Opus Mt Mul En
这是一个基于Transformer架构的多语言到英语的机器翻译模型,支持超过100种语言的翻译任务。
下载量 173.61k
发布时间 : 3/2/2022
模型简介
该模型专注于将多种语言翻译成英语,采用标准化预处理和SentencePiece分词技术,适用于大规模多语言翻译场景。
模型特点
广泛的语言支持
支持超过100种语言的翻译任务,涵盖全球主要语系和地区性语言。
标准化预处理
采用标准化预处理流程,确保输入文本的一致性和翻译质量。
SentencePiece分词
使用spm32k SentencePiece分词技术,有效处理多语言文本。
模型能力
多语言文本翻译
大规模并行翻译
跨语言信息转换
使用案例
跨语言交流
多语言内容本地化
将非英语内容翻译成英语,便于全球传播。
学术研究
多语言文献翻译
帮助研究人员获取非英语学术资料的英文版本。
🚀 多语言到英语翻译模型
本项目提供了一个多语言到英语的翻译模型,支持众多语言的翻译,采用了Transformer架构,具备一定的翻译性能,可用于多种语言的翻译任务。
🚀 快速开始
你可以通过以下链接获取相关资源:
- 原始权重下载:opus2m-2020-08-01.zip
- 测试集翻译:opus2m-2020-08-01.test.txt
- 测试集得分:opus2m-2020-08-01.eval.txt
- OPUS说明文档:mul-eng
✨ 主要特性
- 多语言支持:支持众多语言的翻译,涵盖了全球多种语系。
- 模型架构:采用Transformer架构进行翻译任务。
- 预处理:经过归一化和SentencePiece(spm32k,spm32k)预处理。
📚 详细文档
模型信息
属性 | 详情 |
---|---|
源语言组 | 多种语言 |
目标语言组 | 英语 |
模型类型 | Transformer |
源语言 | 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 |
目标语言 | eng |
预处理 | 归一化 + SentencePiece(spm32k,spm32k) |
基准测试
测试集 | 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 |
📄 许可证
本项目采用Apache-2.0许可证。
M2m100 418M
MIT
M2M100是一个多语言编码器-解码器模型,支持100种语言的9900个翻译方向
机器翻译 支持多种语言
M
facebook
1.6M
299
Opus Mt Fr En
Apache-2.0
基于Transformer的法语到英语神经机器翻译模型,由Helsinki-NLP团队开发,采用OPUS多语数据集训练。
机器翻译 支持多种语言
O
Helsinki-NLP
1.2M
44
Opus Mt Ar En
Apache-2.0
基于OPUS数据训练的阿拉伯语到英语的机器翻译模型,采用transformer-align架构
机器翻译 支持多种语言
O
Helsinki-NLP
579.41k
42
M2m100 1.2B
MIT
M2M100是一个支持100种语言的多语言机器翻译模型,可直接在9900个翻译方向之间进行翻译。
机器翻译 支持多种语言
M
facebook
501.82k
167
Indictrans2 Indic En 1B
MIT
支持25种印度语言与英语互译的1.1B参数规模机器翻译模型,由AI4Bharat项目开发
机器翻译
Transformers 支持多种语言

I
ai4bharat
473.63k
14
Opus Mt En Zh
Apache-2.0
基于Transformer架构的英汉多方言翻译模型,支持英语到13种汉语变体的翻译任务
机器翻译 支持多种语言
O
Helsinki-NLP
442.08k
367
Opus Mt Zh En
由赫尔辛基大学开发的基于OPUS语料库的中文到英语机器翻译模型
机器翻译 支持多种语言
O
Helsinki-NLP
441.24k
505
Mbart Large 50 Many To Many Mmt
基于mBART-large-50微调的多语言机器翻译模型,支持50种语言间的互译
机器翻译 支持多种语言
M
facebook
404.66k
357
Opus Mt De En
Apache-2.0
opus-mt-de-en 是一个基于 transformer-align 架构的德语到英语的机器翻译模型,由 Helsinki-NLP 团队开发。
机器翻译 支持多种语言
O
Helsinki-NLP
404.33k
44
Opus Mt Es En
Apache-2.0
这是一个基于Transformer架构的西班牙语到英语的机器翻译模型,由Helsinki-NLP团队开发。
机器翻译
Transformers 支持多种语言

O
Helsinki-NLP
385.40k
71
精选推荐AI模型
Llama 3 Typhoon V1.5x 8b Instruct
专为泰语设计的80亿参数指令模型,性能媲美GPT-3.5-turbo,优化了应用场景、检索增强生成、受限生成和推理任务
大型语言模型
Transformers 支持多种语言

L
scb10x
3,269
16
Cadet Tiny
Openrail
Cadet-Tiny是一个基于SODA数据集训练的超小型对话模型,专为边缘设备推理设计,体积仅为Cosmo-3B模型的2%左右。
对话系统
Transformers 英语

C
ToddGoldfarb
2,691
6
Roberta Base Chinese Extractive Qa
基于RoBERTa架构的中文抽取式问答模型,适用于从给定文本中提取答案的任务。
问答系统 中文
R
uer
2,694
98