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

art-eng

  • source group: Artificial languages

  • target group: English

  • OPUS readme: art-eng

  • model: transformer

  • source language(s): afh_Latn avk_Latn dws_Latn epo ido ido_Latn ile_Latn ina_Latn jbo jbo_Cyrl jbo_Latn ldn_Latn lfn_Cyrl lfn_Latn nov_Latn qya qya_Latn sjn_Latn tlh_Latn tzl tzl_Latn vol_Latn

  • target language(s): eng

  • model: transformer

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

  • download original weights: opus2m-2020-07-31.zip

  • test set translations: opus2m-2020-07-31.test.txt

  • test set scores: opus2m-2020-07-31.eval.txt

Benchmarks

testset BLEU chr-F
Tatoeba-test.afh-eng.afh.eng 1.2 0.099
Tatoeba-test.avk-eng.avk.eng 0.4 0.105
Tatoeba-test.dws-eng.dws.eng 1.6 0.076
Tatoeba-test.epo-eng.epo.eng 34.6 0.530
Tatoeba-test.ido-eng.ido.eng 12.7 0.310
Tatoeba-test.ile-eng.ile.eng 4.6 0.218
Tatoeba-test.ina-eng.ina.eng 5.8 0.254
Tatoeba-test.jbo-eng.jbo.eng 0.2 0.115
Tatoeba-test.ldn-eng.ldn.eng 0.7 0.083
Tatoeba-test.lfn-eng.lfn.eng 1.8 0.172
Tatoeba-test.multi.eng 11.6 0.287
Tatoeba-test.nov-eng.nov.eng 5.1 0.215
Tatoeba-test.qya-eng.qya.eng 0.7 0.113
Tatoeba-test.sjn-eng.sjn.eng 0.9 0.090
Tatoeba-test.tlh-eng.tlh.eng 0.2 0.124
Tatoeba-test.tzl-eng.tzl.eng 1.4 0.109
Tatoeba-test.vol-eng.vol.eng 0.5 0.115

System Info:

  • hf_name: art-eng

  • source_languages: art

  • target_languages: eng

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

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

  • languages: ['eo', 'io', 'art', 'en']

  • src_constituents: {'sjn_Latn', 'tzl', 'vol_Latn', 'qya', 'tlh_Latn', 'ile_Latn', 'ido_Latn', 'tzl_Latn', 'jbo_Cyrl', 'jbo', 'lfn_Latn', 'nov_Latn', 'dws_Latn', 'ldn_Latn', 'avk_Latn', 'lfn_Cyrl', 'ina_Latn', 'jbo_Latn', 'epo', 'afh_Latn', 'qya_Latn', 'ido'}

  • tgt_constituents: {'eng'}

  • src_multilingual: True

  • tgt_multilingual: False

  • prepro: normalization + SentencePiece (spm32k,spm32k)

  • url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/art-eng/opus2m-2020-07-31.zip

  • url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/art-eng/opus2m-2020-07-31.test.txt

  • src_alpha3: art

  • tgt_alpha3: eng

  • short_pair: art-en

  • chrF2_score: 0.287

  • bleu: 11.6

  • brevity_penalty: 1.0

  • ref_len: 73037.0

  • src_name: Artificial languages

  • tgt_name: English

  • train_date: 2020-07-31

  • src_alpha2: art

  • tgt_alpha2: en

  • prefer_old: False

  • long_pair: art-eng

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41