translation
mask_token: 
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Helsinki-NLP/opus-mt-uk-ru
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-uk-ru") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-uk-ru")
source group: Ukrainian
target group: Russian
OPUS readme: ukr-rus
model: transformer-align
source language(s): ukr
target language(s): rus
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.ukr.rus | 69.2 | 0.826 |
hf_name: ukr-rus
source_languages: ukr
target_languages: rus
opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ukr-rus/README.md
original_repo: Tatoeba-Challenge
tags: ['translation']
languages: ['uk', 'ru']
src_constituents: {'ukr'}
tgt_constituents: {'rus'}
src_multilingual: False
tgt_multilingual: False
prepro: normalization + SentencePiece (spm32k,spm32k)
url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-rus/opus-2020-06-17.zip
url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ukr-rus/opus-2020-06-17.test.txt
src_alpha3: ukr
tgt_alpha3: rus
short_pair: uk-ru
chrF2_score: 0.826
bleu: 69.2
brevity_penalty: 0.992
ref_len: 60387.0
src_name: Ukrainian
tgt_name: Russian
train_date: 2020-06-17
src_alpha2: uk
tgt_alpha2: ru
prefer_old: False
long_pair: ukr-rus
helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
port_machine: brutasse
port_time: 2020-08-21-14:41
API endpoint
