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

  • source group: English

  • target group: Semitic languages

  • OPUS readme: eng-sem

  • model: transformer

  • source language(s): eng

  • target language(s): acm afb amh apc ara arq ary arz heb mlt tir

  • 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-amh.eng.amh 11.2 0.480
Tatoeba-test.eng-ara.eng.ara 12.7 0.417
Tatoeba-test.eng-heb.eng.heb 33.8 0.564
Tatoeba-test.eng-mlt.eng.mlt 18.7 0.554
Tatoeba-test.eng.multi 23.5 0.486
Tatoeba-test.eng-tir.eng.tir 2.7 0.248

System Info: