translation
mask_token: 
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Helsinki-NLP/opus-mt-es-uk
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-es-uk") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-es-uk")
source group: Spanish
target group: Ukrainian
OPUS readme: spa-ukr
model: transformer-align
source language(s): spa
target language(s): ukr
model: transformer-align
pre-processing: normalization + SentencePiece (spm32k,spm32k)
download original weights: opus-2020-06-17.zip
test set translations: opus-2020-06-17.test.txt
test set scores: opus-2020-06-17.eval.txt
| testset | BLEU | chr-F |
|---|---|---|
| Tatoeba-test.spa.ukr | 41.3 | 0.614 |
hf_name: spa-ukr
source_languages: spa
target_languages: ukr
opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/spa-ukr/README.md
original_repo: Tatoeba-Challenge
tags: ['translation']
languages: ['es', 'uk']
src_constituents: {'spa'}
tgt_constituents: {'ukr'}
src_multilingual: False
tgt_multilingual: False
prepro: normalization + SentencePiece (spm32k,spm32k)
url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-ukr/opus-2020-06-17.zip
url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/spa-ukr/opus-2020-06-17.test.txt
src_alpha3: spa
tgt_alpha3: ukr
short_pair: es-uk
chrF2_score: 0.614
bleu: 41.3
brevity_penalty: 0.983
ref_len: 53833.0
src_name: Spanish
tgt_name: Ukrainian
train_date: 2020-06-17
src_alpha2: es
tgt_alpha2: uk
prefer_old: False
long_pair: spa-ukr
helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
port_machine: brutasse
port_time: 2020-08-21-14:41
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