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

iir-eng

  • source group: Indo-Iranian languages

  • target group: English

  • OPUS readme: iir-eng

  • model: transformer

  • source language(s): asm awa ben bho gom guj hif_Latn hin jdt_Cyrl kur_Arab kur_Latn mai mar npi ori oss pan_Guru pes pes_Latn pes_Thaa pnb pus rom san_Deva sin snd_Arab tgk_Cyrl tly_Latn urd zza

  • 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 8.1 0.324
newsdev2019-engu-gujeng.guj.eng 8.1 0.309
newstest2014-hien-hineng.hin.eng 12.1 0.380
newstest2019-guen-gujeng.guj.eng 6.0 0.280
Tatoeba-test.asm-eng.asm.eng 13.9 0.327
Tatoeba-test.awa-eng.awa.eng 7.0 0.219
Tatoeba-test.ben-eng.ben.eng 42.5 0.576
Tatoeba-test.bho-eng.bho.eng 27.3 0.452
Tatoeba-test.fas-eng.fas.eng 5.6 0.262
Tatoeba-test.guj-eng.guj.eng 15.9 0.350
Tatoeba-test.hif-eng.hif.eng 10.1 0.247
Tatoeba-test.hin-eng.hin.eng 36.5 0.544
Tatoeba-test.jdt-eng.jdt.eng 11.4 0.094
Tatoeba-test.kok-eng.kok.eng 6.6 0.256
Tatoeba-test.kur-eng.kur.eng 3.4 0.149
Tatoeba-test.lah-eng.lah.eng 17.4 0.301
Tatoeba-test.mai-eng.mai.eng 65.4 0.703
Tatoeba-test.mar-eng.mar.eng 22.5 0.468
Tatoeba-test.multi.eng 21.3 0.424
Tatoeba-test.nep-eng.nep.eng 3.4 0.185
Tatoeba-test.ori-eng.ori.eng 4.8 0.244
Tatoeba-test.oss-eng.oss.eng 1.6 0.173
Tatoeba-test.pan-eng.pan.eng 14.8 0.348
Tatoeba-test.pus-eng.pus.eng 1.1 0.182
Tatoeba-test.rom-eng.rom.eng 2.8 0.185
Tatoeba-test.san-eng.san.eng 2.8 0.185
Tatoeba-test.sin-eng.sin.eng 22.8 0.474
Tatoeba-test.snd-eng.snd.eng 8.2 0.287
Tatoeba-test.tgk-eng.tgk.eng 11.9 0.321
Tatoeba-test.tly-eng.tly.eng 0.9 0.076
Tatoeba-test.urd-eng.urd.eng 23.9 0.438
Tatoeba-test.zza-eng.zza.eng 0.6 0.098

System Info:

  • hf_name: iir-eng

  • source_languages: iir

  • target_languages: eng

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

  • original_repo: Tatoeba-Challenge

  • tags: ['translation']

  • languages: ['bn', 'or', 'gu', 'mr', 'ur', 'hi', 'ps', 'os', 'as', 'si', 'iir', 'en']

  • src_constituents: {'pnb', 'gom', 'ben', 'hif_Latn', 'ori', 'guj', 'pan_Guru', 'snd_Arab', 'npi', 'mar', 'urd', 'pes', 'bho', 'kur_Arab', 'tgk_Cyrl', 'hin', 'kur_Latn', 'pes_Thaa', 'pus', 'san_Deva', 'oss', 'tly_Latn', 'jdt_Cyrl', 'asm', 'zza', 'rom', 'mai', 'pes_Latn', 'awa', 'sin'}

  • tgt_constituents: {'eng'}

  • src_multilingual: True

  • tgt_multilingual: False

  • prepro: normalization + SentencePiece (spm32k,spm32k)

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

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

  • src_alpha3: iir

  • tgt_alpha3: eng

  • short_pair: iir-en

  • chrF2_score: 0.424

  • bleu: 21.3

  • brevity_penalty: 1.0

  • ref_len: 67026.0

  • src_name: Indo-Iranian languages

  • tgt_name: English

  • train_date: 2020-08-01

  • src_alpha2: iir

  • tgt_alpha2: en

  • prefer_old: False

  • long_pair: iir-eng

  • helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535

  • transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b

  • port_machine: brutasse

  • port_time: 2020-08-21-14:41