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Helsinki-NLP/opus-mt-zle-zle Helsinki-NLP/opus-mt-zle-zle
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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-zle") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-zle-zle")
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zle-zle

  • source group: East Slavic languages

  • target group: East Slavic languages

  • OPUS readme: zle-zle

  • model: transformer

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

  • target language(s): bel bel_Latn orv_Cyrl rus ukr

  • model: transformer

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

  • a sentence initial language token is required in the form of >>id<< (id = valid target language ID)

  • download original weights: opus-2020-07-27.zip

  • test set translations: opus-2020-07-27.test.txt

  • test set scores: opus-2020-07-27.eval.txt

Benchmarks

testset BLEU chr-F
Tatoeba-test.bel-rus.bel.rus 57.1 0.758
Tatoeba-test.bel-ukr.bel.ukr 55.5 0.751
Tatoeba-test.multi.multi 58.0 0.742
Tatoeba-test.orv-rus.orv.rus 5.8 0.226
Tatoeba-test.orv-ukr.orv.ukr 2.5 0.161
Tatoeba-test.rus-bel.rus.bel 50.5 0.714
Tatoeba-test.rus-orv.rus.orv 0.3 0.129
Tatoeba-test.rus-ukr.rus.ukr 63.9 0.794
Tatoeba-test.ukr-bel.ukr.bel 51.3 0.719
Tatoeba-test.ukr-orv.ukr.orv 0.3 0.106
Tatoeba-test.ukr-rus.ukr.rus 68.7 0.825

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