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

  • source group: English

  • target group: Baltic languages

  • OPUS readme: eng-bat

  • model: transformer

  • source language(s): eng

  • target language(s): lav lit ltg prg_Latn sgs

  • 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
newsdev2017-enlv-englav.eng.lav 24.0 0.546
newsdev2019-enlt-englit.eng.lit 20.9 0.533
newstest2017-enlv-englav.eng.lav 18.3 0.506
newstest2019-enlt-englit.eng.lit 13.6 0.466
Tatoeba-test.eng-lav.eng.lav 42.8 0.652
Tatoeba-test.eng-lit.eng.lit 37.1 0.650
Tatoeba-test.eng.multi 37.0 0.616
Tatoeba-test.eng-prg.eng.prg 0.5 0.130
Tatoeba-test.eng-sgs.eng.sgs 4.1 0.178

System Info: