opus-mt-afa-en Open-source Multilingual Translation Model - Free Support for Afro-Asiatic Language to English Translation

Opus Mt Afa En

Developed by Helsinki-NLP

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

Machine Translation

Transformers

Supports Multiple LanguagesOpen Source License:Apache-2.0 #Multidialect Arabic Support #Low-Resource Language Optimization #African Language Translation

Downloads 30

Release Time : 3/2/2022

Model Overview

The model specializes in translation tasks from Afro-Asiatic languages to English, supporting languages such as Arabic, Hebrew, Amharic, and more.

Model Features

Multilingual Support

Supports translation from multiple Afro-Asiatic languages to English, including Arabic, Hebrew, Amharic, and more.

High-Performance Translation

Demonstrates excellent performance on multiple test sets, particularly in Maltese and Hebrew translation tasks.

Standardized Preprocessing

Utilizes standardization and SentencePiece for preprocessing, improving translation accuracy and consistency.

Model Capabilities

Text Translation

Multilingual Support

High-Performance Translation

Use Cases

Language Translation

Arabic to English Translation

Translates Arabic text into English, suitable for cross-language communication and information retrieval.

BLEU score 36.6, chr-F score 0.543

Hebrew to English Translation

Translates Hebrew text into English, suitable for academic research and business communication.

BLEU score 42.7, chr-F score 0.591

🚀 Afro-Asiatic Languages to English Translation Model

This project focuses on the translation from Afro - Asiatic languages to English. It provides a transformer - based model with detailed benchmarks and system information.

✨ Features

Multilingual Support: Supports multiple Afro - Asiatic languages including acm, afb, amh, etc., for translation to English.
Transformer Model: Utilizes a transformer model for translation tasks.
Pre - processing: Applies normalization and SentencePiece (spm32k, spm32k) for pre - processing.

📦 Installation

No specific installation steps are provided in the original document.

📚 Documentation

afa - eng

Source Group: Afro - Asiatic languages
Target Group: English
OPUS Readme: [afa - eng](https://github.com/Helsinki - NLP/Tatoeba - Challenge/tree/master/models/afa - eng/README.md)
Model: transformer
Source Language(s): acm, afb, amh, apc, ara, arq, ary, arz, hau_Latn, heb, kab, mlt, rif_Latn, shy_Latn, som, tir
Target Language(s): eng
Pre - processing: normalization + SentencePiece (spm32k, spm32k)
Download Original Weights: [opus2m - 2020 - 07 - 31.zip](https://object.pouta.csc.fi/Tatoeba - MT - models/afa - eng/opus2m - 2020 - 07 - 31.zip)
Test Set Translations: [opus2m - 2020 - 07 - 31.test.txt](https://object.pouta.csc.fi/Tatoeba - MT - models/afa - eng/opus2m - 2020 - 07 - 31.test.txt)
Test Set Scores: [opus2m - 2020 - 07 - 31.eval.txt](https://object.pouta.csc.fi/Tatoeba - MT - models/afa - eng/opus2m - 2020 - 07 - 31.eval.txt)

Benchmarks

Testset	BLEU	chr - F
Tatoeba - test.amh - eng.amh.eng	35.9	0.550
Tatoeba - test.ara - eng.ara.eng	36.6	0.543
Tatoeba - test.hau - eng.hau.eng	11.9	0.327
Tatoeba - test.heb - eng.heb.eng	42.7	0.591
Tatoeba - test.kab - eng.kab.eng	4.3	0.213
Tatoeba - test.mlt - eng.mlt.eng	44.3	0.618
Tatoeba - test.multi.eng	27.1	0.464
Tatoeba - test.rif - eng.rif.eng	3.5	0.141
Tatoeba - test.shy - eng.shy.eng	0.6	0.125
Tatoeba - test.som - eng.som.eng	23.6	0.472
Tatoeba - test.tir - eng.tir.eng	13.1	0.328

System Info

Property	Details
hf_name	afa - eng
source_languages	afa
target_languages	eng
opus_readme_url	[https://github.com/Helsinki - NLP/Tatoeba - Challenge/tree/master/models/afa - eng/README.md](https://github.com/Helsinki - NLP/Tatoeba - Challenge/tree/master/models/afa - eng/README.md)
original_repo	Tatoeba - Challenge
tags	['translation']
languages	['so', 'ti', 'am', 'he', 'mt', 'ar', 'afa', 'en']
src_constituents	{'som', 'rif_Latn', 'tir', 'kab', 'arq', 'afb', 'amh', 'arz', 'heb', 'shy_Latn', 'apc', 'mlt', 'thv', 'ara', 'hau_Latn', 'acm', 'ary'}
tgt_constituents	{'eng'}
src_multilingual	True
tgt_multilingual	False
prepro	normalization + SentencePiece (spm32k, spm32k)
url_model	[https://object.pouta.csc.fi/Tatoeba - MT - models/afa - eng/opus2m - 2020 - 07 - 31.zip](https://object.pouta.csc.fi/Tatoeba - MT - models/afa - eng/opus2m - 2020 - 07 - 31.zip)
url_test_set	[https://object.pouta.csc.fi/Tatoeba - MT - models/afa - eng/opus2m - 2020 - 07 - 31.test.txt](https://object.pouta.csc.fi/Tatoeba - MT - models/afa - eng/opus2m - 2020 - 07 - 31.test.txt)
src_alpha3	afa
tgt_alpha3	eng
short_pair	afa - en
chrF2_score	0.46399999999999997
bleu	27.1
brevity_penalty	1.0
ref_len	69373.0
src_name	Afro - Asiatic languages
tgt_name	English
train_date	2020 - 07 - 31
src_alpha2	afa
tgt_alpha2	en
prefer_old	False
long_pair	afa - 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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