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Helsinki-NLP/opus-mt-gmq-gmq Helsinki-NLP/opus-mt-gmq-gmq
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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-gmq-gmq") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-gmq-gmq")
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gmq-gmq

  • source group: North Germanic languages

  • target group: North Germanic languages

  • OPUS readme: gmq-gmq

  • model: transformer

  • source language(s): dan fao isl nno nob swe

  • target language(s): dan fao isl nno nob swe

  • 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: opus-2020-07-27.zip

  • test set translations: opus-2020-07-27.test.txt

  • test set scores: opus-2020-07-27.eval.txt

Benchmarks

testset BLEU chr-F
Tatoeba-test.dan-fao.dan.fao 8.1 0.173
Tatoeba-test.dan-isl.dan.isl 52.5 0.827
Tatoeba-test.dan-nor.dan.nor 62.8 0.772
Tatoeba-test.dan-swe.dan.swe 67.6 0.802
Tatoeba-test.fao-dan.fao.dan 11.3 0.306
Tatoeba-test.fao-isl.fao.isl 26.3 0.359
Tatoeba-test.fao-nor.fao.nor 36.8 0.531
Tatoeba-test.fao-swe.fao.swe 0.0 0.632
Tatoeba-test.isl-dan.isl.dan 67.0 0.739
Tatoeba-test.isl-fao.isl.fao 14.5 0.243
Tatoeba-test.isl-nor.isl.nor 51.8 0.674
Tatoeba-test.isl-swe.isl.swe 100.0 1.000
Tatoeba-test.multi.multi 64.7 0.782
Tatoeba-test.nor-dan.nor.dan 65.6 0.797
Tatoeba-test.nor-fao.nor.fao 9.4 0.362
Tatoeba-test.nor-isl.nor.isl 38.8 0.587
Tatoeba-test.nor-nor.nor.nor 51.9 0.721
Tatoeba-test.nor-swe.nor.swe 66.5 0.789
Tatoeba-test.swe-dan.swe.dan 67.6 0.802
Tatoeba-test.swe-fao.swe.fao 0.0 0.268
Tatoeba-test.swe-isl.swe.isl 65.8 0.914
Tatoeba-test.swe-nor.swe.nor 60.6 0.755

System Info:

  • hf_name: gmq-gmq

  • source_languages: gmq

  • target_languages: gmq

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

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

  • languages: ['da', 'nb', 'sv', 'is', 'nn', 'fo', 'gmq']

  • src_constituents: {'dan', 'nob', 'nob_Hebr', 'swe', 'isl', 'nno', 'non_Latn', 'fao'}

  • tgt_constituents: {'dan', 'nob', 'nob_Hebr', 'swe', 'isl', 'nno', 'non_Latn', 'fao'}

  • src_multilingual: True

  • tgt_multilingual: True

  • prepro: normalization + SentencePiece (spm32k,spm32k)

  • url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opus-2020-07-27.zip

  • url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/gmq-gmq/opus-2020-07-27.test.txt

  • src_alpha3: gmq

  • tgt_alpha3: gmq

  • short_pair: gmq-gmq

  • chrF2_score: 0.782

  • bleu: 64.7

  • brevity_penalty: 0.9940000000000001

  • ref_len: 49385.0

  • src_name: North Germanic languages

  • tgt_name: North Germanic languages

  • train_date: 2020-07-27

  • src_alpha2: gmq

  • tgt_alpha2: gmq

  • prefer_old: False

  • long_pair: gmq-gmq

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41