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

  • source group: English

  • target group: Atlantic-Congo languages

  • OPUS readme: eng-alv

  • model: transformer

  • source language(s): eng

  • target language(s): ewe fuc fuv ibo kin lin lug nya run sag sna swh toi_Latn tso umb wol xho yor zul

  • 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
Tatoeba-test.eng-ewe.eng.ewe 4.9 0.212
Tatoeba-test.eng-ful.eng.ful 0.6 0.079
Tatoeba-test.eng-ibo.eng.ibo 3.5 0.255
Tatoeba-test.eng-kin.eng.kin 10.5 0.510
Tatoeba-test.eng-lin.eng.lin 1.1 0.273
Tatoeba-test.eng-lug.eng.lug 5.3 0.340
Tatoeba-test.eng.multi 11.4 0.429
Tatoeba-test.eng-nya.eng.nya 18.1 0.595
Tatoeba-test.eng-run.eng.run 13.9 0.484
Tatoeba-test.eng-sag.eng.sag 5.3 0.194
Tatoeba-test.eng-sna.eng.sna 26.2 0.623
Tatoeba-test.eng-swa.eng.swa 1.0 0.141
Tatoeba-test.eng-toi.eng.toi 7.0 0.224
Tatoeba-test.eng-tso.eng.tso 46.7 0.643
Tatoeba-test.eng-umb.eng.umb 7.8 0.359
Tatoeba-test.eng-wol.eng.wol 6.8 0.191
Tatoeba-test.eng-xho.eng.xho 27.1 0.629
Tatoeba-test.eng-yor.eng.yor 17.4 0.356
Tatoeba-test.eng-zul.eng.zul 34.1 0.729

System Info:

  • hf_name: eng-alv

  • source_languages: eng

  • target_languages: alv

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

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

  • languages: ['en', 'sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'alv']

  • src_constituents: {'eng'}

  • tgt_constituents: {'sna', 'kin', 'wol', 'ibo', 'swh', 'sag', 'ewe', 'zul', 'fuc', 'lug', 'tso', 'lin', 'nya', 'yor', 'run', 'xho', 'fuv', 'toi_Latn', 'umb'}

  • src_multilingual: False

  • tgt_multilingual: True

  • prepro: normalization + SentencePiece (spm32k,spm32k)

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

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

  • src_alpha3: eng

  • tgt_alpha3: alv

  • short_pair: en-alv

  • chrF2_score: 0.429

  • bleu: 11.4

  • brevity_penalty: 1.0

  • ref_len: 10603.0

  • src_name: English

  • tgt_name: Atlantic-Congo languages

  • train_date: 2020-08-01

  • src_alpha2: en

  • tgt_alpha2: alv

  • prefer_old: False

  • long_pair: eng-alv

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41