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---
library_name: transformers
language:
- de
- en
- es
- fr
- lt
- lv
- prg
- pt
- sgs
tags:
- translation
- opus-mt-tc-bible
license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-bat-deu_eng_fra_por_spa
results:
- task:
name: Translation lit-deu
type: translation
args: lit-deu
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-deu
metrics:
- name: BLEU
type: bleu
value: 23.7
- name: chr-F
type: chrf
value: 0.53223
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-eng
metrics:
- name: BLEU
type: bleu
value: 32.6
- name: chr-F
type: chrf
value: 0.59361
- task:
name: Translation lit-fra
type: translation
args: lit-fra
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-fra
metrics:
- name: BLEU
type: bleu
value: 30.0
- name: chr-F
type: chrf
value: 0.56786
- task:
name: Translation lit-por
type: translation
args: lit-por
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-por
metrics:
- name: BLEU
type: bleu
value: 28.2
- name: chr-F
type: chrf
value: 0.55393
- task:
name: Translation lit-spa
type: translation
args: lit-spa
dataset:
name: flores200-devtest
type: flores200-devtest
args: lit-spa
metrics:
- name: BLEU
type: bleu
value: 20.9
- name: chr-F
type: chrf
value: 0.49041
- task:
name: Translation lav-deu
type: translation
args: lav-deu
dataset:
name: flores101-devtest
type: flores_101
args: lav deu devtest
metrics:
- name: BLEU
type: bleu
value: 23.8
- name: chr-F
type: chrf
value: 0.54001
- task:
name: Translation lav-fra
type: translation
args: lav-fra
dataset:
name: flores101-devtest
type: flores_101
args: lav fra devtest
metrics:
- name: BLEU
type: bleu
value: 29.4
- name: chr-F
type: chrf
value: 0.57002
- task:
name: Translation lav-por
type: translation
args: lav-por
dataset:
name: flores101-devtest
type: flores_101
args: lav por devtest
metrics:
- name: BLEU
type: bleu
value: 26.7
- name: chr-F
type: chrf
value: 0.55155
- task:
name: Translation lav-spa
type: translation
args: lav-spa
dataset:
name: flores101-devtest
type: flores_101
args: lav spa devtest
metrics:
- name: BLEU
type: bleu
value: 20.8
- name: chr-F
type: chrf
value: 0.49259
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: flores101-devtest
type: flores_101
args: lit eng devtest
metrics:
- name: BLEU
type: bleu
value: 32.1
- name: chr-F
type: chrf
value: 0.59073
- task:
name: Translation lit-por
type: translation
args: lit-por
dataset:
name: flores101-devtest
type: flores_101
args: lit por devtest
metrics:
- name: BLEU
type: bleu
value: 27.8
- name: chr-F
type: chrf
value: 0.55106
- task:
name: Translation lav-deu
type: translation
args: lav-deu
dataset:
name: ntrex128
type: ntrex128
args: lav-deu
metrics:
- name: BLEU
type: bleu
value: 18.5
- name: chr-F
type: chrf
value: 0.47317
- task:
name: Translation lav-eng
type: translation
args: lav-eng
dataset:
name: ntrex128
type: ntrex128
args: lav-eng
metrics:
- name: BLEU
type: bleu
value: 19.7
- name: chr-F
type: chrf
value: 0.53734
- task:
name: Translation lav-fra
type: translation
args: lav-fra
dataset:
name: ntrex128
type: ntrex128
args: lav-fra
metrics:
- name: BLEU
type: bleu
value: 19.6
- name: chr-F
type: chrf
value: 0.47843
- task:
name: Translation lav-por
type: translation
args: lav-por
dataset:
name: ntrex128
type: ntrex128
args: lav-por
metrics:
- name: BLEU
type: bleu
value: 19.3
- name: chr-F
type: chrf
value: 0.47027
- task:
name: Translation lav-spa
type: translation
args: lav-spa
dataset:
name: ntrex128
type: ntrex128
args: lav-spa
metrics:
- name: BLEU
type: bleu
value: 22.7
- name: chr-F
type: chrf
value: 0.49428
- task:
name: Translation lit-deu
type: translation
args: lit-deu
dataset:
name: ntrex128
type: ntrex128
args: lit-deu
metrics:
- name: BLEU
type: bleu
value: 19.4
- name: chr-F
type: chrf
value: 0.50279
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: ntrex128
type: ntrex128
args: lit-eng
metrics:
- name: BLEU
type: bleu
value: 28.1
- name: chr-F
type: chrf
value: 0.56642
- task:
name: Translation lit-fra
type: translation
args: lit-fra
dataset:
name: ntrex128
type: ntrex128
args: lit-fra
metrics:
- name: BLEU
type: bleu
value: 22.6
- name: chr-F
type: chrf
value: 0.51276
- task:
name: Translation lit-por
type: translation
args: lit-por
dataset:
name: ntrex128
type: ntrex128
args: lit-por
metrics:
- name: BLEU
type: bleu
value: 22.6
- name: chr-F
type: chrf
value: 0.50864
- task:
name: Translation lit-spa
type: translation
args: lit-spa
dataset:
name: ntrex128
type: ntrex128
args: lit-spa
metrics:
- name: BLEU
type: bleu
value: 25.9
- name: chr-F
type: chrf
value: 0.53105
- task:
name: Translation lav-eng
type: translation
args: lav-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: lav-eng
metrics:
- name: BLEU
type: bleu
value: 21.5
- name: chr-F
type: chrf
value: 0.63015
- task:
name: Translation lit-deu
type: translation
args: lit-deu
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: lit-deu
metrics:
- name: BLEU
type: bleu
value: 47.5
- name: chr-F
type: chrf
value: 0.66527
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: lit-eng
metrics:
- name: BLEU
type: bleu
value: 58.9
- name: chr-F
type: chrf
value: 0.72975
- task:
name: Translation lit-spa
type: translation
args: lit-spa
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: lit-spa
metrics:
- name: BLEU
type: bleu
value: 49.9
- name: chr-F
type: chrf
value: 0.67956
- 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: 55.5
- name: chr-F
type: chrf
value: 0.71003
- task:
name: Translation lav-eng
type: translation
args: lav-eng
dataset:
name: newstest2017
type: wmt-2017-news
args: lav-eng
metrics:
- name: BLEU
type: bleu
value: 22.0
- name: chr-F
type: chrf
value: 0.49729
- task:
name: Translation lit-eng
type: translation
args: lit-eng
dataset:
name: newstest2019
type: wmt-2019-news
args: lit-eng
metrics:
- name: BLEU
type: bleu
value: 31.2
- name: chr-F
type: chrf
value: 0.59971
---
# opus-mt-tc-bible-big-bat-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 Baltic languages (bat) 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): lav lit prg sgs
- 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/bat-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/bat-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-bat-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-bat-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/bat-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/bat-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/bat-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/bat-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 |
|----------|---------|-------|-------|-------|--------|
| lav-eng | tatoeba-test-v2021-08-07 | 0.63015 | 21.5 | 1631 | 11213 |
| lit-deu | tatoeba-test-v2021-08-07 | 0.66527 | 47.5 | 1115 | 8531 |
| lit-eng | tatoeba-test-v2021-08-07 | 0.72975 | 58.9 | 2528 | 17855 |
| lit-spa | tatoeba-test-v2021-08-07 | 0.67956 | 49.9 | 454 | 2751 |
| lav-deu | flores101-devtest | 0.54001 | 23.8 | 1012 | 25094 |
| lav-fra | flores101-devtest | 0.57002 | 29.4 | 1012 | 28343 |
| lav-por | flores101-devtest | 0.55155 | 26.7 | 1012 | 26519 |
| lav-spa | flores101-devtest | 0.49259 | 20.8 | 1012 | 29199 |
| lit-eng | flores101-devtest | 0.59073 | 32.1 | 1012 | 24721 |
| lit-por | flores101-devtest | 0.55106 | 27.8 | 1012 | 26519 |
| lit-deu | flores200-devtest | 0.53223 | 23.7 | 1012 | 25094 |
| lit-eng | flores200-devtest | 0.59361 | 32.6 | 1012 | 24721 |
| lit-fra | flores200-devtest | 0.56786 | 30.0 | 1012 | 28343 |
| lit-por | flores200-devtest | 0.55393 | 28.2 | 1012 | 26519 |
| lit-spa | flores200-devtest | 0.49041 | 20.9 | 1012 | 29199 |
| lav-eng | newstest2017 | 0.49729 | 22.0 | 2001 | 47511 |
| lit-eng | newstest2019 | 0.59971 | 31.2 | 1000 | 25878 |
| lav-deu | ntrex128 | 0.47317 | 18.5 | 1997 | 48761 |
| lav-eng | ntrex128 | 0.53734 | 19.7 | 1997 | 47673 |
| lav-fra | ntrex128 | 0.47843 | 19.6 | 1997 | 53481 |
| lav-por | ntrex128 | 0.47027 | 19.3 | 1997 | 51631 |
| lav-spa | ntrex128 | 0.49428 | 22.7 | 1997 | 54107 |
| lit-deu | ntrex128 | 0.50279 | 19.4 | 1997 | 48761 |
| lit-eng | ntrex128 | 0.56642 | 28.1 | 1997 | 47673 |
| lit-fra | ntrex128 | 0.51276 | 22.6 | 1997 | 53481 |
| lit-por | ntrex128 | 0.50864 | 22.6 | 1997 | 51631 |
| lit-spa | ntrex128 | 0.53105 | 25.9 | 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 17:27:51 EEST 2024
* port machine: LM0-400-22516.local
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