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Initial commit
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---
library_name: transformers
language:
- aa
- am
- ar
- arc
- bcw
- byn
- cop
- daa
- de
- dsh
- en
- es
- fr
- gde
- gnd
- ha
- hbo
- he
- hig
- irk
- jpa
- kab
- ker
- kqp
- ktb
- kxc
- lln
- lme
- meq
- mfh
- mfi
- mfk
- mif
- mpg
- mqb
- mt
- muy
- oar
- om
- pbi
- phn
- pt
- rif
- sgw
- shi
- shy
- so
- sur
- syc
- syr
- taq
- thv
- ti
- tig
- tmc
- tmh
- tmr
- ttr
- tzm
- wal
- xed
- zgh
tags:
- translation
- opus-mt-tc-bible
license: apache-2.0
model-index:
- name: opus-mt-tc-bible-big-deu_eng_fra_por_spa-afa
results:
- task:
name: Translation deu-hau
type: translation
args: deu-hau
dataset:
name: flores200-devtest
type: flores200-devtest
args: deu-hau
metrics:
- name: BLEU
type: bleu
value: 11.4
- name: chr-F
type: chrf
value: 0.40471
- task:
name: Translation deu-heb
type: translation
args: deu-heb
dataset:
name: flores200-devtest
type: flores200-devtest
args: deu-heb
metrics:
- name: BLEU
type: bleu
value: 18.1
- name: chr-F
type: chrf
value: 0.48645
- task:
name: Translation deu-mlt
type: translation
args: deu-mlt
dataset:
name: flores200-devtest
type: flores200-devtest
args: deu-mlt
metrics:
- name: BLEU
type: bleu
value: 17.5
- name: chr-F
type: chrf
value: 0.54079
- task:
name: Translation eng-arz
type: translation
args: eng-arz
dataset:
name: flores200-devtest
type: flores200-devtest
args: eng-arz
metrics:
- name: BLEU
type: bleu
value: 11.1
- name: chr-F
type: chrf
value: 0.42804
- task:
name: Translation eng-hau
type: translation
args: eng-hau
dataset:
name: flores200-devtest
type: flores200-devtest
args: eng-hau
metrics:
- name: BLEU
type: bleu
value: 20.4
- name: chr-F
type: chrf
value: 0.49023
- task:
name: Translation eng-heb
type: translation
args: eng-heb
dataset:
name: flores200-devtest
type: flores200-devtest
args: eng-heb
metrics:
- name: BLEU
type: bleu
value: 27.1
- name: chr-F
type: chrf
value: 0.56635
- task:
name: Translation eng-mlt
type: translation
args: eng-mlt
dataset:
name: flores200-devtest
type: flores200-devtest
args: eng-mlt
metrics:
- name: BLEU
type: bleu
value: 34.9
- name: chr-F
type: chrf
value: 0.68334
- task:
name: Translation fra-hau
type: translation
args: fra-hau
dataset:
name: flores200-devtest
type: flores200-devtest
args: fra-hau
metrics:
- name: BLEU
type: bleu
value: 13.2
- name: chr-F
type: chrf
value: 0.42731
- task:
name: Translation fra-heb
type: translation
args: fra-heb
dataset:
name: flores200-devtest
type: flores200-devtest
args: fra-heb
metrics:
- name: BLEU
type: bleu
value: 19.1
- name: chr-F
type: chrf
value: 0.49683
- task:
name: Translation fra-mlt
type: translation
args: fra-mlt
dataset:
name: flores200-devtest
type: flores200-devtest
args: fra-mlt
metrics:
- name: BLEU
type: bleu
value: 20.4
- name: chr-F
type: chrf
value: 0.56844
- task:
name: Translation por-hau
type: translation
args: por-hau
dataset:
name: flores200-devtest
type: flores200-devtest
args: por-hau
metrics:
- name: BLEU
type: bleu
value: 13.6
- name: chr-F
type: chrf
value: 0.42593
- task:
name: Translation por-heb
type: translation
args: por-heb
dataset:
name: flores200-devtest
type: flores200-devtest
args: por-heb
metrics:
- name: BLEU
type: bleu
value: 19.7
- name: chr-F
type: chrf
value: 0.50345
- task:
name: Translation por-mlt
type: translation
args: por-mlt
dataset:
name: flores200-devtest
type: flores200-devtest
args: por-mlt
metrics:
- name: BLEU
type: bleu
value: 21.5
- name: chr-F
type: chrf
value: 0.58913
- task:
name: Translation spa-heb
type: translation
args: spa-heb
dataset:
name: flores200-devtest
type: flores200-devtest
args: spa-heb
metrics:
- name: BLEU
type: bleu
value: 13.5
- name: chr-F
type: chrf
value: 0.45249
- task:
name: Translation spa-mlt
type: translation
args: spa-mlt
dataset:
name: flores200-devtest
type: flores200-devtest
args: spa-mlt
metrics:
- name: BLEU
type: bleu
value: 12.7
- name: chr-F
type: chrf
value: 0.51077
- task:
name: Translation deu-ara
type: translation
args: deu-ara
dataset:
name: flores101-devtest
type: flores_101
args: deu ara devtest
metrics:
- name: BLEU
type: bleu
value: 15.7
- name: chr-F
type: chrf
value: 0.47927
- task:
name: Translation deu-hau
type: translation
args: deu-hau
dataset:
name: flores101-devtest
type: flores_101
args: deu hau devtest
metrics:
- name: BLEU
type: bleu
value: 10.6
- name: chr-F
type: chrf
value: 0.39583
- task:
name: Translation eng-hau
type: translation
args: eng-hau
dataset:
name: flores101-devtest
type: flores_101
args: eng hau devtest
metrics:
- name: BLEU
type: bleu
value: 19.0
- name: chr-F
type: chrf
value: 0.47807
- task:
name: Translation eng-mlt
type: translation
args: eng-mlt
dataset:
name: flores101-devtest
type: flores_101
args: eng mlt devtest
metrics:
- name: BLEU
type: bleu
value: 32.9
- name: chr-F
type: chrf
value: 0.67196
- task:
name: Translation fra-mlt
type: translation
args: fra-mlt
dataset:
name: flores101-devtest
type: flores_101
args: fra mlt devtest
metrics:
- name: BLEU
type: bleu
value: 19.9
- name: chr-F
type: chrf
value: 0.56271
- task:
name: Translation por-heb
type: translation
args: por-heb
dataset:
name: flores101-devtest
type: flores_101
args: por heb devtest
metrics:
- name: BLEU
type: bleu
value: 19.6
- name: chr-F
type: chrf
value: 0.49378
- task:
name: Translation spa-ara
type: translation
args: spa-ara
dataset:
name: flores101-devtest
type: flores_101
args: spa ara devtest
metrics:
- name: BLEU
type: bleu
value: 11.7
- name: chr-F
type: chrf
value: 0.44988
- task:
name: Translation deu-hau
type: translation
args: deu-hau
dataset:
name: ntrex128
type: ntrex128
args: deu-hau
metrics:
- name: BLEU
type: bleu
value: 12.5
- name: chr-F
type: chrf
value: 0.41931
- task:
name: Translation deu-heb
type: translation
args: deu-heb
dataset:
name: ntrex128
type: ntrex128
args: deu-heb
metrics:
- name: BLEU
type: bleu
value: 13.3
- name: chr-F
type: chrf
value: 0.43961
- task:
name: Translation deu-mlt
type: translation
args: deu-mlt
dataset:
name: ntrex128
type: ntrex128
args: deu-mlt
metrics:
- name: BLEU
type: bleu
value: 15.1
- name: chr-F
type: chrf
value: 0.49871
- task:
name: Translation eng-hau
type: translation
args: eng-hau
dataset:
name: ntrex128
type: ntrex128
args: eng-hau
metrics:
- name: BLEU
type: bleu
value: 23.2
- name: chr-F
type: chrf
value: 0.51601
- task:
name: Translation eng-heb
type: translation
args: eng-heb
dataset:
name: ntrex128
type: ntrex128
args: eng-heb
metrics:
- name: BLEU
type: bleu
value: 20.3
- name: chr-F
type: chrf
value: 0.50625
- task:
name: Translation eng-mlt
type: translation
args: eng-mlt
dataset:
name: ntrex128
type: ntrex128
args: eng-mlt
metrics:
- name: BLEU
type: bleu
value: 29.0
- name: chr-F
type: chrf
value: 0.62552
- task:
name: Translation eng-som
type: translation
args: eng-som
dataset:
name: ntrex128
type: ntrex128
args: eng-som
metrics:
- name: BLEU
type: bleu
value: 13.5
- name: chr-F
type: chrf
value: 0.46845
- task:
name: Translation fra-hau
type: translation
args: fra-hau
dataset:
name: ntrex128
type: ntrex128
args: fra-hau
metrics:
- name: BLEU
type: bleu
value: 14.5
- name: chr-F
type: chrf
value: 0.43729
- task:
name: Translation fra-heb
type: translation
args: fra-heb
dataset:
name: ntrex128
type: ntrex128
args: fra-heb
metrics:
- name: BLEU
type: bleu
value: 13.9
- name: chr-F
type: chrf
value: 0.43855
- task:
name: Translation fra-mlt
type: translation
args: fra-mlt
dataset:
name: ntrex128
type: ntrex128
args: fra-mlt
metrics:
- name: BLEU
type: bleu
value: 17.3
- name: chr-F
type: chrf
value: 0.51640
- task:
name: Translation por-hau
type: translation
args: por-hau
dataset:
name: ntrex128
type: ntrex128
args: por-hau
metrics:
- name: BLEU
type: bleu
value: 15.1
- name: chr-F
type: chrf
value: 0.44408
- task:
name: Translation por-heb
type: translation
args: por-heb
dataset:
name: ntrex128
type: ntrex128
args: por-heb
metrics:
- name: BLEU
type: bleu
value: 15.0
- name: chr-F
type: chrf
value: 0.45739
- task:
name: Translation por-mlt
type: translation
args: por-mlt
dataset:
name: ntrex128
type: ntrex128
args: por-mlt
metrics:
- name: BLEU
type: bleu
value: 18.2
- name: chr-F
type: chrf
value: 0.53719
- task:
name: Translation spa-hau
type: translation
args: spa-hau
dataset:
name: ntrex128
type: ntrex128
args: spa-hau
metrics:
- name: BLEU
type: bleu
value: 14.8
- name: chr-F
type: chrf
value: 0.44695
- task:
name: Translation spa-heb
type: translation
args: spa-heb
dataset:
name: ntrex128
type: ntrex128
args: spa-heb
metrics:
- name: BLEU
type: bleu
value: 14.5
- name: chr-F
type: chrf
value: 0.45509
- task:
name: Translation spa-mlt
type: translation
args: spa-mlt
dataset:
name: ntrex128
type: ntrex128
args: spa-mlt
metrics:
- name: BLEU
type: bleu
value: 17.7
- name: chr-F
type: chrf
value: 0.53631
- task:
name: Translation deu-ara
type: translation
args: deu-ara
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: deu-ara
metrics:
- name: BLEU
type: bleu
value: 20.2
- name: chr-F
type: chrf
value: 0.49517
- task:
name: Translation deu-heb
type: translation
args: deu-heb
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: deu-heb
metrics:
- name: BLEU
type: bleu
value: 35.8
- name: chr-F
type: chrf
value: 0.56943
- task:
name: Translation eng-heb
type: translation
args: eng-heb
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-heb
metrics:
- name: BLEU
type: bleu
value: 34.9
- name: chr-F
type: chrf
value: 0.57708
- task:
name: Translation eng-mlt
type: translation
args: eng-mlt
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: eng-mlt
metrics:
- name: BLEU
type: bleu
value: 29.5
- name: chr-F
type: chrf
value: 0.61044
- task:
name: Translation fra-heb
type: translation
args: fra-heb
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: fra-heb
metrics:
- name: BLEU
type: bleu
value: 37.5
- name: chr-F
type: chrf
value: 0.58681
- task:
name: Translation por-heb
type: translation
args: por-heb
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: por-heb
metrics:
- name: BLEU
type: bleu
value: 41.0
- name: chr-F
type: chrf
value: 0.61593
- task:
name: Translation spa-ara
type: translation
args: spa-ara
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: spa-ara
metrics:
- name: BLEU
type: bleu
value: 23.9
- name: chr-F
type: chrf
value: 0.53669
- task:
name: Translation spa-heb
type: translation
args: spa-heb
dataset:
name: tatoeba-test-v2021-08-07
type: tatoeba_mt
args: spa-heb
metrics:
- name: BLEU
type: bleu
value: 41.2
- name: chr-F
type: chrf
value: 0.61966
- task:
name: Translation eng-ara
type: translation
args: eng-ara
dataset:
name: tico19-test
type: tico19-test
args: eng-ara
metrics:
- name: BLEU
type: bleu
value: 25.4
- name: chr-F
type: chrf
value: 0.56288
- task:
name: Translation eng-hau
type: translation
args: eng-hau
dataset:
name: tico19-test
type: tico19-test
args: eng-hau
metrics:
- name: BLEU
type: bleu
value: 22.2
- name: chr-F
type: chrf
value: 0.50060
- task:
name: Translation fra-ara
type: translation
args: fra-ara
dataset:
name: tico19-test
type: tico19-test
args: fra-ara
metrics:
- name: BLEU
type: bleu
value: 13.8
- name: chr-F
type: chrf
value: 0.39785
- task:
name: Translation por-ara
type: translation
args: por-ara
dataset:
name: tico19-test
type: tico19-test
args: por-ara
metrics:
- name: BLEU
type: bleu
value: 16.0
- name: chr-F
type: chrf
value: 0.44442
- task:
name: Translation spa-ara
type: translation
args: spa-ara
dataset:
name: tico19-test
type: tico19-test
args: spa-ara
metrics:
- name: BLEU
type: bleu
value: 16.5
- name: chr-F
type: chrf
value: 0.45429
- task:
name: Translation eng-hau
type: translation
args: eng-hau
dataset:
name: newstest2021
type: wmt-2021-news
args: eng-hau
metrics:
- name: BLEU
type: bleu
value: 13.1
- name: chr-F
type: chrf
value: 0.43617
---
# opus-mt-tc-bible-big-deu_eng_fra_por_spa-afa
## 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 Afro-Asiatic languages (afa).
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**: 2024-05-29
- **License:** Apache-2.0
- **Language(s):**
- Source Language(s): deu eng fra por spa
- Target Language(s): aar acm afb amh apc ara arc arq arz bcw byn cop daa dsh gde gnd hau hbo heb hig irk jpa kab ker kqp ktb kxc lln lme meq mfh mfi mfk mif mlt mpg mqb muy oar orm pbi phn rif sgw shi shy som sur syc syr taq thv tig tir tmc tmh tmr ttr tzm wal xed zgh
- Valid Target Language Labels: >>aal<< >>aar<< >>aas<< >>acm<< >>afb<< >>agj<< >>ahg<< >>aij<< >>aiw<< >>ajw<< >>akk<< >>alw<< >>amh<< >>amw<< >>anc<< >>ank<< >>apc<< >>ara<< >>arc<< >>arq<< >>arv<< >>arz<< >>auj<< >>auo<< >>awn<< >>bbt<< >>bcq<< >>bcw<< >>bcy<< >>bde<< >>bdm<< >>bdn<< >>bds<< >>bej<< >>bhm<< >>bhn<< >>bhs<< >>bid<< >>bjf<< >>bji<< >>bnl<< >>bob<< >>bol<< >>bsw<< >>bta<< >>btf<< >>bux<< >>bva<< >>bvf<< >>bvh<< >>bvw<< >>bwo<< >>bwr<< >>bxe<< >>bxq<< >>byn<< >>cie<< >>ckl<< >>ckq<< >>cky<< >>cla<< >>cnu<< >>cop<< >>cop_Copt<< >>cuv<< >>daa<< >>dal<< >>dbb<< >>dbp<< >>dbq<< >>dbr<< >>dgh<< >>dim<< >>dkx<< >>dlk<< >>dme<< >>dot<< >>dox<< >>doz<< >>drs<< >>dsh<< >>dwa<< >>egy<< >>elo<< >>fie<< >>fkk<< >>fli<< >>gab<< >>gde<< >>gdf<< >>gdk<< >>gdl<< >>gdq<< >>gdu<< >>gea<< >>gek<< >>gew<< >>gex<< >>gez<< >>gft<< >>gha<< >>gho<< >>gid<< >>gis<< >>giz<< >>gji<< >>glo<< >>glw<< >>gnc<< >>gnd<< >>gou<< >>gow<< >>gqa<< >>grd<< >>grr<< >>gru<< >>gwd<< >>gwn<< >>har<< >>hau<< >>hau_Latn<< >>hbb<< >>hbo<< >>hbo_Hebr<< >>hdy<< >>heb<< >>hed<< >>hia<< >>hig<< >>hna<< >>hod<< >>hoh<< >>hrt<< >>hss<< >>huy<< >>hwo<< >>hya<< >>inm<< >>ior<< >>irk<< >>jaf<< >>jbe<< >>jbn<< >>jeu<< >>jia<< >>jie<< >>jii<< >>jim<< >>jmb<< >>jmi<< >>jnj<< >>jpa<< >>jpa_Hebr<< >>jrb<< >>juu<< >>kab<< >>kai<< >>kbz<< >>kcn<< >>kcs<< >>ker<< >>kil<< >>kkr<< >>kks<< >>kna<< >>kof<< >>kot<< >>kpa<< >>kqd<< >>kqp<< >>kqx<< >>ksq<< >>ktb<< >>ktc<< >>kuh<< >>kul<< >>kvf<< >>kvi<< >>kvj<< >>kwl<< >>kxc<< >>ldd<< >>lhs<< >>liq<< >>lln<< >>lme<< >>lsd<< >>maf<< >>mcn<< >>mcw<< >>mdx<< >>meq<< >>mes<< >>mew<< >>mey<< >>mfh<< >>mfi<< >>mfj<< >>mfk<< >>mfl<< >>mfm<< >>mid<< >>mif<< >>mje<< >>mjs<< >>mkf<< >>mlj<< >>mlr<< >>mlt<< >>mlw<< >>mmf<< >>mmy<< >>mou<< >>moz<< >>mpg<< >>mpi<< >>mpk<< >>mqb<< >>mrt<< >>mse<< >>msv<< >>mtl<< >>mub<< >>mug<< >>muj<< >>muu<< >>muy<< >>mvh<< >>mvz<< >>mxf<< >>mxu<< >>mys<< >>myz<< >>mzb<< >>nbh<< >>ndm<< >>ngi<< >>ngs<< >>ngw<< >>ngx<< >>nja<< >>nmi<< >>nnc<< >>nnn<< >>noz<< >>nxm<< >>oar<< >>oar_Hebr<< >>oar_Syrc<< >>orm<< >>oua<< >>pbi<< >>pcw<< >>phn<< >>phn_Phnx<< >>pip<< >>piy<< >>plj<< >>pqa<< >>rel<< >>rif<< >>rif_Latn<< >>rzh<< >>saa<< >>sam<< >>say<< >>scw<< >>sds<< >>sgw<< >>she<< >>shi<< >>shi_Latn<< >>shv<< >>shy<< >>shy_Latn<< >>sid<< >>sir<< >>siz<< >>sjs<< >>smp<< >>sok<< >>som<< >>sor<< >>sqr<< >>sqt<< >>ssn<< >>ssy<< >>stv<< >>sur<< >>swn<< >>swq<< >>swy<< >>syc<< >>syk<< >>syn<< >>syr<< >>tak<< >>tal<< >>tan<< >>taq<< >>tax<< >>tdk<< >>tez<< >>tgd<< >>thv<< >>tia<< >>tig<< >>tir<< >>tjo<< >>tmc<< >>tmh<< >>tmr<< >>tmr_Hebr<< >>tng<< >>tqq<< >>trg<< >>trj<< >>tru<< >>tsb<< >>tsh<< >>ttr<< >>twc<< >>tzm<< >>tzm_Latn<< >>tzm_Tfng<< >>ubi<< >>udl<< >>uga<< >>vem<< >>wal<< >>wbj<< >>wji<< >>wka<< >>wle<< >>xaa<< >>xan<< >>xeb<< >>xed<< >>xhd<< >>xmd<< >>xmj<< >>xna<< >>xpu<< >>xqt<< >>xsa<< >>ymm<< >>zah<< >>zay<< >>zaz<< >>zen<< >>zgh<< >>zim<< >>ziz<< >>zns<< >>zrn<< >>zua<< >>zuy<< >>zwa<<
- **Original Model**: [opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-afa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.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-afa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-29)
- [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. `>>aar<<`
## 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 = [
">>kab<< Tu seras parmi nous demain.",
">>heb<< Let's get out of here while we can."
]
model_name = "pytorch-models/opus-mt-tc-bible-big-deu_eng_fra_por_spa-afa"
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:
# Azekka ad tiliḍ yid-i
# בוא נצא מכאן כל עוד אנחנו יכולים.
```
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-afa")
print(pipe(">>kab<< Tu seras parmi nous demain."))
# expected output: Azekka ad tiliḍ yid-i
```
## 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_2024-05-29.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/deu+eng+fra+por+spa-afa/opusTCv20230926max50+bt+jhubc_transformer-big_2024-05-29.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-afa/opusTCv20230926max50%2Bbt%2Bjhubc_transformer-big_2024-05-29)
* 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-afa/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-afa/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-ara | tatoeba-test-v2021-08-07 | 0.49517 | 20.2 | 1209 | 6324 |
| deu-heb | tatoeba-test-v2021-08-07 | 0.56943 | 35.8 | 3090 | 20341 |
| eng-ara | tatoeba-test-v2021-08-07 | 0.46273 | 17.3 | 10305 | 61356 |
| eng-heb | tatoeba-test-v2021-08-07 | 0.57708 | 34.9 | 10519 | 63628 |
| eng-mlt | tatoeba-test-v2021-08-07 | 0.61044 | 29.5 | 203 | 899 |
| fra-ara | tatoeba-test-v2021-08-07 | 0.42223 | 10.4 | 1569 | 7956 |
| fra-heb | tatoeba-test-v2021-08-07 | 0.58681 | 37.5 | 3281 | 20655 |
| por-heb | tatoeba-test-v2021-08-07 | 0.61593 | 41.0 | 719 | 4423 |
| spa-ara | tatoeba-test-v2021-08-07 | 0.53669 | 23.9 | 1511 | 7547 |
| spa-heb | tatoeba-test-v2021-08-07 | 0.61966 | 41.2 | 1849 | 12112 |
| deu-ara | flores101-devtest | 0.47927 | 15.7 | 1012 | 21357 |
| eng-hau | flores101-devtest | 0.47807 | 19.0 | 1012 | 27730 |
| eng-mlt | flores101-devtest | 0.67196 | 32.9 | 1012 | 22169 |
| fra-mlt | flores101-devtest | 0.56271 | 19.9 | 1012 | 22169 |
| por-heb | flores101-devtest | 0.49378 | 19.6 | 1012 | 20749 |
| spa-ara | flores101-devtest | 0.44988 | 11.7 | 1012 | 21357 |
| deu-ara | flores200-devtest | 0.661 | 0.0 | 1012 | 5 |
| deu-hau | flores200-devtest | 0.40471 | 11.4 | 1012 | 27730 |
| deu-heb | flores200-devtest | 0.48645 | 18.1 | 1012 | 20238 |
| deu-mlt | flores200-devtest | 0.54079 | 17.5 | 1012 | 22169 |
| eng-ara | flores200-devtest | 0.627 | 0.0 | 1012 | 5 |
| eng-arz | flores200-devtest | 0.42804 | 11.1 | 1012 | 21034 |
| eng-hau | flores200-devtest | 0.49023 | 20.4 | 1012 | 27730 |
| eng-heb | flores200-devtest | 0.56635 | 27.1 | 1012 | 20238 |
| eng-mlt | flores200-devtest | 0.68334 | 34.9 | 1012 | 22169 |
| eng-som | flores200-devtest | 0.42814 | 9.9 | 1012 | 25991 |
| fra-ara | flores200-devtest | 0.631 | 0.0 | 1012 | 5 |
| fra-hau | flores200-devtest | 0.42731 | 13.2 | 1012 | 27730 |
| fra-heb | flores200-devtest | 0.49683 | 19.1 | 1012 | 20238 |
| fra-mlt | flores200-devtest | 0.56844 | 20.4 | 1012 | 22169 |
| por-ara | flores200-devtest | 0.622 | 0.0 | 1012 | 5 |
| por-hau | flores200-devtest | 0.42593 | 13.6 | 1012 | 27730 |
| por-heb | flores200-devtest | 0.50345 | 19.7 | 1012 | 20238 |
| por-mlt | flores200-devtest | 0.58913 | 21.5 | 1012 | 22169 |
| spa-ara | flores200-devtest | 0.587 | 0.0 | 1012 | 5 |
| spa-hau | flores200-devtest | 0.40309 | 9.4 | 1012 | 27730 |
| spa-heb | flores200-devtest | 0.45249 | 13.5 | 1012 | 20238 |
| spa-mlt | flores200-devtest | 0.51077 | 12.7 | 1012 | 22169 |
| eng-hau | newstest2021 | 0.43617 | 13.1 | 1000 | 32966 |
| deu-hau | ntrex128 | 0.41931 | 12.5 | 1997 | 54982 |
| deu-heb | ntrex128 | 0.43961 | 13.3 | 1997 | 39624 |
| deu-mlt | ntrex128 | 0.49871 | 15.1 | 1997 | 43308 |
| eng-hau | ntrex128 | 0.51601 | 23.2 | 1997 | 54982 |
| eng-heb | ntrex128 | 0.50625 | 20.3 | 1997 | 39624 |
| eng-mlt | ntrex128 | 0.62552 | 29.0 | 1997 | 43308 |
| eng-som | ntrex128 | 0.46845 | 13.5 | 1997 | 49351 |
| fra-hau | ntrex128 | 0.43729 | 14.5 | 1997 | 54982 |
| fra-heb | ntrex128 | 0.43855 | 13.9 | 1997 | 39624 |
| fra-mlt | ntrex128 | 0.51640 | 17.3 | 1997 | 43308 |
| fra-som | ntrex128 | 0.41813 | 9.6 | 1997 | 49351 |
| por-hau | ntrex128 | 0.44408 | 15.1 | 1997 | 54982 |
| por-heb | ntrex128 | 0.45739 | 15.0 | 1997 | 39624 |
| por-mlt | ntrex128 | 0.53719 | 18.2 | 1997 | 43308 |
| por-som | ntrex128 | 0.41367 | 9.3 | 1997 | 49351 |
| spa-hau | ntrex128 | 0.44695 | 14.8 | 1997 | 54982 |
| spa-heb | ntrex128 | 0.45509 | 14.5 | 1997 | 39624 |
| spa-mlt | ntrex128 | 0.53631 | 17.7 | 1997 | 43308 |
| spa-som | ntrex128 | 0.41755 | 9.1 | 1997 | 49351 |
| eng-ara | tico19-test | 0.56288 | 25.4 | 2100 | 51339 |
| eng-hau | tico19-test | 0.50060 | 22.2 | 2100 | 64509 |
| fra-amh | tico19-test | 3.575 | 1.3 | 2100 | 44782 |
| fra-hau | tico19-test | 5.071 | 1.8 | 2100 | 64509 |
| fra-orm | tico19-test | 4.044 | 1.8 | 2100 | 50032 |
| fra-som | tico19-test | 2.698 | 0.9 | 2100 | 63654 |
| fra-tir | tico19-test | 4.151 | 1.4 | 2100 | 46685 |
| por-amh | tico19-test | 3.799 | 1.4 | 2100 | 44782 |
| por-ara | tico19-test | 0.44442 | 16.0 | 2100 | 51339 |
| por-hau | tico19-test | 5.786 | 2.0 | 2100 | 64509 |
| por-orm | tico19-test | 4.613 | 2.0 | 2100 | 50032 |
| por-som | tico19-test | 3.413 | 1.2 | 2100 | 63654 |
| por-tir | tico19-test | 5.092 | 1.6 | 2100 | 46685 |
| spa-amh | tico19-test | 3.831 | 1.4 | 2100 | 44782 |
| spa-ara | tico19-test | 0.45429 | 16.5 | 2100 | 51339 |
| spa-hau | tico19-test | 5.790 | 1.9 | 2100 | 64509 |
| spa-orm | tico19-test | 4.617 | 1.9 | 2100 | 50032 |
| spa-som | tico19-test | 3.402 | 1.2 | 2100 | 63654 |
| spa-tir | tico19-test | 5.033 | 1.6 | 2100 | 46685 |
## 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 08:58:38 EEST 2024
* port machine: LM0-400-22516.local