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Helsinki-NLP/opus-mt-sla-en Helsinki-NLP/opus-mt-sla-en
49 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-sla-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-sla-en")
Uploaded in S3

sla-eng

  • source group: Slavic languages

  • target group: English

  • OPUS readme: sla-eng

  • model: transformer

  • source 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

  • 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-ceseng.ces.eng 26.7 0.542
newstest2009-ceseng.ces.eng 25.2 0.534
newstest2010-ceseng.ces.eng 25.9 0.545
newstest2011-ceseng.ces.eng 26.8 0.544
newstest2012-ceseng.ces.eng 25.6 0.536
newstest2012-ruseng.rus.eng 32.5 0.588
newstest2013-ceseng.ces.eng 28.8 0.556
newstest2013-ruseng.rus.eng 26.4 0.532
newstest2014-csen-ceseng.ces.eng 31.4 0.591
newstest2014-ruen-ruseng.rus.eng 29.6 0.576
newstest2015-encs-ceseng.ces.eng 28.2 0.545
newstest2015-enru-ruseng.rus.eng 28.1 0.551
newstest2016-encs-ceseng.ces.eng 30.0 0.567
newstest2016-enru-ruseng.rus.eng 27.4 0.548
newstest2017-encs-ceseng.ces.eng 26.5 0.537
newstest2017-enru-ruseng.rus.eng 31.0 0.574
newstest2018-encs-ceseng.ces.eng 27.9 0.548
newstest2018-enru-ruseng.rus.eng 26.8 0.545
newstest2019-ruen-ruseng.rus.eng 29.1 0.562
Tatoeba-test.bel-eng.bel.eng 42.5 0.609
Tatoeba-test.bul-eng.bul.eng 55.4 0.697
Tatoeba-test.ces-eng.ces.eng 53.1 0.688
Tatoeba-test.csb-eng.csb.eng 23.1 0.446
Tatoeba-test.dsb-eng.dsb.eng 31.1 0.467
Tatoeba-test.hbs-eng.hbs.eng 56.1 0.702
Tatoeba-test.hsb-eng.hsb.eng 46.2 0.597
Tatoeba-test.mkd-eng.mkd.eng 54.5 0.680
Tatoeba-test.multi.eng 53.2 0.683
Tatoeba-test.orv-eng.orv.eng 12.1 0.292
Tatoeba-test.pol-eng.pol.eng 51.1 0.671
Tatoeba-test.rue-eng.rue.eng 19.6 0.389
Tatoeba-test.rus-eng.rus.eng 54.1 0.686
Tatoeba-test.slv-eng.slv.eng 43.4 0.610
Tatoeba-test.ukr-eng.ukr.eng 53.8 0.685

System Info:

  • hf_name: sla-eng

  • source_languages: sla

  • target_languages: eng

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

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

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

  • src_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'}

  • tgt_constituents: {'eng'}

  • src_multilingual: True

  • tgt_multilingual: False

  • prepro: normalization + SentencePiece (spm32k,spm32k)

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

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

  • src_alpha3: sla

  • tgt_alpha3: eng

  • short_pair: sla-en

  • chrF2_score: 0.6829999999999999

  • bleu: 53.2

  • brevity_penalty: 0.9740000000000001

  • ref_len: 70897.0

  • src_name: Slavic languages

  • tgt_name: English

  • train_date: 2020-08-01

  • src_alpha2: sla

  • tgt_alpha2: en

  • prefer_old: False

  • long_pair: sla-eng

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41