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

  • source group: Indic languages

  • target group: English

  • OPUS readme: inc-eng

  • model: transformer

  • source language(s): asm awa ben bho gom guj hif_Latn hin mai mar npi ori pan_Guru pnb rom san_Deva sin snd_Arab urd

  • target language(s): eng

  • model: transformer

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

  • 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
newsdev2014-hineng.hin.eng 8.9 0.341
newsdev2019-engu-gujeng.guj.eng 8.7 0.321
newstest2014-hien-hineng.hin.eng 13.1 0.396
newstest2019-guen-gujeng.guj.eng 6.5 0.290
Tatoeba-test.asm-eng.asm.eng 18.1 0.363
Tatoeba-test.awa-eng.awa.eng 6.2 0.222
Tatoeba-test.ben-eng.ben.eng 44.7 0.595
Tatoeba-test.bho-eng.bho.eng 29.4 0.458
Tatoeba-test.guj-eng.guj.eng 19.3 0.383
Tatoeba-test.hif-eng.hif.eng 3.7 0.220
Tatoeba-test.hin-eng.hin.eng 38.6 0.564
Tatoeba-test.kok-eng.kok.eng 6.6 0.287
Tatoeba-test.lah-eng.lah.eng 16.0 0.272
Tatoeba-test.mai-eng.mai.eng 75.6 0.796
Tatoeba-test.mar-eng.mar.eng 25.9 0.497
Tatoeba-test.multi.eng 29.0 0.502
Tatoeba-test.nep-eng.nep.eng 4.5 0.198
Tatoeba-test.ori-eng.ori.eng 5.0 0.226
Tatoeba-test.pan-eng.pan.eng 17.4 0.375
Tatoeba-test.rom-eng.rom.eng 1.7 0.174
Tatoeba-test.san-eng.san.eng 5.0 0.173
Tatoeba-test.sin-eng.sin.eng 31.2 0.511
Tatoeba-test.snd-eng.snd.eng 45.7 0.670
Tatoeba-test.urd-eng.urd.eng 25.6 0.456

System Info: