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---
library_name: transformers
language:
- bru
- cmo
- de
- en
- es
- fr
- kha
- km
- kxm
- mnw
- ngt
- pt
- 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-mkh
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: 33.9
- name: chr-F
type: chrf
value: 0.53535
- 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: 42.6
- name: chr-F
type: chrf
value: 0.60021
- 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: 0.54168
- 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: 35.9
- name: chr-F
type: chrf
value: 0.55046
- 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: 0.50262
- 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: 33.9
- name: chr-F
type: chrf
value: 0.53623
- 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: 42.7
- name: chr-F
type: chrf
value: 0.59986
- 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: 35.7
- name: chr-F
type: chrf
value: 0.54819
- task:
name: Translation deu-vie
type: translation
args: deu-vie
dataset:
name: ntrex128
type: ntrex128
args: deu-vie
metrics:
- name: BLEU
type: bleu
value: 31.2
- name: chr-F
type: chrf
value: 0.51996
- task:
name: Translation eng-vie
type: translation
args: eng-vie
dataset:
name: ntrex128
type: ntrex128
args: eng-vie
metrics:
- name: BLEU
type: bleu
value: 42.7
- name: chr-F
type: chrf
value: 0.60050
- task:
name: Translation fra-vie
type: translation
args: fra-vie
dataset:
name: ntrex128
type: ntrex128
args: fra-vie
metrics:
- name: BLEU
type: bleu
value: 31.7
- name: chr-F
type: chrf
value: 0.51988
- 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: 0.52931
- task:
name: Translation spa-vie
type: translation
args: spa-vie
dataset:
name: ntrex128
type: ntrex128
args: spa-vie
metrics:
- name: BLEU
type: bleu
value: 33.1
- name: chr-F
type: chrf
value: 0.53347
- 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: 25.3
- name: chr-F
type: chrf
value: 0.45222
- 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: 39.0
- name: chr-F
type: chrf
value: 0.56413
- 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: 35.6
- name: chr-F
type: chrf
value: 0.53078
- 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: 0.43068
- 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: 34.0
- name: chr-F
type: chrf
value: 0.51783
---
# opus-mt-tc-bible-big-deu_eng_fra_por_spa-mkh
## 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 Mon-Khmer languages (mkh).
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): deu eng fra por spa
- Target Language(s): bru cmo kha khm kxm mnw ngt vie wbm
- Valid Target Language Labels: >>aem<< >>alk<< >>aml<< >>bbh<< >>bdq<< >>bgk<< >>bgl<< >>blr<< >>brb<< >>bru<< >>brv<< >>btq<< >>caq<< >>cbn<< >>cma<< >>cmo<< >>cog<< >>crv<< >>crw<< >>cua<< >>cwg<< >>dnu<< >>hal<< >>hld<< >>hnu<< >>hre<< >>huo<< >>jah<< >>jeh<< >>jhi<< >>kdt<< >>kha<< >>khf<< >>khm<< >>kjg<< >>kjm<< >>knq<< >>kns<< >>kpm<< >>krr<< >>krv<< >>kta<< >>ktv<< >>kuf<< >>kxm<< >>kxy<< >>lbn<< >>lbo<< >>lcp<< >>lnh<< >>lwl<< >>lyg<< >>mef<< >>mhe<< >>mlf<< >>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<< >>phg<< >>pkt<< >>pll<< >>ply<< >>pnx<< >>prk<< >>prt<< >>puo<< >>rbb<< >>ren<< >>ril<< >>rka<< >>rmx<< >>sbo<< >>scb<< >>scq<< >>sct<< >>sea<< >>sed<< >>sii<< >>smu<< >>spu<< >>sqq<< >>ssm<< >>sss<< >>stg<< >>sti<< >>stt<< >>stu<< >>syo<< >>sza<< >>szc<< >>tdf<< >>tdr<< >>tea<< >>tef<< >>thm<< >>tkz<< >>tlq<< >>tmo<< >>tnz<< >>tou<< >>tpu<< >>tth<< >>tto<< >>tyh<< >>uuu<< >>vie<< >>vwa<< >>wbm<< >>xao<< >>xkk<< >>xnh<< >>xxx<< >>yin<< >>zng<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-30.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-mkh/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/deu%2Beng%2Bfra%2Bpor%2Bspa-mkh/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. `>>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 = [
">>bru<< Replace this with text in an accepted source language.",
">>wbm<< This is the second sentence."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-mkh"
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-deu_eng_fra_por_spa-mkh")
print(pipe(">>bru<< 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/deu+eng+fra+por+spa-mkh/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/deu%2Beng%2Bfra%2Bpor%2Bspa-mkh/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/deu+eng+fra+por+spa-mkh/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-mkh/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 | 0.45222 | 25.3 | 400 | 3768 |
| eng-kha | tatoeba-test-v2021-08-07 | 9.076 | 0.4 | 1314 | 9269 |
| eng-vie | tatoeba-test-v2021-08-07 | 0.56413 | 39.0 | 2500 | 24427 |
| fra-vie | tatoeba-test-v2021-08-07 | 0.53078 | 35.6 | 1299 | 13219 |
| spa-vie | tatoeba-test-v2021-08-07 | 0.51783 | 34.0 | 594 | 4740 |
| deu-vie | flores101-devtest | 0.53623 | 33.9 | 1012 | 33331 |
| eng-khm | flores101-devtest | 0.42022 | 1.4 | 1012 | 7006 |
| eng-vie | flores101-devtest | 0.59986 | 42.7 | 1012 | 33331 |
| por-vie | flores101-devtest | 0.54819 | 35.7 | 1012 | 33331 |
| deu-vie | flores200-devtest | 0.53535 | 33.9 | 1012 | 33331 |
| eng-khm | flores200-devtest | 0.41987 | 1.3 | 1012 | 7006 |
| eng-vie | flores200-devtest | 0.60021 | 42.6 | 1012 | 33331 |
| fra-khm | flores200-devtest | 0.40241 | 2.3 | 1012 | 7006 |
| fra-vie | flores200-devtest | 0.54168 | 34.6 | 1012 | 33331 |
| por-khm | flores200-devtest | 0.41582 | 2.3 | 1012 | 7006 |
| por-vie | flores200-devtest | 0.55046 | 35.9 | 1012 | 33331 |
| spa-vie | flores200-devtest | 0.50262 | 28.1 | 1012 | 33331 |
| deu-khm | ntrex128 | 0.44917 | 3.2 | 1997 | 15866 |
| deu-vie | ntrex128 | 0.51996 | 31.2 | 1997 | 64655 |
| eng-khm | ntrex128 | 0.50215 | 1.6 | 1997 | 15866 |
| eng-vie | ntrex128 | 0.60050 | 42.7 | 1997 | 64655 |
| fra-khm | ntrex128 | 0.44024 | 2.3 | 1997 | 15866 |
| fra-vie | ntrex128 | 0.51988 | 31.7 | 1997 | 64655 |
| por-khm | ntrex128 | 0.46752 | 2.4 | 1997 | 15866 |
| por-vie | ntrex128 | 0.52931 | 33.3 | 1997 | 64655 |
| spa-khm | ntrex128 | 0.46166 | 2.5 | 1997 | 15866 |
| spa-vie | ntrex128 | 0.53347 | 33.1 | 1997 | 64655 |
| eng-khm | tico19-test | 0.54267 | 3.4 | 2100 | 15810 |
| fra-khm | tico19-test | 0.45333 | 4.8 | 2100 | 15810 |
| por-khm | tico19-test | 0.52339 | 6.8 | 2100 | 15810 |
| spa-khm | tico19-test | 0.51848 | 6.8 | 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 10:19:52 EEST 2024
* port machine: LM0-400-22516.local
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