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

  • source group: English

  • target group: Slavic languages

  • OPUS readme: eng-sla

  • model: transformer

  • source language(s): eng

  • target language(s): bel bel_Latn bos_Latn bul bul_Latn ces csb_Latn dsb hrv hsb mkd orv_Cyrl pol rue rus slv srp_Cyrl srp_Latn ukr

  • 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
newssyscomb2009-engces.eng.ces 20.1 0.484
news-test2008-engces.eng.ces 17.7 0.461
newstest2009-engces.eng.ces 19.1 0.479
newstest2010-engces.eng.ces 19.3 0.483
newstest2011-engces.eng.ces 20.4 0.486
newstest2012-engces.eng.ces 18.3 0.461
newstest2012-engrus.eng.rus 27.4 0.551
newstest2013-engces.eng.ces 21.5 0.489
newstest2013-engrus.eng.rus 20.9 0.490
newstest2015-encs-engces.eng.ces 21.1 0.496
newstest2015-enru-engrus.eng.rus 24.5 0.536
newstest2016-encs-engces.eng.ces 23.6 0.515
newstest2016-enru-engrus.eng.rus 23.0 0.519
newstest2017-encs-engces.eng.ces 19.2 0.474
newstest2017-enru-engrus.eng.rus 25.0 0.541
newstest2018-encs-engces.eng.ces 19.3 0.479
newstest2018-enru-engrus.eng.rus 22.3 0.526
newstest2019-encs-engces.eng.ces 20.4 0.486
newstest2019-enru-engrus.eng.rus 24.0 0.506
Tatoeba-test.eng-bel.eng.bel 22.9 0.489
Tatoeba-test.eng-bul.eng.bul 46.7 0.652
Tatoeba-test.eng-ces.eng.ces 42.7 0.624
Tatoeba-test.eng-csb.eng.csb 1.4 0.210
Tatoeba-test.eng-dsb.eng.dsb 1.4 0.165
Tatoeba-test.eng-hbs.eng.hbs 40.3 0.616
Tatoeba-test.eng-hsb.eng.hsb 14.3 0.344
Tatoeba-test.eng-mkd.eng.mkd 44.1 0.635
Tatoeba-test.eng.multi 41.0 0.610
Tatoeba-test.eng-orv.eng.orv 0.3 0.014
Tatoeba-test.eng-pol.eng.pol 42.0 0.637
Tatoeba-test.eng-rue.eng.rue 0.3 0.012
Tatoeba-test.eng-rus.eng.rus 40.5 0.612
Tatoeba-test.eng-slv.eng.slv 18.8 0.357
Tatoeba-test.eng-ukr.eng.ukr 38.8 0.600

System Info:

  • hf_name: eng-sla

  • source_languages: eng

  • target_languages: sla

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

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

  • languages: ['en', 'be', 'hr', 'mk', 'cs', 'ru', 'pl', 'bg', 'uk', 'sl', 'sla']

  • src_constituents: {'eng'}

  • tgt_constituents: {'bel', 'hrv', 'orv_Cyrl', 'mkd', 'bel_Latn', 'srp_Latn', 'bul_Latn', 'ces', 'bos_Latn', 'csb_Latn', 'dsb', 'hsb', 'rus', 'srp_Cyrl', 'pol', 'rue', 'bul', 'ukr', 'slv'}

  • src_multilingual: False

  • tgt_multilingual: True

  • prepro: normalization + SentencePiece (spm32k,spm32k)

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

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

  • src_alpha3: eng

  • tgt_alpha3: sla

  • short_pair: en-sla

  • chrF2_score: 0.61

  • bleu: 41.0

  • brevity_penalty: 0.976

  • ref_len: 64809.0

  • src_name: English

  • tgt_name: Slavic languages

  • train_date: 2020-08-01

  • src_alpha2: en

  • tgt_alpha2: sla

  • prefer_old: False

  • long_pair: eng-sla

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41