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

  • source group: Germanic languages

  • target group: English

  • OPUS readme: gem-eng

  • model: transformer

  • source language(s): afr ang_Latn dan deu enm_Latn fao frr fry gos got_Goth gsw isl ksh ltz nds nld nno nob nob_Hebr non_Latn pdc sco stq swe swg yid

  • 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
newssyscomb2009-deueng.deu.eng 27.2 0.542
news-test2008-deueng.deu.eng 26.3 0.536
newstest2009-deueng.deu.eng 25.1 0.531
newstest2010-deueng.deu.eng 28.3 0.569
newstest2011-deueng.deu.eng 26.0 0.543
newstest2012-deueng.deu.eng 26.8 0.550
newstest2013-deueng.deu.eng 30.2 0.570
newstest2014-deen-deueng.deu.eng 30.7 0.574
newstest2015-ende-deueng.deu.eng 32.1 0.581
newstest2016-ende-deueng.deu.eng 36.9 0.624
newstest2017-ende-deueng.deu.eng 32.8 0.588
newstest2018-ende-deueng.deu.eng 40.2 0.640
newstest2019-deen-deueng.deu.eng 36.8 0.614
Tatoeba-test.afr-eng.afr.eng 62.8 0.758
Tatoeba-test.ang-eng.ang.eng 10.5 0.262
Tatoeba-test.dan-eng.dan.eng 61.6 0.754
Tatoeba-test.deu-eng.deu.eng 49.7 0.665
Tatoeba-test.enm-eng.enm.eng 23.9 0.491
Tatoeba-test.fao-eng.fao.eng 23.4 0.446
Tatoeba-test.frr-eng.frr.eng 10.2 0.184
Tatoeba-test.fry-eng.fry.eng 29.6 0.486
Tatoeba-test.gos-eng.gos.eng 17.8 0.352
Tatoeba-test.got-eng.got.eng 0.1 0.058
Tatoeba-test.gsw-eng.gsw.eng 15.3 0.333
Tatoeba-test.isl-eng.isl.eng 51.0 0.669
Tatoeba-test.ksh-eng.ksh.eng 6.7 0.266
Tatoeba-test.ltz-eng.ltz.eng 33.0 0.505
Tatoeba-test.multi.eng 54.0 0.687
Tatoeba-test.nds-eng.nds.eng 33.6 0.529
Tatoeba-test.nld-eng.nld.eng 58.9 0.733
Tatoeba-test.non-eng.non.eng 37.3 0.546
Tatoeba-test.nor-eng.nor.eng 54.9 0.696
Tatoeba-test.pdc-eng.pdc.eng 29.6 0.446
Tatoeba-test.sco-eng.sco.eng 40.5 0.581
Tatoeba-test.stq-eng.stq.eng 14.5 0.361
Tatoeba-test.swe-eng.swe.eng 62.0 0.745
Tatoeba-test.swg-eng.swg.eng 17.1 0.334
Tatoeba-test.yid-eng.yid.eng 19.4 0.400

System Info:

  • hf_name: gem-eng

  • source_languages: gem

  • target_languages: eng

  • opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/gem-eng/README.md

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

  • languages: ['da', 'sv', 'af', 'nn', 'fy', 'fo', 'de', 'nb', 'nl', 'is', 'en', 'lb', 'yi', 'gem']

  • src_constituents: {'ksh', 'enm_Latn', 'got_Goth', 'stq', 'dan', 'swe', 'afr', 'pdc', 'gos', 'nno', 'fry', 'gsw', 'fao', 'deu', 'swg', 'sco', 'nob', 'nld', 'isl', 'eng', 'ltz', 'nob_Hebr', 'ang_Latn', 'frr', 'non_Latn', 'yid', 'nds'}

  • tgt_constituents: {'eng'}

  • src_multilingual: True

  • tgt_multilingual: False

  • prepro: normalization + SentencePiece (spm32k,spm32k)

  • url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.zip

  • url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/gem-eng/opus2m-2020-08-01.test.txt

  • src_alpha3: gem

  • tgt_alpha3: eng

  • short_pair: gem-en

  • chrF2_score: 0.687

  • bleu: 54.0

  • brevity_penalty: 0.993

  • ref_len: 72120.0

  • src_name: Germanic languages

  • tgt_name: English

  • train_date: 2020-08-01

  • src_alpha2: gem

  • tgt_alpha2: en

  • prefer_old: False

  • long_pair: gem-eng

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41