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

  • source group: English

  • target group: Western Malayo-Polynesian languages

  • OPUS readme: eng-pqw

  • model: transformer

  • source language(s): eng

  • target language(s): akl_Latn ceb cha dtp hil iba ilo ind jav jav_Java mad max_Latn min mlg pag pau sun tmw_Latn war zlm_Latn zsm_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-2020-08-01.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 3.0 0.143
Tatoeba-test.eng-ceb.eng.ceb 11.4 0.432
Tatoeba-test.eng-cha.eng.cha 1.4 0.189
Tatoeba-test.eng-dtp.eng.dtp 0.6 0.139
Tatoeba-test.eng-hil.eng.hil 17.7 0.525
Tatoeba-test.eng-iba.eng.iba 14.6 0.365
Tatoeba-test.eng-ilo.eng.ilo 34.0 0.590
Tatoeba-test.eng-jav.eng.jav 6.2 0.299
Tatoeba-test.eng-mad.eng.mad 2.6 0.154
Tatoeba-test.eng-mlg.eng.mlg 34.3 0.518
Tatoeba-test.eng-msa.eng.msa 31.1 0.561
Tatoeba-test.eng.multi 17.5 0.422
Tatoeba-test.eng-pag.eng.pag 19.8 0.507
Tatoeba-test.eng-pau.eng.pau 1.2 0.129
Tatoeba-test.eng-sun.eng.sun 30.3 0.418
Tatoeba-test.eng-war.eng.war 12.6 0.439

System Info: