transformers pytorch safetensors marian text2text-generation translation opus-mt-tc-bible aai ace agn aia akl alj alp amk aoz apr atq aui ban bcl bep bhz bik bku blz bmk bnp bpr bps btd bth bto bts btx bug buk bvy bzh ceb cgc ch dad dob dtp dww emi en far fil fj frd gfk gil gor haw hil hla hnn hot hvn iba id ifa ifb ifk ifu ify ilo iry itv jv jvn kbm khz kje kne kpg kqe kqf kqw krj kud kwf kzf laa law lcm leu lew lex lid ljp lnd mad mak mbb mbf mbt mee mek mg mgm mh mhy mi mmo mmx mna mnb mog mox mpx mqj mrw ms msm mta mva mvp mvv mwc mwv myw mzz na nak nia nij niu npy nsn nss nwi obo pag pam pau plw pmf pmy pne ppk prf ptp ptu pwg rai rap rej rro rug sas sbl sda sgb sgz sm smk sml snc sps stn su swp sxn tbc tbl tbo tet tgo tgp tkl tl tlx to tpa tpz tte tuc tvl twb twu txa ty ubr uvl viv war wed wuv xsb xsi yml license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
language:
- aai
- ace
- agn
- aia
- akl
- alj
- alp
- amk
- aoz
- apr
- atq
- aui
- ban
- bcl
- bep
- bhz
- bik
- bku
- blz
- bmk
- bnp
- bpr
- bps
- btd
- bth
- bto
- bts
- btx
- bug
- buk
- bvy
- bzh
- ceb
- cgc
- ch
- dad
- dob
- dtp
- dww
- emi
- en
- far
- fil
- fj
- frd
- gfk
- gil
- gor
- haw
- hil
- hla
- hnn
- hot
- hvn
- iba
- id
- ifa
- ifb
- ifk
- ifu
- ify
- ilo
- iry
- itv
- jv
- jvn
- kbm
- khz
- kje
- kne
- kpg
- kqe
- kqf
- kqw
- krj
- kud
- kwf
- kzf
- laa
- law
- lcm
- leu
- lew
- lex
- lid
- ljp
- lnd
- mad
- mak
- mbb
- mbf
- mbt
- mee
- mek
- mg
- mgm
- mh
- mhy
- mi
- mmo
- mmx
- mna
- mnb
- mog
- mox
- mpx
- mqj
- mrw
- ms
- msm
- mta
- mva
- mvp
- mvv
- mwc
- mwv
- myw
- mzz
- na
- nak
- nia
- nij
- niu
- npy
- nsn
- nss
- nwi
- obo
- pag
- pam
- pau
- plw
- pmf
- pmy
- pne
- ppk
- prf
- ptp
- ptu
- pwg
- rai
- rap
- rej
- rro
- rug
- sas
- sbl
- sda
- sgb
- sgz
- sm
- smk
- sml
- snc
- sps
- stn
- su
- swp
- sxn
- tbc
- tbl
- tbo
- tet
- tgo
- tgp
- tkl
- tl
- tlx
- to
- tpa
- tpz
- tte
- tuc
- tvl
- twb
- twu
- txa
- ty
- ubr
- uvl
- viv
- war
- wed
- wuv
- xsb
- xsi
- yml
library_name: transformers
license: apache-2.0
tags:
- translation
- opus-mt-tc-bible
model-index:
- name: opus-mt-tc-bible-big-poz-en
  results:
  - task:
      type: translation
      name: Translation multi-eng
    dataset:
      name: tatoeba-test-v2020-07-28-v2023-09-26
      type: tatoeba_mt
      args: multi-eng
    metrics:
    - type: bleu
      value: 30.5
      name: BLEU
    - type: chrf
      value: 0.48821
      name: chr-F
---
# opus-mt-tc-bible-big-poz-en

## 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 Malayo-Polynesian languages (poz) to English (en).

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 akl alj alp amk aoz apr atq aui ban bcl bep bhz bik bku blz bmk bnp bpr bps btd bth bto bts btx bug buk bvy 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 laa law lcm leu lew lex lid ljp lnd mad mah mak max mbb mbf mbt mee mek mgm mhy mlg mmo mmx mna mnb mog mox mpx mqj mri mrw msa msm mta mva mvp mvv mwc mwv myw mzz nak nau nia nij niu npy nsn nss nwi obo pag pam pau plt plw pmf pmy pne ppk prf ptp ptu pwg rai rap 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 tkl tlx tmw ton tpa tpz tte tuc tvl twb twu txa ubr uvl viv war wed wuv xsb xsi yml zlm zsm
  - Target Language(s): eng
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-08-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/poz-eng/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/poz-eng/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/)

## 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 = [
    "Dapat sila'y may mga ideyang pangahas.",
    "Dia memang seorang pekerja keras."
]

model_name = "pytorch-models/opus-mt-tc-bible-big-poz-en"
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:
#     They should have some ideas.
#     He was a hard worker.
```

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-poz-en")
print(pipe("Dapat sila'y may mga ideyang pangahas."))

# expected output: They should have some ideas.
```

## 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/poz-eng/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/poz-eng/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/poz-eng/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/poz-eng/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-eng | tatoeba-test-v2020-07-28-v2023-09-26 | 0.48821 | 30.5 | 10000 | 75409 |

## 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 13:00:57 EEST 2024
* port machine: LM0-400-22516.local
