Opus-mt-en-roa Open-source Translation Model - Free Deployment for English to Romance Languages Translation

Opus Mt En Roa

Developed by Helsinki-NLP

This is a multilingual translation model based on the Transformer architecture, supporting translation tasks from English to various Romance languages.

Machine Translation Supports Multiple LanguagesOpen Source License:Apache-2.0 #Multilingual translation #Romance language support #SentencePiece tokenization

Downloads 1,238

Release Time : 3/2/2022

Model Overview

This model is specifically designed for translating English text into multiple Romance languages, including French, Spanish, Italian, Portuguese, etc. It employs standardized preprocessing and SentencePiece tokenization technology, supporting multiple target language identifiers.

Model Features

Multilingual Support

Supports translation from English to 35 Romance languages, including major languages such as French, Spanish, Italian, etc.

Standardized Preprocessing

Utilizes standardized preprocessing and SentencePiece tokenization to ensure input text consistency and model processing efficiency.

High-Performance Translation

Performs exceptionally well on multiple test sets, achieving high BLEU and chr-F scores, particularly excelling in Spanish and French translation tasks.

Model Capabilities

English to Romance languages text translation

Multilingual translation support

Standardized text processing

Use Cases

Multilingual Translation

News Translation

Translate English news into Romance languages such as French, Spanish, etc.

Achieved a BLEU score of 27.6 on the newsdev2016 test set.

Social Media Content Translation

Translate English social media content into multiple Romance languages.

Achieved a BLEU score of 50.1 for Spanish translation on the Tatoeba-test set.

🚀 eng-roa

This project focuses on translating English to Romance languages, offering a transformer - based model with specific pre - processing steps and providing detailed test set scores.

🚀 Quick Start

For quick access to the model and related resources:

Original Weights: You can download the original weights from opus2m - 2020 - 08 - 01.zip.
Test Set Translations: Check the test set translations at opus2m - 2020 - 08 - 01.test.txt.
Test Set Scores: Find the test set scores in opus2m - 2020 - 08 - 01.eval.txt.

✨ Features

Translation Scope: It supports translation from English to multiple Romance languages, including arg, ast, cat, etc.
Model Type: A transformer model is used for the translation task.
Pre - processing: The pre - processing involves normalization and SentencePiece (spm32k, spm32k).
Language Token Requirement: A sentence initial language token in the form of >>id<< (id = valid target language ID) is required.

📦 Installation

No specific installation steps are provided in the original README.

💻 Usage Examples

No code examples are provided in the original README.

📚 Documentation

eng - roa Details

Source Group: English
Target Group: Romance languages
OPUS Readme: eng - roa

Model Information

Property	Details
Model Type	transformer
Source Language(s)	eng
Target 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
Pre - processing	normalization + SentencePiece (spm32k, spm32k)

System Info

hf_name: eng - roa
source_languages: eng
target_languages: roa
opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-roa/README.md
original_repo: Tatoeba - Challenge
tags: ['translation']
languages: ['en', 'it', 'ca', 'rm', 'es', 'ro', 'gl', 'co', 'wa', 'pt', 'oc', 'an', 'id', 'fr', 'ht', 'roa']
src_constituents: {'eng'}
tgt_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'}
src_multilingual: False
tgt_multilingual: True
prepro: normalization + SentencePiece (spm32k, spm32k)
url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-roa/opus2m-2020-08-01.zip
url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-roa/opus2m-2020-08-01.test.txt
src_alpha3: eng
tgt_alpha3: roa
short_pair: en - roa
chrF2_score: 0.636
bleu: 42.9
brevity_penalty: 0.978
ref_len: 72751.0
src_name: English
tgt_name: Romance languages
train_date: 2020 - 08 - 01
src_alpha2: en
tgt_alpha2: roa
prefer_old: False
long_pair: eng - roa
helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
port_machine: brutasse
port_time: 2020 - 08 - 21 - 14:41

🔧 Technical Details

No technical implementation details are provided in the original README.

📄 License

The project is licensed under the Apache - 2.0 license.

Benchmarks

testset	BLEU	chr - F
newsdev2016 - enro - engron.eng.ron	27.6	0.567
newsdiscussdev2015 - enfr - engfra.eng.fra	30.2	0.575
newsdiscusstest2015 - enfr - engfra.eng.fra	35.5	0.612
newssyscomb2009 - engfra.eng.fra	27.9	0.570
newssyscomb2009 - engita.eng.ita	29.3	0.590
newssyscomb2009 - engspa.eng.spa	29.6	0.570
news - test2008 - engfra.eng.fra	25.2	0.538
news - test2008 - engspa.eng.spa	27.3	0.548
newstest2009 - engfra.eng.fra	26.9	0.560
newstest2009 - engita.eng.ita	28.7	0.583
newstest2009 - engspa.eng.spa	29.0	0.568
newstest2010 - engfra.eng.fra	29.3	0.574
newstest2010 - engspa.eng.spa	34.2	0.601
newstest2011 - engfra.eng.fra	31.4	0.592
newstest2011 - engspa.eng.spa	35.0	0.599
newstest2012 - engfra.eng.fra	29.5	0.576
newstest2012 - engspa.eng.spa	35.5	0.603
newstest2013 - engfra.eng.fra	29.9	0.567
newstest2013 - engspa.eng.spa	32.1	0.578
newstest2016 - enro - engron.eng.ron	26.1	0.551
Tatoeba - test.eng - arg.eng.arg	1.4	0.125
Tatoeba - test.eng - ast.eng.ast	17.8	0.406
Tatoeba - test.eng - cat.eng.cat	48.3	0.676
Tatoeba - test.eng - cos.eng.cos	3.2	0.275
Tatoeba - test.eng - egl.eng.egl	0.2	0.084
Tatoeba - test.eng - ext.eng.ext	11.2	0.344
Tatoeba - test.eng - fra.eng.fra	45.3	0.637
Tatoeba - test.eng - frm.eng.frm	1.1	0.221
Tatoeba - test.eng - gcf.eng.gcf	0.6	0.118
Tatoeba - test.eng - glg.eng.glg	44.2	0.645
Tatoeba - test.eng - hat.eng.hat	28.0	0.502
Tatoeba - test.eng - ita.eng.ita	45.6	0.674
Tatoeba - test.eng - lad.eng.lad	8.2	0.322
Tatoeba - test.eng - lij.eng.lij	1.4	0.182
Tatoeba - test.eng - lld.eng.lld	0.8	0.217
Tatoeba - test.eng - lmo.eng.lmo	0.7	0.190
Tatoeba - test.eng - mfe.eng.mfe	91.9	0.956
Tatoeba - test.eng - msa.eng.msa	31.1	0.548
Tatoeba - test.eng.multi	42.9	0.636
Tatoeba - test.eng - mwl.eng.mwl	2.1	0.234
Tatoeba - test.eng - oci.eng.oci	7.9	0.297
Tatoeba - test.eng - pap.eng.pap	44.1	0.648
Tatoeba - test.eng - pms.eng.pms	2.1	0.190
Tatoeba - test.eng - por.eng.por	41.8	0.639
Tatoeba - test.eng - roh.eng.roh	3.5	0.261
Tatoeba - test.eng - ron.eng.ron	41.0	0.635
Tatoeba - test.eng - scn.eng.scn	1.7	0.184
Tatoeba - test.eng - spa.eng.spa	50.1	0.689
Tatoeba - test.eng - vec.eng.vec	3.2	0.248
Tatoeba - test.eng - wln.eng.wln	7.2	0.220

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