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https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-sem-en
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Helsinki-NLP/opus-mt-sem-en Helsinki-NLP/opus-mt-sem-en
355 downloads
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pytorch

tf

Contributed by

Language Technology Research Group at the University of Helsinki university
1 team member · 1323 models

How to use this model directly from the 🤗/transformers library:

			
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from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-sem-en") model = AutoModelWithLMHead.from_pretrained("Helsinki-NLP/opus-mt-sem-en")
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sem-eng

  • source group: Semitic languages

  • target group: English

  • OPUS readme: sem-eng

  • model: transformer

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

  • target language(s): eng

  • model: transformer

  • pre-processing: normalization + SentencePiece (spm32k,spm32k)

  • 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.amh-eng.amh.eng 37.5 0.565
Tatoeba-test.ara-eng.ara.eng 38.9 0.566
Tatoeba-test.heb-eng.heb.eng 44.6 0.610
Tatoeba-test.mlt-eng.mlt.eng 53.7 0.688
Tatoeba-test.multi.eng 41.7 0.588
Tatoeba-test.tir-eng.tir.eng 18.3 0.370

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