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Helsinki-NLP/opus-mt-zle-en Helsinki-NLP/opus-mt-zle-en
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last 30 days

pytorch

tf

Contributed by

Language Technology Research Group at the University of Helsinki university
1 team member · 1325 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-zle-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zle-en")
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zle-eng

  • source group: East Slavic languages

  • target group: English

  • OPUS readme: zle-eng

  • model: transformer

  • source language(s): bel bel_Latn orv_Cyrl rue rus ukr

  • target language(s): eng

  • model: transformer

  • pre-processing: normalization + SentencePiece (spm32k,spm32k)

  • download original weights: opus2m-2020-08-01.zip

  • test set translations: opus2m-2020-08-01.test.txt

  • test set scores: opus2m-2020-08-01.eval.txt

Benchmarks

testset BLEU chr-F
newstest2012-ruseng.rus.eng 31.1 0.579
newstest2013-ruseng.rus.eng 24.9 0.522
newstest2014-ruen-ruseng.rus.eng 27.9 0.563
newstest2015-enru-ruseng.rus.eng 26.8 0.541
newstest2016-enru-ruseng.rus.eng 25.8 0.535
newstest2017-enru-ruseng.rus.eng 29.1 0.561
newstest2018-enru-ruseng.rus.eng 25.4 0.537
newstest2019-ruen-ruseng.rus.eng 26.8 0.545
Tatoeba-test.bel-eng.bel.eng 38.3 0.569
Tatoeba-test.multi.eng 50.1 0.656
Tatoeba-test.orv-eng.orv.eng 6.9 0.217
Tatoeba-test.rue-eng.rue.eng 15.4 0.345
Tatoeba-test.rus-eng.rus.eng 52.5 0.674
Tatoeba-test.ukr-eng.ukr.eng 52.1 0.673

System Info: