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opus-2020-07-27.zip

  • dataset: opus
  • model: transformer
  • source language(s): asm hin mar urd
  • target language(s): asm hin mar urd
  • 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: 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.asm-hin.asm.hin 2.6 0.231
Tatoeba-test.hin-asm.hin.asm 9.1 0.262
Tatoeba-test.hin-mar.hin.mar 28.1 0.548
Tatoeba-test.hin-urd.hin.urd 19.9 0.508
Tatoeba-test.mar-hin.mar.hin 11.6 0.466
Tatoeba-test.multi.multi 17.1 0.464
Tatoeba-test.urd-hin.urd.hin 13.5 0.377

opus-2020-09-26.zip

  • dataset: opus
  • model: transformer
  • source language(s): asm awa ben ben_Cyrl ben_Deva ben_Gujr bho dty eng gom guj hif_Latn hin mai mar nep npi ori pan pan_Guru pnb pnb_Guru rmn rmy rom san san_Deva sin snd_Arab urd urd_Deva
  • target language(s): asm awa ben ben_Cyrl ben_Deva ben_Gujr bho dty eng gom guj hif_Latn hin mai mar nep npi ori pan pan_Guru pnb pnb_Guru rmn rmy rom san san_Deva sin snd_Arab urd urd_Deva
  • 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: opus-2020-09-26.zip
  • test set translations: opus-2020-09-26.test.txt
  • test set scores: opus-2020-09-26.eval.txt

Benchmarks

testset BLEU chr-F
newsdev2014-enghin.eng.hin 6.6 0.317
newsdev2014-hineng.hin.eng 9.4 0.359
newsdev2019-engu-engguj.eng.guj 5.4 0.277
newsdev2019-engu-gujeng.guj.eng 10.3 0.336
newstest2014-hien-enghin.eng.hin 9.6 0.333
newstest2014-hien-hineng.hin.eng 12.8 0.406
newstest2019-engu-engguj.eng.guj 6.3 0.284
newstest2019-guen-gujeng.guj.eng 8.0 0.314
Tatoeba-test.asm-eng.asm.eng 20.5 0.394
Tatoeba-test.asm-hin.asm.hin 8.4 0.459
Tatoeba-test.awa-eng.awa.eng 15.5 0.292
Tatoeba-test.ben-eng.ben.eng 38.5 0.547
Tatoeba-test.bho-eng.bho.eng 30.1 0.475
Tatoeba-test.eng-asm.eng.asm 1.9 0.255
Tatoeba-test.eng-awa.eng.awa 0.3 0.019
Tatoeba-test.eng-ben.eng.ben 13.6 0.431
Tatoeba-test.eng-bho.eng.bho 1.1 0.061
Tatoeba-test.eng-guj.eng.guj 16.2 0.363
Tatoeba-test.eng-hif.eng.hif 1.6 0.281
Tatoeba-test.eng-hin.eng.hin 16.6 0.450
Tatoeba-test.eng-kok.eng.kok 2.1 0.004
Tatoeba-test.eng-lah.eng.lah 0.3 0.001
Tatoeba-test.eng-mai.eng.mai 9.3 0.412
Tatoeba-test.eng-mar.eng.mar 19.5 0.460
Tatoeba-test.eng-nep.eng.nep 0.2 0.010
Tatoeba-test.eng-ori.eng.ori 2.4 0.225
Tatoeba-test.eng-pan.eng.pan 8.1 0.325
Tatoeba-test.eng-rom.eng.rom 2.6 0.251
Tatoeba-test.eng-san.eng.san 1.3 0.124
Tatoeba-test.eng-sin.eng.sin 9.2 0.343
Tatoeba-test.eng-snd.eng.snd 9.1 0.377
Tatoeba-test.eng-urd.eng.urd 11.4 0.395
Tatoeba-test.guj-eng.guj.eng 16.2 0.338
Tatoeba-test.hif-eng.hif.eng 4.8 0.308
Tatoeba-test.hin-asm.hin.asm 24.5 0.454
Tatoeba-test.hin-eng.hin.eng 37.0 0.552
Tatoeba-test.hin-mar.hin.mar 30.8 0.596
Tatoeba-test.hin-urd.hin.urd 22.2 0.541
Tatoeba-test.kok-eng.kok.eng 3.7 0.176
Tatoeba-test.lah-eng.lah.eng 18.4 0.295
Tatoeba-test.mai-eng.mai.eng 66.2 0.727
Tatoeba-test.mar-eng.mar.eng 31.7 0.541
Tatoeba-test.mar-hin.mar.hin 16.1 0.540
Tatoeba-test.multi.multi 24.1 0.470
Tatoeba-test.nep-eng.nep.eng 20.9 0.402
Tatoeba-test.ori-eng.ori.eng 7.9 0.263
Tatoeba-test.pan-eng.pan.eng 18.3 0.372
Tatoeba-test.rom-eng.rom.eng 6.2 0.242
Tatoeba-test.san-eng.san.eng 5.2 0.184
Tatoeba-test.sin-eng.sin.eng 24.2 0.469
Tatoeba-test.snd-eng.snd.eng 31.2 0.454
Tatoeba-test.urd-eng.urd.eng 25.0 0.454
Tatoeba-test.urd-hin.urd.hin 24.2 0.503

opus-2020-10-04.zip

  • dataset: opus
  • model: transformer
  • source language(s): asm awa ben ben_Cyrl ben_Deva ben_Gujr bho dty eng gom guj hif_Latn hin mai mar nep npi ori pan pan_Guru pnb pnb_Guru rmn rmy rom san san_Deva sin snd_Arab urd urd_Deva
  • target language(s): asm awa ben ben_Cyrl ben_Deva ben_Gujr bho dty eng gom guj hif_Latn hin mai mar nep npi ori pan pan_Guru pnb pnb_Guru rmn rmy rom san san_Deva sin snd_Arab urd urd_Deva
  • 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: opus-2020-10-04.zip
  • test set translations: opus-2020-10-04.test.txt
  • test set scores: opus-2020-10-04.eval.txt

Benchmarks

testset BLEU chr-F
newsdev2014-enghin.eng.hin 6.8 0.325
newsdev2014-hineng.hin.eng 9.7 0.360
newsdev2019-engu-engguj.eng.guj 5.5 0.279
newsdev2019-engu-gujeng.guj.eng 11.0 0.346
newstest2014-hien-enghin.eng.hin 9.7 0.338
newstest2014-hien-hineng.hin.eng 13.3 0.413
newstest2019-engu-engguj.eng.guj 6.1 0.286
newstest2019-guen-gujeng.guj.eng 8.4 0.316
Tatoeba-test.asm-eng.asm.eng 24.4 0.418
Tatoeba-test.asm-hin.asm.hin 26.5 0.607
Tatoeba-test.awa-eng.awa.eng 15.9 0.300
Tatoeba-test.ben-eng.ben.eng 39.8 0.554
Tatoeba-test.bho-eng.bho.eng 30.9 0.482
Tatoeba-test.eng-asm.eng.asm 2.4 0.268
Tatoeba-test.eng-awa.eng.awa 0.3 0.029
Tatoeba-test.eng-ben.eng.ben 13.4 0.432
Tatoeba-test.eng-bho.eng.bho 1.1 0.074
Tatoeba-test.eng-guj.eng.guj 18.8 0.378
Tatoeba-test.eng-hif.eng.hif 1.5 0.277
Tatoeba-test.eng-hin.eng.hin 16.8 0.454
Tatoeba-test.eng-kok.eng.kok 4.2 0.005
Tatoeba-test.eng-lah.eng.lah 0.3 0.001
Tatoeba-test.eng-mai.eng.mai 15.9 0.561
Tatoeba-test.eng-mar.eng.mar 20.5 0.469
Tatoeba-test.eng-nep.eng.nep 0.7 0.016
Tatoeba-test.eng-ori.eng.ori 1.6 0.239
Tatoeba-test.eng-pan.eng.pan 7.5 0.321
Tatoeba-test.eng-rom.eng.rom 2.6 0.255
Tatoeba-test.eng-san.eng.san 2.2 0.128
Tatoeba-test.eng-sin.eng.sin 9.2 0.356
Tatoeba-test.eng-snd.eng.snd 3.8 0.301
Tatoeba-test.eng-urd.eng.urd 11.7 0.399
Tatoeba-test.guj-eng.guj.eng 19.4 0.365
Tatoeba-test.hif-eng.hif.eng 4.1 0.310
Tatoeba-test.hin-asm.hin.asm 8.9 0.387
Tatoeba-test.hin-eng.hin.eng 37.8 0.559
Tatoeba-test.hin-mar.hin.mar 32.9 0.599
Tatoeba-test.hin-urd.hin.urd 21.8 0.534
Tatoeba-test.kok-eng.kok.eng 4.0 0.240
Tatoeba-test.lah-eng.lah.eng 18.2 0.306
Tatoeba-test.mai-eng.mai.eng 66.2 0.724
Tatoeba-test.mar-eng.mar.eng 33.6 0.552
Tatoeba-test.mar-hin.mar.hin 15.6 0.520
Tatoeba-test.multi.multi 25.1 0.478
Tatoeba-test.nep-eng.nep.eng 24.6 0.433
Tatoeba-test.ori-eng.ori.eng 6.4 0.246
Tatoeba-test.pan-eng.pan.eng 18.4 0.376
Tatoeba-test.rom-eng.rom.eng 6.0 0.237
Tatoeba-test.san-eng.san.eng 4.2 0.182
Tatoeba-test.sin-eng.sin.eng 22.1 0.475
Tatoeba-test.snd-eng.snd.eng 34.3 0.449
Tatoeba-test.urd-eng.urd.eng 25.9 0.462
Tatoeba-test.urd-hin.urd.hin 25.6 0.520

opus-2021-02-24.zip

  • dataset: opus
  • model: transformer
  • source language(s): asm hin mar urd
  • target language(s): asm hin mar urd
  • 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)
  • valid language labels: >>eng<< >>hin<< >>urd<< >>mar<< >>asm<< >>guj<< >>ori<< >>pan_Guru<< >>sin<< >>mai<< >>nep<< >>ben<< >>snd_Arab<< >>rom<< >>rmn<< >>san_Deva<< >>san<< >>ben_Cyrl<< >>dty<< >>rmy<< >>ben_Deva<< >>ben_Gujr<<
  • download: opus-2021-02-24.zip
  • test set translations: opus-2021-02-24.test.txt
  • test set scores: opus-2021-02-24.eval.txt

Benchmarks

testset BLEU chr-F #sent #words BP
newsdev2014.eng-hin 6.8 0.325 520 9538 1.000
newsdev2014.hin-eng 9.7 0.360 520 10406 0.884
newsdev2019-engu.eng-guj 5.5 0.279 1998 39137 0.766
newsdev2019-engu.guj-eng 11.0 0.346 1998 41862 1.000
newstest2014-hien.eng-hin 9.7 0.338 2507 60878 0.957
newstest2014-hien.hin-eng 13.3 0.413 2507 55571 0.958
newstest2019-engu.eng-guj 6.1 0.286 998 21927 0.760
newstest2019-guen.guj-eng 8.4 0.316 1016 17778 1.000
Tatoeba-test.asm-hin 26.5 0.607 4 16 1.000
Tatoeba-test.hin-asm 8.9 0.387 4 14 1.000
Tatoeba-test.hin-mar 32.9 0.599 158 866 1.000
Tatoeba-test.hin-urd 21.8 0.534 239 1618 1.000
Tatoeba-test.mar-hin 15.6 0.518 158 890 1.000
Tatoeba-test.multi-multi 23.3 0.538 802 4985 1.000
Tatoeba-test.urd-hin 25.6 0.520 239 1581 1.000
tico19-test.eng-ben 4.6 0.312 2100 51751 0.757
tico19-test.eng-hin 15.0 0.373 2100 62738 0.909
tico19-test.eng-mar 4.3 0.277 2100 50881 0.713
tico19-test.eng-nep 5.8 0.338 2100 48706 0.777
tico19-test.eng-urd 7.9 0.317 2100 65363 0.802