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Helsinki-NLP/opus-mt-en-zls Helsinki-NLP/opus-mt-en-zls
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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-en-zls") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-zls")
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eng-zls

  • source group: English

  • target group: South Slavic languages

  • OPUS readme: eng-zls

  • model: transformer

  • source language(s): eng

  • target language(s): bos_Latn bul bul_Latn hrv mkd slv srp_Cyrl srp_Latn

  • 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: opus2m-2020-08-02.zip

  • test set translations: opus2m-2020-08-02.test.txt

  • test set scores: opus2m-2020-08-02.eval.txt

Benchmarks

testset BLEU chr-F
Tatoeba-test.eng-bul.eng.bul 47.6 0.657
Tatoeba-test.eng-hbs.eng.hbs 40.7 0.619
Tatoeba-test.eng-mkd.eng.mkd 45.2 0.642
Tatoeba-test.eng.multi 42.7 0.622
Tatoeba-test.eng-slv.eng.slv 17.9 0.351

System Info: