modelId stringlengths 4 81 | tags list | pipeline_tag stringclasses 17 values | config dict | downloads int64 0 59.7M | first_commit timestamp[ns, tz=UTC] | card stringlengths 51 438k | embedding list |
|---|---|---|---|---|---|---|---|
Declan/Independent__model | [] | null | {
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} | 0 | 2020-04-29T13:23:54Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-mt-sv
* source languages: mt
* target languages: sv
* OPUS readme: [mt-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/mt-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/mt-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/mt-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/mt-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.mt.sv | 30.4 | 0.514 |
| [
-0.015406040474772453,
-0.027176963165402412,
-0.0025865084026008844,
0.03915533050894737,
0.029139559715986252,
0.020494289696216583,
-0.0005059564136900008,
-0.0027681696228682995,
-0.04716123640537262,
0.05701708421111107,
0.010473106056451797,
-0.01414462924003601,
0.015011418610811234,
... |
Declan/NPR_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
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} | 3 | 2020-08-19T00:30:48Z | ---
language:
- ca
- es
- os
- eo
- ro
- fy
- cy
- is
- lb
- su
- an
- sq
- fr
- ht
- rm
- cv
- ig
- am
- eu
- tr
- ps
- af
- ny
- ch
- uk
- sl
- lt
- tk
- sg
- ar
- lg
- bg
- be
- ka
- gd
- ja
- si
- br
- mh
- km
- th
- ty
- rw
- te
- mk
- or
- wo
- kl
- mr
- ru
- yo
- hu
- fo
- zh
- ti
- co
- ee
- oc
- sn
- mt
- ts
- pl
- gl
- nb
- bn
- tt
- bo
- lo
- id
- gn
- nv
- hy
- kn
- to
- io
- so
- vi
- da
- fj
- gv
- sm
- nl
- mi
- pt
- hi
- se
- as
- ta
- et
- kw
- ga
- sv
- ln
- na
- mn
- gu
- wa
- lv
- jv
- el
- my
- ba
- it
- hr
- ur
- ce
- nn
- fi
- mg
- rn
- xh
- ab
- de
- cs
- he
- zu
- yi
- ml
- mul
- en
tags:
- translation
license: apache-2.0
---
### mul-eng
* source group: Multiple languages
* target group: English
* OPUS readme: [mul-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mul-eng/README.md)
* model: transformer
* source language(s): abk acm ady afb afh_Latn afr akl_Latn aln amh ang_Latn apc ara arg arq ary arz asm ast avk_Latn awa aze_Latn bak bam_Latn bel bel_Latn ben bho bod bos_Latn bre brx brx_Latn bul bul_Latn cat ceb ces cha che chr chv cjy_Hans cjy_Hant cmn cmn_Hans cmn_Hant cor cos crh crh_Latn csb_Latn cym dan deu dsb dtp dws_Latn egl ell enm_Latn epo est eus ewe ext fao fij fin fkv_Latn fra frm_Latn frr fry fuc fuv gan gcf_Latn gil gla gle glg glv gom gos got_Goth grc_Grek grn gsw guj hat hau_Latn haw heb hif_Latn hil hin hnj_Latn hoc hoc_Latn hrv hsb hun hye iba ibo ido ido_Latn ike_Latn ile_Latn ilo ina_Latn ind isl ita izh jav jav_Java jbo jbo_Cyrl jbo_Latn jdt_Cyrl jpn kab kal kan kat kaz_Cyrl kaz_Latn kek_Latn kha khm khm_Latn kin kir_Cyrl kjh kpv krl ksh kum kur_Arab kur_Latn lad lad_Latn lao lat_Latn lav ldn_Latn lfn_Cyrl lfn_Latn lij lin lit liv_Latn lkt lld_Latn lmo ltg ltz lug lzh lzh_Hans mad mah mai mal mar max_Latn mdf mfe mhr mic min mkd mlg mlt mnw moh mon mri mwl mww mya myv nan nau nav nds niu nld nno nob nob_Hebr nog non_Latn nov_Latn npi nya oci ori orv_Cyrl oss ota_Arab ota_Latn pag pan_Guru pap pau pdc pes pes_Latn pes_Thaa pms pnb pol por ppl_Latn prg_Latn pus quc qya qya_Latn rap rif_Latn roh rom ron rue run rus sag sah san_Deva scn sco sgs shs_Latn shy_Latn sin sjn_Latn slv sma sme smo sna snd_Arab som spa sqi srp_Cyrl srp_Latn stq sun swe swg swh tah tam tat tat_Arab tat_Latn tel tet tgk_Cyrl tha tir tlh_Latn tly_Latn tmw_Latn toi_Latn ton tpw_Latn tso tuk tuk_Latn tur tvl tyv tzl tzl_Latn udm uig_Arab uig_Cyrl ukr umb urd uzb_Cyrl uzb_Latn vec vie vie_Hani vol_Latn vro war wln wol wuu xal xho yid yor yue yue_Hans yue_Hant zho zho_Hans zho_Hant zlm_Latn zsm_Latn zul zza
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newsdev2014-hineng.hin.eng | 8.5 | 0.341 |
| newsdev2015-enfi-fineng.fin.eng | 16.8 | 0.441 |
| newsdev2016-enro-roneng.ron.eng | 31.3 | 0.580 |
| newsdev2016-entr-tureng.tur.eng | 16.4 | 0.422 |
| newsdev2017-enlv-laveng.lav.eng | 21.3 | 0.502 |
| newsdev2017-enzh-zhoeng.zho.eng | 12.7 | 0.409 |
| newsdev2018-enet-esteng.est.eng | 19.8 | 0.467 |
| newsdev2019-engu-gujeng.guj.eng | 13.3 | 0.385 |
| newsdev2019-enlt-liteng.lit.eng | 19.9 | 0.482 |
| newsdiscussdev2015-enfr-fraeng.fra.eng | 26.7 | 0.520 |
| newsdiscusstest2015-enfr-fraeng.fra.eng | 29.8 | 0.541 |
| newssyscomb2009-ceseng.ces.eng | 21.1 | 0.487 |
| newssyscomb2009-deueng.deu.eng | 22.6 | 0.499 |
| newssyscomb2009-fraeng.fra.eng | 25.8 | 0.530 |
| newssyscomb2009-huneng.hun.eng | 15.1 | 0.430 |
| newssyscomb2009-itaeng.ita.eng | 29.4 | 0.555 |
| newssyscomb2009-spaeng.spa.eng | 26.1 | 0.534 |
| news-test2008-deueng.deu.eng | 21.6 | 0.491 |
| news-test2008-fraeng.fra.eng | 22.3 | 0.502 |
| news-test2008-spaeng.spa.eng | 23.6 | 0.514 |
| newstest2009-ceseng.ces.eng | 19.8 | 0.480 |
| newstest2009-deueng.deu.eng | 20.9 | 0.487 |
| newstest2009-fraeng.fra.eng | 25.0 | 0.523 |
| newstest2009-huneng.hun.eng | 14.7 | 0.425 |
| newstest2009-itaeng.ita.eng | 27.6 | 0.542 |
| newstest2009-spaeng.spa.eng | 25.7 | 0.530 |
| newstest2010-ceseng.ces.eng | 20.6 | 0.491 |
| newstest2010-deueng.deu.eng | 23.4 | 0.517 |
| newstest2010-fraeng.fra.eng | 26.1 | 0.537 |
| newstest2010-spaeng.spa.eng | 29.1 | 0.561 |
| newstest2011-ceseng.ces.eng | 21.0 | 0.489 |
| newstest2011-deueng.deu.eng | 21.3 | 0.494 |
| newstest2011-fraeng.fra.eng | 26.8 | 0.546 |
| newstest2011-spaeng.spa.eng | 28.2 | 0.549 |
| newstest2012-ceseng.ces.eng | 20.5 | 0.485 |
| newstest2012-deueng.deu.eng | 22.3 | 0.503 |
| newstest2012-fraeng.fra.eng | 27.5 | 0.545 |
| newstest2012-ruseng.rus.eng | 26.6 | 0.532 |
| newstest2012-spaeng.spa.eng | 30.3 | 0.567 |
| newstest2013-ceseng.ces.eng | 22.5 | 0.498 |
| newstest2013-deueng.deu.eng | 25.0 | 0.518 |
| newstest2013-fraeng.fra.eng | 27.4 | 0.537 |
| newstest2013-ruseng.rus.eng | 21.6 | 0.484 |
| newstest2013-spaeng.spa.eng | 28.4 | 0.555 |
| newstest2014-csen-ceseng.ces.eng | 24.0 | 0.517 |
| newstest2014-deen-deueng.deu.eng | 24.1 | 0.511 |
| newstest2014-fren-fraeng.fra.eng | 29.1 | 0.563 |
| newstest2014-hien-hineng.hin.eng | 14.0 | 0.414 |
| newstest2014-ruen-ruseng.rus.eng | 24.0 | 0.521 |
| newstest2015-encs-ceseng.ces.eng | 21.9 | 0.481 |
| newstest2015-ende-deueng.deu.eng | 25.5 | 0.519 |
| newstest2015-enfi-fineng.fin.eng | 17.4 | 0.441 |
| newstest2015-enru-ruseng.rus.eng | 22.4 | 0.494 |
| newstest2016-encs-ceseng.ces.eng | 23.0 | 0.500 |
| newstest2016-ende-deueng.deu.eng | 30.1 | 0.560 |
| newstest2016-enfi-fineng.fin.eng | 18.5 | 0.461 |
| newstest2016-enro-roneng.ron.eng | 29.6 | 0.562 |
| newstest2016-enru-ruseng.rus.eng | 22.0 | 0.495 |
| newstest2016-entr-tureng.tur.eng | 14.8 | 0.415 |
| newstest2017-encs-ceseng.ces.eng | 20.2 | 0.475 |
| newstest2017-ende-deueng.deu.eng | 26.0 | 0.523 |
| newstest2017-enfi-fineng.fin.eng | 19.6 | 0.465 |
| newstest2017-enlv-laveng.lav.eng | 16.2 | 0.454 |
| newstest2017-enru-ruseng.rus.eng | 24.2 | 0.510 |
| newstest2017-entr-tureng.tur.eng | 15.0 | 0.412 |
| newstest2017-enzh-zhoeng.zho.eng | 13.7 | 0.412 |
| newstest2018-encs-ceseng.ces.eng | 21.2 | 0.486 |
| newstest2018-ende-deueng.deu.eng | 31.5 | 0.564 |
| newstest2018-enet-esteng.est.eng | 19.7 | 0.473 |
| newstest2018-enfi-fineng.fin.eng | 15.1 | 0.418 |
| newstest2018-enru-ruseng.rus.eng | 21.3 | 0.490 |
| newstest2018-entr-tureng.tur.eng | 15.4 | 0.421 |
| newstest2018-enzh-zhoeng.zho.eng | 12.9 | 0.408 |
| newstest2019-deen-deueng.deu.eng | 27.0 | 0.529 |
| newstest2019-fien-fineng.fin.eng | 17.2 | 0.438 |
| newstest2019-guen-gujeng.guj.eng | 9.0 | 0.342 |
| newstest2019-lten-liteng.lit.eng | 22.6 | 0.512 |
| newstest2019-ruen-ruseng.rus.eng | 24.1 | 0.503 |
| newstest2019-zhen-zhoeng.zho.eng | 13.9 | 0.427 |
| newstestB2016-enfi-fineng.fin.eng | 15.2 | 0.428 |
| newstestB2017-enfi-fineng.fin.eng | 16.8 | 0.442 |
| newstestB2017-fien-fineng.fin.eng | 16.8 | 0.442 |
| Tatoeba-test.abk-eng.abk.eng | 2.4 | 0.190 |
| Tatoeba-test.ady-eng.ady.eng | 1.1 | 0.111 |
| Tatoeba-test.afh-eng.afh.eng | 1.7 | 0.108 |
| Tatoeba-test.afr-eng.afr.eng | 53.0 | 0.672 |
| Tatoeba-test.akl-eng.akl.eng | 5.9 | 0.239 |
| Tatoeba-test.amh-eng.amh.eng | 25.6 | 0.464 |
| Tatoeba-test.ang-eng.ang.eng | 11.7 | 0.289 |
| Tatoeba-test.ara-eng.ara.eng | 26.4 | 0.443 |
| Tatoeba-test.arg-eng.arg.eng | 35.9 | 0.473 |
| Tatoeba-test.asm-eng.asm.eng | 19.8 | 0.365 |
| Tatoeba-test.ast-eng.ast.eng | 31.8 | 0.467 |
| Tatoeba-test.avk-eng.avk.eng | 0.4 | 0.119 |
| Tatoeba-test.awa-eng.awa.eng | 9.7 | 0.271 |
| Tatoeba-test.aze-eng.aze.eng | 37.0 | 0.542 |
| Tatoeba-test.bak-eng.bak.eng | 13.9 | 0.395 |
| Tatoeba-test.bam-eng.bam.eng | 2.2 | 0.094 |
| Tatoeba-test.bel-eng.bel.eng | 36.8 | 0.549 |
| Tatoeba-test.ben-eng.ben.eng | 39.7 | 0.546 |
| Tatoeba-test.bho-eng.bho.eng | 33.6 | 0.540 |
| Tatoeba-test.bod-eng.bod.eng | 1.1 | 0.147 |
| Tatoeba-test.bre-eng.bre.eng | 14.2 | 0.303 |
| Tatoeba-test.brx-eng.brx.eng | 1.7 | 0.130 |
| Tatoeba-test.bul-eng.bul.eng | 46.0 | 0.621 |
| Tatoeba-test.cat-eng.cat.eng | 46.6 | 0.636 |
| Tatoeba-test.ceb-eng.ceb.eng | 17.4 | 0.347 |
| Tatoeba-test.ces-eng.ces.eng | 41.3 | 0.586 |
| Tatoeba-test.cha-eng.cha.eng | 7.9 | 0.232 |
| Tatoeba-test.che-eng.che.eng | 0.7 | 0.104 |
| Tatoeba-test.chm-eng.chm.eng | 7.3 | 0.261 |
| Tatoeba-test.chr-eng.chr.eng | 8.8 | 0.244 |
| Tatoeba-test.chv-eng.chv.eng | 11.0 | 0.319 |
| Tatoeba-test.cor-eng.cor.eng | 5.4 | 0.204 |
| Tatoeba-test.cos-eng.cos.eng | 58.2 | 0.643 |
| Tatoeba-test.crh-eng.crh.eng | 26.3 | 0.399 |
| Tatoeba-test.csb-eng.csb.eng | 18.8 | 0.389 |
| Tatoeba-test.cym-eng.cym.eng | 23.4 | 0.407 |
| Tatoeba-test.dan-eng.dan.eng | 50.5 | 0.659 |
| Tatoeba-test.deu-eng.deu.eng | 39.6 | 0.579 |
| Tatoeba-test.dsb-eng.dsb.eng | 24.3 | 0.449 |
| Tatoeba-test.dtp-eng.dtp.eng | 1.0 | 0.149 |
| Tatoeba-test.dws-eng.dws.eng | 1.6 | 0.061 |
| Tatoeba-test.egl-eng.egl.eng | 7.6 | 0.236 |
| Tatoeba-test.ell-eng.ell.eng | 55.4 | 0.682 |
| Tatoeba-test.enm-eng.enm.eng | 28.0 | 0.489 |
| Tatoeba-test.epo-eng.epo.eng | 41.8 | 0.591 |
| Tatoeba-test.est-eng.est.eng | 41.5 | 0.581 |
| Tatoeba-test.eus-eng.eus.eng | 37.8 | 0.557 |
| Tatoeba-test.ewe-eng.ewe.eng | 10.7 | 0.262 |
| Tatoeba-test.ext-eng.ext.eng | 25.5 | 0.405 |
| Tatoeba-test.fao-eng.fao.eng | 28.7 | 0.469 |
| Tatoeba-test.fas-eng.fas.eng | 7.5 | 0.281 |
| Tatoeba-test.fij-eng.fij.eng | 24.2 | 0.320 |
| Tatoeba-test.fin-eng.fin.eng | 35.8 | 0.534 |
| Tatoeba-test.fkv-eng.fkv.eng | 15.5 | 0.434 |
| Tatoeba-test.fra-eng.fra.eng | 45.1 | 0.618 |
| Tatoeba-test.frm-eng.frm.eng | 29.6 | 0.427 |
| Tatoeba-test.frr-eng.frr.eng | 5.5 | 0.138 |
| Tatoeba-test.fry-eng.fry.eng | 25.3 | 0.455 |
| Tatoeba-test.ful-eng.ful.eng | 1.1 | 0.127 |
| Tatoeba-test.gcf-eng.gcf.eng | 16.0 | 0.315 |
| Tatoeba-test.gil-eng.gil.eng | 46.7 | 0.587 |
| Tatoeba-test.gla-eng.gla.eng | 20.2 | 0.358 |
| Tatoeba-test.gle-eng.gle.eng | 43.9 | 0.592 |
| Tatoeba-test.glg-eng.glg.eng | 45.1 | 0.623 |
| Tatoeba-test.glv-eng.glv.eng | 3.3 | 0.119 |
| Tatoeba-test.gos-eng.gos.eng | 20.1 | 0.364 |
| Tatoeba-test.got-eng.got.eng | 0.1 | 0.041 |
| Tatoeba-test.grc-eng.grc.eng | 2.1 | 0.137 |
| Tatoeba-test.grn-eng.grn.eng | 1.7 | 0.152 |
| Tatoeba-test.gsw-eng.gsw.eng | 18.2 | 0.334 |
| Tatoeba-test.guj-eng.guj.eng | 21.7 | 0.373 |
| Tatoeba-test.hat-eng.hat.eng | 34.5 | 0.502 |
| Tatoeba-test.hau-eng.hau.eng | 10.5 | 0.295 |
| Tatoeba-test.haw-eng.haw.eng | 2.8 | 0.160 |
| Tatoeba-test.hbs-eng.hbs.eng | 46.7 | 0.623 |
| Tatoeba-test.heb-eng.heb.eng | 33.0 | 0.492 |
| Tatoeba-test.hif-eng.hif.eng | 17.0 | 0.391 |
| Tatoeba-test.hil-eng.hil.eng | 16.0 | 0.339 |
| Tatoeba-test.hin-eng.hin.eng | 36.4 | 0.533 |
| Tatoeba-test.hmn-eng.hmn.eng | 0.4 | 0.131 |
| Tatoeba-test.hoc-eng.hoc.eng | 0.7 | 0.132 |
| Tatoeba-test.hsb-eng.hsb.eng | 41.9 | 0.551 |
| Tatoeba-test.hun-eng.hun.eng | 33.2 | 0.510 |
| Tatoeba-test.hye-eng.hye.eng | 32.2 | 0.487 |
| Tatoeba-test.iba-eng.iba.eng | 9.4 | 0.278 |
| Tatoeba-test.ibo-eng.ibo.eng | 5.8 | 0.200 |
| Tatoeba-test.ido-eng.ido.eng | 31.7 | 0.503 |
| Tatoeba-test.iku-eng.iku.eng | 9.1 | 0.164 |
| Tatoeba-test.ile-eng.ile.eng | 42.2 | 0.595 |
| Tatoeba-test.ilo-eng.ilo.eng | 29.7 | 0.485 |
| Tatoeba-test.ina-eng.ina.eng | 42.1 | 0.607 |
| Tatoeba-test.isl-eng.isl.eng | 35.7 | 0.527 |
| Tatoeba-test.ita-eng.ita.eng | 54.8 | 0.686 |
| Tatoeba-test.izh-eng.izh.eng | 28.3 | 0.526 |
| Tatoeba-test.jav-eng.jav.eng | 10.0 | 0.282 |
| Tatoeba-test.jbo-eng.jbo.eng | 0.3 | 0.115 |
| Tatoeba-test.jdt-eng.jdt.eng | 5.3 | 0.140 |
| Tatoeba-test.jpn-eng.jpn.eng | 18.8 | 0.387 |
| Tatoeba-test.kab-eng.kab.eng | 3.9 | 0.205 |
| Tatoeba-test.kal-eng.kal.eng | 16.9 | 0.329 |
| Tatoeba-test.kan-eng.kan.eng | 16.2 | 0.374 |
| Tatoeba-test.kat-eng.kat.eng | 31.1 | 0.493 |
| Tatoeba-test.kaz-eng.kaz.eng | 24.5 | 0.437 |
| Tatoeba-test.kek-eng.kek.eng | 7.4 | 0.192 |
| Tatoeba-test.kha-eng.kha.eng | 1.0 | 0.154 |
| Tatoeba-test.khm-eng.khm.eng | 12.2 | 0.290 |
| Tatoeba-test.kin-eng.kin.eng | 22.5 | 0.355 |
| Tatoeba-test.kir-eng.kir.eng | 27.2 | 0.470 |
| Tatoeba-test.kjh-eng.kjh.eng | 2.1 | 0.129 |
| Tatoeba-test.kok-eng.kok.eng | 4.5 | 0.259 |
| Tatoeba-test.kom-eng.kom.eng | 1.4 | 0.099 |
| Tatoeba-test.krl-eng.krl.eng | 26.1 | 0.387 |
| Tatoeba-test.ksh-eng.ksh.eng | 5.5 | 0.256 |
| Tatoeba-test.kum-eng.kum.eng | 9.3 | 0.288 |
| Tatoeba-test.kur-eng.kur.eng | 9.6 | 0.208 |
| Tatoeba-test.lad-eng.lad.eng | 30.1 | 0.475 |
| Tatoeba-test.lah-eng.lah.eng | 11.6 | 0.284 |
| Tatoeba-test.lao-eng.lao.eng | 4.5 | 0.214 |
| Tatoeba-test.lat-eng.lat.eng | 21.5 | 0.402 |
| Tatoeba-test.lav-eng.lav.eng | 40.2 | 0.577 |
| Tatoeba-test.ldn-eng.ldn.eng | 0.8 | 0.115 |
| Tatoeba-test.lfn-eng.lfn.eng | 23.0 | 0.433 |
| Tatoeba-test.lij-eng.lij.eng | 9.3 | 0.287 |
| Tatoeba-test.lin-eng.lin.eng | 2.4 | 0.196 |
| Tatoeba-test.lit-eng.lit.eng | 44.0 | 0.597 |
| Tatoeba-test.liv-eng.liv.eng | 1.6 | 0.115 |
| Tatoeba-test.lkt-eng.lkt.eng | 2.0 | 0.113 |
| Tatoeba-test.lld-eng.lld.eng | 18.3 | 0.312 |
| Tatoeba-test.lmo-eng.lmo.eng | 25.4 | 0.395 |
| Tatoeba-test.ltz-eng.ltz.eng | 35.9 | 0.509 |
| Tatoeba-test.lug-eng.lug.eng | 5.1 | 0.357 |
| Tatoeba-test.mad-eng.mad.eng | 2.8 | 0.123 |
| Tatoeba-test.mah-eng.mah.eng | 5.7 | 0.175 |
| Tatoeba-test.mai-eng.mai.eng | 56.3 | 0.703 |
| Tatoeba-test.mal-eng.mal.eng | 37.5 | 0.534 |
| Tatoeba-test.mar-eng.mar.eng | 22.8 | 0.470 |
| Tatoeba-test.mdf-eng.mdf.eng | 2.0 | 0.110 |
| Tatoeba-test.mfe-eng.mfe.eng | 59.2 | 0.764 |
| Tatoeba-test.mic-eng.mic.eng | 9.0 | 0.199 |
| Tatoeba-test.mkd-eng.mkd.eng | 44.3 | 0.593 |
| Tatoeba-test.mlg-eng.mlg.eng | 31.9 | 0.424 |
| Tatoeba-test.mlt-eng.mlt.eng | 38.6 | 0.540 |
| Tatoeba-test.mnw-eng.mnw.eng | 2.5 | 0.101 |
| Tatoeba-test.moh-eng.moh.eng | 0.3 | 0.110 |
| Tatoeba-test.mon-eng.mon.eng | 13.5 | 0.334 |
| Tatoeba-test.mri-eng.mri.eng | 8.5 | 0.260 |
| Tatoeba-test.msa-eng.msa.eng | 33.9 | 0.520 |
| Tatoeba-test.multi.eng | 34.7 | 0.518 |
| Tatoeba-test.mwl-eng.mwl.eng | 37.4 | 0.630 |
| Tatoeba-test.mya-eng.mya.eng | 15.5 | 0.335 |
| Tatoeba-test.myv-eng.myv.eng | 0.8 | 0.118 |
| Tatoeba-test.nau-eng.nau.eng | 9.0 | 0.186 |
| Tatoeba-test.nav-eng.nav.eng | 1.3 | 0.144 |
| Tatoeba-test.nds-eng.nds.eng | 30.7 | 0.495 |
| Tatoeba-test.nep-eng.nep.eng | 3.5 | 0.168 |
| Tatoeba-test.niu-eng.niu.eng | 42.7 | 0.492 |
| Tatoeba-test.nld-eng.nld.eng | 47.9 | 0.640 |
| Tatoeba-test.nog-eng.nog.eng | 12.7 | 0.284 |
| Tatoeba-test.non-eng.non.eng | 43.8 | 0.586 |
| Tatoeba-test.nor-eng.nor.eng | 45.5 | 0.619 |
| Tatoeba-test.nov-eng.nov.eng | 26.9 | 0.472 |
| Tatoeba-test.nya-eng.nya.eng | 33.2 | 0.456 |
| Tatoeba-test.oci-eng.oci.eng | 17.9 | 0.370 |
| Tatoeba-test.ori-eng.ori.eng | 14.6 | 0.305 |
| Tatoeba-test.orv-eng.orv.eng | 11.0 | 0.283 |
| Tatoeba-test.oss-eng.oss.eng | 4.1 | 0.211 |
| Tatoeba-test.ota-eng.ota.eng | 4.1 | 0.216 |
| Tatoeba-test.pag-eng.pag.eng | 24.3 | 0.468 |
| Tatoeba-test.pan-eng.pan.eng | 16.4 | 0.358 |
| Tatoeba-test.pap-eng.pap.eng | 53.2 | 0.628 |
| Tatoeba-test.pau-eng.pau.eng | 3.7 | 0.173 |
| Tatoeba-test.pdc-eng.pdc.eng | 45.3 | 0.569 |
| Tatoeba-test.pms-eng.pms.eng | 14.0 | 0.345 |
| Tatoeba-test.pol-eng.pol.eng | 41.7 | 0.588 |
| Tatoeba-test.por-eng.por.eng | 51.4 | 0.669 |
| Tatoeba-test.ppl-eng.ppl.eng | 0.4 | 0.134 |
| Tatoeba-test.prg-eng.prg.eng | 4.1 | 0.198 |
| Tatoeba-test.pus-eng.pus.eng | 6.7 | 0.233 |
| Tatoeba-test.quc-eng.quc.eng | 3.5 | 0.091 |
| Tatoeba-test.qya-eng.qya.eng | 0.2 | 0.090 |
| Tatoeba-test.rap-eng.rap.eng | 17.5 | 0.230 |
| Tatoeba-test.rif-eng.rif.eng | 4.2 | 0.164 |
| Tatoeba-test.roh-eng.roh.eng | 24.6 | 0.464 |
| Tatoeba-test.rom-eng.rom.eng | 3.4 | 0.212 |
| Tatoeba-test.ron-eng.ron.eng | 45.2 | 0.620 |
| Tatoeba-test.rue-eng.rue.eng | 21.4 | 0.390 |
| Tatoeba-test.run-eng.run.eng | 24.5 | 0.392 |
| Tatoeba-test.rus-eng.rus.eng | 42.7 | 0.591 |
| Tatoeba-test.sag-eng.sag.eng | 3.4 | 0.187 |
| Tatoeba-test.sah-eng.sah.eng | 5.0 | 0.177 |
| Tatoeba-test.san-eng.san.eng | 2.0 | 0.172 |
| Tatoeba-test.scn-eng.scn.eng | 35.8 | 0.410 |
| Tatoeba-test.sco-eng.sco.eng | 34.6 | 0.520 |
| Tatoeba-test.sgs-eng.sgs.eng | 21.8 | 0.299 |
| Tatoeba-test.shs-eng.shs.eng | 1.8 | 0.122 |
| Tatoeba-test.shy-eng.shy.eng | 1.4 | 0.104 |
| Tatoeba-test.sin-eng.sin.eng | 20.6 | 0.429 |
| Tatoeba-test.sjn-eng.sjn.eng | 1.2 | 0.095 |
| Tatoeba-test.slv-eng.slv.eng | 37.0 | 0.545 |
| Tatoeba-test.sma-eng.sma.eng | 4.4 | 0.147 |
| Tatoeba-test.sme-eng.sme.eng | 8.9 | 0.229 |
| Tatoeba-test.smo-eng.smo.eng | 37.7 | 0.483 |
| Tatoeba-test.sna-eng.sna.eng | 18.0 | 0.359 |
| Tatoeba-test.snd-eng.snd.eng | 28.1 | 0.444 |
| Tatoeba-test.som-eng.som.eng | 23.6 | 0.472 |
| Tatoeba-test.spa-eng.spa.eng | 47.9 | 0.645 |
| Tatoeba-test.sqi-eng.sqi.eng | 46.9 | 0.634 |
| Tatoeba-test.stq-eng.stq.eng | 8.1 | 0.379 |
| Tatoeba-test.sun-eng.sun.eng | 23.8 | 0.369 |
| Tatoeba-test.swa-eng.swa.eng | 6.5 | 0.193 |
| Tatoeba-test.swe-eng.swe.eng | 51.4 | 0.655 |
| Tatoeba-test.swg-eng.swg.eng | 18.5 | 0.342 |
| Tatoeba-test.tah-eng.tah.eng | 25.6 | 0.249 |
| Tatoeba-test.tam-eng.tam.eng | 29.1 | 0.437 |
| Tatoeba-test.tat-eng.tat.eng | 12.9 | 0.327 |
| Tatoeba-test.tel-eng.tel.eng | 21.2 | 0.386 |
| Tatoeba-test.tet-eng.tet.eng | 9.2 | 0.215 |
| Tatoeba-test.tgk-eng.tgk.eng | 12.7 | 0.374 |
| Tatoeba-test.tha-eng.tha.eng | 36.3 | 0.531 |
| Tatoeba-test.tir-eng.tir.eng | 9.1 | 0.267 |
| Tatoeba-test.tlh-eng.tlh.eng | 0.2 | 0.084 |
| Tatoeba-test.tly-eng.tly.eng | 2.1 | 0.128 |
| Tatoeba-test.toi-eng.toi.eng | 5.3 | 0.150 |
| Tatoeba-test.ton-eng.ton.eng | 39.5 | 0.473 |
| Tatoeba-test.tpw-eng.tpw.eng | 1.5 | 0.160 |
| Tatoeba-test.tso-eng.tso.eng | 44.7 | 0.526 |
| Tatoeba-test.tuk-eng.tuk.eng | 18.6 | 0.401 |
| Tatoeba-test.tur-eng.tur.eng | 40.5 | 0.573 |
| Tatoeba-test.tvl-eng.tvl.eng | 55.0 | 0.593 |
| Tatoeba-test.tyv-eng.tyv.eng | 19.1 | 0.477 |
| Tatoeba-test.tzl-eng.tzl.eng | 17.7 | 0.333 |
| Tatoeba-test.udm-eng.udm.eng | 3.4 | 0.217 |
| Tatoeba-test.uig-eng.uig.eng | 11.4 | 0.289 |
| Tatoeba-test.ukr-eng.ukr.eng | 43.1 | 0.595 |
| Tatoeba-test.umb-eng.umb.eng | 9.2 | 0.260 |
| Tatoeba-test.urd-eng.urd.eng | 23.2 | 0.426 |
| Tatoeba-test.uzb-eng.uzb.eng | 19.0 | 0.342 |
| Tatoeba-test.vec-eng.vec.eng | 41.1 | 0.409 |
| Tatoeba-test.vie-eng.vie.eng | 30.6 | 0.481 |
| Tatoeba-test.vol-eng.vol.eng | 1.8 | 0.143 |
| Tatoeba-test.war-eng.war.eng | 15.9 | 0.352 |
| Tatoeba-test.wln-eng.wln.eng | 12.6 | 0.291 |
| Tatoeba-test.wol-eng.wol.eng | 4.4 | 0.138 |
| Tatoeba-test.xal-eng.xal.eng | 0.9 | 0.153 |
| Tatoeba-test.xho-eng.xho.eng | 35.4 | 0.513 |
| Tatoeba-test.yid-eng.yid.eng | 19.4 | 0.387 |
| Tatoeba-test.yor-eng.yor.eng | 19.3 | 0.327 |
| Tatoeba-test.zho-eng.zho.eng | 25.8 | 0.448 |
| Tatoeba-test.zul-eng.zul.eng | 40.9 | 0.567 |
| Tatoeba-test.zza-eng.zza.eng | 1.6 | 0.125 |
### System Info:
- hf_name: mul-eng
- source_languages: mul
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/mul-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ca', 'es', 'os', 'eo', 'ro', 'fy', 'cy', 'is', 'lb', 'su', 'an', 'sq', 'fr', 'ht', 'rm', 'cv', 'ig', 'am', 'eu', 'tr', 'ps', 'af', 'ny', 'ch', 'uk', 'sl', 'lt', 'tk', 'sg', 'ar', 'lg', 'bg', 'be', 'ka', 'gd', 'ja', 'si', 'br', 'mh', 'km', 'th', 'ty', 'rw', 'te', 'mk', 'or', 'wo', 'kl', 'mr', 'ru', 'yo', 'hu', 'fo', 'zh', 'ti', 'co', 'ee', 'oc', 'sn', 'mt', 'ts', 'pl', 'gl', 'nb', 'bn', 'tt', 'bo', 'lo', 'id', 'gn', 'nv', 'hy', 'kn', 'to', 'io', 'so', 'vi', 'da', 'fj', 'gv', 'sm', 'nl', 'mi', 'pt', 'hi', 'se', 'as', 'ta', 'et', 'kw', 'ga', 'sv', 'ln', 'na', 'mn', 'gu', 'wa', 'lv', 'jv', 'el', 'my', 'ba', 'it', 'hr', 'ur', 'ce', 'nn', 'fi', 'mg', 'rn', 'xh', 'ab', 'de', 'cs', 'he', 'zu', 'yi', 'ml', 'mul', 'en']
- src_constituents: {'sjn_Latn', 'cat', 'nan', 'spa', 'ile_Latn', 'pap', 'mwl', 'uzb_Latn', 'mww', 'hil', 'lij', 'avk_Latn', 'lad_Latn', 'lat_Latn', 'bos_Latn', 'oss', 'epo', 'ron', 'fry', 'cym', 'toi_Latn', 'awa', 'swg', 'zsm_Latn', 'zho_Hant', 'gcf_Latn', 'uzb_Cyrl', 'isl', 'lfn_Latn', 'shs_Latn', 'nov_Latn', 'bho', 'ltz', 'lzh', 'kur_Latn', 'sun', 'arg', 'pes_Thaa', 'sqi', 'uig_Arab', 'csb_Latn', 'fra', 'hat', 'liv_Latn', 'non_Latn', 'sco', 'cmn_Hans', 'pnb', 'roh', 'chv', 'ibo', 'bul_Latn', 'amh', 'lfn_Cyrl', 'eus', 'fkv_Latn', 'tur', 'pus', 'afr', 'brx_Latn', 'nya', 'acm', 'ota_Latn', 'cha', 'ukr', 'xal', 'slv', 'lit', 'zho_Hans', 'tmw_Latn', 'kjh', 'ota_Arab', 'war', 'tuk', 'sag', 'myv', 'hsb', 'lzh_Hans', 'ara', 'tly_Latn', 'lug', 'brx', 'bul', 'bel', 'vol_Latn', 'kat', 'gan', 'got_Goth', 'vro', 'ext', 'afh_Latn', 'gla', 'jpn', 'udm', 'mai', 'ary', 'sin', 'tvl', 'hif_Latn', 'cjy_Hant', 'bre', 'ceb', 'mah', 'nob_Hebr', 'crh_Latn', 'prg_Latn', 'khm', 'ang_Latn', 'tha', 'tah', 'tzl', 'aln', 'kin', 'tel', 'ady', 'mkd', 'ori', 'wol', 'aze_Latn', 'jbo', 'niu', 'kal', 'mar', 'vie_Hani', 'arz', 'yue', 'kha', 'san_Deva', 'jbo_Latn', 'gos', 'hau_Latn', 'rus', 'quc', 'cmn', 'yor', 'hun', 'uig_Cyrl', 'fao', 'mnw', 'zho', 'orv_Cyrl', 'iba', 'bel_Latn', 'tir', 'afb', 'crh', 'mic', 'cos', 'swh', 'sah', 'krl', 'ewe', 'apc', 'zza', 'chr', 'grc_Grek', 'tpw_Latn', 'oci', 'mfe', 'sna', 'kir_Cyrl', 'tat_Latn', 'gom', 'ido_Latn', 'sgs', 'pau', 'tgk_Cyrl', 'nog', 'mlt', 'pdc', 'tso', 'srp_Cyrl', 'pol', 'ast', 'glg', 'pms', 'fuc', 'nob', 'qya', 'ben', 'tat', 'kab', 'min', 'srp_Latn', 'wuu', 'dtp', 'jbo_Cyrl', 'tet', 'bod', 'yue_Hans', 'zlm_Latn', 'lao', 'ind', 'grn', 'nav', 'kaz_Cyrl', 'rom', 'hye', 'kan', 'ton', 'ido', 'mhr', 'scn', 'som', 'rif_Latn', 'vie', 'enm_Latn', 'lmo', 'npi', 'pes', 'dan', 'fij', 'ina_Latn', 'cjy_Hans', 'jdt_Cyrl', 'gsw', 'glv', 'khm_Latn', 'smo', 'umb', 'sma', 'gil', 'nld', 'snd_Arab', 'arq', 'mri', 'kur_Arab', 'por', 'hin', 'shy_Latn', 'sme', 'rap', 'tyv', 'dsb', 'moh', 'asm', 'lad', 'yue_Hant', 'kpv', 'tam', 'est', 'frm_Latn', 'hoc_Latn', 'bam_Latn', 'kek_Latn', 'ksh', 'tlh_Latn', 'ltg', 'pan_Guru', 'hnj_Latn', 'cor', 'gle', 'swe', 'lin', 'qya_Latn', 'kum', 'mad', 'cmn_Hant', 'fuv', 'nau', 'mon', 'akl_Latn', 'guj', 'kaz_Latn', 'wln', 'tuk_Latn', 'jav_Java', 'lav', 'jav', 'ell', 'frr', 'mya', 'bak', 'rue', 'ita', 'hrv', 'izh', 'ilo', 'dws_Latn', 'urd', 'stq', 'tat_Arab', 'haw', 'che', 'pag', 'nno', 'fin', 'mlg', 'ppl_Latn', 'run', 'xho', 'abk', 'deu', 'hoc', 'lkt', 'lld_Latn', 'tzl_Latn', 'mdf', 'ike_Latn', 'ces', 'ldn_Latn', 'egl', 'heb', 'vec', 'zul', 'max_Latn', 'pes_Latn', 'yid', 'mal', 'nds'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/mul-eng/opus2m-2020-08-01.test.txt
- src_alpha3: mul
- tgt_alpha3: eng
- short_pair: mul-en
- chrF2_score: 0.518
- bleu: 34.7
- brevity_penalty: 1.0
- ref_len: 72346.0
- src_name: Multiple languages
- tgt_name: English
- train_date: 2020-08-01
- src_alpha2: mul
- tgt_alpha2: en
- prefer_old: False
- long_pair: mul-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
-0.017688052728772163,
-0.009157471358776093,
-0.002984133781865239,
0.06427746266126633,
0.023443957790732384,
0.025435322895646095,
0.005475827492773533,
-0.011381593532860279,
-0.056347042322158813,
0.030250361189246178,
-0.005236182361841202,
-0.04793330654501915,
0.008051380515098572,
... |
Declan/NPR_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
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"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 7 | 2020-04-29T13:24:09Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ng-en
* source languages: ng
* target languages: en
* OPUS readme: [ng-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ng-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ng-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ng-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ng-en/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ng.en | 27.3 | 0.443 |
| [
-0.009876247495412827,
-0.02579931728541851,
-0.00004069972055731341,
0.035657480359077454,
0.02452441304922104,
0.02565355785191059,
-0.0012406465830281377,
-0.003029034473001957,
-0.04687929153442383,
0.05984410271048546,
0.014131646603345871,
-0.007310541812330484,
0.009818937629461288,
... |
Declan/NPR_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9 | null | ---
language:
- sn
- rw
- wo
- ig
- sg
- ee
- zu
- lg
- ts
- ln
- ny
- yo
- rn
- xh
- nic
- en
tags:
- translation
license: apache-2.0
---
### nic-eng
* source group: Niger-Kordofanian languages
* target group: English
* OPUS readme: [nic-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nic-eng/README.md)
* model: transformer
* source language(s): bam_Latn 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-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nic-eng/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nic-eng/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nic-eng/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.bam-eng.bam.eng | 2.4 | 0.090 |
| Tatoeba-test.ewe-eng.ewe.eng | 10.3 | 0.384 |
| Tatoeba-test.ful-eng.ful.eng | 1.2 | 0.114 |
| Tatoeba-test.ibo-eng.ibo.eng | 7.5 | 0.197 |
| Tatoeba-test.kin-eng.kin.eng | 30.7 | 0.481 |
| Tatoeba-test.lin-eng.lin.eng | 3.1 | 0.185 |
| Tatoeba-test.lug-eng.lug.eng | 3.1 | 0.261 |
| Tatoeba-test.multi.eng | 21.3 | 0.377 |
| Tatoeba-test.nya-eng.nya.eng | 31.6 | 0.502 |
| Tatoeba-test.run-eng.run.eng | 24.9 | 0.420 |
| Tatoeba-test.sag-eng.sag.eng | 5.2 | 0.231 |
| Tatoeba-test.sna-eng.sna.eng | 20.1 | 0.374 |
| Tatoeba-test.swa-eng.swa.eng | 4.6 | 0.191 |
| Tatoeba-test.toi-eng.toi.eng | 4.8 | 0.122 |
| Tatoeba-test.tso-eng.tso.eng | 100.0 | 1.000 |
| Tatoeba-test.umb-eng.umb.eng | 9.0 | 0.246 |
| Tatoeba-test.wol-eng.wol.eng | 14.0 | 0.212 |
| Tatoeba-test.xho-eng.xho.eng | 38.2 | 0.558 |
| Tatoeba-test.yor-eng.yor.eng | 21.2 | 0.364 |
| Tatoeba-test.zul-eng.zul.eng | 42.3 | 0.589 |
### System Info:
- hf_name: nic-eng
- source_languages: nic
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nic-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['sn', 'rw', 'wo', 'ig', 'sg', 'ee', 'zu', 'lg', 'ts', 'ln', 'ny', 'yo', 'rn', 'xh', 'nic', 'en']
- src_constituents: {'bam_Latn', '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/nic-eng/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nic-eng/opus2m-2020-08-01.test.txt
- src_alpha3: nic
- tgt_alpha3: eng
- short_pair: nic-en
- chrF2_score: 0.377
- bleu: 21.3
- brevity_penalty: 1.0
- ref_len: 15228.0
- src_name: Niger-Kordofanian languages
- tgt_name: English
- train_date: 2020-08-01
- src_alpha2: nic
- tgt_alpha2: en
- prefer_old: False
- long_pair: nic-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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0.0... |
Declan/NPR_model_v4 | [
"pytorch",
"bert",
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"autotrain_compatible"
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} | 3 | 2020-04-29T13:24:23Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-niu-de
* source languages: niu
* target languages: de
* OPUS readme: [niu-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/niu-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/niu-de/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-de/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-de/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.niu.de | 20.2 | 0.395 |
| [
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0.009889332577586174,
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0.002862371737137437,
0... |
Declan/NPR_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 7 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-niu-en
* source languages: niu
* target languages: en
* OPUS readme: [niu-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/niu-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/niu-en/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-en/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-en/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.niu.en | 46.1 | 0.604 |
| [
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-0.02683086507022381,
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0... |
Declan/NPR_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | 2020-04-29T13:25:02Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-niu-es
* source languages: niu
* target languages: es
* OPUS readme: [niu-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/niu-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/niu-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.niu.es | 24.2 | 0.419 |
| [
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... |
Declan/NPR_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 3 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-niu-fi
* source languages: niu
* target languages: fi
* OPUS readme: [niu-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/niu-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/niu-fi/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-fi/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-fi/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.niu.fi | 24.8 | 0.474 |
| [
-0.019145255908370018,
-0.03460810333490372,
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0.0467761754989624,
0.011423218064010143,
-0.005057724192738533,
0.007726071402430534,
0.0... |
Declan/NewYorkPost_model_v1 | [] | null | {
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}
} | 0 | 2020-04-29T13:25:45Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-niu-fr
* source languages: niu
* target languages: fr
* OPUS readme: [niu-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/niu-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/niu-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.niu.fr | 28.1 | 0.452 |
| [
-0.01587792858481407,
-0.030409149825572968,
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0.03642076998949051,
0.028190942481160164,
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... |
Declan/NewYorkTimes_model_v1 | [] | null | {
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}
} | 0 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-niu-sv
* source languages: niu
* target languages: sv
* OPUS readme: [niu-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/niu-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/niu-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/niu-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.niu.sv | 29.2 | 0.478 |
| [
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-0.02800208143889904,
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0.05320703983306885,
0.0070902020670473576,
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0.01433554757386446,
... |
Declan/NewYorkTimes_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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}
} | 7 | 2020-08-19T00:30:56Z | ---
language:
- nl
- af
tags:
- translation
license: apache-2.0
---
### nld-afr
* source group: Dutch
* target group: Afrikaans
* OPUS readme: [nld-afr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-afr/README.md)
* model: transformer-align
* source language(s): nld
* target language(s): afr
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-afr/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-afr/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-afr/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nld.afr | 57.8 | 0.749 |
### System Info:
- hf_name: nld-afr
- source_languages: nld
- target_languages: afr
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-afr/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['nl', 'af']
- src_constituents: {'nld'}
- tgt_constituents: {'afr'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-afr/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-afr/opus-2020-06-17.test.txt
- src_alpha3: nld
- tgt_alpha3: afr
- short_pair: nl-af
- chrF2_score: 0.7490000000000001
- bleu: 57.8
- brevity_penalty: 1.0
- ref_len: 6823.0
- src_name: Dutch
- tgt_name: Afrikaans
- train_date: 2020-06-17
- src_alpha2: nl
- tgt_alpha2: af
- prefer_old: False
- long_pair: nld-afr
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/NewYorkTimes_model_v3 | [] | null | {
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} | 0 | null | ---
language:
- nl
- ca
tags:
- translation
license: apache-2.0
---
### nld-cat
* source group: Dutch
* target group: Catalan
* OPUS readme: [nld-cat](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-cat/README.md)
* model: transformer-align
* source language(s): nld
* target language(s): cat
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nld.cat | 42.1 | 0.624 |
### System Info:
- hf_name: nld-cat
- source_languages: nld
- target_languages: cat
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-cat/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['nl', 'ca']
- src_constituents: {'nld'}
- tgt_constituents: {'cat'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-cat/opus-2020-06-16.test.txt
- src_alpha3: nld
- tgt_alpha3: cat
- short_pair: nl-ca
- chrF2_score: 0.624
- bleu: 42.1
- brevity_penalty: 0.988
- ref_len: 3942.0
- src_name: Dutch
- tgt_name: Catalan
- train_date: 2020-06-16
- src_alpha2: nl
- tgt_alpha2: ca
- prefer_old: False
- long_pair: nld-cat
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/NewYorkTimes_model_v4 | [] | null | {
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}
} | 0 | 2020-04-29T13:26:09Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nl-en
* source languages: nl
* target languages: en
* OPUS readme: [nl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nl-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-05.zip](https://object.pouta.csc.fi/OPUS-MT-models/nl-en/opus-2019-12-05.zip)
* test set translations: [opus-2019-12-05.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-en/opus-2019-12-05.test.txt)
* test set scores: [opus-2019-12-05.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-en/opus-2019-12-05.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.nl.en | 60.9 | 0.749 |
| [
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0... |
Declan/NewYorkTimes_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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],
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}
}
} | 5 | null | ---
language:
- nl
- eo
tags:
- translation
license: apache-2.0
---
### nld-epo
* source group: Dutch
* target group: Esperanto
* OPUS readme: [nld-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-epo/README.md)
* model: transformer-align
* source language(s): nld
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-epo/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-epo/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-epo/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nld.epo | 16.1 | 0.355 |
### System Info:
- hf_name: nld-epo
- source_languages: nld
- target_languages: epo
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-epo/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['nl', 'eo']
- src_constituents: {'nld'}
- tgt_constituents: {'epo'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-epo/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-epo/opus-2020-06-16.test.txt
- src_alpha3: nld
- tgt_alpha3: epo
- short_pair: nl-eo
- chrF2_score: 0.355
- bleu: 16.1
- brevity_penalty: 0.9359999999999999
- ref_len: 72293.0
- src_name: Dutch
- tgt_name: Esperanto
- train_date: 2020-06-16
- src_alpha2: nl
- tgt_alpha2: eo
- prefer_old: False
- long_pair: nld-epo
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/NewYorkTimes_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
}
} | 3 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nl-es
* source languages: nl
* target languages: es
* OPUS readme: [nl-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nl-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nl-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.nl.es | 51.6 | 0.698 |
| [
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0.052768684923648834,
0.005307465326040983,
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0.0023902282118797302,
... |
Declan/Politico_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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"task_specific_params": {
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}
} | 3 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nl-fi
* source languages: nl
* target languages: fi
* OPUS readme: [nl-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nl-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-02-26.zip](https://object.pouta.csc.fi/OPUS-MT-models/nl-fi/opus-2020-02-26.zip)
* test set translations: [opus-2020-02-26.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-fi/opus-2020-02-26.test.txt)
* test set scores: [opus-2020-02-26.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-fi/opus-2020-02-26.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.nl.fi | 28.6 | 0.569 |
| [
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0.007328676525503397,
0.0... |
Declan/Politico_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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}
}
} | 5 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nl-fr
* source languages: nl
* target languages: fr
* OPUS readme: [nl-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nl-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/nl-fr/opus-2020-01-24.zip)
* test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-fr/opus-2020-01-24.test.txt)
* test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-fr/opus-2020-01-24.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.nl.fr | 51.3 | 0.674 |
| [
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-0.031327251344919205,
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0.008845475502312183,
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... |
Declan/Politico_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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}
}
} | 5 | null | ---
language:
- nl
- no
tags:
- translation
license: apache-2.0
---
### nld-nor
* source group: Dutch
* target group: Norwegian
* OPUS readme: [nld-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-nor/README.md)
* model: transformer-align
* source language(s): nld
* target language(s): nob
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-nor/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-nor/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-nor/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nld.nor | 36.1 | 0.562 |
### System Info:
- hf_name: nld-nor
- source_languages: nld
- target_languages: nor
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-nor/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['nl', 'no']
- src_constituents: {'nld'}
- tgt_constituents: {'nob', 'nno'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-nor/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-nor/opus-2020-06-17.test.txt
- src_alpha3: nld
- tgt_alpha3: nor
- short_pair: nl-no
- chrF2_score: 0.562
- bleu: 36.1
- brevity_penalty: 0.966
- ref_len: 1459.0
- src_name: Dutch
- tgt_name: Norwegian
- train_date: 2020-06-17
- src_alpha2: nl
- tgt_alpha2: no
- prefer_old: False
- long_pair: nld-nor
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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0.0... |
Declan/Politico_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 9 | 2020-04-29T13:28:09Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nl-sv
* source languages: nl
* target languages: sv
* OPUS readme: [nl-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nl-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nl-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nl-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| GlobalVoices.nl.sv | 25.0 | 0.518 |
| [
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Declan/Politico_model_v5 | [
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"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 7 | null | ---
language:
- nl
- uk
tags:
- translation
license: apache-2.0
---
### nld-ukr
* source group: Dutch
* target group: Ukrainian
* OPUS readme: [nld-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-ukr/README.md)
* model: transformer-align
* source language(s): nld
* target language(s): ukr
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nld.ukr | 40.8 | 0.619 |
### System Info:
- hf_name: nld-ukr
- source_languages: nld
- target_languages: ukr
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nld-ukr/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['nl', 'uk']
- src_constituents: {'nld'}
- tgt_constituents: {'ukr'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nld-ukr/opus-2020-06-17.test.txt
- src_alpha3: nld
- tgt_alpha3: ukr
- short_pair: nl-uk
- chrF2_score: 0.619
- bleu: 40.8
- brevity_penalty: 0.992
- ref_len: 51674.0
- src_name: Dutch
- tgt_name: Ukrainian
- train_date: 2020-06-17
- src_alpha2: nl
- tgt_alpha2: uk
- prefer_old: False
- long_pair: nld-ukr
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/Politico_model_v6 | [
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"transformers",
"autotrain_compatible"
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}
} | 3 | null | ---
language:
- no
- da
tags:
- translation
license: apache-2.0
---
### nor-dan
* source group: Norwegian
* target group: Danish
* OPUS readme: [nor-dan](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-dan/README.md)
* model: transformer-align
* source language(s): nno nob
* target language(s): dan
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-dan/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-dan/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-dan/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.dan | 65.0 | 0.792 |
### System Info:
- hf_name: nor-dan
- source_languages: nor
- target_languages: dan
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-dan/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'da']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'dan'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-dan/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-dan/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: dan
- short_pair: no-da
- chrF2_score: 0.792
- bleu: 65.0
- brevity_penalty: 0.995
- ref_len: 9865.0
- src_name: Norwegian
- tgt_name: Danish
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: da
- prefer_old: False
- long_pair: nor-dan
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/Politico_model_v8 | [
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"bert",
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"transformers",
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}
} | 7 | 2020-08-19T00:31:15Z | ---
language:
- no
- de
tags:
- translation
license: apache-2.0
---
### nor-deu
* source group: Norwegian
* target group: German
* OPUS readme: [nor-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-deu/README.md)
* model: transformer-align
* source language(s): nno nob
* target language(s): deu
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-deu/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-deu/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-deu/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.deu | 29.6 | 0.541 |
### System Info:
- hf_name: nor-deu
- source_languages: nor
- target_languages: deu
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-deu/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'de']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'deu'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-deu/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-deu/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: deu
- short_pair: no-de
- chrF2_score: 0.541
- bleu: 29.6
- brevity_penalty: 0.96
- ref_len: 34575.0
- src_name: Norwegian
- tgt_name: German
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: de
- prefer_old: False
- long_pair: nor-deu
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/Reuters_model_v1 | [
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"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 3 | null | ---
language:
- no
- es
tags:
- translation
license: apache-2.0
---
### nor-spa
* source group: Norwegian
* target group: Spanish
* OPUS readme: [nor-spa](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-spa/README.md)
* model: transformer-align
* source language(s): nno nob
* target language(s): spa
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm12k,spm12k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.spa | 34.2 | 0.565 |
### System Info:
- hf_name: nor-spa
- source_languages: nor
- target_languages: spa
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-spa/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'es']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'spa'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm12k,spm12k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-spa/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: spa
- short_pair: no-es
- chrF2_score: 0.565
- bleu: 34.2
- brevity_penalty: 0.997
- ref_len: 7311.0
- src_name: Norwegian
- tgt_name: Spanish
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: es
- prefer_old: False
- long_pair: nor-spa
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/Reuters_model_v2 | [
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"transformers",
"autotrain_compatible"
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}
} | 5 | null | ---
language:
- no
- fi
tags:
- translation
license: apache-2.0
---
### nor-fin
* source group: Norwegian
* target group: Finnish
* OPUS readme: [nor-fin](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-fin/README.md)
* model: transformer-align
* source language(s): nno nob
* target language(s): fin
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fin/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fin/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fin/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.fin | 14.1 | 0.374 |
### System Info:
- hf_name: nor-fin
- source_languages: nor
- target_languages: fin
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-fin/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'fi']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'fin'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fin/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fin/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: fin
- short_pair: no-fi
- chrF2_score: 0.374
- bleu: 14.1
- brevity_penalty: 0.894
- ref_len: 13066.0
- src_name: Norwegian
- tgt_name: Finnish
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: fi
- prefer_old: False
- long_pair: nor-fin
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/Reuters_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 7 | null | ---
language:
- no
- fr
tags:
- translation
license: apache-2.0
---
### nor-fra
* source group: Norwegian
* target group: French
* OPUS readme: [nor-fra](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-fra/README.md)
* model: transformer-align
* source language(s): nno nob
* target language(s): fra
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fra/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fra/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fra/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.fra | 39.1 | 0.578 |
### System Info:
- hf_name: nor-fra
- source_languages: nor
- target_languages: fra
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-fra/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'fr']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'fra'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fra/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-fra/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: fra
- short_pair: no-fr
- chrF2_score: 0.578
- bleu: 39.1
- brevity_penalty: 0.987
- ref_len: 3205.0
- src_name: Norwegian
- tgt_name: French
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: fr
- prefer_old: False
- long_pair: nor-fra
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/Reuters_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | 2020-08-19T00:31:41Z | ---
language:
- no
- nl
tags:
- translation
license: apache-2.0
---
### nor-nld
* source group: Norwegian
* target group: Dutch
* OPUS readme: [nor-nld](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-nld/README.md)
* model: transformer-align
* source language(s): nob
* target language(s): nld
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nld/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nld/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nld/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.nld | 40.2 | 0.596 |
### System Info:
- hf_name: nor-nld
- source_languages: nor
- target_languages: nld
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-nld/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'nl']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'nld'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nld/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nld/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: nld
- short_pair: no-nl
- chrF2_score: 0.596
- bleu: 40.2
- brevity_penalty: 0.9590000000000001
- ref_len: 1535.0
- src_name: Norwegian
- tgt_name: Dutch
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: nl
- prefer_old: False
- long_pair: nor-nld
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/Reuters_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 3 | null | ---
language:
- no
tags:
- translation
license: apache-2.0
---
### nor-nor
* source group: Norwegian
* target group: Norwegian
* OPUS readme: [nor-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-nor/README.md)
* model: transformer-align
* source language(s): nno nob
* target language(s): nno nob
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nor/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nor/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nor/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.nor | 58.4 | 0.784 |
### System Info:
- hf_name: nor-nor
- source_languages: nor
- target_languages: nor
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-nor/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'nob', 'nno'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nor/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-nor/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: nor
- short_pair: no-no
- chrF2_score: 0.784
- bleu: 58.4
- brevity_penalty: 0.988
- ref_len: 6351.0
- src_name: Norwegian
- tgt_name: Norwegian
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: no
- prefer_old: False
- long_pair: nor-nor
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/Reuters_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 7 | null | ---
language:
- no
- pl
tags:
- translation
license: apache-2.0
---
### nor-pol
* source group: Norwegian
* target group: Polish
* OPUS readme: [nor-pol](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-pol/README.md)
* model: transformer-align
* source language(s): nob
* target language(s): pol
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-pol/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-pol/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-pol/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.pol | 20.9 | 0.455 |
### System Info:
- hf_name: nor-pol
- source_languages: nor
- target_languages: pol
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-pol/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'pl']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'pol'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-pol/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-pol/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: pol
- short_pair: no-pl
- chrF2_score: 0.455
- bleu: 20.9
- brevity_penalty: 0.941
- ref_len: 1828.0
- src_name: Norwegian
- tgt_name: Polish
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: pl
- prefer_old: False
- long_pair: nor-pol
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/Reuters_model_v8 | [
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"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 3 | null | ---
language:
- no
- ru
tags:
- translation
license: apache-2.0
---
### nor-rus
* source group: Norwegian
* target group: Russian
* OPUS readme: [nor-rus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-rus/README.md)
* model: transformer-align
* source language(s): nno nob
* target language(s): rus
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-rus/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-rus/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-rus/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.rus | 18.6 | 0.400 |
### System Info:
- hf_name: nor-rus
- source_languages: nor
- target_languages: rus
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-rus/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'ru']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'rus'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-rus/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-rus/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: rus
- short_pair: no-ru
- chrF2_score: 0.4
- bleu: 18.6
- brevity_penalty: 0.958
- ref_len: 10671.0
- src_name: Norwegian
- tgt_name: Russian
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: ru
- prefer_old: False
- long_pair: nor-rus
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/WallStreetJournal_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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}
} | 3 | null | ---
language:
- no
- sv
tags:
- translation
license: apache-2.0
---
### nor-swe
* source group: Norwegian
* target group: Swedish
* OPUS readme: [nor-swe](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-swe/README.md)
* model: transformer-align
* source language(s): nno nob
* target language(s): swe
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-swe/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-swe/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-swe/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.swe | 63.7 | 0.773 |
### System Info:
- hf_name: nor-swe
- source_languages: nor
- target_languages: swe
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-swe/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'sv']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'swe'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-swe/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-swe/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: swe
- short_pair: no-sv
- chrF2_score: 0.773
- bleu: 63.7
- brevity_penalty: 0.9670000000000001
- ref_len: 3672.0
- src_name: Norwegian
- tgt_name: Swedish
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: sv
- prefer_old: False
- long_pair: nor-swe
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/WallStreetJournal_model_v2 | [
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}
} | 7 | null | ---
language:
- no
- uk
tags:
- translation
license: apache-2.0
---
### nor-ukr
* source group: Norwegian
* target group: Ukrainian
* OPUS readme: [nor-ukr](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-ukr/README.md)
* model: transformer-align
* source language(s): nob
* target language(s): ukr
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-ukr/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-ukr/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/nor-ukr/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.nor.ukr | 16.6 | 0.384 |
### System Info:
- hf_name: nor-ukr
- source_languages: nor
- target_languages: ukr
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/nor-ukr/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['no', 'uk']
- src_constituents: {'nob', 'nno'}
- tgt_constituents: {'ukr'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-ukr/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/nor-ukr/opus-2020-06-17.test.txt
- src_alpha3: nor
- tgt_alpha3: ukr
- short_pair: no-uk
- chrF2_score: 0.384
- bleu: 16.6
- brevity_penalty: 1.0
- ref_len: 3982.0
- src_name: Norwegian
- tgt_name: Ukrainian
- train_date: 2020-06-17
- src_alpha2: no
- tgt_alpha2: uk
- prefer_old: False
- long_pair: nor-ukr
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Declan/WallStreetJournal_model_v3 | [
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"transformers",
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}
} | 3 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nso-de
* source languages: nso
* target languages: de
* OPUS readme: [nso-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/nso-de/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-de/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-de/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.nso.de | 24.7 | 0.461 |
| [
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-0.03052113763988018,
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0.011892163194715977,
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0.003475061384961009,
... |
Declan/WallStreetJournal_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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}
} | 7 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nso-en
* source languages: nso
* target languages: en
* OPUS readme: [nso-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nso-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-en/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.nso.en | 48.6 | 0.634 |
| [
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0.... |
Declan/WallStreetJournal_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
}
} | 9 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nso-es
* source languages: nso
* target languages: es
* OPUS readme: [nso-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nso-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.nso.es | 29.5 | 0.485 |
| [
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-0.03134017437696457,
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0.05295349657535553,
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0.006353678647428751,
... |
Declan/WallStreetJournal_model_v6 | [] | null | {
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}
} | 0 | 2020-04-29T13:29:03Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nso-fi
* source languages: nso
* target languages: fi
* OPUS readme: [nso-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nso-fi/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-fi/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-fi/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.nso.fi | 27.8 | 0.523 |
| [
-0.019492562860250473,
-0.03798802196979523,
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0.05016452074050903,
0.015173406340181828,
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0.008900830522179604,
0.... |
Declan/WallStreetJournal_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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}
} | 9 | 2020-04-29T13:29:30Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nso-fr
* source languages: nso
* target languages: fr
* OPUS readme: [nso-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nso-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.nso.fr | 30.7 | 0.488 |
| [
-0.015580405481159687,
-0.03371619060635567,
-0.01004526112228632,
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0.026833780109882355,
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0.053671568632125854,
0.00969518069177866,
-0.008812270127236843,
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... |
Declan/test_model | [] | null | {
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}
} | 0 | 2020-04-29T13:29:46Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-nso-sv
* source languages: nso
* target languages: sv
* OPUS readme: [nso-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/nso-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/nso-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/nso-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.nso.sv | 34.3 | 0.527 |
| [
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0.013417387381196022,
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Declan/test_push | [] | null | {
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}
} | 0 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ny-de
* source languages: ny
* target languages: de
* OPUS readme: [ny-de](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ny-de/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/ny-de/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ny-de/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ny-de/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ny.de | 23.9 | 0.440 |
| [
-0.014410963281989098,
-0.024773305281996727,
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0.02875613607466221,
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0.0... |
DeepChem/ChemBERTa-5M-MLM | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
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}
} | 29 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-om-en
* source languages: om
* target languages: en
* OPUS readme: [om-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/om-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/om-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/om-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/om-en/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.om.en | 27.3 | 0.448 |
| [
-0.01934279128909111,
-0.025377899408340454,
-0.002584679052233696,
0.030236879363656044,
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0.011899511329829693,
0.... |
DeepESP/gpt2-spanish-medium | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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}
}
} | 340 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-pag-fi
* source languages: pag
* target languages: fi
* OPUS readme: [pag-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pag-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-24.zip](https://object.pouta.csc.fi/OPUS-MT-models/pag-fi/opus-2020-01-24.zip)
* test set translations: [opus-2020-01-24.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pag-fi/opus-2020-01-24.test.txt)
* test set scores: [opus-2020-01-24.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pag-fi/opus-2020-01-24.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.pag.fi | 26.7 | 0.496 |
| [
-0.014024884440004826,
-0.03714059665799141,
0.0026832805015146732,
0.03056926466524601,
0.01879420131444931,
0.031069977208971977,
0.010323028080165386,
-0.00033207854721695185,
-0.049458231776952744,
0.05562502145767212,
0.019670572131872177,
-0.006326952949166298,
0.013539229519665241,
... |
DeepPavlov/bert-base-multilingual-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 140 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-pap-es
* source languages: pap
* target languages: es
* OPUS readme: [pap-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pap-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/pap-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pap-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pap-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.pap.es | 32.3 | 0.518 |
| [
-0.008937752805650234,
-0.0351497121155262,
0.0050172461196780205,
0.03720295801758766,
0.02972976677119732,
0.02478216215968132,
0.0015884300228208303,
0.006500047631561756,
-0.049265846610069275,
0.056571103632450104,
0.009391220286488533,
-0.020431099459528923,
0.00972607359290123,
0.05... |
DeepPavlov/xlm-roberta-large-en-ru | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"en",
"ru",
"transformers"
] | feature-extraction | {
"architectures": [
"XLMRobertaModel"
],
"model_type": "xlm-roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 190 | 2020-04-29T13:35:51Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-pl-en
* source languages: pl
* target languages: en
* OPUS readme: [pl-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/pl-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2019-12-18.zip](https://object.pouta.csc.fi/OPUS-MT-models/pl-en/opus-2019-12-18.zip)
* test set translations: [opus-2019-12-18.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/pl-en/opus-2019-12-18.test.txt)
* test set scores: [opus-2019-12-18.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/pl-en/opus-2019-12-18.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.pl.en | 54.9 | 0.701 |
| [
-0.014878750778734684,
-0.03131267800927162,
-0.0027609984390437603,
0.038057394325733185,
0.035819362848997116,
0.022729696705937386,
0.000891355681233108,
-0.002801948692649603,
-0.05732784792780876,
0.054170865565538406,
0.012372812256217003,
-0.015614180825650692,
-0.0022084603551775217,... |
Dilmk2/DialoGPT-small-harrypotter | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 13 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ru-fi
* source languages: ru
* target languages: fi
* OPUS readme: [ru-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ru-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-04-12.zip](https://object.pouta.csc.fi/OPUS-MT-models/ru-fi/opus-2020-04-12.zip)
* test set translations: [opus-2020-04-12.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-fi/opus-2020-04-12.test.txt)
* test set scores: [opus-2020-04-12.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ru-fi/opus-2020-04-12.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.ru.fi | 40.1 | 0.646 |
| [
-0.013932598754763603,
-0.03682563453912735,
0.007220860105007887,
0.03382923826575279,
0.035664770752191544,
0.026854924857616425,
-0.002130958717316389,
-0.007329304702579975,
-0.06320255994796753,
0.05062665045261383,
0.020215345546603203,
-0.014290020801126957,
0.0005655547138303518,
0... |
Dimedrolza/DialoGPT-small-cyberpunk | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
"max_length": 1000
},
"summarization": {
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"max_length": null,
"min_length": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9 | null | ---
language:
- ru
- hy
tags:
- translation
license: apache-2.0
---
### rus-hye
* source group: Russian
* target group: Armenian
* OPUS readme: [rus-hye](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-hye/README.md)
* model: transformer-align
* source language(s): rus
* target language(s): hye hye_Latn
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-hye/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-hye/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-hye/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.rus.hye | 21.7 | 0.494 |
### System Info:
- hf_name: rus-hye
- source_languages: rus
- target_languages: hye
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-hye/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ru', 'hy']
- src_constituents: {'rus'}
- tgt_constituents: {'hye', 'hye_Latn'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-hye/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-hye/opus-2020-06-16.test.txt
- src_alpha3: rus
- tgt_alpha3: hye
- short_pair: ru-hy
- chrF2_score: 0.494
- bleu: 21.7
- brevity_penalty: 1.0
- ref_len: 1602.0
- src_name: Russian
- tgt_name: Armenian
- train_date: 2020-06-16
- src_alpha2: ru
- tgt_alpha2: hy
- prefer_old: False
- long_pair: rus-hye
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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0.016238147392868996,
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-0.0060103097930550575,
... |
DingleyMaillotUrgell/homer-bot | [
"pytorch",
"gpt2",
"text-generation",
"en",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
"conversational": {
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},
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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}
}
} | 12 | null | ---
language:
- ru
- lt
tags:
- translation
license: apache-2.0
---
### rus-lit
* source group: Russian
* target group: Lithuanian
* OPUS readme: [rus-lit](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-lit/README.md)
* model: transformer-align
* source language(s): rus
* target language(s): lit
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-lit/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-lit/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-lit/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.rus.lit | 43.5 | 0.675 |
### System Info:
- hf_name: rus-lit
- source_languages: rus
- target_languages: lit
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-lit/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ru', 'lt']
- src_constituents: {'rus'}
- tgt_constituents: {'lit'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-lit/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-lit/opus-2020-06-17.test.txt
- src_alpha3: rus
- tgt_alpha3: lit
- short_pair: ru-lt
- chrF2_score: 0.675
- bleu: 43.5
- brevity_penalty: 0.937
- ref_len: 14406.0
- src_name: Russian
- tgt_name: Lithuanian
- train_date: 2020-06-17
- src_alpha2: ru
- tgt_alpha2: lt
- prefer_old: False
- long_pair: rus-lit
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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0.024929439648985863,
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0.05... |
Dizoid/Lll | [] | null | {
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}
}
} | 0 | null | ---
language:
- ru
- no
tags:
- translation
license: apache-2.0
---
### rus-nor
* source group: Russian
* target group: Norwegian
* OPUS readme: [rus-nor](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-nor/README.md)
* model: transformer-align
* source language(s): rus
* target language(s): nno nob
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-nor/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-nor/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-nor/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.rus.nor | 20.3 | 0.418 |
### System Info:
- hf_name: rus-nor
- source_languages: rus
- target_languages: nor
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-nor/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ru', 'no']
- src_constituents: {'rus'}
- tgt_constituents: {'nob', 'nno'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-nor/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-nor/opus-2020-06-17.test.txt
- src_alpha3: rus
- tgt_alpha3: nor
- short_pair: ru-no
- chrF2_score: 0.418
- bleu: 20.3
- brevity_penalty: 0.946
- ref_len: 11686.0
- src_name: Russian
- tgt_name: Norwegian
- train_date: 2020-06-17
- src_alpha2: ru
- tgt_alpha2: no
- prefer_old: False
- long_pair: rus-nor
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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0.05... |
Dkwkk/Da | [] | null | {
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} | 0 | null | ---
language:
- ru
- sl
tags:
- translation
license: apache-2.0
---
### rus-slv
* source group: Russian
* target group: Slovenian
* OPUS readme: [rus-slv](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-slv/README.md)
* model: transformer-align
* source language(s): rus
* target language(s): slv
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-slv/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-slv/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/rus-slv/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.rus.slv | 32.3 | 0.492 |
### System Info:
- hf_name: rus-slv
- source_languages: rus
- target_languages: slv
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/rus-slv/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['ru', 'sl']
- src_constituents: {'rus'}
- tgt_constituents: {'slv'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-slv/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/rus-slv/opus-2020-06-17.test.txt
- src_alpha3: rus
- tgt_alpha3: slv
- short_pair: ru-sl
- chrF2_score: 0.49200000000000005
- bleu: 32.3
- brevity_penalty: 0.992
- ref_len: 2135.0
- src_name: Russian
- tgt_name: Slovenian
- train_date: 2020-06-17
- src_alpha2: ru
- tgt_alpha2: sl
- prefer_old: False
- long_pair: rus-slv
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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Waynehillsdev/Waynehills-STT-doogie-server | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
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}
} | 61 | null | ---
language:
- mt
- ar
- he
- ti
- am
- sem
tags:
- translation
license: apache-2.0
---
### sem-sem
* source group: Semitic languages
* target group: Semitic languages
* OPUS readme: [sem-sem](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sem-sem/README.md)
* model: transformer
* source language(s): apc ara arq arz heb mlt
* target language(s): apc ara arq arz heb mlt
* 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](https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ara-ara.ara.ara | 4.2 | 0.200 |
| Tatoeba-test.ara-heb.ara.heb | 34.0 | 0.542 |
| Tatoeba-test.ara-mlt.ara.mlt | 16.6 | 0.513 |
| Tatoeba-test.heb-ara.heb.ara | 18.8 | 0.477 |
| Tatoeba-test.mlt-ara.mlt.ara | 20.7 | 0.388 |
| Tatoeba-test.multi.multi | 27.1 | 0.507 |
### System Info:
- hf_name: sem-sem
- source_languages: sem
- target_languages: sem
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sem-sem/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['mt', 'ar', 'he', 'ti', 'am', 'sem']
- src_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}
- tgt_constituents: {'apc', 'mlt', 'arz', 'ara', 'heb', 'tir', 'arq', 'afb', 'amh', 'acm', 'ary'}
- src_multilingual: True
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/sem-sem/opus-2020-07-27.test.txt
- src_alpha3: sem
- tgt_alpha3: sem
- short_pair: sem-sem
- chrF2_score: 0.507
- bleu: 27.1
- brevity_penalty: 0.972
- ref_len: 13472.0
- src_name: Semitic languages
- tgt_name: Semitic languages
- train_date: 2020-07-27
- src_alpha2: sem
- tgt_alpha2: sem
- prefer_old: False
- long_pair: sem-sem
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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0.0... |
Waynehillsdev/Waynehills_summary_tensorflow | [
"tf",
"t5",
"text2text-generation",
"transformers",
"generated_from_keras_callback",
"autotrain_compatible"
] | text2text-generation | {
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}
} | 5 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sg-en
* source languages: sg
* target languages: en
* OPUS readme: [sg-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sg-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sg-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sg-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sg-en/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sg.en | 32.0 | 0.477 |
| [
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... |
Doohae/p_encoder | [
"pytorch"
] | null | {
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}
}
} | 3 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sg-fr
* source languages: sg
* target languages: fr
* OPUS readme: [sg-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sg-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/sg-fr/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sg-fr/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sg-fr/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sg.fr | 24.9 | 0.420 |
| [
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-0.030335774645209312,
-0.01175461895763874,
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0.054302193224430084,
0.013144970871508121,
-0.00997588224709034,
-0.000036820900277234614,... |
Doohae/q_encoder | [
"pytorch"
] | null | {
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}
} | 3 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sg-sv
* source languages: sg
* target languages: sv
* OPUS readme: [sg-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sg-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/sg-sv/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sg-sv/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sg-sv/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sg.sv | 25.3 | 0.428 |
| [
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0.014272774569690228,
... |
Doohae/roberta | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
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}
} | 3 | null | ---
language:
- sh
- eo
tags:
- translation
license: apache-2.0
---
### hbs-epo
* source group: Serbo-Croatian
* target group: Esperanto
* OPUS readme: [hbs-epo](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hbs-epo/README.md)
* model: transformer-align
* source language(s): bos_Latn hrv srp_Cyrl srp_Latn
* target language(s): epo
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm4k,spm4k)
* download original weights: [opus-2020-06-16.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-epo/opus-2020-06-16.zip)
* test set translations: [opus-2020-06-16.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-epo/opus-2020-06-16.test.txt)
* test set scores: [opus-2020-06-16.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-epo/opus-2020-06-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.hbs.epo | 18.7 | 0.383 |
### System Info:
- hf_name: hbs-epo
- source_languages: hbs
- target_languages: epo
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/hbs-epo/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['sh', 'eo']
- src_constituents: {'hrv', 'srp_Cyrl', 'bos_Latn', 'srp_Latn'}
- tgt_constituents: {'epo'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm4k,spm4k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-epo/opus-2020-06-16.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/hbs-epo/opus-2020-06-16.test.txt
- src_alpha3: hbs
- tgt_alpha3: epo
- short_pair: sh-eo
- chrF2_score: 0.38299999999999995
- bleu: 18.7
- brevity_penalty: 0.9990000000000001
- ref_len: 18457.0
- src_name: Serbo-Croatian
- tgt_name: Esperanto
- train_date: 2020-06-16
- src_alpha2: sh
- tgt_alpha2: eo
- prefer_old: False
- long_pair: hbs-epo
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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DoyyingFace/bert-asian-hate-tweets-asian-unclean-warmup-75 | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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}
} | 37 | null | ---
language:
- be
- hr
- mk
- cs
- ru
- pl
- bg
- uk
- sl
- sla
- en
tags:
- translation
license: apache-2.0
---
### sla-eng
* source group: Slavic languages
* target group: English
* OPUS readme: [sla-eng](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sla-eng/README.md)
* model: transformer
* source language(s): bel bel_Latn bos_Latn bul bul_Latn ces csb_Latn dsb hrv hsb mkd orv_Cyrl pol rue rus slv srp_Cyrl srp_Latn ukr
* target language(s): eng
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-eng/opus2m-2020-08-01.zip)
* test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-eng/opus2m-2020-08-01.test.txt)
* test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-eng/opus2m-2020-08-01.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newssyscomb2009-ceseng.ces.eng | 26.7 | 0.542 |
| newstest2009-ceseng.ces.eng | 25.2 | 0.534 |
| newstest2010-ceseng.ces.eng | 25.9 | 0.545 |
| newstest2011-ceseng.ces.eng | 26.8 | 0.544 |
| newstest2012-ceseng.ces.eng | 25.6 | 0.536 |
| newstest2012-ruseng.rus.eng | 32.5 | 0.588 |
| newstest2013-ceseng.ces.eng | 28.8 | 0.556 |
| newstest2013-ruseng.rus.eng | 26.4 | 0.532 |
| newstest2014-csen-ceseng.ces.eng | 31.4 | 0.591 |
| newstest2014-ruen-ruseng.rus.eng | 29.6 | 0.576 |
| newstest2015-encs-ceseng.ces.eng | 28.2 | 0.545 |
| newstest2015-enru-ruseng.rus.eng | 28.1 | 0.551 |
| newstest2016-encs-ceseng.ces.eng | 30.0 | 0.567 |
| newstest2016-enru-ruseng.rus.eng | 27.4 | 0.548 |
| newstest2017-encs-ceseng.ces.eng | 26.5 | 0.537 |
| newstest2017-enru-ruseng.rus.eng | 31.0 | 0.574 |
| newstest2018-encs-ceseng.ces.eng | 27.9 | 0.548 |
| newstest2018-enru-ruseng.rus.eng | 26.8 | 0.545 |
| newstest2019-ruen-ruseng.rus.eng | 29.1 | 0.562 |
| Tatoeba-test.bel-eng.bel.eng | 42.5 | 0.609 |
| Tatoeba-test.bul-eng.bul.eng | 55.4 | 0.697 |
| Tatoeba-test.ces-eng.ces.eng | 53.1 | 0.688 |
| Tatoeba-test.csb-eng.csb.eng | 23.1 | 0.446 |
| Tatoeba-test.dsb-eng.dsb.eng | 31.1 | 0.467 |
| Tatoeba-test.hbs-eng.hbs.eng | 56.1 | 0.702 |
| Tatoeba-test.hsb-eng.hsb.eng | 46.2 | 0.597 |
| Tatoeba-test.mkd-eng.mkd.eng | 54.5 | 0.680 |
| Tatoeba-test.multi.eng | 53.2 | 0.683 |
| Tatoeba-test.orv-eng.orv.eng | 12.1 | 0.292 |
| Tatoeba-test.pol-eng.pol.eng | 51.1 | 0.671 |
| Tatoeba-test.rue-eng.rue.eng | 19.6 | 0.389 |
| Tatoeba-test.rus-eng.rus.eng | 54.1 | 0.686 |
| Tatoeba-test.slv-eng.slv.eng | 43.4 | 0.610 |
| Tatoeba-test.ukr-eng.ukr.eng | 53.8 | 0.685 |
### System Info:
- hf_name: sla-eng
- source_languages: sla
- target_languages: eng
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sla-eng/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['be', 'hr', 'mk', 'cs', 'ru', 'pl', 'bg', 'uk', 'sl', 'sla', 'en']
- src_constituents: {'bel', 'hrv', 'orv_Cyrl', 'mkd', 'bel_Latn', 'srp_Latn', 'bul_Latn', 'ces', 'bos_Latn', 'csb_Latn', 'dsb', 'hsb', 'rus', 'srp_Cyrl', 'pol', 'rue', 'bul', 'ukr', 'slv'}
- tgt_constituents: {'eng'}
- src_multilingual: True
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/sla-eng/opus2m-2020-08-01.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/sla-eng/opus2m-2020-08-01.test.txt
- src_alpha3: sla
- tgt_alpha3: eng
- short_pair: sla-en
- chrF2_score: 0.6829999999999999
- bleu: 53.2
- brevity_penalty: 0.9740000000000001
- ref_len: 70897.0
- src_name: Slavic languages
- tgt_name: English
- train_date: 2020-08-01
- src_alpha2: sla
- tgt_alpha2: en
- prefer_old: False
- long_pair: sla-eng
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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0.0... |
DoyyingFace/bert-asian-hate-tweets-asian-unclean-with-clean-valid | [
"pytorch",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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}
} | 33 | null | ---
language:
- be
- hr
- mk
- cs
- ru
- pl
- bg
- uk
- sl
- sla
tags:
- translation
license: apache-2.0
---
### sla-sla
* source group: Slavic languages
* target group: Slavic languages
* OPUS readme: [sla-sla](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sla-sla/README.md)
* model: transformer
* source language(s): bel bel_Latn bos_Latn bul bul_Latn ces dsb hrv hsb mkd orv_Cyrl pol rus slv srp_Cyrl srp_Latn ukr
* target language(s): bel bel_Latn bos_Latn bul bul_Latn ces dsb hrv hsb mkd orv_Cyrl pol rus slv srp_Cyrl srp_Latn ukr
* 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](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-sla/opus-2020-07-27.zip)
* test set translations: [opus-2020-07-27.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-sla/opus-2020-07-27.test.txt)
* test set scores: [opus-2020-07-27.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/sla-sla/opus-2020-07-27.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| newstest2012-cesrus.ces.rus | 15.9 | 0.437 |
| newstest2012-rusces.rus.ces | 13.6 | 0.403 |
| newstest2013-cesrus.ces.rus | 19.8 | 0.473 |
| newstest2013-rusces.rus.ces | 17.9 | 0.449 |
| Tatoeba-test.bel-bul.bel.bul | 100.0 | 1.000 |
| Tatoeba-test.bel-ces.bel.ces | 33.5 | 0.630 |
| Tatoeba-test.bel-hbs.bel.hbs | 45.4 | 0.644 |
| Tatoeba-test.bel-mkd.bel.mkd | 19.3 | 0.531 |
| Tatoeba-test.bel-pol.bel.pol | 46.9 | 0.681 |
| Tatoeba-test.bel-rus.bel.rus | 58.5 | 0.767 |
| Tatoeba-test.bel-ukr.bel.ukr | 55.1 | 0.743 |
| Tatoeba-test.bul-bel.bul.bel | 10.7 | 0.423 |
| Tatoeba-test.bul-ces.bul.ces | 36.9 | 0.585 |
| Tatoeba-test.bul-hbs.bul.hbs | 53.7 | 0.807 |
| Tatoeba-test.bul-mkd.bul.mkd | 31.9 | 0.715 |
| Tatoeba-test.bul-pol.bul.pol | 38.6 | 0.607 |
| Tatoeba-test.bul-rus.bul.rus | 44.8 | 0.655 |
| Tatoeba-test.bul-ukr.bul.ukr | 49.9 | 0.691 |
| Tatoeba-test.ces-bel.ces.bel | 30.9 | 0.585 |
| Tatoeba-test.ces-bul.ces.bul | 75.8 | 0.859 |
| Tatoeba-test.ces-hbs.ces.hbs | 50.0 | 0.661 |
| Tatoeba-test.ces-hsb.ces.hsb | 7.9 | 0.246 |
| Tatoeba-test.ces-mkd.ces.mkd | 24.6 | 0.569 |
| Tatoeba-test.ces-pol.ces.pol | 44.3 | 0.652 |
| Tatoeba-test.ces-rus.ces.rus | 50.8 | 0.690 |
| Tatoeba-test.ces-slv.ces.slv | 4.9 | 0.240 |
| Tatoeba-test.ces-ukr.ces.ukr | 52.9 | 0.687 |
| Tatoeba-test.dsb-pol.dsb.pol | 16.3 | 0.367 |
| Tatoeba-test.dsb-rus.dsb.rus | 12.7 | 0.245 |
| Tatoeba-test.hbs-bel.hbs.bel | 32.9 | 0.531 |
| Tatoeba-test.hbs-bul.hbs.bul | 100.0 | 1.000 |
| Tatoeba-test.hbs-ces.hbs.ces | 40.3 | 0.626 |
| Tatoeba-test.hbs-mkd.hbs.mkd | 19.3 | 0.535 |
| Tatoeba-test.hbs-pol.hbs.pol | 45.0 | 0.650 |
| Tatoeba-test.hbs-rus.hbs.rus | 53.5 | 0.709 |
| Tatoeba-test.hbs-ukr.hbs.ukr | 50.7 | 0.684 |
| Tatoeba-test.hsb-ces.hsb.ces | 17.9 | 0.366 |
| Tatoeba-test.mkd-bel.mkd.bel | 23.6 | 0.548 |
| Tatoeba-test.mkd-bul.mkd.bul | 54.2 | 0.833 |
| Tatoeba-test.mkd-ces.mkd.ces | 12.1 | 0.371 |
| Tatoeba-test.mkd-hbs.mkd.hbs | 19.3 | 0.577 |
| Tatoeba-test.mkd-pol.mkd.pol | 53.7 | 0.833 |
| Tatoeba-test.mkd-rus.mkd.rus | 34.2 | 0.745 |
| Tatoeba-test.mkd-ukr.mkd.ukr | 42.7 | 0.708 |
| Tatoeba-test.multi.multi | 48.5 | 0.672 |
| Tatoeba-test.orv-pol.orv.pol | 10.1 | 0.355 |
| Tatoeba-test.orv-rus.orv.rus | 10.6 | 0.275 |
| Tatoeba-test.orv-ukr.orv.ukr | 7.5 | 0.230 |
| Tatoeba-test.pol-bel.pol.bel | 29.8 | 0.533 |
| Tatoeba-test.pol-bul.pol.bul | 36.8 | 0.578 |
| Tatoeba-test.pol-ces.pol.ces | 43.6 | 0.626 |
| Tatoeba-test.pol-dsb.pol.dsb | 0.9 | 0.097 |
| Tatoeba-test.pol-hbs.pol.hbs | 42.4 | 0.644 |
| Tatoeba-test.pol-mkd.pol.mkd | 19.3 | 0.535 |
| Tatoeba-test.pol-orv.pol.orv | 0.7 | 0.109 |
| Tatoeba-test.pol-rus.pol.rus | 49.6 | 0.680 |
| Tatoeba-test.pol-slv.pol.slv | 7.3 | 0.262 |
| Tatoeba-test.pol-ukr.pol.ukr | 46.8 | 0.664 |
| Tatoeba-test.rus-bel.rus.bel | 34.4 | 0.577 |
| Tatoeba-test.rus-bul.rus.bul | 45.5 | 0.657 |
| Tatoeba-test.rus-ces.rus.ces | 48.0 | 0.659 |
| Tatoeba-test.rus-dsb.rus.dsb | 10.7 | 0.029 |
| Tatoeba-test.rus-hbs.rus.hbs | 44.6 | 0.655 |
| Tatoeba-test.rus-mkd.rus.mkd | 34.9 | 0.617 |
| Tatoeba-test.rus-orv.rus.orv | 0.1 | 0.073 |
| Tatoeba-test.rus-pol.rus.pol | 45.2 | 0.659 |
| Tatoeba-test.rus-slv.rus.slv | 30.4 | 0.476 |
| Tatoeba-test.rus-ukr.rus.ukr | 57.6 | 0.751 |
| Tatoeba-test.slv-ces.slv.ces | 42.5 | 0.604 |
| Tatoeba-test.slv-pol.slv.pol | 39.6 | 0.601 |
| Tatoeba-test.slv-rus.slv.rus | 47.2 | 0.638 |
| Tatoeba-test.slv-ukr.slv.ukr | 36.4 | 0.549 |
| Tatoeba-test.ukr-bel.ukr.bel | 36.9 | 0.597 |
| Tatoeba-test.ukr-bul.ukr.bul | 56.4 | 0.733 |
| Tatoeba-test.ukr-ces.ukr.ces | 52.1 | 0.686 |
| Tatoeba-test.ukr-hbs.ukr.hbs | 47.1 | 0.670 |
| Tatoeba-test.ukr-mkd.ukr.mkd | 20.8 | 0.548 |
| Tatoeba-test.ukr-orv.ukr.orv | 0.2 | 0.058 |
| Tatoeba-test.ukr-pol.ukr.pol | 50.1 | 0.695 |
| Tatoeba-test.ukr-rus.ukr.rus | 63.9 | 0.790 |
| Tatoeba-test.ukr-slv.ukr.slv | 14.5 | 0.288 |
### System Info:
- hf_name: sla-sla
- source_languages: sla
- target_languages: sla
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/sla-sla/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['be', 'hr', 'mk', 'cs', 'ru', 'pl', 'bg', 'uk', 'sl', 'sla']
- src_constituents: {'bel', 'hrv', 'orv_Cyrl', 'mkd', 'bel_Latn', 'srp_Latn', 'bul_Latn', 'ces', 'bos_Latn', 'csb_Latn', 'dsb', 'hsb', 'rus', 'srp_Cyrl', 'pol', 'rue', 'bul', 'ukr', 'slv'}
- tgt_constituents: {'bel', 'hrv', 'orv_Cyrl', 'mkd', 'bel_Latn', 'srp_Latn', 'bul_Latn', 'ces', 'bos_Latn', 'csb_Latn', 'dsb', 'hsb', 'rus', 'srp_Cyrl', 'pol', 'rue', 'bul', 'ukr', 'slv'}
- src_multilingual: True
- tgt_multilingual: True
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/sla-sla/opus-2020-07-27.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/sla-sla/opus-2020-07-27.test.txt
- src_alpha3: sla
- tgt_alpha3: sla
- short_pair: sla-sla
- chrF2_score: 0.672
- bleu: 48.5
- brevity_penalty: 1.0
- ref_len: 59320.0
- src_name: Slavic languages
- tgt_name: Slavic languages
- train_date: 2020-07-27
- src_alpha2: sla
- tgt_alpha2: sla
- prefer_old: False
- long_pair: sla-sla
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
-0.005482249893248081,
-0.030721422284841537,
-0.0030798017978668213,
0.04654575511813164,
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-0.008681686595082283,
0.006348902825266123,
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0.05628453567624092,
0.017983242869377136,
-0.02506597340106964,
-0.006666518282145262,
0... |
albert-base-v1 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"AlbertForMaskedLM"
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}
} | 38,156 | 2020-04-29T13:47:47Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sn-es
* source languages: sn
* target languages: es
* OPUS readme: [sn-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sn-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sn-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sn.es | 32.5 | 0.509 |
| [
-0.019437288865447044,
-0.029604772105813026,
-0.0033538707066327333,
0.038362350314855576,
0.02702578343451023,
0.027485273778438568,
-0.006354611366987228,
0.0007086475379765034,
-0.04980509355664253,
0.051026877015829086,
0.002894024830311537,
-0.006697879638522863,
0.0025570436846464872,... |
albert-base-v2 | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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},
"translation_en_to_fr": {
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"translation_en_to_ro": {
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}
}
} | 4,785,283 | 2020-04-29T13:48:05Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sn-fr
* source languages: sn
* target languages: fr
* OPUS readme: [sn-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sn-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sn-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sn.fr | 30.8 | 0.491 |
| [
-0.016745252534747124,
-0.030812231823801994,
-0.011192369274795055,
0.037703875452280045,
0.025192247703671455,
0.024695731699466705,
-0.008829130791127682,
-0.004786650650203228,
-0.050645049661397934,
0.05189144238829613,
0.010130190290510654,
-0.006833007093518972,
-0.002661163453012705,... |
albert-large-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 687 | 2020-04-29T13:48:22Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sn-sv
* source languages: sn
* target languages: sv
* OPUS readme: [sn-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sn-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sn-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sn-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sn.sv | 35.6 | 0.536 |
| [
-0.01965818740427494,
-0.02858627215027809,
-0.002351032104343176,
0.040695589035749435,
0.027561519294977188,
0.023395096883177757,
-0.0020725983195006847,
0.00035668289638124406,
-0.05180903896689415,
0.05381147190928459,
0.01028973888605833,
-0.008175088092684746,
0.012552506290376186,
... |
albert-large-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 26,792 | 2020-04-29T13:48:40Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sq-en
* source languages: sq
* target languages: en
* OPUS readme: [sq-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sq-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sq-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-en/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba.sq.en | 58.4 | 0.732 |
| [
-0.0123189901933074,
-0.032945338636636734,
0.0034963444340974092,
0.043169036507606506,
0.03391813859343529,
0.016420554369688034,
-0.0030064054299145937,
0.0017531472258269787,
-0.05040416121482849,
0.04382745921611786,
-0.004960019141435623,
-0.014375568367540836,
0.009051389992237091,
... |
albert-xlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
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},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 341 | 2020-04-29T13:48:58Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sq-es
* source languages: sq
* target languages: es
* OPUS readme: [sq-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sq-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sq-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| GlobalVoices.sq.es | 23.9 | 0.510 |
| [
-0.011864805594086647,
-0.029092662036418915,
0.00047838463797234,
0.04478392004966736,
0.03472163528203964,
0.015415273606777191,
-0.00310715870000422,
0.0035180733539164066,
-0.04775425046682358,
0.047703780233860016,
-0.007978670299053192,
-0.015946077182888985,
0.014843763783574104,
0.... |
albert-xlarge-v2 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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},
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},
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 2,973 | 2020-04-29T13:49:13Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sq-sv
* source languages: sq
* target languages: sv
* OPUS readme: [sq-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sq-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sq-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sq-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sq.sv | 36.2 | 0.559 |
| [
-0.0136776277795434,
-0.029130108654499054,
0.004272020887583494,
0.04329564422369003,
0.03262180835008621,
0.014956978149712086,
-0.00047442628419958055,
0.0007559327059425414,
-0.049747299402952194,
0.04665979743003845,
-0.0010891035199165344,
-0.013475832529366016,
0.01512855477631092,
... |
albert-xxlarge-v1 | [
"pytorch",
"tf",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
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},
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},
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 7,091 | 2020-04-29T13:49:26Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-srn-en
* source languages: srn
* target languages: en
* OPUS readme: [srn-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/srn-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/srn-en/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-en/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-en/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.srn.en | 40.3 | 0.555 |
| [
-0.01678062044084072,
-0.02817518636584282,
-0.00831504538655281,
0.039029307663440704,
0.02640351839363575,
0.024991780519485474,
-0.0013055222807452083,
-0.003804973093792796,
-0.046280574053525925,
0.05312807112932205,
0.01500481367111206,
-0.01460307464003563,
0.006808919366449118,
0.0... |
albert-xxlarge-v2 | [
"pytorch",
"tf",
"safetensors",
"albert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1909.11942",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"AlbertForMaskedLM"
],
"model_type": "albert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 42,640 | 2020-04-29T13:49:41Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-srn-es
* source languages: srn
* target languages: es
* OPUS readme: [srn-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/srn-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/srn-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.srn.es | 30.4 | 0.481 |
| [
-0.01607321761548519,
-0.030935361981391907,
-0.005938918795436621,
0.039795223623514175,
0.027675317600369453,
0.024414317682385445,
-0.0038594743236899376,
-0.0030547224450856447,
-0.04406289383769035,
0.05169985070824623,
0.009667256847023964,
-0.013435475528240204,
0.004993862006813288,
... |
bert-base-cased-finetuned-mrpc | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 11,644 | 2020-04-29T13:49:55Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-srn-fr
* source languages: srn
* target languages: fr
* OPUS readme: [srn-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/srn-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/srn-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.srn.fr | 28.9 | 0.462 |
| [
-0.0156712606549263,
-0.03096758760511875,
-0.012708934023976326,
0.037348825484514236,
0.02529464103281498,
0.02330772951245308,
-0.006021237466484308,
-0.00763948867097497,
-0.04595862701535225,
0.051659539341926575,
0.011916136369109154,
-0.010820581577718258,
-0.0007165659917518497,
0.... |
bert-base-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"min_length": null,
"no_repeat_ngram_size": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 8,621,271 | 2020-04-29T13:50:10Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-srn-sv
* source languages: srn
* target languages: sv
* OPUS readme: [srn-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/srn-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/srn-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/srn-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.srn.sv | 32.2 | 0.500 |
| [
-0.016952235251665115,
-0.02961435541510582,
-0.005281987600028515,
0.041033487766981125,
0.028488440439105034,
0.02094542607665062,
-0.0016228151507675648,
-0.0040933964774012566,
-0.04729820042848587,
0.055497728288173676,
0.014152743853628635,
-0.014031345956027508,
0.009527132846415043,
... |
bert-base-chinese | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"zh",
"arxiv:1810.04805",
"transformers",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 3,377,486 | 2020-04-29T13:50:24Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ss-en
* source languages: ss
* target languages: en
* OPUS readme: [ss-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ss-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ss-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ss-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ss-en/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ss.en | 30.9 | 0.478 |
| [
-0.020026404410600662,
-0.02733660489320755,
-0.010955766774713993,
0.0412980280816555,
0.02768012322485447,
0.027362743392586708,
-0.0028349673375487328,
0.0012133106356486678,
-0.05290226265788078,
0.0566101111471653,
0.012437286786735058,
-0.012065742164850235,
0.006458846852183342,
0.0... |
bert-base-german-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"exbert",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
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"prefix": null
}
}
} | 175,983 | 2020-04-29T13:50:39Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-ssp-es
* source languages: ssp
* target languages: es
* OPUS readme: [ssp-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/ssp-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/ssp-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/ssp-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/ssp-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.ssp.es | 89.7 | 0.930 |
| [
-0.01423339918255806,
-0.030141785740852356,
-0.006688883062452078,
0.03821958228945732,
0.0301545187830925,
0.022878287360072136,
-0.0005359693896025419,
0.0026032801251858473,
-0.050247929990291595,
0.056084342300891876,
0.007125211879611015,
-0.013940834440290928,
0.005498244892805815,
... |
bert-base-german-dbmdz-cased | [
"pytorch",
"jax",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1,814 | 2020-04-29T13:50:57Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-st-en
* source languages: st
* target languages: en
* OPUS readme: [st-en](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/st-en/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/st-en/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-en/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-en/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.st.en | 45.7 | 0.609 |
| [
-0.01418577041476965,
-0.028001317754387856,
-0.009834697470068932,
0.038807354867458344,
0.029773162677884102,
0.026385625824332237,
0.00014269101666286588,
-0.002843619091436267,
-0.051135577261447906,
0.056341007351875305,
0.011117905378341675,
-0.01403027679771185,
0.006604624446481466,
... |
bert-base-german-dbmdz-uncased | [
"pytorch",
"jax",
"safetensors",
"bert",
"fill-mask",
"de",
"transformers",
"license:mit",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"max_length": null,
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 68,305 | 2020-04-29T13:51:13Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-st-es
* source languages: st
* target languages: es
* OPUS readme: [st-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/st-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/st-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.st.es | 31.3 | 0.499 |
| [
-0.011348684318363667,
-0.028394561260938644,
-0.007157316897064447,
0.03741636872291565,
0.030699780210852623,
0.025641530752182007,
-0.0023479911033064127,
-0.0009783144341781735,
-0.05005502700805664,
0.05543157458305359,
0.006216011010110378,
-0.014665666967630386,
0.0061501748859882355,... |
bert-base-multilingual-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
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"prefix": null
}
}
} | 4,749,504 | 2020-04-29T13:51:34Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-st-fi
* source languages: st
* target languages: fi
* OPUS readme: [st-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/st-fi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/st-fi/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-fi/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-fi/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.st.fi | 28.8 | 0.520 |
| [
-0.015164615586400032,
-0.03371502831578255,
-0.0013795499689877033,
0.03817916288971901,
0.025787511840462685,
0.028309712186455727,
0.0015914889518171549,
-0.004141359589993954,
-0.055821117013692856,
0.05089295282959938,
0.013103986158967018,
-0.01237387303262949,
0.00932731106877327,
0... |
bert-base-multilingual-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
"et",
... | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 328,585 | 2020-04-29T13:51:49Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-st-fr
* source languages: st
* target languages: fr
* OPUS readme: [st-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/st-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/st-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/st-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.st.fr | 30.7 | 0.490 |
| [
-0.012104650028049946,
-0.029841912910342216,
-0.013181304559111595,
0.036750685423612595,
0.02848145365715027,
0.02428114414215088,
-0.0052024247124791145,
-0.00672726659104228,
-0.04832877218723297,
0.054018132388591766,
0.010013185441493988,
-0.011667458340525627,
-0.0007053219014778733,
... |
bert-large-cased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8,214 | 2020-05-12T21:40:41Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-NORWAY
* source languages: sv
* target languages: nb_NO,nb,nn_NO,nn,nog,no_nb,no
* OPUS readme: [sv-nb_NO+nb+nn_NO+nn+nog+no_nb+no](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-nb_NO+nb+nn_NO+nn+nog+no_nb+no/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-nb_NO+nb+nn_NO+nn+nog+no_nb+no/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-nb_NO+nb+nn_NO+nn+nog+no_nb+no/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-nb_NO+nb+nn_NO+nn+nog+no_nb+no/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.no | 39.3 | 0.590 |
| [
-0.012416972778737545,
-0.023729978129267693,
0.001046065939590335,
0.04102308675646782,
0.03290456533432007,
0.011741122230887413,
-0.009010376408696175,
-0.010902064852416515,
-0.050083454698324203,
0.06332484632730484,
0.008010968565940857,
-0.015318368561565876,
0.005319069605320692,
0... |
bert-large-cased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 2,316 | 2020-05-12T21:40:32Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-ZH
* source languages: sv
* target languages: cmn,cn,yue,ze_zh,zh_cn,zh_CN,zh_HK,zh_tw,zh_TW,zh_yue,zhs,zht,zh
* OPUS readme: [sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* a sentence initial language token is required in the form of `>>id<<` (id = valid target language ID)
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-cmn+cn+yue+ze_zh+zh_cn+zh_CN+zh_HK+zh_tw+zh_TW+zh_yue+zhs+zht+zh/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| bible-uedin.sv.zh | 24.2 | 0.342 |
| [
-0.020853424444794655,
-0.03225753456354141,
-0.0009497338905930519,
0.049584999680519104,
0.021560141816735268,
0.017497222870588303,
-0.0008081396226771176,
-0.009995290078222752,
-0.04078751802444458,
0.053128067404031754,
0.00943820271641016,
-0.00923441257327795,
0.016543736681342125,
... |
bert-large-cased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 388,769 | 2020-04-29T13:52:29Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-af
* source languages: sv
* target languages: af
* OPUS readme: [sv-af](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-af/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-af/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-af/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-af/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.af | 44.4 | 0.623 |
| [
-0.01646343059837818,
-0.032250016927719116,
-0.001705040456727147,
0.040604546666145325,
0.028176208958029747,
0.022241581231355667,
0.0016488838009536266,
-0.0018168243113905191,
-0.05021768808364868,
0.05401906371116638,
0.009408470243215561,
-0.007126869633793831,
0.012568837963044643,
... |
bert-large-uncased-whole-word-masking-finetuned-squad | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"question-answering",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"BertForQuestionAnswering"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
"text-generation": {
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},
"translation_en_to_de": {
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},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 480,510 | 2020-04-29T13:52:41Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-ase
* source languages: sv
* target languages: ase
* OPUS readme: [sv-ase](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-ase/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-ase/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ase/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ase/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.ase | 40.5 | 0.572 |
| [
-0.013531362637877464,
-0.03155975416302681,
0.007620497141033411,
0.031283918768167496,
0.038196664303541183,
0.013799442909657955,
0.0029963082633912563,
0.00021108833607286215,
-0.05424084514379501,
0.053743135184049606,
0.004925841465592384,
-0.004870586097240448,
0.01055233832448721,
... |
bert-large-uncased-whole-word-masking | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
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},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 76,685 | 2020-04-29T13:53:03Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-bcl
* source languages: sv
* target languages: bcl
* OPUS readme: [sv-bcl](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-bcl/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-bcl/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-bcl/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-bcl/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.bcl | 39.5 | 0.607 |
| [
-0.016759686172008514,
-0.02087964490056038,
0.0005199289880692959,
0.04109744727611542,
0.024054955691099167,
0.02088460512459278,
0.006316446699202061,
0.0031911979895085096,
-0.04339348524808884,
0.049122776836156845,
0.010579443536698818,
-0.016768217086791992,
0.011608085595071316,
0.... |
bert-large-uncased | [
"pytorch",
"tf",
"jax",
"safetensors",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"transformers",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
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"prefix": null
},
"text-generation": {
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"max_length": null
},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 1,058,496 | 2020-04-29T13:53:28Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-bem
* source languages: sv
* target languages: bem
* OPUS readme: [sv-bem](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-bem/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-bem/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-bem/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-bem/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.bem | 22.3 | 0.473 |
| [
-0.018502937629818916,
-0.032558634877204895,
0.0023784381337463856,
0.04048726707696915,
0.030712643638253212,
0.02450881339609623,
0.005873582791537046,
0.0006199518102221191,
-0.0434538833796978,
0.05667421594262123,
0.006610406097024679,
-0.015457520261406898,
0.010597657412290573,
0.0... |
ctrl | [
"pytorch",
"tf",
"ctrl",
"en",
"arxiv:1909.05858",
"arxiv:1910.09700",
"transformers",
"license:bsd-3-clause",
"has_space"
] | null | {
"architectures": null,
"model_type": "ctrl",
"task_specific_params": {
"conversational": {
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},
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},
"text-generation": {
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},
"translation_en_to_de": {
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"prefix": null
},
"translation_en_to_fr": {
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"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 17,007 | 2020-04-29T13:54:02Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-bi
* source languages: sv
* target languages: bi
* OPUS readme: [sv-bi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-bi/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-bi/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-bi/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-bi/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.bi | 30.8 | 0.496 |
| [
-0.019490348175168037,
-0.025495529174804688,
0.003333507338538766,
0.03956287354230881,
0.02155712991952896,
0.02218366228044033,
0.0031248251907527447,
0.0005159344291314483,
-0.044382959604263306,
0.050417233258485794,
0.006737901829183102,
-0.0176641047000885,
0.015765083953738213,
0.0... |
distilbert-base-cased-distilled-squad | [
"pytorch",
"tf",
"rust",
"safetensors",
"openvino",
"distilbert",
"question-answering",
"en",
"dataset:squad",
"arxiv:1910.01108",
"arxiv:1910.09700",
"transformers",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"has_space"
] | question-answering | {
"architectures": [
"DistilBertForQuestionAnswering"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 257,745 | 2020-04-29T13:54:29Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-bzs
* source languages: sv
* target languages: bzs
* OPUS readme: [sv-bzs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-bzs/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-bzs/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-bzs/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-bzs/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.bzs | 29.4 | 0.484 |
| [
-0.016843870282173157,
-0.030883632600307465,
-0.003053459106013179,
0.04123523458838463,
0.02472548745572567,
0.023578522726893425,
0.002119176322594285,
0.0017430539010092616,
-0.04436877742409706,
0.052066851407289505,
0.014401664026081562,
-0.014996137470006943,
0.01286276988685131,
0.... |
distilbert-base-cased | [
"pytorch",
"tf",
"onnx",
"distilbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"license:apache-2.0",
"has_space"
] | null | {
"architectures": null,
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 574,859 | 2020-04-29T13:54:41Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-ceb
* source languages: sv
* target languages: ceb
* OPUS readme: [sv-ceb](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-ceb/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-ceb/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ceb/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ceb/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.ceb | 39.2 | 0.609 |
| [
-0.014031704515218735,
-0.025438033044338226,
-0.004184320569038391,
0.0348963625729084,
0.03355003520846367,
0.020555684342980385,
-0.0024367200676351786,
-0.001073420513421297,
-0.05089770257472992,
0.054659634828567505,
0.008214105851948261,
-0.011920290999114513,
0.010117584839463234,
... |
distilbert-base-multilingual-cased | [
"pytorch",
"tf",
"onnx",
"safetensors",
"distilbert",
"fill-mask",
"multilingual",
"af",
"sq",
"ar",
"an",
"hy",
"ast",
"az",
"ba",
"eu",
"bar",
"be",
"bn",
"inc",
"bs",
"br",
"bg",
"my",
"ca",
"ceb",
"ce",
"zh",
"cv",
"hr",
"cs",
"da",
"nl",
"en",
... | fill-mask | {
"architectures": [
"DistilBertForMaskedLM"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 8,339,633 | 2020-04-29T13:55:29Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-crs
* source languages: sv
* target languages: crs
* OPUS readme: [sv-crs](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-crs/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-crs/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-crs/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-crs/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.crs | 32.4 | 0.512 |
| [
-0.0169678945094347,
-0.02684083953499794,
-0.00031479718745686114,
0.03856533765792847,
0.0310565996915102,
0.017772413790225983,
-0.00047410899423994124,
-0.00009213864541379735,
-0.04308989271521568,
0.05452718585729599,
0.012138043530285358,
-0.013340798206627369,
0.013370456174015999,
... |
distilbert-base-uncased-finetuned-sst-2-english | [
"pytorch",
"tf",
"rust",
"safetensors",
"distilbert",
"text-classification",
"en",
"dataset:sst2",
"dataset:glue",
"arxiv:1910.01108",
"doi:10.57967/hf/0181",
"transformers",
"license:apache-2.0",
"model-index",
"has_space"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
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"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 3,060,704 | 2020-04-29T13:56:00Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-ee
* source languages: sv
* target languages: ee
* OPUS readme: [sv-ee](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-ee/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-ee/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ee/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ee/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.ee | 29.7 | 0.508 |
| [
-0.015157999470829964,
-0.02489834651350975,
0.0022204043343663216,
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0.04... |
gpt2 | [
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"tf",
"jax",
"tflite",
"rust",
"safetensors",
"gpt2",
"text-generation",
"en",
"doi:10.57967/hf/0039",
"transformers",
"exbert",
"license:mit",
"has_space"
] | text-generation | {
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}
} | 21,488,226 | 2020-04-29T13:57:44Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-fi
* source languages: sv
* target languages: fi
* OPUS readme: [sv-fi](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-fi/README.md)
* dataset: opus+bt
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus+bt-2020-04-07.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-fi/opus+bt-2020-04-07.zip)
* test set translations: [opus+bt-2020-04-07.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-fi/opus+bt-2020-04-07.test.txt)
* test set scores: [opus+bt-2020-04-07.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-fi/opus+bt-2020-04-07.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| fiskmo_testset.sv.fi | 26.9 | 0.623 |
| Tatoeba.sv.fi | 45.2 | 0.678 |
| [
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0.05084419250488281,
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openai-gpt | [
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"tf",
"rust",
"safetensors",
"openai-gpt",
"text-generation",
"en",
"arxiv:1705.11168",
"arxiv:1803.02324",
"arxiv:1910.09700",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
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}
} | 65,432 | 2020-04-29T13:57:57Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-fj
* source languages: sv
* target languages: fj
* OPUS readme: [sv-fj](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-fj/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-21.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-fj/opus-2020-01-21.zip)
* test set translations: [opus-2020-01-21.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-fj/opus-2020-01-21.test.txt)
* test set scores: [opus-2020-01-21.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-fj/opus-2020-01-21.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.fj | 27.8 | 0.504 |
| [
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0.008812790736556053,
0.0... |
xlnet-large-cased | [
"pytorch",
"tf",
"xlnet",
"text-generation",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1906.08237",
"transformers",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
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],
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}
}
} | 16,389 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-mos
* source languages: sv
* target languages: mos
* OPUS readme: [sv-mos](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-mos/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-mos/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-mos/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-mos/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.mos | 22.4 | 0.379 |
| [
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0.016527920961380005,
... |
123abhiALFLKFO/distilbert-base-uncased-finetuned-cola | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | text-classification | {
"architectures": [
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}
} | 57 | 2020-04-29T14:10:41Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-sv-ro
* source languages: sv
* target languages: ro
* OPUS readme: [sv-ro](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/sv-ro/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/sv-ro/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ro/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/sv-ro/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.sv.ro | 29.5 | 0.510 |
| [
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-0.01978135295212269,
0.008341281674802303,
0.0... |
AAli/distilbert-base-uncased-finetuned-cola | [] | null | {
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}
} | 0 | null | ---
language:
- tl
- de
tags:
- translation
license: apache-2.0
---
### tgl-deu
* source group: Tagalog
* target group: German
* OPUS readme: [tgl-deu](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-deu/README.md)
* model: transformer-align
* source language(s): tgl_Latn
* target language(s): deu
* model: transformer-align
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-06-17.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-deu/opus-2020-06-17.zip)
* test set translations: [opus-2020-06-17.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-deu/opus-2020-06-17.test.txt)
* test set scores: [opus-2020-06-17.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-deu/opus-2020-06-17.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.tgl.deu | 22.7 | 0.473 |
### System Info:
- hf_name: tgl-deu
- source_languages: tgl
- target_languages: deu
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/tgl-deu/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['tl', 'de']
- src_constituents: {'tgl_Latn'}
- tgt_constituents: {'deu'}
- src_multilingual: False
- tgt_multilingual: False
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-deu/opus-2020-06-17.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/tgl-deu/opus-2020-06-17.test.txt
- src_alpha3: tgl
- tgt_alpha3: deu
- short_pair: tl-de
- chrF2_score: 0.473
- bleu: 22.7
- brevity_penalty: 0.9690000000000001
- ref_len: 2453.0
- src_name: Tagalog
- tgt_name: German
- train_date: 2020-06-17
- src_alpha2: tl
- tgt_alpha2: de
- prefer_old: False
- long_pair: tgl-deu
- helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535
- transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b
- port_machine: brutasse
- port_time: 2020-08-21-14:41 | [
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AG/pretraining | [
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} | 2 | 2020-05-06T03:08:09Z | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-tn-fr
* source languages: tn
* target languages: fr
* OPUS readme: [tn-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tn-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/tn-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tn-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tn-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.tn.fr | 29.0 | 0.474 |
| [
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0.05041661113500595,
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... |
ASCCCCCCCC/bert-base-chinese-finetuned-amazon_zh_20000 | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"transformers",
"generated_from_trainer"
] | text-classification | {
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}
} | 43 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-tzo-es
* source languages: tzo
* target languages: es
* OPUS readme: [tzo-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tzo-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/tzo-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tzo-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tzo-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.tzo.es | 20.8 | 0.381 |
| [
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0.012306762859225273,
-0.020037641748785973,
0.00823224149644374... |
AbidineVall/my-new-shiny-tokenizer | [] | null | {
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}
} | 0 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-yo-es
* source languages: yo
* target languages: es
* OPUS readme: [yo-es](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yo-es/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/yo-es/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-es/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-es/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.yo.es | 22.0 | 0.393 |
| [
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Abirate/code_net_new_tokenizer_from_WPiece_bert_algorithm | [] | null | {
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} | 0 | null | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-yo-fr
* source languages: yo
* target languages: fr
* OPUS readme: [yo-fr](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/yo-fr/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/yo-fr/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-fr/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/yo-fr/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.yo.fr | 24.1 | 0.408 |
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AdapterHub/bert-base-uncased-pf-mit_movie_trivia | [
"bert",
"en",
"arxiv:2104.08247",
"adapter-transformers",
"token-classification",
"adapterhub:ner/mit_movie_trivia"
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} | 11 | null | ---
language:
- it
- he
tags:
- translation
license: apache-2.0
---
### it-he
* source group: Italian
* target group: Hebrew
* OPUS readme: [ita-heb](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-heb/README.md)
* model: transformer
* source language(s): ita
* target language(s): heb
* model: transformer
* pre-processing: normalization + SentencePiece (spm32k,spm32k)
* download original weights: [opus-2020-12-10.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.zip)
* test set translations: [opus-2020-12-10.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.test.txt)
* test set scores: [opus-2020-12-10.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| Tatoeba-test.ita.heb | 38.5 | 0.593 |
### System Info:
- hf_name: it-he
- source_languages: ita
- target_languages: heb
- opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/ita-heb/README.md
- original_repo: Tatoeba-Challenge
- tags: ['translation']
- languages: ['it', 'he']
- src_constituents: ('Italian', {'ita'})
- tgt_constituents: ('Hebrew', {'heb'})
- src_multilingual: False
- tgt_multilingual: False
- long_pair: ita-heb
- prepro: normalization + SentencePiece (spm32k,spm32k)
- url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.zip
- url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/ita-heb/opus-2020-12-10.test.txt
- src_alpha3: ita
- tgt_alpha3: heb
- chrF2_score: 0.593
- bleu: 38.5
- brevity_penalty: 0.985
- ref_len: 9796.0
- src_name: Italian
- tgt_name: Hebrew
- train_date: 2020-12-10 00:00:00
- src_alpha2: it
- tgt_alpha2: he
- prefer_old: False
- short_pair: it-he
- helsinki_git_sha: b317f78a3ec8a556a481b6a53dc70dc11769ca96
- transformers_git_sha: 1310e1a758edc8e89ec363db76863c771fbeb1de
- port_machine: LM0-400-22516.local
- port_time: 2020-12-11-16:02 | [
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} | 36 | null | ## ParsBERT: Transformer-based Model for Persian Language Understanding
ParsBERT is a monolingual language model based on Google’s BERT architecture with the same configurations as BERT-Base.
Paper presenting ParsBERT: [arXiv:2005.12515](https://arxiv.org/abs/2005.12515)
All the models (downstream tasks) are uncased and trained with whole word masking. (coming soon stay tuned)
---
## Introduction
This model is pre-trained on a large Persian corpus with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 2M documents. A large subset of this corpus was crawled manually.
As a part of ParsBERT methodology, an extensive pre-processing combining POS tagging and WordPiece segmentation was carried out to bring the corpus into a proper format. This process produces more than 40M true sentences.
## Evaluation
ParsBERT is evaluated on three NLP downstream tasks: Sentiment Analysis (SA), Text Classification, and Named Entity Recognition (NER). For this matter and due to insufficient resources, two large datasets for SA and two for text classification were manually composed, which are available for public use and benchmarking. ParsBERT outperformed all other language models, including multilingual BERT and other hybrid deep learning models for all tasks, improving the state-of-the-art performance in Persian language modeling.
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
### Sentiment Analysis (SA) task
| Dataset | ParsBERT | mBERT | DeepSentiPers |
|:--------------------------:|:---------:|:-----:|:-------------:|
| Digikala User Comments | 81.74* | 80.74 | - |
| SnappFood User Comments | 88.12* | 87.87 | - |
| SentiPers (Multi Class) | 71.11* | - | 69.33 |
| SentiPers (Binary Class) | 92.13* | - | 91.98 |
### Text Classification (TC) task
| Dataset | ParsBERT | mBERT |
|:-----------------:|:--------:|:-----:|
| Digikala Magazine | 93.59* | 90.72 |
| Persian News | 97.19* | 95.79 |
### Named Entity Recognition (NER) task
| Dataset | ParsBERT | mBERT | MorphoBERT | Beheshti-NER | LSTM-CRF | Rule-Based CRF | BiLSTM-CRF |
|:-------:|:--------:|:--------:|:----------:|:--------------:|:----------:|:----------------:|:------------:|
| PEYMA | 93.10* | 86.64 | - | 90.59 | - | 84.00 | - |
| ARMAN | 98.79* | 95.89 | 89.9 | 84.03 | 86.55 | - | 77.45 |
**If you tested ParsBERT on a public dataset and you want to add your results to the table above, open a pull request or contact us. Also make sure to have your code available online so we can add it as a reference**
## How to use
### TensorFlow 2.0
```python
from transformers import AutoConfig, AutoTokenizer, TFAutoModel
config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
text = "ما در هوشواره معتقدیم با انتقال صحیح دانش و آگاهی، همه افراد میتوانند از ابزارهای هوشمند استفاده کنند. شعار ما هوش مصنوعی برای همه است."
tokenizer.tokenize(text)
>>> ['ما', 'در', 'هوش', '##واره', 'معتقدیم', 'با', 'انتقال', 'صحیح', 'دانش', 'و', 'اگاهی', '،', 'همه', 'افراد', 'میتوانند', 'از', 'ابزارهای', 'هوشمند', 'استفاده', 'کنند', '.', 'شعار', 'ما', 'هوش', 'مصنوعی', 'برای', 'همه', 'است', '.']
```
### Pytorch
```python
from transformers import AutoConfig, AutoTokenizer, AutoModel
config = AutoConfig.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
tokenizer = AutoTokenizer.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
model = AutoModel.from_pretrained("HooshvareLab/bert-base-parsbert-uncased")
```
## NLP Tasks Tutorial
Coming soon stay tuned
## Cite
Please cite the following paper in your publication if you are using [ParsBERT](https://arxiv.org/abs/2005.12515) in your research:
```markdown
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Acknowledgments
We hereby, express our gratitude to the [Tensorflow Research Cloud (TFRC) program](https://tensorflow.org/tfrc) for providing us with the necessary computation resources. We also thank [Hooshvare](https://hooshvare.com) Research Group for facilitating dataset gathering and scraping online text resources.
## Contributors
- Mehrdad Farahani: [Linkedin](https://www.linkedin.com/in/m3hrdadfi/), [Twitter](https://twitter.com/m3hrdadfi), [Github](https://github.com/m3hrdadfi)
- Mohammad Gharachorloo: [Linkedin](https://www.linkedin.com/in/mohammad-gharachorloo/), [Twitter](https://twitter.com/MGharachorloo), [Github](https://github.com/baarsaam)
- Marzieh Farahani: [Linkedin](https://www.linkedin.com/in/marziehphi/), [Twitter](https://twitter.com/marziehphi), [Github](https://github.com/marziehphi)
- Mohammad Manthouri: [Linkedin](https://www.linkedin.com/in/mohammad-manthouri-aka-mansouri-07030766/), [Twitter](https://twitter.com/mmanthouri), [Github](https://github.com/mmanthouri)
- Hooshvare Team: [Official Website](https://hooshvare.com/), [Linkedin](https://www.linkedin.com/company/hooshvare), [Twitter](https://twitter.com/hooshvare), [Github](https://github.com/hooshvare), [Instagram](https://www.instagram.com/hooshvare/)
## Releases
### Release v0.1 (May 27, 2019)
This is the first version of our ParsBERT based on BERT<sub>BASE</sub>
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} | 0 | null | ---
language:
- zh
inference:
parameters:
max_new_tokens: 128
do_sample: True
license: apache-2.0
---
# Wenzhong-GPT2-3.5B
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
善于处理NLG任务，目前最大的，中文版的GPT2
Focused on handling NLG tasks, the current largest, Chinese GPT2.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言生成 NLG| 闻仲 Wenzhong | GPT2 | 3.5B | 中文 Chinese |
## 模型信息 Model Information
为了可以获得一个强大的单向语言模型，我们采用GPT模型结构，并且应用于中文语料上。具体地，这个模型拥有30层解码器和35亿参数，这比原本的GPT2-XL还要大。我们在100G的中文语料上预训练，这消耗了32个NVIDIA A100显卡大约28小时。据我们所知，它是目前最大的中文的GPT模型。
To obtain a robust unidirectional language model, we adopt the GPT model structure and apply it to the Chinese corpus. Specifically, this model has 30 decoder layers and 3.5 billion parameters, which is larger than the original GPT2-XL. We pre-train it on 100G of Chinese corpus, which consumes 32 NVIDIA A100 GPUs for about 28 hours. To the best of our knowledge, it is the largest Chinese GPT model currently available.
## 使用 Usage
### 加载模型 Loading Models
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('IDEA-CCNL/Wenzhong-GPT2-3.5B')
model = GPT2Model.from_pretrained('IDEA-CCNL/Wenzhong-GPT2-3.5B')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
### 使用示例 Usage Examples
```python
from transformers import pipeline, set_seed
set_seed(55)
generator = pipeline('text-generation', model='IDEA-CCNL/Wenzhong-GPT2-3.5B')
generator("北京位于", max_length=30, num_return_sequences=1)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
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} | 5 | null | ---
language:
- en
inference: false
license: apache-2.0
---
# Yuyuan-GPT2-3.5B
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
目前最大的，医疗领域的生成语言模型GPT2。
The currently largest, generative language model GPT2 in the medical domain.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 特殊 Special | 领域 Domain | 余元 Yuyuan | GPT2 | 3.5B | - |
## 模型信息 Model Information
我们采用与Wenzhong-GPT2-3.5B相同的架构，在50GB的医学(PubMed)语料库上进行预训练。我们使用了32个NVIDIA A100显卡大约7天。我们的Yuyuan-GPT2-3.5B是医疗领域最大的开源的GPT2模型。进一步地，模型可以通过计算困惑度（PPL）来判断事实。为了完成问答功能，我们将短语模式从疑问的形式转换为了陈述句。
We adopt the same architecture as Wenzhong-GPT2-3.5B to be pre-trained on 50 GB medical (PubMed) corpus. We use 32 NVIDIA A100 GPUs for about 7 days. Our Yuyuan-GPT2-3.5B is the largest open-source GPT2 model in the medical domain. We further allow the model to judge facts by computing perplexity (PPL). To accomplish question-and-answer functionality, we transform the phrase pattern from interrogative to declarative.
## 使用 Usage
### 加载模型 Loading Models
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('IDEA-CCNL/Yuyuan-GPT2-3.5B')
model = GPT2Model.from_pretrained('IDEA-CCNL/Yuyuan-GPT2-3.5B')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
### 使用示例 Usage Examples
```python
from transformers import pipeline, set_seed
set_seed(55)
generator = pipeline('text-generation', model='IDEA-CCNL/Yuyuan-GPT2-3.5B')
generator("Diabetics should not eat", max_length=30, num_return_sequences=1)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
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Akash7897/my-newtokenizer | [] | null | {
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}
} | 0 | null | ---
language:
- zh
license: apache-2.0
widget:
- text: "生活的真谛是[MASK]。"
---
# Zhouwenwang-Unified-1.3B
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
与追一科技合作探索的中文统一模型，13亿参数的编码器结构模型。
The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure model with 1.3B parameters.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 特殊 Special | 探索 Exploration | 周文王 Zhouwenwang | 待定 TBD | 1.3B | 中文 Chinese |
## 模型信息 Model Information
IDEA研究院认知计算中心联合追一科技有限公司提出的具有新结构的大模型。该模型在预训练阶段时考虑统一LM和MLM的任务，这让其同时具备生成和理解的能力，并且增加了旋转位置编码技术。目前已有13亿参数的Zhouwenwang-Unified-1.3B大模型，是中文领域中可以同时做LM和MLM任务的最大的模型。我们后续会持续在模型规模、知识融入、监督辅助任务等方向不断优化。
A large-scale model (Zhouwenwang-Unified-1.3B) with a new structure proposed by IDEA CCNL and Zhuiyi Technology. The model considers the task of unifying LM (Language Modeling) and MLM (Masked Language Modeling) during the pre-training phase, which gives it both generative and comprehension capabilities, and applys rotational position encoding. At present, Zhouwenwang-Unified-1.3B with 13B parameters is the largest Chinese model that can do both LM and MLM tasks. In the future, we will continue to optimize it in the direction of model size, knowledge incorporation, and supervisory assistance tasks.
### 下游任务 Performance
下游中文任务的得分（没有做任何数据增强）。
Scores on downstream chinese tasks (without any data augmentation)
| 模型 Model | afqmc | tnews | iflytek | ocnli | cmnli | wsc | csl |
| :--------: | :-----: | :----: | :-----: | :----: | :----: | :----: | :----: |
| roberta-wwm-ext-large | 0.7514 | 0.5872 | 0.6152 | 0.7770 | 0.8140 | 0.8914 | 0.8600 |
| Zhouwenwang-Unified-1.3B | 0.7463 | 0.6036 | 0.6288 | 0.7654 | 0.7741 | 0.8849 | 0. 8777 |
## 使用 Usage
因为[transformers](https://github.com/huggingface/transformers)库中是没有 Zhouwenwang-Unified-1.3B相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。
Since there is no structure of Zhouwenwang-Unified-1.3B in [transformers library](https://github.com/huggingface/transformers), you can find the structure of Zhouwenwang-Unified-1.3B and run the codes in [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
```shell
git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
```
### 加载模型 Loading Models
```python
from fengshen import RoFormerModel
from fengshen import RoFormerConfig
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B")
config = RoFormerConfig.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B")
model = RoFormerModel.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B")
```
### 使用示例 Usage Examples
你可以使用该模型进行续写任务。
You can use the model for continuation writing tasks.
```python
from fengshen import RoFormerModel
from transformers import AutoTokenizer
import torch
import numpy as np
sentence = '清华大学位于'
max_length = 32
tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B")
model = RoFormerModel.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-1.3B")
for i in range(max_length):
encode = torch.tensor(
[[tokenizer.cls_token_id]+tokenizer.encode(sentence, add_special_tokens=False)]).long()
logits = model(encode)[0]
logits = torch.nn.functional.linear(
logits, model.embeddings.word_embeddings.weight)
logits = torch.nn.functional.softmax(
logits, dim=-1).cpu().detach().numpy()[0]
sentence = sentence + \
tokenizer.decode(int(np.random.choice(logits.shape[1], p=logits[-1])))
if sentence[-1] == '。':
break
print(sentence)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
``` | [
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0.05... |
Akash7897/test-clm | [] | null | {
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}
}
} | 0 | null | ---
language:
- zh
license: apache-2.0
widget:
- text: "生活的真谛是[MASK]。"
---
# Zhouwenwang-Unified-110M
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
- Docs: [Fengshenbang-Docs](https://fengshenbang-doc.readthedocs.io/)
## 简介 Brief Introduction
与追一科技合作探索的中文统一模型，1.1亿参数的编码器结构模型。
The Chinese unified model explored in cooperation with Zhuiyi Technology, the encoder structure model with 110M parameters.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 特殊 Special | 探索 Exploration | 周文王 Zhouwenwang | 待定 TBD | 110M | 中文 Chinese |
## 模型信息 Model Information
IDEA研究院认知计算中心联合追一科技有限公司提出的具有新结构的大模型。该模型在预训练阶段时考虑统一LM和MLM的任务，这让其同时具备生成和理解的能力，并且增加了旋转位置编码技术。我们后续会持续在模型规模、知识融入、监督辅助任务等方向不断优化。
A large-scale model (Zhouwenwang-Unified-1.3B) with a new structure proposed by IDEA CCNL and Zhuiyi Technology. The model considers the task of unifying LM (Language Modeling) and MLM (Masked Language Modeling) during the pre-training phase, which gives it both generative and comprehension capabilities, and applys rotational position encoding. In the future, we will continue to optimize it in the direction of model size, knowledge incorporation, and supervisory assistance tasks.
## 使用 Usage
因为[transformers](https://github.com/huggingface/transformers)库中是没有 Zhouwenwang-Unified-110M相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。
Since there is no structure of Zhouwenwang-Unified-110M in [transformers library](https://github.com/huggingface/transformers), you can find the structure of Zhouwenwang-Unified-110M and run the codes in [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
```shell
git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
```
### 加载模型 Loading Models
```python
from fengshen import RoFormerModel
from fengshen import RoFormerConfig
from transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M")
config = RoFormerConfig.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M")
model = RoFormerModel.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M")
```
### 使用示例 Usage Examples
你可以使用该模型进行续写任务。
You can use the model for continuation writing tasks.
```python
from fengshen import RoFormerModel
from transformers import AutoTokenizer
import torch
import numpy as np
sentence = '清华大学位于'
max_length = 32
tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M")
model = RoFormerModel.from_pretrained("IDEA-CCNL/Zhouwenwang-Unified-110M")
for i in range(max_length):
encode = torch.tensor(
[[tokenizer.cls_token_id]+tokenizer.encode(sentence, add_special_tokens=False)]).long()
logits = model(encode)[0]
logits = torch.nn.functional.linear(
logits, model.embeddings.word_embeddings.weight)
logits = torch.nn.functional.softmax(
logits, dim=-1).cpu().detach().numpy()[0]
sentence = sentence + \
tokenizer.decode(int(np.random.choice(logits.shape[1], p=logits[-1])))
if sentence[-1] == '。':
break
print(sentence)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
| [
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Akashamba/distilbert-base-uncased-finetuned-ner | [] | null | {
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}
} | 0 | null | ---
tags:
- conversational
---
# Rick And Morty DialoGPT Model | [
-0.028104610741138458,
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0.04... |
Akashpb13/Galician_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"gl",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"model_for_talk",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
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}
}
} | 7 | null | ---
widget:
- text: "My name is Mark and I live in London. I am a postgraduate student at Queen Mary University."
language:
- en
license: mit
---
# Hate Speech Classifier for Social Media Content in English Language
A monolingual model for hate speech classification of social media content in English language. The model was trained on 103190 YouTube comments and tested on an independent test set of 20554 YouTube comments. It is based on English BERT base pre-trained language model.
## Tokenizer
During training the text was preprocessed using the original English BERT base tokenizer. We suggest the same tokenizer is used for inference.
## Model output
The model classifies each input into one of four distinct classes:
* 0 - acceptable
* 1 - inappropriate
* 2 - offensive
* 3 - violent | [
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... |
Akashpb13/Hausa_xlsr | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"ha",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"generated_from_trainer",
"hf-asr-leaderboard",
"model_for_talk",
"mozilla-foundation/common_voice_8_0",
"robust-speech-event",
"license:apache-2.0",
"model-index",
"... | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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},
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"prefix": null
},
"translation_en_to_fr": {
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},
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}
}
} | 31 | null | ---
widget:
- text: "Ciao, mi chiamo Marcantonio, sono di Roma. Studio informatica all'Università di Roma."
language:
- it
license: mit
---
# Hate Speech Classifier for Social Media Content in Italian Language
A monolingual model for hate speech classification of social media content in Italian language. The model was trained on 119,670 YouTube comments and tested on an independent test set of 21,072 YouTube comments. It is based on Italian ALBERTO pre-trained language model.
## Tokenizer
During training the text was preprocessed using the original Italian ALBERTO tokenizer. We suggest the same tokenizer is used for inference.
## Model output
The model classifies each input into one of four distinct classes:
* 0 - acceptable
* 1 - inappropriate
* 2 - offensive
* 3 - violent | [
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0.... |
AkshaySg/gramCorrection | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
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},
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},
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},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 4 | null | ---
datasets:
- squad_v2
- wiki_qa
language:
- en
metrics:
- accuracy
pipeline_tag: question-answering
---
A distilbert model fine-tuned for question answering. | [
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0.052303191274404526,
0.015803705900907516,
0.011370775289833546,
0... |
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