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

  • source group: English

  • target group: Philippine languages

  • OPUS readme: eng-phi

  • model: transformer

  • source language(s): eng

  • target language(s): akl_Latn ceb hil ilo pag war

  • 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-2020-08-01.zip

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

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

Benchmarks

testset BLEU chr-F
Tatoeba-test.eng-akl.eng.akl 7.1 0.245
Tatoeba-test.eng-ceb.eng.ceb 10.5 0.435
Tatoeba-test.eng-hil.eng.hil 18.0 0.506
Tatoeba-test.eng-ilo.eng.ilo 33.4 0.590
Tatoeba-test.eng.multi 13.1 0.392
Tatoeba-test.eng-pag.eng.pag 19.4 0.481
Tatoeba-test.eng-war.eng.war 12.8 0.441

System Info: