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

alv-eng

  • source group: Atlantic-Congo languages

  • target group: English

  • OPUS readme: alv-eng

  • model: transformer

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

  • 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.ewe-eng.ewe.eng 6.3 0.328
Tatoeba-test.ful-eng.ful.eng 0.4 0.108
Tatoeba-test.ibo-eng.ibo.eng 4.5 0.196
Tatoeba-test.kin-eng.kin.eng 30.7 0.511
Tatoeba-test.lin-eng.lin.eng 2.8 0.213
Tatoeba-test.lug-eng.lug.eng 3.4 0.140
Tatoeba-test.multi.eng 20.9 0.376
Tatoeba-test.nya-eng.nya.eng 38.7 0.492
Tatoeba-test.run-eng.run.eng 24.5 0.417
Tatoeba-test.sag-eng.sag.eng 5.5 0.177
Tatoeba-test.sna-eng.sna.eng 26.9 0.412
Tatoeba-test.swa-eng.swa.eng 4.9 0.196
Tatoeba-test.toi-eng.toi.eng 3.9 0.147
Tatoeba-test.tso-eng.tso.eng 76.7 0.957
Tatoeba-test.umb-eng.umb.eng 4.0 0.195
Tatoeba-test.wol-eng.wol.eng 3.7 0.170
Tatoeba-test.xho-eng.xho.eng 38.9 0.556
Tatoeba-test.yor-eng.yor.eng 25.1 0.412
Tatoeba-test.zul-eng.zul.eng 46.1 0.623

System Info:

  • hf_name: alv-eng

  • source_languages: alv

  • target_languages: eng

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

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

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

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

  • tgt_constituents: {'eng'}

  • src_multilingual: True

  • tgt_multilingual: False

  • prepro: normalization + SentencePiece (spm32k,spm32k)

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

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

  • src_alpha3: alv

  • tgt_alpha3: eng

  • short_pair: alv-en

  • chrF2_score: 0.376

  • bleu: 20.9

  • brevity_penalty: 1.0

  • ref_len: 15208.0

  • src_name: Atlantic-Congo languages

  • tgt_name: English

  • train_date: 2020-07-31

  • src_alpha2: alv

  • tgt_alpha2: en

  • prefer_old: False

  • long_pair: alv-eng

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41