translation
mask_token: 
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Helsinki-NLP/opus-mt-it-ar
pytorch
tf
How to use this model directly from the
🤗/transformers library:
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-it-ar") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-it-ar")
source group: Italian
target group: Arabic
OPUS readme: ita-ara
model: transformer
source language(s): ita
target language(s): ara
model: transformer
pre-processing: normalization + SentencePiece (spm32k,spm32k)
download original weights: opus-2020-07-03.zip
test set translations: opus-2020-07-03.test.txt
test set scores: opus-2020-07-03.eval.txt
| testset | BLEU | chr-F |
|---|---|---|
| Tatoeba-test.ita.ara | 21.9 | 0.517 |
hf_name: ita-ara
source_languages: ita
target_languages: ara
opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-ara/README.md
original_repo: Tatoeba-Challenge
tags: ['translation']
languages: ['it', 'ar']
src_constituents: {'ita'}
tgt_constituents: {'apc', 'ara', 'arq_Latn', 'arq', 'afb', 'ara_Latn', 'apc_Latn', 'arz'}
src_multilingual: False
tgt_multilingual: False
prepro: normalization + SentencePiece (spm32k,spm32k)
url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-ara/opus-2020-07-03.zip
url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-ara/opus-2020-07-03.test.txt
src_alpha3: ita
tgt_alpha3: ara
short_pair: it-ar
chrF2_score: 0.517
bleu: 21.9
brevity_penalty: 0.95
ref_len: 1161.0
src_name: Italian
tgt_name: Arabic
train_date: 2020-07-03
src_alpha2: it
tgt_alpha2: ar
prefer_old: False
long_pair: ita-ara
helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
port_machine: brutasse
port_time: 2020-08-21-14:41
API endpoint
