The opus-mt-roa-en open-source translation model enables free translation from multiple Romance languages to English!

Opus Mt Roa En

Developed by Helsinki-NLP

This is a Transformer model supporting translation from multiple Romance languages to English, developed by the Helsinki-NLP team.

Machine Translation Supports Multiple LanguagesOpen Source License:Apache-2.0 #Romance language translation #High BLEU score #News translation optimization

Downloads 3,686

Release Time : 3/2/2022

Model Overview

The model specializes in translating various Romance languages (such as Italian, French, Spanish, etc.) into English, utilizing the Transformer architecture, and is suitable for multilingual translation tasks.

Model Features

Multilingual Support

Supports translation from multiple Romance languages to English, including Italian, French, Spanish, etc.

High-Performance Translation

Performs excellently on multiple test sets, with high BLEU and chr-F scores.

Standardized Preprocessing

Employs standardized and SentencePiece preprocessing techniques to enhance translation quality.

Model Capabilities

Text translation

Multilingual support

High-quality translation

Use Cases

Language Translation

News Translation

Translates news content from Romance languages into English.

Achieved a BLEU score of 37.1 on the newsdev2016-enro test set.

Daily Conversation Translation

Translates daily conversations from Romance languages into English.

Performed well on the Tatoeba-test set, e.g., Italian-to-English BLEU score of 64.8.

🚀 roa-eng Translation Model

This project focuses on translating from Romance languages to English. It provides a transformer - based model with specific pre - processing steps and offers various evaluation metrics and model - related information.

📚 Documentation

General Information

Source Group: Romance languages
Target Group: English
OPUS Readme: roa-eng
Model: Transformer
Source Language(s): arg ast cat cos egl ext fra frm_Latn gcf_Latn glg hat ind ita lad lad_Latn lij lld_Latn lmo max_Latn mfe min mwl oci pap pms por roh ron scn spa tmw_Latn vec wln zlm_Latn zsm_Latn
Target Language(s): eng
Pre - processing: normalization + SentencePiece (spm32k,spm32k)
Download Original Weights: opus2m-2020-08-01.zip
Test Set Translations: opus2m-2020-08-01.test.txt
Test Set Scores: opus2m-2020-08-01.eval.txt

Benchmarks

Testset	BLEU	chr - F
newsdev2016 - enro - roneng.ron.eng	37.1	0.631
newsdiscussdev2015 - enfr - fraeng.fra.eng	31.6	0.564
newsdiscusstest2015 - enfr - fraeng.fra.eng	36.1	0.592
newssyscomb2009 - fraeng.fra.eng	29.3	0.563
newssyscomb2009 - itaeng.ita.eng	33.1	0.589
newssyscomb2009 - spaeng.spa.eng	29.2	0.562
news - test2008 - fraeng.fra.eng	25.2	0.533
news - test2008 - spaeng.spa.eng	26.6	0.542
newstest2009 - fraeng.fra.eng	28.6	0.557
newstest2009 - itaeng.ita.eng	32.0	0.580
newstest2009 - spaeng.spa.eng	28.9	0.559
newstest2010 - fraeng.fra.eng	29.9	0.573
newstest2010 - spaeng.spa.eng	33.3	0.596
newstest2011 - fraeng.fra.eng	31.2	0.585
newstest2011 - spaeng.spa.eng	32.3	0.584
newstest2012 - fraeng.fra.eng	31.3	0.580
newstest2012 - spaeng.spa.eng	35.3	0.606
newstest2013 - fraeng.fra.eng	31.9	0.575
newstest2013 - spaeng.spa.eng	32.8	0.592
newstest2014 - fren - fraeng.fra.eng	34.6	0.611
newstest2016 - enro - roneng.ron.eng	35.8	0.614
Tatoeba - test.arg - eng.arg.eng	38.7	0.512
Tatoeba - test.ast - eng.ast.eng	35.2	0.520
Tatoeba - test.cat - eng.cat.eng	54.9	0.703
Tatoeba - test.cos - eng.cos.eng	68.1	0.666
Tatoeba - test.egl - eng.egl.eng	6.7	0.209
Tatoeba - test.ext - eng.ext.eng	24.2	0.427
Tatoeba - test.fra - eng.fra.eng	53.9	0.691
Tatoeba - test.frm - eng.frm.eng	25.7	0.423
Tatoeba - test.gcf - eng.gcf.eng	14.8	0.288
Tatoeba - test.glg - eng.glg.eng	54.6	0.703
Tatoeba - test.hat - eng.hat.eng	37.0	0.540
Tatoeba - test.ita - eng.ita.eng	64.8	0.768
Tatoeba - test.lad - eng.lad.eng	21.7	0.452
Tatoeba - test.lij - eng.lij.eng	11.2	0.299
Tatoeba - test.lld - eng.lld.eng	10.8	0.273
Tatoeba - test.lmo - eng.lmo.eng	5.8	0.260
Tatoeba - test.mfe - eng.mfe.eng	63.1	0.819
Tatoeba - test.msa - eng.msa.eng	40.9	0.592
Tatoeba - test.multi.eng	54.9	0.697
Tatoeba - test.mwl - eng.mwl.eng	44.6	0.674
Tatoeba - test.oci - eng.oci.eng	20.5	0.404
Tatoeba - test.pap - eng.pap.eng	56.2	0.669
Tatoeba - test.pms - eng.pms.eng	10.3	0.324
Tatoeba - test.por - eng.por.eng	59.7	0.738
Tatoeba - test.roh - eng.roh.eng	14.8	0.378
Tatoeba - test.ron - eng.ron.eng	55.2	0.703
Tatoeba - test.scn - eng.scn.eng	10.2	0.259
Tatoeba - test.spa - eng.spa.eng	56.2	0.714
Tatoeba - test.vec - eng.vec.eng	13.8	0.317
Tatoeba - test.wln - eng.wln.eng	17.3	0.323

System Info

Property	Details
hf_name	roa - eng
source_languages	roa
target_languages	eng
opus_readme_url	https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/roa-eng/README.md
original_repo	Tatoeba - Challenge
tags	['translation']
languages	['it', 'ca', 'rm', 'es', 'ro', 'gl', 'co', 'wa', 'pt', 'oc', 'an', 'id', 'fr', 'ht', 'roa', 'en']
src_constituents	{'ita', 'cat', 'roh', 'spa', 'pap', 'lmo', 'mwl', 'lij', 'lad_Latn', 'ext', 'ron', 'ast', 'glg', 'pms', 'zsm_Latn', 'gcf_Latn', 'lld_Latn', 'min', 'tmw_Latn', 'cos', 'wln', 'zlm_Latn', 'por', 'egl', 'oci', 'vec', 'arg', 'ind', 'fra', 'hat', 'lad', 'max_Latn', 'frm_Latn', 'scn', 'mfe'}
tgt_constituents	{'eng'}
src_multilingual	True
tgt_multilingual	False
prepro	normalization + SentencePiece (spm32k,spm32k)
url_model	https://object.pouta.csc.fi/Tatoeba-MT-models/roa-eng/opus2m-2020-08-01.zip
url_test_set	https://object.pouta.csc.fi/Tatoeba-MT-models/roa-eng/opus2m-2020-08-01.test.txt
src_alpha3	roa
tgt_alpha3	eng
short_pair	roa - en
chrF2_score	0.6970000000000001
bleu	54.9
brevity_penalty	0.9790000000000001
ref_len	74762.0
src_name	Romance languages
tgt_name	English
train_date	2020 - 08 - 01
src_alpha2	roa
tgt_alpha2	en
prefer_old	False
long_pair	roa - eng
helsinki_git_sha	480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
transformers_git_sha	2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
port_machine	brutasse
port_time	2020 - 08 - 21 - 14:41

📄 License

This project is licensed under the Apache - 2.0 license.

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