license stringlengths 2 30 | tags stringlengths 2 513 | is_nc bool 1 class | readme_section stringlengths 201 597k | hash stringlengths 32 32 |
|---|---|---|---|---|
creativeml-openrail-m | ['stable-diffusion', 'text-to-image'] | false | Chinese Digital Art Diffusion **Trigger Words: CNDigitalArt Style** This is a fine-tuned Stable Diffusion model trained on some of the **Chinese Digital Arts** style that usually uses on Chinese Interactive Reading (Visual Novel) platforms such as **Orange Light** [66rpg.com](https://66rpg.com) or **NetEase Interactive Reading Platform** [avg.163.com](https://avg.163.com/). _if you don't know what that is, don't worry, it's just one of those really big thing in China that majority of Westerners had no clue about._  Use the tokens **_CNDigitalArt Style_** in your prompts to test and experiment it yourself. **EXAMPLES:** _These results were tested on the 2000 Steps model [ **CNDigitalArt_2000.ckpt**](https://huggingface.co/CultivatorX/Chinese-Digital-Art/blob/main/CNDigitalArt_2000.ckpt). I just did 20 batches of -1 seeds in random for each of the prompt (most of which isn't that good) but it does have some really good ones. Prompt: **a portrait of Megan Fox in CNDigitalArt Style** Negative prompt: _lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, two faces, two heads_ Steps: 20, Sampler: Euler, CFG scale: 7, Seed: 593563256, Face restoration: GFPGAN, Size: 512x512, Model hash: 2258c119  Prompt: **a portrait of Scarlett Johansson in CNDigitalArt Style** Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, two faces, two heads Steps: 20, Sampler: Euler, CFG scale: 7, Seed: 4272335413, Face restoration: GFPGAN, Size: 512x512, Model hash: 2258c119 ===================================================================== ===================================================================== Prompt: **a portrait of Emma Watson in CNDigitalArt Style** Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, two faces, two heads Steps: 20, Sampler: Euler, CFG scale: 7, Seed: 3813059825, Face restoration: GFPGAN, Size: 512x512, Model hash: 2258c119  Prompt: **a portrait of Zendaya in CNDigitalArt Style** Negative prompt: lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name, two faces, two heads Steps: 20, Sampler: Euler, CFG scale: 7, Seed: 962052606, Face restoration: GFPGAN, Size: 512x512, Model hash: 2258c119 | 7bf21751272789432c2aa42be445e70e |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | UD v2.5 benchmarking pipeline for UD_Lithuanian-ALKSNIS | Feature | Description | | --- | --- | | **Name** | `lt_udv25_lithuanianalksnis_trf` | | **Version** | `0.0.1` | | **spaCy** | `>=3.2.1,<3.3.0` | | **Default Pipeline** | `experimental_char_ner_tokenizer`, `transformer`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Components** | `experimental_char_ner_tokenizer`, `transformer`, `senter`, `tagger`, `morphologizer`, `parser`, `experimental_edit_tree_lemmatizer` | | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | | **Sources** | [Universal Dependencies v2.5](https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3105) (Zeman, Daniel; et al.) | | **License** | `CC BY-SA 4.0` | | **Author** | [Explosion](https://explosion.ai) | | 8c65efc69c7e62e2d3598e8efff6166b |
cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Label Scheme <details> <summary>View label scheme (3674 labels for 6 components)</summary> | Component | Labels | | --- | --- | | **`experimental_char_ner_tokenizer`** | `TOKEN` | | **`senter`** | `I`, `S` | | **`tagger`** | `.`, `akr.`, `bdv.aukšt.mot.dgs.K.`, `bdv.aukšt.mot.dgs.V.`, `bdv.aukšt.mot.dgs.Vt.`, `bdv.aukšt.mot.dgs.Įn.`, `bdv.aukšt.mot.vns.G.`, `bdv.aukšt.mot.vns.K.`, `bdv.aukšt.mot.vns.V.`, `bdv.aukšt.vyr.dgs.G.`, `bdv.aukšt.vyr.dgs.K.`, `bdv.aukšt.vyr.dgs.N.`, `bdv.aukšt.vyr.dgs.V.`, `bdv.aukšt.vyr.dgs.Vt.`, `bdv.aukšt.vyr.dgs.Įn.`, `bdv.aukšt.vyr.vns.G.`, `bdv.aukšt.vyr.vns.K.`, `bdv.aukšt.vyr.vns.N.`, `bdv.aukšt.vyr.vns.V.`, `bdv.aukšt.vyr.vns.Vt.`, `bdv.aukšt.vyr.vns.Įn.`, `bdv.aukšč.bev.`, `bdv.aukšč.mot.dgs.G.`, `bdv.aukšč.mot.dgs.K.`, `bdv.aukšč.mot.dgs.V.`, `bdv.aukšč.mot.dgs.Įn.`, `bdv.aukšč.mot.vns.K.`, `bdv.aukšč.mot.vns.V.`, `bdv.aukšč.mot.vns.Vt.`, `bdv.aukšč.mot.vns.Įn.`, `bdv.aukšč.vyr.dgs.G.`, `bdv.aukšč.vyr.dgs.K.`, `bdv.aukšč.vyr.dgs.V.`, `bdv.aukšč.vyr.dgs.Vt.`, `bdv.aukšč.vyr.dgs.Įn.`, `bdv.aukšč.vyr.vns.G.`, `bdv.aukšč.vyr.vns.K.`, `bdv.aukšč.vyr.vns.V.`, `bdv.aukšč.vyr.vns.Įn.`, `bdv.aukšč.įvardž.mot.vns.K.`, `bdv.nelygin.`, `bdv.nelygin..vyr.vns.K.`, `bdv.nelygin.bev.`, `bdv.nelygin.mot.dgs.G.`, `bdv.nelygin.mot.dgs.K.`, `bdv.nelygin.mot.dgs.N.`, `bdv.nelygin.mot.dgs.V`, `bdv.nelygin.mot.dgs.V.`, `bdv.nelygin.mot.dgs.Vt.`, `bdv.nelygin.mot.dgs.Įn.`, `bdv.nelygin.mot.vns.G.`, `bdv.nelygin.mot.vns.K.`, `bdv.nelygin.mot.vns.N.`, `bdv.nelygin.mot.vns.V.`, `bdv.nelygin.mot.vns.Vt.`, `bdv.nelygin.mot.vns.Įn.`, `bdv.nelygin.vyr.dgs.G.`, `bdv.nelygin.vyr.dgs.K.`, `bdv.nelygin.vyr.dgs.N.`, `bdv.nelygin.vyr.dgs.V.`, `bdv.nelygin.vyr.dgs.Vt.`, `bdv.nelygin.vyr.dgs.Įn.`, `bdv.nelygin.vyr.vns.G.`, `bdv.nelygin.vyr.vns.K.`, `bdv.nelygin.vyr.vns.N.`, `bdv.nelygin.vyr.vns.V.`, `bdv.nelygin.vyr.vns.Vt.`, `bdv.nelygin.vyr.vns.Įn.`, `bdv.nelygin.įvardž.mot.dgs.G.`, `bdv.nelygin.įvardž.mot.dgs.K.`, `bdv.nelygin.įvardž.mot.dgs.N.`, `bdv.nelygin.įvardž.mot.dgs.V.`, `bdv.nelygin.įvardž.mot.dgs.Įn.`, `bdv.nelygin.įvardž.mot.vns.G.`, `bdv.nelygin.įvardž.mot.vns.K.`, `bdv.nelygin.įvardž.mot.vns.N.`, `bdv.nelygin.įvardž.mot.vns.V.`, `bdv.nelygin.įvardž.mot.vns.Vt.`, `bdv.nelygin.įvardž.mot.vns.Įn.`, `bdv.nelygin.įvardž.vyr.dgs.G.`, `bdv.nelygin.įvardž.vyr.dgs.K.`, `bdv.nelygin.įvardž.vyr.dgs.V.`, `bdv.nelygin.įvardž.vyr.dgs.Vt.`, `bdv.nelygin.įvardž.vyr.dgs.Įn.`, `bdv.nelygin.įvardž.vyr.vns.G.`, `bdv.nelygin.įvardž.vyr.vns.K.`, `bdv.nelygin.įvardž.vyr.vns.N.`, `bdv.nelygin.įvardž.vyr.vns.V.`, `bdv.nelygin.įvardž.vyr.vns.Vt.`, `bdv.nelygin.įvardž.vyr.vns.Įn.`, `būdv.nelygin.įvardž.vyr.dgs.K.`, `dkt.`, `dkt.bendr.dgs.V.`, `dkt.bendr.vns.K.`, `dkt.bendr.vns.N.`, `dkt.bendr.vns.V.`, `dkt.mot.`, `dkt.mot.dgs.G.`, `dkt.mot.dgs.K.`, `dkt.mot.dgs.N.`, `dkt.mot.dgs.V.`, `dkt.mot.dgs.Vt.`, `dkt.mot.dgs.Įn.`, `dkt.mot.vns.G.`, `dkt.mot.vns.Il.`, `dkt.mot.vns.K`, `dkt.mot.vns.K.`, `dkt.mot.vns.N.`, `dkt.mot.vns.V.`, `dkt.mot.vns.Vt.`, `dkt.mot.vns.Įn.`, `dkt.mot.vns.Įv.`, `dkt.mot.vns.Š.`, `dkt.sngr.vyr.dgs.G.`, `dkt.sngr.vyr.dgs.K.`, `dkt.sngr.vyr.dgs.V.`, `dkt.sngr.vyr.dgs.Įn.`, `dkt.sngr.vyr.vns.G.`, `dkt.sngr.vyr.vns.K.`, `dkt.sngr.vyr.vns.N.`, `dkt.sngr.vyr.vns.V.`, `dkt.sngr.vyr.vns.Įn.`, `dkt.tikr.`, `dkt.tikr.mot.`, `dkt.tikr.mot.dgs.K.`, `dkt.tikr.mot.vns.G.`, `dkt.tikr.mot.vns.K.`, `dkt.tikr.mot.vns.N.`, `dkt.tikr.mot.vns.V.`, `dkt.tikr.mot.vns.Vt.`, `dkt.tikr.mot.vns.Įn.`, `dkt.tikr.vyr.dgs.K.`, `dkt.tikr.vyr.vns.G.`, `dkt.tikr.vyr.vns.K.`, `dkt.tikr.vyr.vns.N.`, `dkt.tikr.vyr.vns.V.`, `dkt.tikr.vyr.vns.Vt.`, `dkt.tikr.vyr.vns.Įn.`, `dkt.vyr.`, `dkt.vyr.dgs.G.`, `dkt.vyr.dgs.K.`, `dkt.vyr.dgs.N.`, `dkt.vyr.dgs.V.`, `dkt.vyr.dgs.Vt.`, `dkt.vyr.dgs.v.`, `dkt.vyr.dgs.Įn.`, `dkt.vyr.vns,K.`, `dkt.vyr.vns.G.`, `dkt.vyr.vns.Il.`, `dkt.vyr.vns.K.`, `dkt.vyr.vns.N.`, `dkt.vyr.vns.V.`, `dkt.vyr.vns.Vt.`, `dkt.vyr.vns.vt.`, `dkt.vyr.vns.Įn.`, `dkt.vyr.vns.Š.`, `dktv.mot.vns.K.`, `dll`, `dll.`, `dlv.neveik.es.mot.vns.V.`, `jng.`, `jst.`, `kita`, `kita.`, `prl.G.`, `prl.K.`, `prl.Įn.`, `prv.aukšt.`, `prv.aukšč.`, `prv.nelygin.`, `prv.neygin.`, `prv.sampl.nelygin.`, `samp.įv.mot.dgs.N.`, `sampl.dll.`, `sampl.jng.`, `sampl.jst.`, `sampl.prv.`, `sampl.prv.nelyg.`, `sampl.prv.nelygin.`, `sampl.sktv.`, `sampl.sktv.raid.kiek.`, `sampl.sutr.`, `sampl.užs.`, `sampl.vksm.pad.es.`, `sampl.įv.`, `sampl.įv.G.`, `sampl.įv.K.`, `sampl.įv.V.`, `sampl.įv.bev.`, `sampl.įv.mot.dgs.G.`, `sampl.įv.mot.dgs.K.`, `sampl.įv.mot.dgs.V.`, `sampl.įv.mot.dgs.Vt.`, `sampl.įv.mot.dgs.Įn.`, `sampl.įv.mot.vns.G.`, `sampl.įv.mot.vns.K.`, `sampl.įv.mot.vns.N.`, `sampl.įv.mot.vns.V.`, `sampl.įv.mot.vns.Vt.`, `sampl.įv.mot.vns.Įn.`, `sampl.įv.vyr.dgs.G.`, `sampl.įv.vyr.dgs.K.`, `sampl.įv.vyr.dgs.N.`, `sampl.įv.vyr.dgs.V.`, `sampl.įv.vyr.dgs.Vt.`, `sampl.įv.vyr.dgs.Įn.`, `sampl.įv.vyr.vns.G.`, `sampl.įv.vyr.vns.K.`, `sampl.įv.vyr.vns.V.`, `sampl.įv.vyr.vns.Vt.`, `sampl.įv.vyr.vns.Įn.`, `sampl.įv.Įn.`, `sktv.`, `sktv.arab`, `sktv.arab.`, `sktv.kelint.mot.vns.Vt.`, `sktv.kelint.įvardž.mot.vns.V.`, `sktv.kelint.įvardž.vyr.vns.G.`, `sktv.kiek.mot.V.`, `sktv.kiek.vyr.dgs.G.`, `sktv.mišr.`, `sktv.mišr.kelint.įvardž.mot.vns.G.`, `sktv.mišr.kelint.įvardž.mot.vns.K.`, `sktv.mišr.kelint.įvardž.mot.vns.V.`, `sktv.mišr.kelint.įvardž.vyr.vns.G.`, `sktv.mišr.kelint.įvardž.vyr.vns.K.`, `sktv.mišr.kelint.įvardž.vyr.vns.Vt.`, `sktv.raid.daugin.vyr.G.`, `sktv.raid.daugin.vyr.K.`, `sktv.raid.kelint.bev.`, `sktv.raid.kelint.mot.vns.K.`, `sktv.raid.kelint.mot.vns.V.`, `sktv.raid.kelint.mot.vns.Vt.`, `sktv.raid.kelint.vyr.dgs.K.`, `sktv.raid.kelint.vyr.dgs.V.`, `sktv.raid.kelint.vyr.dgs.Vt.`, `sktv.raid.kelint.vyr.dgs.Įn.`, `sktv.raid.kelint.vyr.vns.G.`, `sktv.raid.kelint.vyr.vns.K.`, `sktv.raid.kelint.vyr.vns.V.`, `sktv.raid.kelint.vyr.vns.Vt.`, `sktv.raid.kelint.įvardž.mot.vns.G.`, `sktv.raid.kelint.įvardž.mot.vns.K.`, `sktv.raid.kelint.įvardž.mot.vns.N.`, `sktv.raid.kelint.įvardž.mot.vns.V.`, `sktv.raid.kelint.įvardž.mot.vns.Vt.`, `sktv.raid.kelint.įvardž.vyr.dgs.K.`, `sktv.raid.kelint.įvardž.vyr.dgs.N.`, `sktv.raid.kelint.įvardž.vyr.dgs.V.`, `sktv.raid.kelint.įvardž.vyr.dgs.Įn.`, `sktv.raid.kelint.įvardž.vyr.vns.G.`, `sktv.raid.kelint.įvardž.vyr.vns.K.`, `sktv.raid.kelint.įvardž.vyr.vns.V.`, `sktv.raid.kiek.`, `sktv.raid.kiek.K.`, `sktv.raid.kiek.mot.G.`, `sktv.raid.kiek.mot.K.`, `sktv.raid.kiek.mot.N.`, `sktv.raid.kiek.mot.V.`, `sktv.raid.kiek.mot.Vt.`, `sktv.raid.kiek.mot.dgs.V.`, `sktv.raid.kiek.mot.vns.G.`, `sktv.raid.kiek.mot.vns.K.`, `sktv.raid.kiek.mot.vns.Įn.`, `sktv.raid.kiek.mot.Įn.`, `sktv.raid.kiek.vyr.G.`, `sktv.raid.kiek.vyr.K.`, `sktv.raid.kiek.vyr.N.`, `sktv.raid.kiek.vyr.V.`, `sktv.raid.kiek.vyr.Vt.`, `sktv.raid.kiek.vyr.dgs.K.`, `sktv.raid.kiek.vyr.dgs.V.`, `sktv.raid.kiek.vyr.vns.G.`, `sktv.raid.kiek.vyr.vns.K.`, `sktv.raid.kiek.vyr.vns.V.`, `sktv.raid.kiek.vyr.Įn.`, `sktv.raid.kiekin.mot.vns.G.`, `sktv.raid.kiekin.mot.vns.V.`, `sktv.raid.kuopin.G.`, `sktv.rom.`, `skyr.`, `sutr.`, `tęs`, `tęs.`, `tęs.sktv.raid.kelint.vyr.vns.G.`, `tęs.įv.vyr.dgs.G.`, `tęs.įv.vyr.dgs.N.`, `tęs.įv.vyr.vns.G.`, `tęs.įv.vyr.vns.N.`, `tęs.įv.vyr.vns.V.`, `tęs.įv.vyr.vns.Įn.`, `užs.`, `vksm.asm.liep.dgs.1.`, `vksm.asm.liep.dgs.2.`, `vksm.asm.liep.vns.2.`, `vksm.asm.liep.vns.3.`, `vksm.asm.neig.liep.dgs.2.`, `vksm.asm.neig.liep.vns.2.`, `vksm.asm.neig.sngr.liep.dgs.2.`, `vksm.asm.neig.sngr.tar.3.`, `vksm.asm.neig.sngr.tar.dgs.1.`, `vksm.asm.neig.sngr.tar.vns.1.`, `vksm.asm.neig.sngr.tar.vns.3.`, `vksm.asm.neig.sngr.tiesiog.būs.vns.2.`, `vksm.asm.neig.sngr.tiesiog.būs.vns.3.`, `vksm.asm.neig.sngr.tiesiog.būt-k.3.`, `vksm.asm.neig.sngr.tiesiog.būt-k.dgs.3.`, `vksm.asm.neig.sngr.tiesiog.būt-k.vns.1.`, `vksm.asm.neig.sngr.tiesiog.būt-k.vns.3.`, `vksm.asm.neig.sngr.tiesiog.es.3.`, `vksm.asm.neig.sngr.tiesiog.es.dgs.3.`, `vksm.asm.neig.sngr.tiesiog.es.vns.1.`, `vksm.asm.neig.sngr.tiesiog.es.vns.3.`, 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`įv.vyr.dgs.G.`, `įv.vyr.dgs.K.`, `įv.vyr.dgs.N.`, `įv.vyr.dgs.V.`, `įv.vyr.dgs.Vt.`, `įv.vyr.dgs.Įn.`, `įv.vyr.dvisk.G.`, `įv.vyr.dvisk.K.`, `įv.vyr.dvisk.V.`, `įv.vyr.vns.G.`, `įv.vyr.vns.K.`, `įv.vyr.vns.N.`, `įv.vyr.vns.V.`, `įv.vyr.vns.Vt.`, `įv.vyr.vns.Įn.`, `įv.vyr.Įn,`, `įv.Įn.`, `įv.įvardž.bev.`, `įv.įvardž.mot.vns.K.`, `įv.įvardž.mot.vns.V.` | | **`morphologizer`** | `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Pos\|VerbForm=Inf`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Ger`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `POS=PUNCT`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Abbr=Yes\|POS=X`, `AdpType=Prep\|Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Degree=Pos\|POS=ADV`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Degree=Pos\|Hyph=Yes\|POS=ADV`, `Hyph=Yes\|POS=X`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=SCONJ`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|POS=PRON\|PronType=Ind`, `POS=PART`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|POS=DET\|PronType=Dem`, `Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Ger`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Inf`, `Degree=Cmp\|POS=ADV`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|NumForm=Digit\|POS=NUM`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Hyph=Yes\|POS=PART`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `AdpType=Prep\|Case=Acc\|POS=ADP`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Fem\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|NumForm=Roman\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Degree=Sup\|POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Mood=Nec\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Masc\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `AdpType=Prep\|Case=Ins\|POS=ADP`, `Case=Gen\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=INTJ`, `Definite=Ind\|Gender=Neut\|NumForm=Word\|NumType=Ord\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Neg`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Neut\|Hyph=Yes\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|POS=PRON\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Hyph=Yes\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Ger`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|POS=NOUN`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Hyph=Yes\|POS=SCONJ`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Acc\|Definite=Def\|Gender=Masc\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Definite=Def\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|POS=NOUN`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Ger`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Ger`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Mood=Cnd\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Ger`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Hyph=Yes\|POS=ADV`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Hyph=Yes\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Def\|Gender=Fem\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|NumForm=Word\|NumType=Card\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Def\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Foreign=Yes\|POS=X`, `Case=Acc\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=PROPN`, `Aspect=Perf\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Ger`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Ger`, `Case=Nom\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Gender=Fem\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Definite=Ind\|Hyph=Yes\|POS=NUM`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Ger`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Dat\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Neut\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=AUX\|Polarity=Pos\|VerbForm=Inf`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Hyph=Yes\|POS=CCONJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Ger`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Int`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Dual\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Aspect=Perf\|Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Pos\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Gender=Neut\|Mood=Nec\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Dat\|Definite=Ind\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Definite=Def\|Gender=Neut\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Dual\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Foreign=Yes\|Hyph=Yes\|POS=X`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Ins\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Definite=Ind\|Degree=Sup\|Gender=Neut\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=ADV`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=SYM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Mult\|POS=NUM`, `Case=Nom\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Definite=Ind\|NumForm=Combi\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Acc\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Dat\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|POS=PRON\|PronType=Neg`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Dat\|Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Int`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Voc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Loc\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Gen\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Ins\|Gender=Fem\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ill\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Loc\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Definite=Ind\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Gen\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Hyph=Yes\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `POS=VERB\|Polarity=Neg\|VerbForm=Inf`, `Aspect=Perf\|Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Ger`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Aspect=Perf\|Case=Ins\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres`, `Definite=Ind\|Gender=Masc\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Loc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Cnd\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Conv`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `POS=VERB\|Polarity=Neg\|Reflex=Yes\|VerbForm=Inf`, `Case=Dat\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Case=Ins\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=AUX\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Gender=Fem\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Ill\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Abbr=Yes\|Hyph=Yes\|POS=X`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Peri`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Definite=Def\|Gender=Masc\|NumForm=Combi\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Def\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Gender=Fem\|POS=PROPN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN\|Reflex=Yes`, `Case=Gen\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Ins\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Case=Ins\|Definite=Def\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Loc\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Dat\|Gender=Masc\|NumForm=Word\|NumType=Card\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Definite=Ind\|Gender=Neut\|Mood=Nec\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Dat\|Definite=Ind\|Number=Sing\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Conv`, `Case=Acc\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Gender=Neut\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Case=Nom\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=X`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Neg`, `Aspect=Hab\|Mood=Ind\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Int`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=AUX\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|VerbForm=Fin`, `Hyph=Yes\|POS=INTJ`, `Aspect=Perf\|Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Aspect=Hab\|Mood=Ind\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Com\|Number=Sing\|POS=NOUN`, `Aspect=Hab\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Definite=Ind\|Gender=Neut\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Ind\|NumForm=Word\|NumType=Sets\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Mult\|POS=NUM`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Definite=Ind\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Hab\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Fut\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=NOUN`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Aspect=Perf\|Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN\|Reflex=Yes`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Mood=Nec\|Number=Sing\|POS=VERB\|Polarity=Neg\|VerbForm=Part`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Definite=Ind\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Ins\|Definite=Ind\|POS=PRON\|PronType=Ind`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|VerbForm=Fin`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Nom\|Gender=Fem\|NumType=Card\|POS=NUM`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Definite=Ind\|Hyph=Yes\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Polarity=Pos\|VerbForm=Conv`, `Case=Loc\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Ind\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Definite=Ind\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|Hyph=Yes\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Fem\|NumForm=Word\|Number=Sing\|POS=NUM`, `Case=Loc\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Definite=Ind\|Gender=Fem\|NumForm=Word\|Number=Sing\|POS=NUM`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Definite=Def\|Gender=Masc\|NumForm=Word\|NumType=Ord\|Number=Plur\|POS=NUM`, `Case=Nom\|Definite=Def\|Gender=Fem\|Number=Sing\|POS=VERB\|Polarity=Pos\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|VerbForm=Conv`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Pos\|Reflex=Yes\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Loc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Ins\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Neg\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Definite=Ind\|Gender=Fem\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Fem\|POS=VERB\|Polarity=Pos\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Tense=Past\|Voice=Pass`, `Case=Acc\|Definite=Ind\|Gender=Masc\|Number=Plur\|POS=VERB\|Polarity=Pos\|Reflex=Yes\|Tense=Past\|VerbForm=Part\|Voice=Pass` | | **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `advmod:emph`, `amod`, `appos`, `case`, `cc`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `flat`, `flat:foreign`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `nummod:gov`, `obj`, `obl`, `obl:arg`, `orphan`, `parataxis`, `punct`, `xcomp` | | **`experimental_edit_tree_lemmatizer`** | `2`, `3`, `5`, `7`, `9`, `12`, `16`, `18`, `19`, `21`, `24`, `26`, `30`, `32`, `34`, `37`, `39`, `41`, `43`, `44`, `46`, `48`, `50`, `52`, `55`, `59`, `62`, `64`, `66`, `68`, `70`, `72`, `74`, `75`, `77`, `79`, `81`, `84`, `86`, `88`, `90`, `92`, `94`, `96`, `98`, `101`, `103`, `105`, `107`, `109`, `110`, `111`, `113`, `115`, `117`, `119`, `121`, `123`, `125`, `127`, `129`, `131`, `133`, `135`, `137`, `139`, `142`, `146`, `148`, `151`, `153`, `155`, `158`, `162`, `165`, `167`, `168`, `170`, `173`, `175`, `177`, `180`, `182`, `184`, `185`, `187`, `189`, `190`, `194`, `195`, `196`, `197`, `200`, `202`, `204`, `205`, `206`, `207`, `208`, `209`, `211`, `213`, `216`, `217`, `219`, `220`, `222`, `224`, `225`, `227`, `231`, `234`, `238`, `242`, `246`, `249`, `251`, `252`, `255`, `258`, `261`, `263`, `265`, `267`, `269`, `272`, `274`, `276`, `278`, `281`, `284`, `285`, `287`, `289`, `292`, `294`, `295`, `297`, `299`, `301`, `303`, `306`, `308`, `310`, `313`, `314`, `317`, `319`, `323`, `325`, `328`, `331`, `333`, `336`, `339`, `341`, `344`, `346`, `350`, `353`, `356`, `359`, `360`, `363`, `366`, `368`, `371`, `374`, `376`, `378`, `380`, `382`, `384`, `385`, `387`, `389`, `390`, `391`, `393`, `395`, `397`, `402`, `403`, `404`, `406`, `408`, `409`, `413`, `415`, `417`, `419`, `420`, `423`, `424`, `426`, `429`, `432`, `434`, `436`, `439`, `442`, `445`, `447`, `448`, `450`, `452`, `455`, `456`, `458`, `460`, `463`, `465`, `468`, `472`, `475`, `477`, `480`, `482`, `483`, `485`, `487`, `488`, `489`, `491`, `492`, `494`, `496`, `497`, `500`, `501`, `502`, `504`, `505`, `506`, `508`, `509`, `513`, `515`, `518`, `519`, `521`, `522`, `523`, `525`, `527`, `529`, `533`, `535`, `538`, 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cc-by-sa-4.0 | ['spacy', 'token-classification'] | false | Accuracy | Type | Score | | --- | --- | | `TOKEN_F` | 99.96 | | `TOKEN_P` | 99.94 | | `TOKEN_R` | 99.98 | | `TOKEN_ACC` | 100.00 | | `SENTS_F` | 95.65 | | `SENTS_P` | 96.84 | | `SENTS_R` | 94.49 | | `TAG_ACC` | 95.43 | | `POS_ACC` | 98.07 | | `MORPH_ACC` | 95.50 | | `DEP_UAS` | 88.11 | | `DEP_LAS` | 83.62 | | `LEMMA_ACC` | 90.46 | | 1199a4ea0792abd149c6f394d11160d0 |
apache-2.0 | ['generated_from_trainer'] | false | finetuned_sentence_itr2_2e-05_all_26_02_2022-04_09_01 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4676 - Accuracy: 0.8299 - F1: 0.8892 | 31f8d59d00b4eb614e73de71b96be1a9 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-finetuned-digits This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0605 - Accuracy: 0.9846 | 4cd762fa31bf363f2a93eafdc260fb62 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4808 | 1.0 | 620 | 0.3103 | 0.9696 | | 0.1877 | 2.0 | 1240 | 0.1043 | 0.9791 | | 0.1478 | 3.0 | 1860 | 0.0727 | 0.9827 | | 0.1611 | 4.0 | 2480 | 0.0644 | 0.9842 | | 0.0993 | 5.0 | 3100 | 0.0605 | 0.9846 | | c8070499196bc3f6f3b8da8389ac55e4 |
apache-2.0 | ['generated_from_trainer'] | false | qnli This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.3608 - Accuracy: 0.9138 | 22e91c26a37bc6eb058ad84c14f2ecb0 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4.0 | 500371e9e2073711b5aec790830e9169 |
apache-2.0 | [] | false | Model description
The REALM checkpoint finetuned with Natural Question(NQ) dataset, converted from the TF checkpoint provided by Google Language.
The original paper, code, and checkpoints can be found [here](https://github.com/google-research/language/tree/master/language/realm).
| 740d3e62cfa2674fd4c04965a614551c |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Wav2Vec2-Large-XLSR-53-Swedish Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Swedish using the [Common Voice](https://huggingface.co/datasets/common_voice). The training data amounts to 402 MB. When using this model, make sure that your speech input is sampled at 16kHz. | 1fd4925c3caf5a004dc808bb3a1d77a2 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Usage The model can be used directly (without a language model) as follows: ```python import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "sv-SE", split="test[:2%]"). processor = Wav2Vec2Processor.from_pretrained("birgermoell/wav2vec2-swedish-common-voice") model = Wav2Vec2ForCTC.from_pretrained("birgermoell/wav2vec2-swedish-common-voice") resampler = torchaudio.transforms.Resample(48_000, 16_000) | 339e8b3aacf6d1e63686d9deb05667f9 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Evaluation The model can be evaluated as follows on the {language} test data of Common Voice. ```python import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re test_dataset = load_dataset("common_voice", "sv-SE", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("birgermoell/wav2vec2-swedish-common-voice") model = Wav2Vec2ForCTC.from_pretrained("birgermoell/wav2vec2-swedish-common-voice") model.to("cuda") chars_to_ignore_regex = '[\,\?\.\!\-\;\:\"\“]' resampler = torchaudio.transforms.Resample(48_000, 16_000) | 07fcda392888878714ad47232f061167 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | We need to read the aduio files as arrays def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) ``` **Test Result**: 36.91 % | 43087675ab748c1c7bce65bed6444365 |
apache-2.0 | ['audio', 'automatic-speech-recognition', 'speech', 'xlsr-fine-tuning-week'] | false | Training The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](https://colab.research.google.com/drive/1KkD4PeZwnIwxxxOP1bUE7XTZMK7-SzRj?usp=sharing) | 1c6bfbb18b297e789dbeb4f649ddbece |
apache-2.0 | ['generated_from_trainer'] | false | SEED0042 This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the SST2 dataset. It achieves the following results on the evaluation set: - Loss: 0.1754 - Accuracy: 0.9507 | 3c13b04c4b84f241cb2517eaa4638403 |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - distributed_type: not_parallel - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3 | 247fdf6c025a0afbc3c4c8cb6e5e7f16 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 2105 | 0.2056 | 0.9358 | | 0.2549 | 2.0 | 4210 | 0.1850 | 0.9438 | | 0.1162 | 3.0 | 6315 | 0.1754 | 0.9507 | | 391fca8f87439049e99d2134410837ef |
apache-2.0 | ['generated_from_trainer'] | false | albert-base-v2-finetuned-ner This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0700 - Precision: 0.9301 - Recall: 0.9376 - F1: 0.9338 - Accuracy: 0.9852 | bedd8eca2d1c4c591dfdcfc9552c0aba |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.096 | 1.0 | 1756 | 0.0752 | 0.9163 | 0.9201 | 0.9182 | 0.9811 | | 0.0481 | 2.0 | 3512 | 0.0761 | 0.9169 | 0.9293 | 0.9231 | 0.9830 | | 0.0251 | 3.0 | 5268 | 0.0700 | 0.9301 | 0.9376 | 0.9338 | 0.9852 | | e86c15ce55741b13532ca8d08637f99d |
mit | [] | false | Description A BERT model pre-trained on PubMed abstracts, and continual pre-trained on clinical notes ([MIMIC-III](https://mimic.physionet.org/)). We try combining two domains that have fewer overlaps with general knowledge text corpora: EHRs and biomedical papers. We hope this model can serve better results on clinical-related downstream tasks such as readmissions. This model is trained on 500000 clinical notes randomly sampled from MIMIC datasets, with 120k steps of training. We also used whole word masking to enhance the coherence of the language model. All notes are chunked into a length of 128 tokens. Pre-trained model: https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract | 3d8677761a7ca47698bb325fb42aea14 |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | wav2vec 2.0 base model pretrained on librispeech 960h This HuggingFace repository provides all the necessary tools to extract wav2vec2 embeddings from a pretrained model. For a better experience, we encourage you to learn more about [SpeechBrain](https://speechbrain.github.io). The wav2vec2 model has entirely been pretrained with SpeechBrain (not with fairseq or HuggingFace). The performance of the model is the following: | Release | Test WER | GPUs | |:-------------:|:--------------:| :--------:| | 22-09-22 | 7.X | 1xV100 32GB | | 6289d4c0c76c449a8f3650a80b679afb |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | Pipeline description This w2v2 system is composed of 2 different but linked blocks: - A convolutional backend to extract features from the raw waveform. - A latent encoder made of a transformer network. The obtained embeddings are the output of the transformer after going through each block. | 0caf4b6007d791ba2498d88765b4b9f6 |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | Extracting embeddings for your own audio files ```python from speechbrain.pretrained import WaveformEncoder ssl_model = WaveformEncoder.from_hparams(source="speechbrain/ssl-wav2vec2-base-librispeech", savedir="speechbrain/ssl-wav2vec2-base-librispeech") ssl_model.encode_file("mywavfile.wav") ``` | d4c090ab65cfecb7d3c32630fc5983cf |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | Training The model was trained with SpeechBrain. To train it from scratch follow these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ```bash cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ```bash cd recipes/LibriSpeech/self-supervised-learning/wav2vec2 python train_sb_wav2vec2.py hparams/wav2vec2_base.yaml --data_folder=your_data_folder ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1eXA6HQtiKfgrPejvvoKvRRfTEvOI3BQt?usp=sharing). | f74b29e93dc17bd60446bb9209de512e |
apache-2.0 | ['pretraining', 'CTC', 'pytorch', 'speechbrain', 'speech'] | false | Referencing SpeechBrain ``` @misc{SB2021, author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\\\\url{https://github.com/speechbrain/speechbrain}}, } ``` | 9652655d3a77562fc0563ff40ac12032 |
apache-2.0 | ['argumentation'] | false | Generate the conclusion of an argument This model is a version of [`gpt-neo-2.7B`](https://huggingface.co/EleutherAI/gpt-neo-2.7B), where all parameters (both weights and biases) have been finetuned on the task of generating the conclusion of an argument given its premises. It was trained as part of a University of Melbourne [research project](https://github.com/Hunt-Laboratory/language-model-optimization) evaluating how large language models can best be optimized to perform argumentative reasoning tasks. Code used for optimization and evaluation can be found in the project [GitHub repository](https://github.com/Hunt-Laboratory/language-model-optimization). A paper reporting on model evaluation is currently under review. | f8febbfb4e75e521bafd92c8fc8aca1e |
apache-2.0 | ['generated_from_keras_callback', 'dpr'] | false | dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2 This model(google/bert_uncased_L-2_H-128_A-2) was trained from scratch on training data: data.retriever.nq-adv-hn-train(facebookresearch/DPR). It achieves the following results on the evaluation set: | e3d3563c850239d6eeb1155e941cf35b |
apache-2.0 | ['generated_from_keras_callback', 'dpr'] | false | Evaluation data evaluation dataset: facebook-dpr-dev-dataset from official DPR github |model_name|data_name|num of queries|num of passages|R@10|R@20|R@50|R@100|R@100| |---|---|---|---|---|---|---|---|---| |nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2(our)|nq-dev dataset|6445|199795|60.53%|68.28%|76.07%|80.98%|91.45%| |nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2(our)|nq-dev dataset|6445|199795|65.43%|71.99%|79.03%|83.24%|92.11%| |*facebook/dpr-ctx_encoder-single-nq-base(hf/fb)|nq-dev dataset|6445|199795|40.94%|49.27%|59.05%|66.00%|82.00%| evaluation dataset: UKPLab/beir test data but we have used first 2lac passage only. |model_name|data_name|num of queries|num of passages|R@10|R@20|R@50|R@100|R@100| |---|---|---|---|---|---|---|---|---| |nlpconnect/dpr-ctx_encoder_bert_uncased_L-2_H-128_A-2(our)|nq-test dataset|3452|200001|49.68%|59.06%|69.40%|75.75%|89.28%| |nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2(our)|nq-test dataset|3452|200001|51.62%|61.09%|70.10%|76.07%|88.70%| |*facebook/dpr-ctx_encoder-single-nq-base(hf/fb)|nq-test dataset|3452|200001|32.93%|43.74%|56.95%|66.30%|83.92%| Note: * means we have evaluated on same eval dataset. | 5417faa81ae81cb907a7b0bda93b9f96 |
apache-2.0 | ['generated_from_keras_callback', 'dpr'] | false | Usage (HuggingFace Transformers) ```python passage_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2") query_encoder = TFAutoModel.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-12_H-128_A-2") p_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-ctx_encoder_bert_uncased_L-12_H-128_A-2") q_tokenizer = AutoTokenizer.from_pretrained("nlpconnect/dpr-question_encoder_bert_uncased_L-12_H-128_A-2") def get_title_text_combined(passage_dicts): res = [] for p in passage_dicts: res.append(tuple((p['title'], p['text']))) return res processed_passages = get_title_text_combined(passage_dicts) def extracted_passage_embeddings(processed_passages, model_config): passage_inputs = tokenizer.batch_encode_plus( processed_passages, add_special_tokens=True, truncation=True, padding="max_length", max_length=model_config.passage_max_seq_len, return_token_type_ids=True ) passage_embeddings = passage_encoder.predict([np.array(passage_inputs['input_ids']), np.array(passage_inputs['attention_mask']), np.array(passage_inputs['token_type_ids'])], batch_size=512, verbose=1) return passage_embeddings passage_embeddings = extracted_passage_embeddings(processed_passages, model_config) def extracted_query_embeddings(queries, model_config): query_inputs = tokenizer.batch_encode_plus( queries, add_special_tokens=True, truncation=True, padding="max_length", max_length=model_config.query_max_seq_len, return_token_type_ids=True ) query_embeddings = query_encoder.predict([np.array(query_inputs['input_ids']), np.array(query_inputs['attention_mask']), np.array(query_inputs['token_type_ids'])], batch_size=512, verbose=1) return query_embeddings query_embeddings = extracted_query_embeddings(queries, model_config) ``` | 2d8039f50ae6c25add6118dd331f7c6b |
mit | [] | false | HD Emoji on Stable Diffusion This is the `<HDemoji-object>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:            | ad56818671abb27d5aab99b4300f0cdd |
apache-2.0 | ['generated_from_trainer'] | false | whisper-small-libirClean-vs-commonNative-en This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the librispeech_asr dataset. It achieves the following results on the evaluation set: - Loss: 2.3358 - Wer: 85.5379 | fe25334b49c545d7b30b62a3c5ad284f |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 50 - mixed_precision_training: Native AMP | 6056646906995f64277c770a75a736b3 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 1.2481 | 0.08 | 10 | 3.5688 | 21.1895 | | 0.7793 | 0.16 | 20 | 2.8307 | 38.9990 | | 0.5443 | 0.24 | 30 | 2.4196 | 67.0458 | | 0.4484 | 0.32 | 40 | 2.2903 | 71.1732 | | 0.4086 | 0.4 | 50 | 2.3358 | 85.5379 | | b20238477af73342e92cbb6de7bf6752 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | plng_f222 Dreambooth model trained by andylive with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | 1adb3765d9f6fa28d48f93eefdbef3ca |
mit | [] | false | Liminal spaces 2.0 on Stable Diffusion This is the `liminal image` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`:                     | 35e11a62cdb91ab552e7d08596fc023b |
cc-by-4.0 | ['generated_from_trainer'] | false | roberta-base-squad2-ta-qna-roberta3e This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4671 | 26ccb4c8c0a0d864bee87a4125ea0cd6 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 | 42942f4d5fcb70ee04744f426071d5e9 |
cc-by-4.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 87 | 0.5221 | | No log | 2.0 | 174 | 0.4408 | | No log | 3.0 | 261 | 0.4671 | | ca6b3a74bd73430da4dda587e4652814 |
apache-2.0 | ['generated_from_trainer'] | false | edos-2023-baseline-bert-base-uncased-label_sexist This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0519 - F1: 0.9825 | 96a89fab8ef4034ed85cd6f7a5966d9f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.4448 | 0.57 | 400 | 0.3022 | 0.8232 | | 0.3283 | 1.14 | 800 | 0.2306 | 0.8786 | | 0.2588 | 1.71 | 1200 | 0.1721 | 0.9149 | | 0.2169 | 2.29 | 1600 | 0.1420 | 0.9338 | | 0.1855 | 2.86 | 2000 | 0.0976 | 0.9556 | | 0.139 | 3.43 | 2400 | 0.0724 | 0.9718 | | 0.1276 | 4.0 | 2800 | 0.0552 | 0.9785 | | 0.0943 | 4.57 | 3200 | 0.0519 | 0.9825 | | d82e52f090be8bd6aec1ec27f8d7f126 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1453 | c9eb6dc9d71a1bf8f53f13c460cee924 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 1.2065 | 1.0 | 5577 | 1.1289 | | 0.9226 | 2.0 | 11154 | 1.1019 | | 0.7411 | 3.0 | 16731 | 1.1453 | | f1b295431a9d4159170dfb744cfb9aaa |
mit | ['generated_from_keras_callback'] | false | nandysoham16/Catalan_language-clustered This model is a fine-tuned version of [nandysoham16/13-clustered_aug](https://huggingface.co/nandysoham16/13-clustered_aug) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6225 - Train End Logits Accuracy: 0.8646 - Train Start Logits Accuracy: 0.8472 - Validation Loss: 0.2992 - Validation End Logits Accuracy: 0.9091 - Validation Start Logits Accuracy: 1.0 - Epoch: 0 | c8100b258d65f0e571a9a44fd5377f97 |
mit | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch | |:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:| | 0.6225 | 0.8646 | 0.8472 | 0.2992 | 0.9091 | 1.0 | 0 | | c7795627551888b6859ee32ac4f1f42d |
apache-2.0 | ['generated_from_keras_callback'] | false | tw-sentiment-finetuned This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2039 - Train Accuracy: 0.9171 - Validation Loss: 0.4805 - Validation Accuracy: 0.8237 - Epoch: 2 | 21a6fec8243dead68e8126c974865a24 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 0.4620 | 0.7977 | 0.3893 | 0.8332 | 0 | | 0.3238 | 0.8596 | 0.4674 | 0.8362 | 1 | | 0.2039 | 0.9171 | 0.4805 | 0.8237 | 2 | | 7e5ff72d04bb13d20e9e2f0aafc9c6c7 |
mit | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 128 - seed: 4 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 128 - total_eval_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 75 | 72cf936fde0577fb550fa704a5466c64 |
apache-2.0 | ['generated_from_trainer'] | false | w2v2-xlsr-indonesian This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.2496 - Wer: 0.2116 | 5c0fee926821afc298a465a5d97f57ff |
apache-2.0 | ['generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 300 - num_epochs: 60.0 - mixed_precision_training: Native AMP | 9615c12e577576d56c111241a69d1cfe |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 9.828 | 3.23 | 100 | 3.3112 | 1.0 | | 2.99 | 6.45 | 200 | 2.8944 | 1.0 | | 2.8597 | 9.68 | 300 | 2.8430 | 1.0 | | 2.6267 | 12.9 | 400 | 1.6697 | 0.9975 | | 1.3279 | 16.13 | 500 | 0.5147 | 0.5183 | | 0.8697 | 19.35 | 600 | 0.3648 | 0.3825 | | 0.7173 | 22.58 | 700 | 0.3172 | 0.3332 | | 0.6268 | 25.81 | 800 | 0.3027 | 0.2900 | | 0.5733 | 29.03 | 900 | 0.2921 | 0.2710 | | 0.5283 | 32.26 | 1000 | 0.2731 | 0.2546 | | 0.4865 | 35.48 | 1100 | 0.2690 | 0.2397 | | 0.4559 | 38.71 | 1200 | 0.2581 | 0.2330 | | 0.4352 | 41.94 | 1300 | 0.2620 | 0.2254 | | 0.4265 | 45.16 | 1400 | 0.2496 | 0.2228 | | 0.3987 | 48.39 | 1500 | 0.2571 | 0.2219 | | 0.3977 | 51.61 | 1600 | 0.2514 | 0.2138 | | 0.3786 | 54.84 | 1700 | 0.2533 | 0.2142 | | 0.3763 | 58.06 | 1800 | 0.2496 | 0.2116 | | 4b5c9c4db16ae43286f480be1513def6 |
apache-2.0 | ['translation'] | false | eng-cus * source group: English * target group: Cushitic languages * OPUS readme: [eng-cus](https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cus/README.md) * model: transformer * source language(s): eng * target language(s): som * model: transformer * pre-processing: normalization + SentencePiece (spm12k,spm12k) * download original weights: [opus2m-2020-08-01.zip](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.zip) * test set translations: [opus2m-2020-08-01.test.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.test.txt) * test set scores: [opus2m-2020-08-01.eval.txt](https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.eval.txt) | 083b333048195b70bebefe9980991c76 |
apache-2.0 | ['translation'] | false | System Info: - hf_name: eng-cus - source_languages: eng - target_languages: cus - opus_readme_url: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/eng-cus/README.md - original_repo: Tatoeba-Challenge - tags: ['translation'] - languages: ['en', 'so', 'cus'] - src_constituents: {'eng'} - tgt_constituents: {'som'} - src_multilingual: False - tgt_multilingual: True - prepro: normalization + SentencePiece (spm12k,spm12k) - url_model: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.zip - url_test_set: https://object.pouta.csc.fi/Tatoeba-MT-models/eng-cus/opus2m-2020-08-01.test.txt - src_alpha3: eng - tgt_alpha3: cus - short_pair: en-cus - chrF2_score: 0.17300000000000001 - bleu: 16.0 - brevity_penalty: 1.0 - ref_len: 3.0 - src_name: English - tgt_name: Cushitic languages - train_date: 2020-08-01 - src_alpha2: en - tgt_alpha2: cus - prefer_old: False - long_pair: eng-cus - helsinki_git_sha: 480fcbe0ee1bf4774bcbe6226ad9f58e63f6c535 - transformers_git_sha: 2207e5d8cb224e954a7cba69fa4ac2309e9ff30b - port_machine: brutasse - port_time: 2020-08-21-14:41 | 6e6b74b98e861fecc6349288ee23ebbb |
apache-2.0 | [] | false | Enformer Enformer model. It was introduced in the paper [Effective gene expression prediction from sequence by integrating long-range interactions.](https://www.nature.com/articles/s41592-021-01252-x) by Avsec et al. and first released in [this repository](https://github.com/deepmind/deepmind-research/tree/master/enformer). This particular model was trained on sequences of 196,608 basepairs, target length 896, with shift augmentation but without reverse complement, on poisson loss objective. Final human pearson R of ~0.45. This repo contains the weights of the PyTorch implementation by Phil Wang as seen in the [enformer-pytorch repository](https://github.com/lucidrains/enformer-pytorch). Disclaimer: The team releasing Enformer did not write a model card for this model so this model card has been written by the Hugging Face team. | 1720bd42a75563097282ee886c997900 |
mit | [] | false | model by Mizuzora This your the Stable Diffusion model fine-tuned the Fang yuan-002 concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: **an anime art of sks Fang_Yuan** You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). Here are the images used for training this concept:     | e7b072c931e84b8d1df72384af53b3c0 |
apache-2.0 | ['generated_from_trainer'] | false | test-trainer This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.5802 - Accuracy: 0.8505 - F1: 0.8935 | 656e879fc021c8d4955247b94d4e11e1 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.4443 | 0.8039 | 0.8485 | | 0.5584 | 2.0 | 918 | 0.3841 | 0.8431 | 0.8810 | | 0.3941 | 3.0 | 1377 | 0.5802 | 0.8505 | 0.8935 | | 26a1a0291e4c7f882b800b54d8331493 |
apache-2.0 | ['generated_from_trainer'] | false | bert-nlp-project-ft-google-ds-imdb This model is a fine-tuned version of [jestemleon/bert-nlp-project-google](https://huggingface.co/jestemleon/bert-nlp-project-google) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2646 - Accuracy: 0.9455 - F1: 0.9446 | 84c2d86c3354fb7cb2b8a22d5cad446f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.2684 | 0.38 | 750 | 0.1850 | 0.9315 | 0.9295 | | 0.2105 | 0.75 | 1500 | 0.1734 | 0.9407 | 0.9385 | | 0.1767 | 1.12 | 2250 | 0.2229 | 0.9337 | 0.9346 | | 0.1259 | 1.5 | 3000 | 0.2029 | 0.9423 | 0.9401 | | 0.1187 | 1.88 | 3750 | 0.2554 | 0.9353 | 0.9359 | | 0.0778 | 2.25 | 4500 | 0.2759 | 0.9433 | 0.9418 | | 0.06 | 2.62 | 5250 | 0.2663 | 0.9443 | 0.9428 | | 0.0551 | 3.0 | 6000 | 0.2646 | 0.9455 | 0.9446 | | 136796fc45a9f3197ab4986ef28bbb7b |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_vp-100k_s364 Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | b95a801b3f2f8522f3905bcdd20f9ace |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_add_GLUE_Experiment_qnli_192 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE QNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.6649 - Accuracy: 0.5949 | c9ce91cb66af93f7f7de7134eba0a0f9 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6936 | 1.0 | 410 | 0.6930 | 0.5054 | | 0.6793 | 2.0 | 820 | 0.6684 | 0.5823 | | 0.6511 | 3.0 | 1230 | 0.6650 | 0.5938 | | 0.6385 | 4.0 | 1640 | 0.6649 | 0.5949 | | 0.6306 | 5.0 | 2050 | 0.6668 | 0.5923 | | 0.6215 | 6.0 | 2460 | 0.6783 | 0.5931 | | 0.6137 | 7.0 | 2870 | 0.6969 | 0.5852 | | 0.6046 | 8.0 | 3280 | 0.6888 | 0.5881 | | 0.5964 | 9.0 | 3690 | 0.6977 | 0.5799 | | 13eaf76d99f5e0b5a47741e1a60bd49e |
apache-2.0 | [] | false | Details of byt5-is-ocr-post-processing-modern-texts *Note: This model is almost the same as [atlijas/byt5-is-ocr-post-processing-old-texts](https://huggingface.co/atlijas/byt5-is-ocr-post-processing-old-texts/). The only difference is the amount of epochs trained.* This model generates a revised version of a given Icelandic OCRed text. The model was trained with [simpleT5](https://github.com/Shivanandroy/simpleT5) on 900.000 lines (\~7.000.000 tokens) of which only 50.000 (\~400.000 tokens) were from real OCRed texts. The rest were extracted from [The Icelandic Gigaword Corpus](https://clarin.is/en/resources/gigaword/) and augmented with artificial errors. It can be assumed that increasing the amount of OCRed data can significantly improve the model. For inference, it is recommended to feed the model one line (not necessarily whole sentences, though) at a time. | 5932d5fb069731e8a207d556d6e91e80 |
apache-2.0 | [] | false | Evaluation results The test set for this model consists of various Icelandic texts from the the 80's and 90's. On it, the model achieves a chrF error rate reduction of 30.1%, with the original text's score being 95.2, and the processed one's 96.7. The model achieves a proportional BLEU improvement of 19.8%, with the original text's BLEU score being 97.55 and the processed one's 98.0. | c0764db55ccd89aa0ba178d66040c3b0 |
apache-2.0 | ['translation'] | false | opus-mt-af-sv * source languages: af * target languages: sv * OPUS readme: [af-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/af-sv/README.md) * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: [opus-2020-01-08.zip](https://object.pouta.csc.fi/OPUS-MT-models/af-sv/opus-2020-01-08.zip) * test set translations: [opus-2020-01-08.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-sv/opus-2020-01-08.test.txt) * test set scores: [opus-2020-01-08.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/af-sv/opus-2020-01-08.eval.txt) | 52418547c22450601be3507299796af2 |
apache-2.0 | ['generated_from_keras_callback'] | false | Rocketknight1/marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.6862 - Validation Loss: 0.8050 - Epoch: 2 | 6af4ddf6581aa101fc0cc39df483a821 |
apache-2.0 | ['generated_from_keras_callback'] | false | Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 1.0615 | 0.8832 | 0 | | 0.7983 | 0.8211 | 1 | | 0.6862 | 0.8050 | 2 | | 300161f4734eb9cac235608b6238adcb |
apache-2.0 | ['generated_from_trainer'] | false | small-vanilla-target-glue-rte This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.8377 - Accuracy: 0.6390 | 585670051f571398707b4d06d6151703 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4103 | 6.41 | 500 | 1.1929 | 0.6318 | | 0.0583 | 12.82 | 1000 | 2.3816 | 0.6029 | | 0.0263 | 19.23 | 1500 | 2.7340 | 0.6065 | | 0.0129 | 25.64 | 2000 | 2.8019 | 0.6354 | | 0.015 | 32.05 | 2500 | 2.5423 | 0.6426 | | 0.0168 | 38.46 | 3000 | 2.7661 | 0.6606 | | 0.0112 | 44.87 | 3500 | 2.8761 | 0.6318 | | 0.0119 | 51.28 | 4000 | 2.9552 | 0.6426 | | 0.0163 | 57.69 | 4500 | 2.8377 | 0.6390 | | 01c65a81438e822262cdb7567cb80b1c |
cc-by-4.0 | ['espnet', 'audio', 'automatic-speech-recognition'] | false | `Emiru_Tsunoo/aishell_asr_train_asr_streaming_transformer_raw_zh_char_sp_valid.acc.ave` ♻️ Imported from https://zenodo.org/record/4604023/ This model was trained by Emiru Tsunoo using aishell/asr1 recipe in [espnet](https://github.com/espnet/espnet/). | df47a048bccaf70f37ff0e2f0894d474 |
apache-2.0 | ['generated_from_trainer'] | false | vit-base-patch16-224-in21k-eurosat This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.3394 - Accuracy: 0.9802 | b67ad68690dd8de7189a078aa354e97d |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.7076 | 0.98 | 33 | 0.6119 | 0.9696 | | 0.4469 | 1.98 | 66 | 0.4190 | 0.9788 | | 0.3497 | 2.98 | 99 | 0.3555 | 0.9788 | | 0.3048 | 3.98 | 132 | 0.3394 | 0.9802 | | 0.2983 | 4.98 | 165 | 0.3394 | 0.9802 | | a812fa5b004ff3fde7a745a009cb2115 |
apache-2.0 | ['automatic-speech-recognition', 'pt'] | false | exp_w2v2t_pt_r-wav2vec2_s957 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition using the train split of [Common Voice 7.0 (pt)](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 5d5acb6cd9a0f8eed2ba84c3f4894596 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'SepFormer', 'DPRNN', 'Convtasnet', 'speechbrain'] | false | SI-SNR Estimator introduced for the REAL-M dataset This repository provides the Separator models to be able to train a blind SI-SNR estimator with the recipe provided in the SpeechBrain repository to complement the REAL-M real-life speech separation dataset. The SI-SNR estimator is trained with a recordings from the WHAMR! and LibriMix datasets. We used a mixture of 9 separators to create the training data on the fly. This model is released together with the REAL-M dataset for source separation on in-the-wild speech mixtures. The REAL-M dataset can downloaded from [this link](https://sourceseparationresearch.com/static/REAL-M-v0.1.0.tar.gz). The paper for the REAL-M dataset can be found on [this arxiv link](https://arxiv.org/pdf/2110.10812.pdf). | ae7cad9b07ea906895ea8898883d6965 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'SepFormer', 'DPRNN', 'Convtasnet', 'speechbrain'] | false | Install SpeechBrain First of all, currently you need to install SpeechBrain: ```bash pip install speechbrain ``` Please notice that we encourage you to read our tutorials and learn more about [SpeechBrain](https://speechbrain.github.io). | f0d478e6fae6b4b9a7edbde40a06b743 |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'SepFormer', 'DPRNN', 'Convtasnet', 'speechbrain'] | false | Training The model was trained with SpeechBrain (fc2eabb7). To train it from scratch follows these steps: 1. Clone SpeechBrain: ```bash git clone https://github.com/speechbrain/speechbrain/ ``` 2. Install it: ``` cd speechbrain pip install -r requirements.txt pip install -e . ``` 3. Run Training: ``` cd recipes/REAL-M/sisnr-estimation python train.py hparams/pool_sisnrestimator.yaml --data_folder /yourLibri2Mixpath --base_folder_dm /yourLibriSpeechpath --rir_path /yourpathforwhamrRIRs --dynamic_mixing True --use_whamr_train True --whamr_data_folder /yourpath/whamr --base_folder_dm_whamr /yourpath/wsj0-processed/si_tr_s ``` You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1NGncbjvLeGfbUqmVi6ej-NH9YQn5vBmI). | 11a53240a01009817ae88ca59dac252d |
apache-2.0 | ['Source Separation', 'Speech Separation', 'Audio Source Separation', 'SepFormer', 'DPRNN', 'Convtasnet', 'speechbrain'] | false | Referencing REAL-M ```bibtex @misc{subakan2021realm, title={REAL-M: Towards Speech Separation on Real Mixtures}, author={Cem Subakan and Mirco Ravanelli and Samuele Cornell and François Grondin}, year={2021}, eprint={2110.10812}, archivePrefix={arXiv}, primaryClass={eess.AS} } ``` | 0c19264cff3dffd177e02a2310899a60 |
apache-2.0 | ['generated_from_trainer'] | false | wav2vec2-base-timit-demo-google-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5155 - Wer: 0.3388 | 52a052cba2771e330993feab01cccf6f |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 3.5822 | 1.0 | 500 | 2.4127 | 1.0 | | 0.9838 | 2.01 | 1000 | 0.5401 | 0.5363 | | 0.4308 | 3.01 | 1500 | 0.4380 | 0.4592 | | 0.3086 | 4.02 | 2000 | 0.4409 | 0.4503 | | 0.2324 | 5.02 | 2500 | 0.4148 | 0.4041 | | 0.202 | 6.02 | 3000 | 0.4214 | 0.3882 | | 0.1595 | 7.03 | 3500 | 0.4489 | 0.3875 | | 0.1383 | 8.03 | 4000 | 0.4225 | 0.3858 | | 0.1246 | 9.04 | 4500 | 0.4512 | 0.3846 | | 0.104 | 10.04 | 5000 | 0.4676 | 0.3875 | | 0.0949 | 11.04 | 5500 | 0.4389 | 0.3683 | | 0.0899 | 12.05 | 6000 | 0.4964 | 0.3803 | | 0.0854 | 13.05 | 6500 | 0.5397 | 0.3798 | | 0.0728 | 14.06 | 7000 | 0.4823 | 0.3666 | | 0.065 | 15.06 | 7500 | 0.5187 | 0.3648 | | 0.0573 | 16.06 | 8000 | 0.5378 | 0.3715 | | 0.0546 | 17.07 | 8500 | 0.5239 | 0.3705 | | 0.0573 | 18.07 | 9000 | 0.5094 | 0.3554 | | 0.0478 | 19.08 | 9500 | 0.5334 | 0.3657 | | 0.0673 | 20.08 | 10000 | 0.5300 | 0.3528 | | 0.0434 | 21.08 | 10500 | 0.5314 | 0.3528 | | 0.0363 | 22.09 | 11000 | 0.5540 | 0.3512 | | 0.0326 | 23.09 | 11500 | 0.5514 | 0.3510 | | 0.0332 | 24.1 | 12000 | 0.5439 | 0.3492 | | 0.0275 | 25.1 | 12500 | 0.5273 | 0.3432 | | 0.0267 | 26.1 | 13000 | 0.5068 | 0.3430 | | 0.0243 | 27.11 | 13500 | 0.5131 | 0.3388 | | 0.0228 | 28.11 | 14000 | 0.5247 | 0.3406 | | 0.0227 | 29.12 | 14500 | 0.5155 | 0.3388 | | b9bfc5240375b7dab11c8eac3f805382 |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Whisper Base Te - Bharat Ramanathan This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2455 - Wer: 42.6485 | de196edc76ae750657ca4e2a6c9f2ddc |
apache-2.0 | ['whisper-event', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:-------:| | 0.6341 | 0.1 | 500 | 0.3894 | 60.7108 | | 0.349 | 0.2 | 1000 | 0.3081 | 52.0935 | | 0.2792 | 0.3 | 1500 | 0.2874 | 49.7079 | | 0.2433 | 0.4 | 2000 | 0.2720 | 47.5657 | | 0.2224 | 1.06 | 2500 | 0.2632 | 45.2288 | | 0.2058 | 1.16 | 3000 | 0.2529 | 44.3038 | | 0.1944 | 1.26 | 3500 | 0.2519 | 44.5959 | | 0.1869 | 1.36 | 4000 | 0.2475 | 43.7196 | | 0.1811 | 2.03 | 4500 | 0.2451 | 43.3301 | | 0.1775 | 2.13 | 5000 | 0.2455 | 42.6485 | | 9d1e17760186c5fba3a5583337b5a9c7 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | mt5-small-finetuned-amazon-en-es This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.0171 - Rouge1: 16.778 - Rouge2: 8.0849 - Rougel: 16.5329 - Rougelsum: 16.4302 | 0793bd37afe020ad3b82a7b6867c8129 |
apache-2.0 | ['summarization', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:| | 3.4297 | 1.0 | 1209 | 3.1211 | 17.6479 | 8.1669 | 17.1554 | 17.0276 | | 3.4217 | 2.0 | 2418 | 3.0394 | 16.4501 | 8.3991 | 16.2225 | 16.2214 | | 3.2701 | 3.0 | 3627 | 3.0427 | 16.3473 | 7.5173 | 16.1924 | 16.098 | | 3.1888 | 4.0 | 4836 | 3.0283 | 15.3718 | 6.8591 | 15.0889 | 14.9769 | | 3.1204 | 5.0 | 6045 | 3.0256 | 17.5963 | 8.331 | 17.1812 | 17.0733 | | 3.072 | 6.0 | 7254 | 3.0189 | 16.5811 | 8.1764 | 16.28 | 16.207 | | 3.0386 | 7.0 | 8463 | 3.0171 | 17.1018 | 8.4785 | 16.8196 | 16.7681 | | 3.0193 | 8.0 | 9672 | 3.0171 | 16.778 | 8.0849 | 16.5329 | 16.4302 | | 3d4cbfdc0ded89c2e673635fdb3b8144 |
apache-2.0 | [] | false | bert-base-fr-cased We are sharing smaller versions of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) that handle a custom number of languages. Unlike [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased), our versions give exactly the same representations produced by the original model which preserves the original accuracy. For more information please visit our paper: [Load What You Need: Smaller Versions of Multilingual BERT](https://www.aclweb.org/anthology/2020.sustainlp-1.16.pdf). | 1d514e2ca3aace99f4cd830c8a93f479 |
apache-2.0 | [] | false | How to use ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Geotrend/bert-base-fr-cased") model = AutoModel.from_pretrained("Geotrend/bert-base-fr-cased") ``` To generate other smaller versions of multilingual transformers please visit [our Github repo](https://github.com/Geotrend-research/smaller-transformers). | 158cb9624da8dfddce1ccb359be6828e |
cc-by-sa-4.0 | ['long-documents'] | false | Disclaimer 🚧 ⚠️ This is an experimental version of HAT, trying to make HAT a native part of Transformers library. Please use ONLY [kiddothe2b/hierarchical-transformer-base-4096](https://huggingface.co/kiddothe2b/hierarchical-transformer-base-4096) for the moment. | be2991c92ce1498a67f8e5e6d68033ec |
openrail | [] | false | Usage DialoGPT small version, finetuned on Morty's sequences (Rick and Morty Cartoon character). Simple snippet of how to infer of this model: ```python from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('s3nh/DialoGPT-small-morty') model = AutoModelWithLMHead.from_pretrained('s3nh/DialoGPT-small-morty') for step in range(4): new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt') bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids chat_history_ids = model.generate( bot_input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=3, do_sample=True, top_k=100, top_p=0.7, temperature=0.8 ) print("MortyBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) | 8fdef385b5ae2b02facd077fc2ef6aad |
mit | [] | false | galah on Stable Diffusion This is the `<galah>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:      | 2022248b6e708c5e6350a32e23642dd4 |
apache-2.0 | ['automatic-speech-recognition', 'en'] | false | exp_w2v2t_en_r-wav2vec2_s44 Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0). When using this model, make sure that your speech input is sampled at 16kHz. This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool. | 97d6d1c14b5deb30d422a0bbdb48fcfb |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | aaureeliaav1 Dreambooth model trained by akahnn with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept: | 327872ae11e6052058080dced98f5d36 |
creativeml-openrail-m | ['text-to-image', 'stable-diffusion'] | false | yelanlan Dreambooth model trained by jiaheillu Sample pictures of this concept:         | cd6b194e9f33b1e96c43c7c8ec93cbc6 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert_sa_GLUE_Experiment_data_aug_stsb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the GLUE STSB dataset. It achieves the following results on the evaluation set: - Loss: 2.9982 - Pearson: 0.2057 - Spearmanr: 0.2115 - Combined Score: 0.2086 | 86a3bdb661ace299c6c1f7a7af371395 |
apache-2.0 | ['generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr | Combined Score | |:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|:--------------:| | 0.7165 | 1.0 | 1259 | 2.9982 | 0.2057 | 0.2115 | 0.2086 | | 0.1449 | 2.0 | 2518 | 3.4353 | 0.1748 | 0.1797 | 0.1773 | | 0.0735 | 3.0 | 3777 | 3.0788 | 0.1911 | 0.1920 | 0.1915 | | 0.0475 | 4.0 | 5036 | 3.2439 | 0.1597 | 0.1573 | 0.1585 | | 0.0349 | 5.0 | 6295 | 3.3386 | 0.1631 | 0.1676 | 0.1654 | | 0.0298 | 6.0 | 7554 | 3.3579 | 0.1710 | 0.1787 | 0.1748 | | 8afbb49fb94324363959623e0a60e609 |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_11_0', 'generated_from_trainer'] | false | wav2vec2-xls-r-300m-tr This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_11_0 - TR dataset. It achieves the following results on the evaluation set: - Loss: 0.3179 - Wer: 0.2863 - Cer: 0.0681 | d4a593c9a944f5f3bfc12caf368593aa |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_11_0', 'generated_from_trainer'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30.0 - mixed_precision_training: Native AMP | b40bdcb8afe5291057ab294e51f61cdb |
apache-2.0 | ['automatic-speech-recognition', 'mozilla-foundation/common_voice_11_0', 'generated_from_trainer'] | false | Training results | Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:| | No log | 0.71 | 400 | 1.7290 | 0.9804 | 0.4797 | | 4.5435 | 1.42 | 800 | 0.4810 | 0.5774 | 0.1450 | | 0.523 | 2.12 | 1200 | 0.3859 | 0.4812 | 0.1156 | | 0.3449 | 2.83 | 1600 | 0.3492 | 0.4498 | 0.1095 | | 0.2814 | 3.54 | 2000 | 0.3660 | 0.4466 | 0.1099 | | 0.2814 | 4.25 | 2400 | 0.3766 | 0.4235 | 0.1043 | | 0.2463 | 4.96 | 2800 | 0.3416 | 0.4119 | 0.1010 | | 0.2296 | 5.66 | 3200 | 0.3322 | 0.4013 | 0.0979 | | 0.2143 | 6.37 | 3600 | 0.3370 | 0.3956 | 0.0972 | | 0.1955 | 7.08 | 4000 | 0.3401 | 0.4033 | 0.0998 | | 0.1955 | 7.79 | 4400 | 0.3375 | 0.3889 | 0.0962 | | 0.1845 | 8.5 | 4800 | 0.3455 | 0.3752 | 0.0923 | | 0.1752 | 9.2 | 5200 | 0.3336 | 0.3718 | 0.0925 | | 0.1705 | 9.91 | 5600 | 0.3145 | 0.3653 | 0.0892 | | 0.1585 | 10.62 | 6000 | 0.3410 | 0.3737 | 0.0922 | | 0.1585 | 11.33 | 6400 | 0.3296 | 0.3664 | 0.0899 | | 0.1474 | 12.04 | 6800 | 0.3492 | 0.3590 | 0.0899 | | 0.1485 | 12.74 | 7200 | 0.3176 | 0.3506 | 0.0867 | | 0.137 | 13.45 | 7600 | 0.3532 | 0.3600 | 0.0890 | | 0.1291 | 14.16 | 8000 | 0.3318 | 0.3571 | 0.0873 | | 0.1291 | 14.87 | 8400 | 0.3353 | 0.3548 | 0.0883 | | 0.1274 | 15.58 | 8800 | 0.3235 | 0.3396 | 0.0823 | | 0.1198 | 16.28 | 9200 | 0.3259 | 0.3389 | 0.0832 | | 0.1164 | 16.99 | 9600 | 0.3263 | 0.3411 | 0.0844 | | 0.1119 | 17.7 | 10000 | 0.3254 | 0.3377 | 0.0824 | | 0.1119 | 18.41 | 10400 | 0.3243 | 0.3331 | 0.0812 | | 0.1054 | 19.12 | 10800 | 0.3223 | 0.3239 | 0.0790 | | 0.1017 | 19.82 | 11200 | 0.3054 | 0.3190 | 0.0774 | | 0.0964 | 20.53 | 11600 | 0.3278 | 0.3237 | 0.0785 | | 0.0903 | 21.24 | 12000 | 0.3167 | 0.3177 | 0.0774 | | 0.0903 | 21.95 | 12400 | 0.3331 | 0.3124 | 0.0766 | | 0.0886 | 22.65 | 12800 | 0.3099 | 0.3089 | 0.0745 | | 0.0836 | 23.36 | 13200 | 0.3171 | 0.3048 | 0.0731 | | 0.0796 | 24.07 | 13600 | 0.3158 | 0.3041 | 0.0733 | | 0.0739 | 24.78 | 14000 | 0.3203 | 0.3003 | 0.0721 | | 0.0739 | 25.49 | 14400 | 0.3138 | 0.2974 | 0.0713 | | 0.0742 | 26.19 | 14800 | 0.3197 | 0.2959 | 0.0711 | | 0.07 | 26.9 | 15200 | 0.3232 | 0.2952 | 0.0703 | | 0.0654 | 27.61 | 15600 | 0.3243 | 0.2939 | 0.0701 | | 0.0631 | 28.32 | 16000 | 0.3213 | 0.2876 | 0.0688 | | 0.0631 | 29.03 | 16400 | 0.3151 | 0.2880 | 0.0685 | | 0.0607 | 29.73 | 16800 | 0.3184 | 0.2867 | 0.0681 | | 6116ee108ae7fa4ae0c89c152e0992a7 |
apache-2.0 | ['whisper-event'] | false | Whisper Telugu Small This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Telugu data available from multiple publicly available ASR corpuses. It has been fine-tuned as a part of the Whisper fine-tuning sprint. | 3e8dd121a365d52bbbb02cdc7c43e465 |
apache-2.0 | ['whisper-event'] | false | Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1.7e-05 - train_batch_size: 48 - eval_batch_size: 32 - seed: 22 - optimizer: adamw_bnb_8bit - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 15000 - training_steps: 26856 (terminated upon convergence. Initially set to 89520 steps) - mixed_precision_training: True | 9266451da076959bea22d541428e9611 |
apache-2.0 | ['generated_from_trainer'] | false | distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.6908 | f05d3721b7df42e98b78611e8f3fbf54 |
mit | [] | false | Dancing cactus on Stable Diffusion This is the `<dancing-cactus>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`:     | e0355049698aa98fd2d2ea5849d18e19 |
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