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https://api-inference.huggingface.co/models/Helsinki-NLP/opus-mt-en-itc
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Helsinki-NLP/opus-mt-en-itc Helsinki-NLP/opus-mt-en-itc
105 downloads
last 30 days

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-en-itc") model = AutoModelWithLMHead.from_pretrained("Helsinki-NLP/opus-mt-en-itc")
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eng-itc

  • source group: English

  • target group: Italic languages

  • OPUS readme: eng-itc

  • model: transformer

  • source language(s): eng

  • target language(s): arg ast cat cos egl ext fra frm_Latn gcf_Latn glg hat ind ita lad lad_Latn lat_Latn lij lld_Latn lmo max_Latn mfe min mwl oci pap pms por roh ron scn spa tmw_Latn vec wln zlm_Latn zsm_Latn

  • 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
newsdev2016-enro-engron.eng.ron 27.1 0.565
newsdiscussdev2015-enfr-engfra.eng.fra 29.9 0.574
newsdiscusstest2015-enfr-engfra.eng.fra 35.3 0.609
newssyscomb2009-engfra.eng.fra 27.7 0.567
newssyscomb2009-engita.eng.ita 28.6 0.586
newssyscomb2009-engspa.eng.spa 29.8 0.569
news-test2008-engfra.eng.fra 25.0 0.536
news-test2008-engspa.eng.spa 27.1 0.548
newstest2009-engfra.eng.fra 26.7 0.557
newstest2009-engita.eng.ita 28.9 0.583
newstest2009-engspa.eng.spa 28.9 0.567
newstest2010-engfra.eng.fra 29.6 0.574
newstest2010-engspa.eng.spa 33.8 0.598
newstest2011-engfra.eng.fra 30.9 0.590
newstest2011-engspa.eng.spa 34.8 0.598
newstest2012-engfra.eng.fra 29.1 0.574
newstest2012-engspa.eng.spa 34.9 0.600
newstest2013-engfra.eng.fra 30.1 0.567
newstest2013-engspa.eng.spa 31.8 0.576
newstest2016-enro-engron.eng.ron 25.9 0.548
Tatoeba-test.eng-arg.eng.arg 1.6 0.120
Tatoeba-test.eng-ast.eng.ast 17.2 0.389
Tatoeba-test.eng-cat.eng.cat 47.6 0.668
Tatoeba-test.eng-cos.eng.cos 4.3 0.287
Tatoeba-test.eng-egl.eng.egl 0.9 0.101
Tatoeba-test.eng-ext.eng.ext 8.7 0.287
Tatoeba-test.eng-fra.eng.fra 44.9 0.635
Tatoeba-test.eng-frm.eng.frm 1.0 0.225
Tatoeba-test.eng-gcf.eng.gcf 0.7 0.115
Tatoeba-test.eng-glg.eng.glg 44.9 0.648
Tatoeba-test.eng-hat.eng.hat 30.9 0.533
Tatoeba-test.eng-ita.eng.ita 45.4 0.673
Tatoeba-test.eng-lad.eng.lad 5.6 0.279
Tatoeba-test.eng-lat.eng.lat 12.1 0.380
Tatoeba-test.eng-lij.eng.lij 1.4 0.183
Tatoeba-test.eng-lld.eng.lld 0.5 0.199
Tatoeba-test.eng-lmo.eng.lmo 0.7 0.187
Tatoeba-test.eng-mfe.eng.mfe 83.6 0.909
Tatoeba-test.eng-msa.eng.msa 31.3 0.549
Tatoeba-test.eng.multi 38.0 0.588
Tatoeba-test.eng-mwl.eng.mwl 2.7 0.322
Tatoeba-test.eng-oci.eng.oci 8.2 0.293
Tatoeba-test.eng-pap.eng.pap 46.7 0.663
Tatoeba-test.eng-pms.eng.pms 2.1 0.194
Tatoeba-test.eng-por.eng.por 41.2 0.635
Tatoeba-test.eng-roh.eng.roh 2.6 0.237
Tatoeba-test.eng-ron.eng.ron 40.6 0.632
Tatoeba-test.eng-scn.eng.scn 1.6 0.181
Tatoeba-test.eng-spa.eng.spa 49.5 0.685
Tatoeba-test.eng-vec.eng.vec 1.6 0.223
Tatoeba-test.eng-wln.eng.wln 7.1 0.250

System Info:

  • hf_name: eng-itc

  • source_languages: eng

  • target_languages: itc

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

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

  • languages: ['en', 'it', 'ca', 'rm', 'es', 'ro', 'gl', 'sc', 'co', 'wa', 'pt', 'oc', 'an', 'id', 'fr', 'ht', 'itc']

  • src_constituents: {'eng'}

  • tgt_constituents: {'ita', 'cat', 'roh', 'spa', 'pap', 'bjn', 'lmo', 'mwl', 'lij', 'lat_Latn', 'lad_Latn', 'pcd', 'lat_Grek', 'ext', 'ron', 'ast', 'glg', 'pms', 'zsm_Latn', 'srd', 'gcf_Latn', 'lld_Latn', 'min', 'tmw_Latn', 'cos', 'wln', 'zlm_Latn', 'por', 'egl', 'oci', 'vec', 'arg', 'ind', 'fra', 'hat', 'lad', 'max_Latn', 'frm_Latn', 'scn', 'mfe'}

  • src_multilingual: False

  • tgt_multilingual: True

  • prepro: normalization + SentencePiece (spm32k,spm32k)

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

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

  • src_alpha3: eng

  • tgt_alpha3: itc

  • short_pair: en-itc

  • chrF2_score: 0.588

  • bleu: 38.0

  • brevity_penalty: 0.9670000000000001

  • ref_len: 73951.0

  • src_name: English

  • tgt_name: Italic languages

  • train_date: 2020-08-01

  • src_alpha2: en

  • tgt_alpha2: itc

  • prefer_old: False

  • long_pair: eng-itc

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41