ita_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:
# Ani jur lyer bha shane kynsan-kynsan.
# ¿Estás ocupado?
```

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-aav-fra_ita_por_spa")
print(pipe(">>spa<< Ani jur lyer bha shane kynsan-kynsan."))

# expected output: Ani jur lyer bha shane kynsan-kynsan.
```

## 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_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/aav-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.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/aav-fra%2Bita%2Bpor%2Bspa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-08-17)
* test set translations: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aav-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.test.txt)
* test set scores: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/aav-fra+ita+por+spa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.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 |
|----------|---------|-------|-------|-------|--------|
| multi-multi | tatoeba-test-v2020-07-28-v2023-09-26 | 0.48590 | 29.3 | 3861 | 27219 |

## 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 17:06:57 EEST 2024
* port machine: LM0-400-22516.local