README.md

---
library_name: transformers
language:
- aai
- ace
- agn
- aia
- alj
- alp
- amk
- aoz
- apr
- atq
- aui
- ban
- bcl
- bep
- bhz
- bku
- blz
- bmk
- bnp
- bpr
- bps
- btd
- bth
- bto
- bts
- btx
- bug
- buk
- bzh
- ceb
- cgc
- ch
- dad
- dob
- dtp
- dww
- emi
- es
- far
- fil
- fj
- fr
- frd
- gfk
- gil
- gor
- haw
- hil
- hla
- hnn
- hot
- hvn
- iba
- id
- ifa
- ifb
- ifk
- ifu
- ify
- ilo
- iry
- it
- itv
- jv
- jvn
- kbm
- khz
- kje
- kne
- kpg
- kqe
- kqf
- kqw
- krj
- kud
- kwf
- kzf
- law
- lcm
- leu
- lew
- lex
- lid
- ljp
- lnd
- mad
- mak
- mbb
- mbf
- mbt
- mee
- mek
- mg
- mh
- mhy
- mi
- mmo
- mmx
- mna
- mnb
- mog
- mox
- mpx
- mqj
- mrw
- ms
- msm
- mta
- mva
- mvp
- mwc
- mwv
- myw
- mzz
- na
- nak
- nia
- nij
- npy
- nsn
- nss
- nwi
- obo
- pag
- pam
- pau
- plw
- pmf
- pne
- ppk
- prf
- pt
- ptp
- ptu
- pwg
- rai
- rej
- rro
- rug
- sas
- sbl
- sda
- sgb
- sgz
- sm
- smk
- sml
- snc
- sps
- stn
- su
- swp
- sxn
- tbc
- tbl
- tbo
- tet
- tgo
- tgp
- tl
- tlx
- to
- tpa
- tpz
- tte
- tuc
- twb
- twu
- txa
- ty
- ubr
- uvl
- viv
- war
- wed
- wuv
- xsb
- xsi
- yml

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

license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-map-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: 34.9
       - name: chr-F
         type: chrf
         value: 0.55799
---
# opus-mt-tc-bible-big-map-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 Austronesian languages (map) 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): aai ace agn aia alj alp amk aoz apr atq aui ban bcl bep bhz bku blz bmk bnp bpr bps btd bth bto bts btx bug buk bzh ceb cgc cha dad dob dtp dww emi far fij fil frd gfk gil gor haw hil hla hnn hot hvn iba ifa ifb ifk ifu ify ilo ind iry itv jak jav jvn kbm khz kje kne kpg kqe kqf kqw krj kud kwf kzf law lcm leu lew lex lid ljp lnd mad mah mak mbb mbf mbt mee mek mhy mlg mmo mmx mna mnb mog mox mpx mqj mri mrw msa msm mta mva mvp mwc mwv myw mzz nak nau nia nij npy nsn nss nwi obo pag pam pau plt plw pmf pne ppk prf ptp ptu pwg rai rej rro rug sas sbl sda sgb sgz smk sml smo snc sps stn sun swp sxn tah tbc tbl tbo tet tgl tgo tgp tlx ton tpa tpz tte tuc twb twu txa ubr uvl viv war wed wuv xsb xsi yml zsm
  - 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/map-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/map-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 = [
    ">>fra<< Bag-ong iroy akong gusto.",
    ">>fra<< Usá, duhá, tuló, upat, limá, unom, pitó, waló, siyam, napúlò."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-map-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:
#     J'ai gardé ma bouche, et je t'ai donné l'ordre de mon âme.
#     Il y avait là un homme, nommé d'une femme, qui était vêtu d'une longue robe, et qui avait une ceinture d'or autour de la poitrine.
```

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-map-fra_ita_por_spa")
print(pipe(">>fra<< Bag-ong iroy akong gusto."))

# expected output: J'ai gardé ma bouche, et je t'ai donné l'ordre de mon âme.
```

## 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/map-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/map-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/map-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/map-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 | 0.55799 | 34.9 | 2097 | 15222 |

## 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 12:10:43 EEST 2024
* port machine: LM0-400-22516.local