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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-27 00:47:30	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 533 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-27 00:47:21	card stringlengths 11 1.01M
marcolatella/emotion_trained	marcolatella	2021-12-10T23:23:20Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "dataset:tweet_eval", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - f1 model-index: - name: emotion_trained results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: emotion metrics: - name: F1 type: f1 value: 0.7377785764567545 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion_trained This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 0.9362 - F1: 0.7378 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6.961635072722524e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 0 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 204 \| 0.7468 \| 0.6599 \| \| No log \| 2.0 \| 408 \| 0.6829 \| 0.7369 \| \| 0.5184 \| 3.0 \| 612 \| 0.8089 \| 0.7411 \| \| 0.5184 \| 4.0 \| 816 \| 0.9362 \| 0.7378 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
explosion/ro_udv25_romaniannonstandard_trf	explosion	2021-12-10T23:04:41Z	0	0	spacy	[ "spacy", "token-classification", "ro", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - ro license: cc-by-sa-4.0 model-index: - name: ro_udv25_romaniannonstandard_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9385375334 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9765972953 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9364320998 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9399476397 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9256250793 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8749206752 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9699570815 --- UD v2.5 benchmarking pipeline for UD_Romanian-Nonstandard \| Feature \| Description \| \| --- \| --- \| \| Name \| `ro_udv25_romaniannonstandard_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) \| ### Label Scheme <details> <summary>View label scheme (7445 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `AdpType=Prep\\|Case=Acc`, `Afp`, `Afpf--n`, `Afpfp-n`, `Afpfpon`, `Afpfpoy`, `Afpfprn`, `Afpfpry`, `Afpfson`, `Afpfsoy`, `Afpfsrn`, `Afpfsry`, `Afpmp-n`, `Afpmpoy`, `Afpmprn`, `Afpmpry`, `Afpmpvy`, `Afpms-n`, `Afpmsoy`, `Afpmsrn`, `Afpmsry`, `Afpmsvn`, `Afpmsvy`, `COLON`, `COMMA`, `Cccsp`, `Cccsz`, `Ccssp`, `Ccssz`, `Cscsp`, `Csssp`, `DASH`, `DBLQ`, `Dd3-po---e`, `Dd3-po---o`, `Dd3fpo`, `Dd3fpr`, `Dd3fpr---e`, `Dd3fpr---o`, `Dd3fso`, `Dd3fso---e`, `Dd3fso---o`, `Dd3fsr`, `Dd3fsr---e`, `Dd3fsr---o`, `Dd3mpo`, `Dd3mpr`, `Dd3mpr---e`, `Dd3mpr---o`, `Dd3mso`, `Dd3mso---e`, `Dd3mso---o`, `Dd3msr`, `Dd3msr---e`, `Dd3msr---o`, `Dh1mp`, `Dh1ms`, `Dh2mp`, `Dh2ms`, `Dh3fp`, `Dh3mp`, `Dh3ms`, `Di3--r`, `Di3-po`, `Di3-sr`, `Di3fp`, `Di3fpo`, `Di3fpr`, `Di3fso`, `Di3fsr`, `Di3mpr`, `Di3mso`, `Di3msr`, `Ds1fp-p`, `Ds1fp-s`, `Ds1fsop`, `Ds1fsos`, `Ds1fsrp`, `Ds1fsrs`, `Ds1mp-p`, `Ds1mp-s`, `Ds1ms-p`, `Ds1ms-s`, `Ds2fp-p`, `Ds2fp-s`, `Ds2fsop`, `Ds2fsos`, `Ds2fsrp`, `Ds2fsrs`, `Ds2mp-p`, `Ds2mp-s`, `Ds2ms-p`, `Ds2ms-s`, `Ds3fp-s`, `Ds3fsos`, `Ds3fsrs`, `Ds3mp-s`, `Ds3ms-s`, `Dw3--r`, `Dw3-po`, `Dw3fpr`, `Dw3fso`, `Dw3fsr`, `Dw3mpr`, `Dw3mso`, `Dw3msr`, `Dz3fpr`, `Dz3fsr`, `Dz3msr`, `EXCL`, `EXCLHELLIP`, `HELLIP`, `I`, `LPAR`, `M`, `Mc-p-l`, `Mcfp-l`, `Mcfpol`, `Mcfprln`, `Mcfsoln`, `Mcfsoly`, `Mcfsrln`, `Mcfsrly`, `Mcmp-l`, `Mcms-ln`, `Mcmsoly`, `Mcmsrl`, `Mcmsrly`, `Mffsrln`, `Ml-po`, `Mlfpr`, `Mlmpr`, `Mmfpr-n`, `Mmmpr-n`, `Mmmsr-n`, `Mo---l`, `Mo---ln`, `Mo-s-r`, `Mofprln`, `Mofprly`, `Mofs-l`, `Mofs-ly`, `Mofsrln`, `Mofsrly`, `Momp-ln`, `Moms-l`, `Moms-ln`, `Momsoly`, `Momsrly`, `Ncfpoy`, `Ncfprn`, `Ncfpry`, `Ncfpvy`, `Ncfson`, `Ncfsoy`, `Ncfsrn`, `Ncfsry`, `Ncfsvn`, `Ncfsvy`, `Ncmpoy`, `Ncmprn`, `Ncmpry`, `Ncmpvy`, `Ncmson`, `Ncmsoy`, `Ncmsrn`, `Ncmsry`, `Ncmsvn`, `Ncmsvy`, `Ncnsrn`, `Np`, `Npfpoy`, `Npfprn`, `Npfpry`, `Npfsoy`, `Npfsrn`, `Npfsry`, `Npfsvn`, `Npmpoy`, `Npmprn`, `Npmpry`, `Npmsoy`, `Npmsrn`, `Npmsry`, `Npmsvn`, `Npmsvy`, `PERIOD`, `Pd3-po`, `Pd3-po---o`, `Pd3fpo`, `Pd3fpr`, `Pd3fso`, `Pd3fsr`, `Pd3mpo`, `Pd3mpr`, `Pd3mso`, `Pd3msr`, `Ph1mp`, `Ph1ms`, `Ph2mp`, `Ph2ms`, `Ph3--r`, `Ph3fp`, `Ph3fsr`, `Ph3mp`, `Ph3mpo`, `Ph3mpr`, `Ph3ms`, `Ph3mso`, `Pi3--r`, `Pi3-po`, `Pi3-so`, `Pi3-sr`, `Pi3fpo`, `Pi3fpr`, `Pi3fso`, `Pi3fsr`, `Pi3mpo`, `Pi3mpr`, `Pi3mpry`, `Pi3mso`, `Pi3msr`, `Pi3msry`, `Pp1-pa--------s`, `Pp1-pa--------w`, `Pp1-pd--------s`, `Pp1-pd--------w`, `Pp1-pr`, `Pp1-sa--------s`, `Pp1-sa--------w`, `Pp1-sd--------s`, `Pp1-sd--------w`, `Pp1-sr`, `Pp2-pa--------s`, `Pp2-pa--------w`, `Pp2-pd--------s`, `Pp2-pd--------w`, `Pp2-po`, `Pp2-pr`, `Pp2-sa--------s`, `Pp2-sa--------w`, `Pp2-sd--------s`, `Pp2-sd--------w`, `Pp2-so`, `Pp2-sr`, `Pp3-pd--------s`, `Pp3-pd--------w`, `Pp3-po`, `Pp3-pr`, `Pp3-sd--------w`, `Pp3-so`, `Pp3fpa--------s`, `Pp3fpa--------w`, `Pp3fpr`, `Pp3fsa--------s`, `Pp3fsa--------w`, `Pp3fsd--------s`, `Pp3fso`, `Pp3fsoy`, `Pp3fsr`, `Pp3mpa--------s`, `Pp3mpa--------w`, `Pp3mpo`, `Pp3mpr`, `Pp3msa--------s`, `Pp3msa--------w`, `Pp3msd--------s`, `Pp3mso`, `Pp3msr`, `Pp3msry`, `Ps1fp-p`, `Ps1fp-s`, `Ps1fsrp`, `Ps1fsrs`, `Ps1mp-p`, `Ps1ms-p`, `Ps1ms-s`, `Ps2fp-p`, `Ps2fp-s`, `Ps2fsrp`, `Ps2fsrs`, `Ps2mp-s`, `Ps2ms-p`, `Ps2ms-s`, `Ps3fp-s`, `Ps3fsrs`, `Ps3mp-s`, `Ps3ms-s`, `Pw3--r`, `Pw3-po`, `Pw3-pr`, `Pw3-pry`, `Pw3-so`, `Pw3fpr`, `Pw3fpry`, `Pw3fso`, `Pw3fsr`, `Pw3fsry`, `Pw3mpr`, `Pw3mpry`, `Pw3mso`, `Pw3msr`, `Pw3msry`, `Px3--a--------s`, `Px3--a--------w`, `Px3--d--------s`, `Px3--d--------w`, `Px3--d-------w`, `Pz3-so`, `Pz3-sr`, `Pz3fsr`, `Pz3mso`, `Pz3msr`, `QUEST`, `QUOT`, `Qn`, `Qs`, `Qz`, `RPAR`, `Rg`, `Ri`, `Rw`, `Rz`, `SCOLON`, `Sp`, `Spca`, `Spcg`, `Spsa`, `Spsd`, `Spsg`, `TILDA`, `Td-po`, `Tdfpr`, `Tdfso`, `Tdfsr`, `Tdmpr`, `Tdmso`, `Tdmsr`, `Tf-so`, `Tffsr`, `Tfmso`, `Tfmsr`, `Ti-po`, `Ti-pr`, `Tifso`, `Tifsr`, `Timso`, `Timsr`, `Tsfpr`, `Tsfso`, `Tsfsr`, `Tsmpr`, `Tsmsr`, `Vag-----p`, `Vag-----z`, `Vaii1p`, `Vaii1s`, `Vaii2p`, `Vaii2s`, `Vaii3p`, `Vaii3s`, `Vail3s`, `Vaip1p`, `Vaip1s`, `Vaip2p`, `Vaip2s`, `Vaip3`, `Vaip3p`, `Vaip3s`, `Vais1p`, `Vais1s`, `Vais2p`, `Vais2s`, `Vais3p`, `Vais3s`, `Vam-2p`, `Vam-2p---l`, `Vam-2s--p`, `Vam-2s--z`, `Vam-2s-p`, `Vam-2s-z`, `Vamip3p`, `Vamip3s`, `Vamn`, `Vamsp3`, `Van`, `Van------l`, `Vap`, `Vap--sm-p`, `Vasp1p`, `Vasp1s`, `Vasp2p`, `Vasp2s`, `Vasp3`, `Vasp3s`, `Vmg-----p`, `Vmg-----z`, `Vmii1p`, `Vmii1s`, `Vmii2p`, `Vmii2s`, `Vmii3p`, `Vmii3s`, `Vmil1s`, `Vmil2p`, `Vmil2s`, `Vmil3p`, `Vmil3s`, `Vmip1p`, `Vmip1s`, `Vmip2p`, `Vmip2s`, `Vmip3`, `Vmip3p`, `Vmip3s`, `Vmis1p`, `Vmis1s`, `Vmis2p`, `Vmis2s`, `Vmis3p`, `Vmis3s`, `Vmm-2p`, `Vmm-2p---l`, `Vmm-2s--p`, `Vmm-2s--z`, `Vmn`, `Vmn------l`, `Vmp`, `Vmp--pf-p`, `Vmp--pf-z`, `Vmp--pm-p`, `Vmp--pm-z`, `Vmp--sf-p--o`, `Vmp--sf-p--r`, `Vmp--sf-z--r`, `Vmp--sm-p`, `Vmp--sm-z`, `Vmsp1p`, `Vmsp1s`, `Vmsp2p`, `Vmsp2s`, `Vmsp3`, `Vmsp3s`, `X`, `Y` \| \| `morphologizer` \| `AdpType=Prep\\|Case=Acc\\|POS=ADP`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `POS=PUNCT`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `POS=ADV\\|PronType=Int,Rel`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Weak`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `POS=ADV`, `Case=Acc,Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Ind`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Acc,Nom\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `POS=CCONJ\\|Polarity=Pos`, `Compound=Yes\\|POS=SCONJ\\|Polarity=Pos`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Weak`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Acc,Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `POS=PART\\|PartType=Sub`, `Mood=Sub\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Imp\\|VerbForm=Fin`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Strong`, `POS=VERB\\|VerbForm=Inf`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `POS=ADV\\|Polarity=Neg`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Weak`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|Position=Prenom\\|PronType=Dem`, `POS=AUX\\|Polarity=Pos\\|VerbForm=Ger`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin`, `POS=VERB\\|Polarity=Pos\\|VerbForm=Ger`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Strong`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Imp`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Strong`, `Case=Voc\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `POS=INTJ`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin`, `POS=SCONJ\\|Polarity=Pos`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|VerbForm=Fin`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Strength=Weak`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres`, `Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Part`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc,Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind`, `AdpType=Prep\\|Case=Acc\\|Compound=Yes\\|POS=ADP`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Dat,Gen\\|Definite=Def\\|Number=Sing\\|POS=DET\\|PronType=Art`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Acc,Nom\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Neg`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Ind`, `Case=Acc,Nom\\|POS=DET\\|Person=3\\|PronType=Int,Rel`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Strength=Strong`, `Case=Voc\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Strength=Weak`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Weak`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Strength=Weak`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Strength=Weak`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Pres`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Weak`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Strong`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Ind`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Strength=Strong`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|Position=Postnom\\|PronType=Dem`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|NumForm=Word\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|NumForm=Word\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres`, `POS=AUX\\|VerbForm=Part`, `POS=VERB\\|VerbForm=Part`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin`, `POS=PART\\|PartType=Inf`, `Case=Dat,Gen\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Part`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Art`, `Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `NumForm=Word\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Art`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Strong`, `Mood=Sub\\|POS=AUX\\|Person=3\\|Tense=Pres`, `Gender=Masc\\|NumForm=Word\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Dat\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Weak`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Strength=Strong`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Weak`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Part`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Strong`, `Mood=Imp\\|Number=Sing\\|POS=AUX\\|Person=2\\|Polarity=Pos`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|Position=Prenom\\|PronType=Dem`, `Case=Dat,Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Strong`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Strength=Strong`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|PronType=Prs`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `NumForm=Digit\\|POS=NUM`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Imp\\|VerbForm=Fin`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Part`, `POS=PROPN`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Neg`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Compound=Yes\\|POS=CCONJ\\|Polarity=Neg`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|VerbForm=Fin`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Strength=Strong`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|PronType=Prs`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Strength=Strong`, `POS=AUX\\|VerbForm=Inf`, `AdpType=Prep\\|Case=Gen\\|Compound=Yes\\|POS=ADP`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|Position=Postnom\\|PronType=Dem`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|PronType=Prs`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|PronType=Prs`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Dat,Gen\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|NumForm=Word\\|NumType=Frac\\|Number=Sing\\|POS=NUM`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Dat,Gen\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc,Nom\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=2\\|PronType=Emp`, `Case=Acc,Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Acc,Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=VERB\\|Polarity=Neg\\|VerbForm=Ger`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Int,Rel`, `Case=Dat,Gen\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Emp`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Emp`, `Gender=Fem\\|NumForm=Word\\|NumType=Ord\\|Number=Sing\\|POS=NUM`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Weak`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Variant=Long\\|VerbForm=Fin`, `Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Emp`, `Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Part`, `Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|PronType=Prs`, `Case=Voc\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|Position=Prenom\\|PronType=Dem`, `Case=Acc,Nom\\|Definite=Def\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Part`, `Mood=Sub\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin`, `Case=Dat,Gen\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Int,Rel`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Neg`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|PronType=Prs`, `Mood=Sub\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Strength=Weak`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|Position=Prenom\\|PronType=Dem`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Imp\\|VerbForm=Fin`, `Case=Voc\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Compound=Yes\\|POS=CCONJ\\|Polarity=Pos`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|Position=Postnom\\|PronType=Dem`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|Position=Postnom\\|PronType=Dem`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin`, `Case=Dat,Gen\\|Gender=Fem\\|NumForm=Word\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Case=Acc,Nom\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Voc\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Art`, `Case=Dat\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Strong`, `Case=Dat,Gen\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Case=Acc,Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `AdpType=Prep\\|Case=Gen\\|POS=ADP`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Past`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Strength=Strong`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|PronType=Prs`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Neg`, `Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Emp`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Past`, `Case=Dat,Gen\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Ind`, `Gender=Fem\\|NumForm=Word\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Part`, `Case=Acc,Nom\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|PronType=Tot`, `Case=Acc,Nom\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Acc,Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|PronType=Prs`, `POS=VERB\\|Variant=Long\\|VerbForm=Inf`, `Definite=Ind\\|Gender=Masc\\|NumForm=Word\\|NumType=Ord\\|Number=Sing\\|POS=NUM`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `AdpType=Prep\\|Case=Dat\\|POS=ADP`, `Case=Dat,Gen\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Strength=Weak`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Strength=Weak`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Strength=Strong`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|NumForm=Word\\|NumType=Ord\\|Number=Sing\\|POS=NUM`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|PronType=Prs`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|PronType=Prs`, `Compound=Yes\\|POS=ADV\\|Polarity=Neg`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Art`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|PronType=Prs`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Case=Dat,Gen\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|PronType=Tot`, `Gender=Masc\\|NumForm=Word\\|NumType=Ord\\|Number=Sing\\|POS=NUM`, `Definite=Ind\\|NumForm=Word\\|NumType=Ord\\|POS=NUM`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Ind`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|PronType=Prs`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Imp\\|VerbForm=Fin`, `Case=Acc,Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `POS=AUX\\|Variant=Long\\|VerbForm=Inf`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Dat,Gen\\|Definite=Ind\\|Gender=Fem\\|NumForm=Word\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Ind`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|Position=Postnom\\|PronType=Dem`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Past\\|VerbForm=Fin`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Part`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Imp\\|VerbForm=Fin`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Strength=Strong`, `POS=X`, `Case=Dat,Gen\\|Number=Plur\\|POS=DET\\|Person=3\\|Position=Prenom\\|PronType=Dem`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|VerbForm=Fin`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|PronType=Prs`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|PronType=Prs`, `Case=Dat,Gen\\|Number=Plur\\|POS=DET\\|Person=3\\|Position=Postnom\\|PronType=Dem`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Strength=Weak`, `Case=Dat,Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|PronType=Prs`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|PronType=Prs`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=NUM`, `Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Emp`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|PronType=Tot`, `Case=Acc,Nom\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin`, `Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Part`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|NumForm=Word\\|NumType=Ord\\|Number=Sing\\|POS=NUM`, `Case=Dat,Gen\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|PronType=Prs`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Mood=Sub\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc,Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Mood=Imp\\|Number=Plur\\|POS=AUX\\|Person=2`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `POS=AUX\\|Polarity=Neg\\|VerbForm=Ger`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Dat,Gen\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Mood=Sub\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres`, `Case=Acc,Nom\\|POS=DET\\|Person=3\\|PronType=Ind`, `Case=Voc\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Dat,Gen\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Neg`, `POS=CCONJ\\|Polarity=Neg`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Voc\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Acc,Nom\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Case=Dat,Gen\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|Position=Postnom\\|PronType=Dem`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|Polite=Form\\|PronType=Prs`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|Position=Prenom\\|PronType=Dem`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Case=Acc,Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Acc,Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Past`, `Gender=Masc\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|VerbForm=Part`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Emp`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|Position=Postnom\\|PronType=Dem`, `Case=Voc\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Mood=Sub\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Voc\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `POS=PRON\\|Polarity=Pos`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin`, `Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Emp`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Imp\\|VerbForm=Fin`, `Case=Acc,Nom\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Ind`, `Case=Dat,Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Imp\\|VerbForm=Fin`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=2\\|PronType=Emp`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Imp`, `Case=Dat,Gen\\|Number=Plur\\|POS=PRON\\|Person=3\\|Position=Postnom\\|PronType=Dem`, `Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc,Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Mood=Sub\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres`, `Case=Acc,Nom\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Emp`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres`, `Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Emp`, `Case=Dat,Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Mood=Sub\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Compound=Yes\\|POS=ADP\\|Polarity=Pos`, `Mood=Sub\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Pres`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Emp`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=ADJ`, `Case=Voc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|Position=Prenom\\|PronType=Dem`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pqp\\|VerbForm=Fin`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Ind`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Emp`, `Case=Dat,Gen\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Mood=Imp\\|Number=Sing\\|POS=AUX\\|Person=2\\|Polarity=Neg`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Past\\|VerbForm=Fin`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `POS=ADV\\|PronType=Ind`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|NumForm=Word\\|NumType=Ord\\|Number=Sing\\|POS=NUM`, `POS=AUX\\|Polarity=Pos`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Art`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Imp`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Mood=Sub\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres`, `NumForm=Roman\\|NumType=Ord\\|Number=Sing\\|POS=NUM`, `Case=Voc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Past`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|Gender=Masc\\|NumForm=Word\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pqp\\|VerbForm=Fin`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Imp`, `Definite=Ind\\|Gender=Masc\\|NumForm=Word\\|NumType=Ord\\|Number=Plur\\|POS=NUM`, `Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Ind`, `Mood=Imp\\|Number=Plur\\|POS=AUX\\|Person=2\\|Variant=Long\\|VerbForm=Fin`, `Mood=Imp\\|Number=Plur\\|POS=AUX\\|Person=2\\|Variant=Long`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|PronType=Prs`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Imp`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Art`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Int,Rel`, `Case=Acc,Nom\\|POS=PRON\\|Person=3\\|PronType=Emp`, `NumForm=Word\\|NumType=Ord\\|POS=NUM`, `Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Emp`, `Case=Dat,Gen\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Int,Rel`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Int,Rel`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=1\\|PronType=Emp`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=1\\|PronType=Emp`, `Mood=Imp\\|Number=Plur\\|POS=AUX\\|Person=2\\|VerbForm=Fin`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Art`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Masc\\|NumForm=Word\\|NumType=Ord\\|Number=Sing\\|POS=NUM`, `Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Emp`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Art`, `Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|POS=ADJ`, `Mood=Sub\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres`, `Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Ind`, `Case=Voc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Past`, `Case=Dat,Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Fem\\|NumForm=Word\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Masc\\|NumForm=Word\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Dat,Gen\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos`, `Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Int,Rel`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pqp\\|VerbForm=Fin`, `Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pqp\\|VerbForm=Fin`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Neg`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pqp`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes\\|Strength=Weak`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes\\|Strength=Weak`, `Case=Dat,Gen\\|Number=Plur\\|POS=PRON\\|Person=2\\|Polite=Form\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Mood=Ind\\|POS=AUX\\|Person=3\\|Tense=Pres`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Neg`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes\\|Strength=Strong`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Dat,Gen\\|Number=Sing\\|POS=PRON\\|Person=2\\|Polite=Form\\|PronType=Prs`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Int,Rel`, `Case=Acc,Nom\\|Gender=Masc\\|NumForm=Word\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Acc,Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|POS=ADJ`, `POS=DET`, `Case=Acc,Nom\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=ADP`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|NumForm=Word\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Int,Rel`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|NumForm=Word\\|NumType=Ord\\|Number=Plur\\|POS=NUM`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Fem\\|NumType=Mult\\|Number=Plur\\|POS=NUM`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Masc\\|NumType=Mult\\|Number=Plur\\|POS=NUM`, `Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Imp`, `Case=Dat,Gen\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Mood=Ind\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Dat,Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Emp`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Fem\\|NumForm=Word\\|NumType=Ord\\|Number=Plur\\|POS=NUM`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Neg`, `Case=Dat,Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Degree=Pos\\|POS=ADJ`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Case=Acc,Nom\\|Definite=Ind\\|Gender=Masc\\|NumType=Mult\\|Number=Sing\\|POS=NUM`, `Case=Acc,Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Part`, `Case=Acc,Nom\\|Definite=Def\\|Gender=Masc\\|NumForm=Word\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Acc,Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADV\\|Person=3\\|PronType=Int,Rel`, `Case=Dat,Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|Polite=Form\\|PronType=Prs` \| \| `parser` \| `ROOT`, `acl`, `advcl`, `advcl:tcl`, `advmod`, `advmod:tmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `ccomp:pmod`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `expl:impers`, `expl:pass`, `expl:poss`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:agent`, `nmod:pmod`, `nmod:tmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `1`, `3`, `4`, `6`, `8`, `12`, `14`, `16`, `19`, `23`, `29`, `30`, `32`, `35`, `37`, `39`, `40`, `45`, `46`, `47`, `51`, `53`, `54`, `57`, `61`, `63`, `65`, `66`, `69`, `33`, `71`, `73`, `76`, `79`, `80`, `84`, `86`, `87`, `88`, `89`, `92`, `95`, `97`, `100`, `103`, `105`, `107`, `110`, `112`, `113`, `115`, `117`, `120`, `121`, `123`, `125`, `126`, `128`, `130`, `132`, `133`, `136`, `140`, `143`, `145`, `147`, `58`, `148`, `151`, `154`, `157`, `159`, `163`, `165`, `167`, `171`, `174`, `176`, `178`, `180`, `182`, `184`, `185`, `187`, `188`, `190`, `192`, `196`, `197`, `199`, `200`, `202`, `206`, `208`, `210`, `211`, `213`, `215`, `216`, `219`, `221`, `223`, `225`, `226`, `228`, `230`, `232`, `236`, `238`, `241`, `242`, `244`, `246`, `248`, `251`, `253`, `255`, `258`, `260`, `264`, `265`, `267`, `272`, `275`, `278`, `280`, `281`, `284`, `286`, `287`, `290`, `291`, `292`, `295`, `296`, `298`, `300`, `301`, `302`, `305`, `306`, `307`, `309`, `310`, `312`, `314`, `315`, `317`, `319`, `321`, `323`, `324`, `327`, `330`, `332`, `334`, `335`, `337`, `339`, `340`, `343`, `344`, `345`, `346`, `350`, `351`, `353`, `355`, `357`, `360`, `362`, `366`, `368`, `369`, `370`, `371`, `224`, `374`, `376`, `378`, `379`, `381`, `384`, `385`, `386`, `388`, `389`, `391`, `392`, `393`, `396`, `398`, `399`, `403`, `406`, `408`, `411`, `413`, `415`, `418`, `422`, `423`, `426`, `427`, `431`, `433`, `436`, `438`, `440`, `442`, `445`, `448`, `449`, `450`, `451`, `452`, `454`, `455`, `457`, `459`, `460`, `462`, `464`, `466`, `468`, `471`, `472`, `473`, `474`, `475`, `478`, `481`, `482`, `485`, `486`, `488`, `490`, `492`, `494`, `495`, `497`, `498`, `499`, `501`, `503`, `504`, `506`, `508`, `510`, `513`, `514`, `515`, `516`, `518`, `519`, `521`, `523`, `524`, `526`, `527`, `528`, `530`, `533`, `96`, `537`, `538`, `539`, `542`, `544`, `545`, 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97.66 \| \| `MORPH_ACC` \| 93.64 \| \| `DEP_UAS` \| 92.56 \| \| `DEP_LAS` \| 87.49 \| \| `LEMMA_ACC` \| 93.99 \|
explosion/pl_udv25_polishlfg_trf	explosion	2021-12-10T22:19:07Z	0	0	spacy	[ "spacy", "token-classification", "pl", "license:gpl-3.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - pl license: gpl-3.0 model-index: - name: pl_udv25_polishlfg_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9561865506 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9904587436 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9542019693 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9592490842 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9738866205 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.9554648182 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9988538682 --- UD v2.5 benchmarking pipeline for UD_Polish-LFG \| Feature \| Description \| \| --- \| --- \| \| Name \| `pl_udv25_polishlfg_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 \| `GPL 3.0` \| \| Author \| [Explosion](https://explosion.ai) \| ### Label Scheme <details> <summary>View label scheme (4947 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `adj:pl:acc:f:com`, `adj:pl:acc:f:pos`, `adj:pl:acc:f:sup`, `adj:pl:acc:m1:com`, `adj:pl:acc:m1:pos`, `adj:pl:acc:m1:sup`, `adj:pl:acc:m2:pos`, `adj:pl:acc:m3:com`, `adj:pl:acc:m3:pos`, `adj:pl:acc:m3:sup`, `adj:pl:acc:n:com`, `adj:pl:acc:n:pos`, `adj:pl:acc:n:sup`, `adj:pl:dat:f:pos`, `adj:pl:dat:m1:com`, `adj:pl:dat:m1:pos`, `adj:pl:dat:m3:pos`, `adj:pl:dat:n:pos`, `adj:pl:gen:f:com`, `adj:pl:gen:f:pos`, `adj:pl:gen:f:sup`, `adj:pl:gen:m1:com`, `adj:pl:gen:m1:pos`, `adj:pl:gen:m1:sup`, `adj:pl:gen:m2:pos`, `adj:pl:gen:m2:sup`, `adj:pl:gen:m3:com`, `adj:pl:gen:m3:pos`, `adj:pl:gen:m3:sup`, `adj:pl:gen:n:com`, `adj:pl:gen:n:pos`, `adj:pl:inst:f:pos`, `adj:pl:inst:m1:pos`, `adj:pl:inst:m2:pos`, `adj:pl:inst:m3:pos`, `adj:pl:inst:n:pos`, `adj:pl:loc:f:pos`, 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`impt:pl:sec:imperf`, `impt:pl:sec:perf`, `impt:sg:sec:imperf`, `impt:sg:sec:perf`, `inf:imperf`, `inf:perf`, `interj`, `interp`, `num:pl:acc:f:congr`, `num:pl:acc:f:rec`, `num:pl:acc:m1:rec`, `num:pl:acc:m2:congr`, `num:pl:acc:m2:rec`, `num:pl:acc:m3:congr`, `num:pl:acc:m3:rec`, `num:pl:acc:n:congr`, `num:pl:acc:n:rec`, `num:pl:dat:f:congr`, `num:pl:dat:m1:congr`, `num:pl:dat:n:congr`, `num:pl:gen:f:congr`, `num:pl:gen:m1:congr`, `num:pl:gen:m2:congr`, `num:pl:gen:m3:congr`, `num:pl:gen:m3:rec`, `num:pl:gen:n:congr`, `num:pl:inst:f:congr`, `num:pl:inst:m1:congr`, `num:pl:inst:m2:congr`, `num:pl:inst:m3:congr`, `num:pl:inst:n:congr`, `num:pl:loc:f:congr`, `num:pl:loc:m1:congr`, `num:pl:loc:m3:congr`, `num:pl:loc:n:congr`, `num:pl:nom:f:congr`, `num:pl:nom:m1:congr`, `num:pl:nom:m2:congr`, `num:pl:nom:m3:congr`, `num:pl:nom:n:congr`, `pact:pl:acc:f:imperf:aff`, `pact:pl:acc:m1:imperf:aff`, `pact:pl:acc:m2:imperf:aff`, `pact:pl:acc:m3:imperf:aff`, `pact:pl:acc:n:imperf:aff`, 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`pact:sg:loc:m2:imperf:aff`, `pact:sg:loc:m3:imperf:aff`, `pact:sg:loc:n:imperf:aff`, `pact:sg:nom:f:imperf:aff`, `pact:sg:nom:m1:imperf:aff`, `pact:sg:nom:m2:imperf:aff`, `pact:sg:nom:m3:imperf:aff`, `pact:sg:nom:n:imperf:aff`, `pant:perf`, `pcon:imperf`, `ppas:pl:acc:f:imperf:aff`, `ppas:pl:acc:f:perf:aff`, `ppas:pl:acc:m1:perf:aff`, `ppas:pl:acc:m2:perf:aff`, `ppas:pl:acc:m3:imperf:aff`, `ppas:pl:acc:m3:perf:aff`, `ppas:pl:acc:m3:perf:neg`, `ppas:pl:acc:n:perf:aff`, `ppas:pl:dat:f:imperf:aff`, `ppas:pl:dat:f:perf:aff`, `ppas:pl:dat:m1:perf:aff`, `ppas:pl:dat:m3:perf:aff`, `ppas:pl:gen:f:imperf:aff`, `ppas:pl:gen:f:imperf:neg`, `ppas:pl:gen:f:perf:aff`, `ppas:pl:gen:m1:imperf:aff`, `ppas:pl:gen:m1:perf:aff`, `ppas:pl:gen:m2:perf:aff`, `ppas:pl:gen:m3:imperf:aff`, `ppas:pl:gen:m3:perf:aff`, `ppas:pl:gen:n:imperf:aff`, `ppas:pl:gen:n:perf:aff`, `ppas:pl:inst:f:perf:aff`, `ppas:pl:inst:m1:perf:aff`, `ppas:pl:inst:m3:perf:aff`, `ppas:pl:inst:n:perf:aff`, `ppas:pl:loc:f:imperf:neg`, `ppas:pl:loc:f:perf:aff`, `ppas:pl:loc:m1:imperf:aff`, `ppas:pl:loc:m3:imperf:aff`, `ppas:pl:loc:m3:perf:aff`, `ppas:pl:loc:n:imperf:aff`, `ppas:pl:loc:n:perf:aff`, `ppas:pl:nom:f:imperf:aff`, `ppas:pl:nom:f:imperf:neg`, `ppas:pl:nom:f:perf:aff`, `ppas:pl:nom:m1:imperf:aff`, `ppas:pl:nom:m1:perf:aff`, `ppas:pl:nom:m2:perf:aff`, `ppas:pl:nom:m3:imperf:aff`, `ppas:pl:nom:m3:perf:aff`, `ppas:pl:nom:n:imperf:aff`, `ppas:pl:nom:n:perf:aff`, `ppas:sg:acc:f:imperf:aff`, `ppas:sg:acc:f:perf:aff`, `ppas:sg:acc:m1:perf:aff`, `ppas:sg:acc:m2:perf:aff`, `ppas:sg:acc:m3:perf:aff`, `ppas:sg:acc:n:perf:aff`, `ppas:sg:dat:f:perf:aff`, `ppas:sg:dat:m1:perf:aff`, `ppas:sg:gen:f:imperf:aff`, `ppas:sg:gen:f:perf:aff`, `ppas:sg:gen:f:perf:neg`, `ppas:sg:gen:m1:imperf:aff`, `ppas:sg:gen:m1:perf:aff`, `ppas:sg:gen:m2:perf:aff`, `ppas:sg:gen:m3:imperf:aff`, `ppas:sg:gen:m3:perf:aff`, `ppas:sg:gen:n:imperf:aff`, `ppas:sg:gen:n:perf:aff`, `ppas:sg:inst:f:imperf:neg`, `ppas:sg:inst:f:perf:aff`, `ppas:sg:inst:m1:imperf:aff`, `ppas:sg:inst:m1:perf:aff`, `ppas:sg:inst:m3:perf:aff`, `ppas:sg:inst:n:perf:aff`, `ppas:sg:inst:n:perf:neg`, `ppas:sg:loc:f:imperf:aff`, `ppas:sg:loc:f:perf:aff`, `ppas:sg:loc:m3:imperf:aff`, `ppas:sg:loc:m3:perf:aff`, `ppas:sg:loc:n:imperf:aff`, `ppas:sg:loc:n:perf:aff`, `ppas:sg:nom:f:imperf:aff`, `ppas:sg:nom:f:imperf:neg`, `ppas:sg:nom:f:perf:aff`, `ppas:sg:nom:f:perf:neg`, `ppas:sg:nom:m1:imperf:aff`, `ppas:sg:nom:m1:perf:aff`, `ppas:sg:nom:m2:imperf:aff`, `ppas:sg:nom:m2:perf:aff`, `ppas:sg:nom:m3:imperf:aff`, `ppas:sg:nom:m3:perf:aff`, `ppas:sg:nom:m3:perf:neg`, `ppas:sg:nom:n:imperf:aff`, `ppas:sg:nom:n:perf:aff`, `ppron12:pl:acc:f:pri`, `ppron12:pl:acc:m1:pri`, `ppron12:pl:acc:m1:sec`, `ppron12:pl:acc:n:sec`, `ppron12:pl:dat:f:pri`, `ppron12:pl:dat:f:sec`, `ppron12:pl:dat:m1:pri`, `ppron12:pl:dat:m1:sec`, `ppron12:pl:gen:f:pri`, `ppron12:pl:gen:m1:pri`, `ppron12:pl:gen:m1:sec`, `ppron12:pl:inst:m1:pri`, `ppron12:pl:inst:m1:sec`, `ppron12:pl:loc:m1:pri`, `ppron12:pl:loc:m1:sec`, `ppron12:pl:nom:f:pri`, `ppron12:pl:nom:m1:pri`, `ppron12:pl:nom:m1:sec`, `ppron12:sg:acc:f:pri:akc`, `ppron12:sg:acc:f:sec:akc`, `ppron12:sg:acc:f:sec:nakc`, `ppron12:sg:acc:m1:pri:akc`, `ppron12:sg:acc:m1:pri:nakc`, `ppron12:sg:acc:m1:sec:akc`, `ppron12:sg:acc:m1:sec:nakc`, `ppron12:sg:acc:m2:pri:akc`, `ppron12:sg:acc:m2:sec:nakc`, `ppron12:sg:acc:m3:pri:akc`, `ppron12:sg:dat:f:pri:akc`, `ppron12:sg:dat:f:pri:nakc`, `ppron12:sg:dat:f:sec:akc`, `ppron12:sg:dat:f:sec:nakc`, `ppron12:sg:dat:m1:pri:akc`, `ppron12:sg:dat:m1:pri:nakc`, `ppron12:sg:dat:m1:sec:akc`, `ppron12:sg:dat:m1:sec:nakc`, `ppron12:sg:dat:m2:sec:nakc`, `ppron12:sg:dat:n:pri:nakc`, `ppron12:sg:gen:f:pri:akc`, `ppron12:sg:gen:f:sec:akc`, `ppron12:sg:gen:f:sec:nakc`, `ppron12:sg:gen:m1:pri:akc`, `ppron12:sg:gen:m1:sec:akc`, `ppron12:sg:gen:m1:sec:nakc`, `ppron12:sg:gen:m2:sec:akc`, `ppron12:sg:inst:f:pri`, `ppron12:sg:inst:f:sec`, `ppron12:sg:inst:m1:pri`, `ppron12:sg:inst:m1:sec`, `ppron12:sg:loc:f:pri`, `ppron12:sg:loc:f:sec`, `ppron12:sg:loc:m1:pri`, `ppron12:sg:loc:m1:sec`, `ppron12:sg:nom:f:pri`, `ppron12:sg:nom:f:sec`, `ppron12:sg:nom:m1:pri`, `ppron12:sg:nom:m1:sec`, `ppron12:sg:nom:m2:sec`, `ppron3:pl:acc:f:ter:akc:npraep`, `ppron3:pl:acc:f:ter:akc:praep`, `ppron3:pl:acc:m1:ter:akc:npraep`, `ppron3:pl:acc:m1:ter:akc:praep`, `ppron3:pl:acc:m2:ter:akc:npraep`, `ppron3:pl:acc:m3:ter:akc:npraep`, `ppron3:pl:acc:n:ter:akc:npraep`, `ppron3:pl:acc:n:ter:akc:praep`, `ppron3:pl:dat:f:ter:akc:npraep`, `ppron3:pl:dat:f:ter:akc:praep`, `ppron3:pl:dat:m1:ter:akc:npraep`, `ppron3:pl:dat:m3:ter:akc:praep`, `ppron3:pl:dat:n:ter:akc:npraep`, `ppron3:pl:gen:f:ter:akc:npraep`, `ppron3:pl:gen:f:ter:akc:praep`, `ppron3:pl:gen:m1:ter:akc:npraep`, `ppron3:pl:gen:m1:ter:akc:praep`, `ppron3:pl:gen:m2:ter:akc:npraep`, `ppron3:pl:gen:m3:ter:akc:npraep`, `ppron3:pl:gen:m3:ter:akc:praep`, `ppron3:pl:gen:n:ter:akc:npraep`, `ppron3:pl:gen:n:ter:akc:praep`, `ppron3:pl:inst:f:ter:akc:npraep`, `ppron3:pl:inst:f:ter:akc:praep`, `ppron3:pl:inst:m1:ter:akc:praep`, `ppron3:pl:inst:m2:ter:akc:praep`, `ppron3:pl:inst:n:ter:akc:praep`, `ppron3:pl:loc:f:ter:akc:praep`, `ppron3:pl:loc:m1:ter:akc:praep`, `ppron3:pl:loc:m3:ter:akc:praep`, `ppron3:pl:loc:n:ter:akc:praep`, `ppron3:pl:nom:f:ter:akc:npraep`, `ppron3:pl:nom:m1:ter:akc:npraep`, `ppron3:pl:nom:m3:ter:akc:npraep`, `ppron3:pl:nom:n:ter:akc:npraep`, `ppron3:sg:acc:f:ter:akc:npraep`, `ppron3:sg:acc:f:ter:akc:praep`, `ppron3:sg:acc:m1:ter:akc:npraep`, `ppron3:sg:acc:m1:ter:akc:praep`, `ppron3:sg:acc:m1:ter:nakc:npraep`, `ppron3:sg:acc:m1:ter:nakc:praep`, `ppron3:sg:acc:m2:ter:akc:praep`, `ppron3:sg:acc:m2:ter:nakc:npraep`, `ppron3:sg:acc:m3:ter:akc:praep`, `ppron3:sg:acc:m3:ter:nakc:npraep`, `ppron3:sg:acc:m3:ter:nakc:praep`, `ppron3:sg:acc:n:ter:akc:npraep`, `ppron3:sg:acc:n:ter:akc:praep`, `ppron3:sg:dat:f:ter:akc:npraep`, `ppron3:sg:dat:f:ter:akc:praep`, `ppron3:sg:dat:m1:ter:akc:npraep`, `ppron3:sg:dat:m1:ter:akc:praep`, `ppron3:sg:dat:m1:ter:nakc:npraep`, `ppron3:sg:dat:m2:ter:nakc:npraep`, `ppron3:sg:dat:m3:ter:nakc:npraep`, `ppron3:sg:dat:n:ter:nakc:npraep`, `ppron3:sg:gen:f:ter:akc:npraep`, `ppron3:sg:gen:f:ter:akc:praep`, `ppron3:sg:gen:m1:ter:akc:npraep`, `ppron3:sg:gen:m1:ter:akc:praep`, `ppron3:sg:gen:m1:ter:nakc:npraep`, `ppron3:sg:gen:m2:ter:akc:npraep`, `ppron3:sg:gen:m3:ter:akc:npraep`, `ppron3:sg:gen:m3:ter:akc:praep`, `ppron3:sg:gen:m3:ter:nakc:npraep`, `ppron3:sg:gen:n:ter:akc:npraep`, `ppron3:sg:gen:n:ter:akc:praep`, `ppron3:sg:gen:n:ter:nakc:npraep`, `ppron3:sg:inst:f:ter:akc:npraep`, `ppron3:sg:inst:f:ter:akc:praep`, `ppron3:sg:inst:m1:ter:akc:npraep`, `ppron3:sg:inst:m1:ter:akc:praep`, `ppron3:sg:inst:m2:ter:akc:npraep`, `ppron3:sg:inst:m2:ter:akc:praep`, `ppron3:sg:inst:m3:ter:akc:npraep`, `ppron3:sg:inst:m3:ter:akc:praep`, `ppron3:sg:inst:n:ter:akc:npraep`, `ppron3:sg:inst:n:ter:akc:praep`, `ppron3:sg:loc:f:ter:akc:praep`, `ppron3:sg:loc:m1:ter:akc:praep`, `ppron3:sg:loc:m2:ter:akc:praep`, `ppron3:sg:loc:m3:ter:akc:praep`, `ppron3:sg:loc:n:ter:akc:praep`, `ppron3:sg:nom:f:ter:akc:npraep`, `ppron3:sg:nom:m1:ter:akc:npraep`, `ppron3:sg:nom:m2:ter:akc:npraep`, `ppron3:sg:nom:m3:ter:akc:npraep`, `ppron3:sg:nom:n:ter:akc:npraep`, `praet:pl:f:imperf`, `praet:pl:f:perf`, `praet:pl:m1:imperf`, `praet:pl:m1:perf`, `praet:pl:m2:imperf`, `praet:pl:m2:perf`, `praet:pl:m3:imperf`, `praet:pl:m3:perf`, `praet:pl:n:imperf`, `praet:pl:n:perf`, `praet:sg:f:imperf`, `praet:sg:f:perf`, `praet:sg:m1:imperf`, `praet:sg:m1:imperf:agl`, `praet:sg:m1:imperf:nagl`, `praet:sg:m1:perf`, `praet:sg:m1:perf:agl`, `praet:sg:m1:perf:nagl`, `praet:sg:m2:imperf`, `praet:sg:m2:imperf:nagl`, `praet:sg:m2:perf`, `praet:sg:m2:perf:nagl`, `praet:sg:m3:imperf`, `praet:sg:m3:imperf:nagl`, `praet:sg:m3:perf`, `praet:sg:m3:perf:nagl`, `praet:sg:n:imperf`, `praet:sg:n:perf`, `pred`, `prep:acc`, `prep:acc:nwok`, `prep:acc:wok`, `prep:dat`, `prep:gen`, `prep:gen:nwok`, `prep:gen:wok`, `prep:inst`, `prep:inst:nwok`, `prep:inst:wok`, `prep:loc`, `prep:loc:nwok`, `prep:loc:wok`, `prep:nom`, `qub`, `qub:nwok`, `qub:wok`, `siebie:acc`, `siebie:dat`, `siebie:gen`, `siebie:inst`, `siebie:loc`, `subst:pl:acc:f`, `subst:pl:acc:m1`, `subst:pl:acc:m2`, `subst:pl:acc:m3`, `subst:pl:acc:n`, `subst:pl:dat:f`, `subst:pl:dat:m1`, `subst:pl:dat:m3`, `subst:pl:dat:n`, `subst:pl:gen:f`, `subst:pl:gen:m1`, `subst:pl:gen:m2`, `subst:pl:gen:m3`, `subst:pl:gen:n`, `subst:pl:inst:f`, `subst:pl:inst:m1`, `subst:pl:inst:m2`, `subst:pl:inst:m3`, `subst:pl:inst:n`, `subst:pl:loc:f`, `subst:pl:loc:m1`, `subst:pl:loc:m2`, `subst:pl:loc:m3`, `subst:pl:loc:n`, `subst:pl:nom:f`, `subst:pl:nom:m1`, `subst:pl:nom:m2`, `subst:pl:nom:m3`, `subst:pl:nom:n`, `subst:pl:voc:m1`, `subst:sg:acc:f`, `subst:sg:acc:m1`, `subst:sg:acc:m2`, `subst:sg:acc:m3`, `subst:sg:acc:n`, `subst:sg:dat:f`, `subst:sg:dat:m1`, `subst:sg:dat:m2`, `subst:sg:dat:m3`, `subst:sg:dat:n`, `subst:sg:gen:f`, `subst:sg:gen:m1`, `subst:sg:gen:m2`, `subst:sg:gen:m3`, `subst:sg:gen:n`, `subst:sg:inst:f`, `subst:sg:inst:m1`, `subst:sg:inst:m2`, `subst:sg:inst:m3`, `subst:sg:inst:n`, `subst:sg:loc:f`, `subst:sg:loc:m1`, `subst:sg:loc:m2`, `subst:sg:loc:m3`, `subst:sg:loc:n`, `subst:sg:nom:f`, `subst:sg:nom:m1`, `subst:sg:nom:m2`, `subst:sg:nom:m3`, `subst:sg:nom:n`, `subst:sg:voc:f`, `subst:sg:voc:m1`, `subst:sg:voc:m3`, `winien:pl:f:imperf`, `winien:pl:m1:imperf`, `winien:pl:m3:imperf`, `winien:sg:f:imperf`, `winien:sg:m1:imperf`, `winien:sg:m3:imperf`, `winien:sg:n:imperf` \| \| `morphologizer` \| `Case=Ins\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc3`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `POS=PUNCT\\|PunctType=Peri`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc3`, `Aspect=Imp\\|Gender=Fem\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `POS=PUNCT\\|PunctType=Comm`, `Case=Nom\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc1`, `Agglutination=Nagl\\|Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `AdpType=Prep\\|POS=ADP`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `AdpType=Prep\\|POS=ADP\\|Variant=Short`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc3`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc3`, `Aspect=Perf\\|Gender=Fem\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc1`, `POS=SCONJ`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Aspect=Perf\\|POS=VERB\\|VerbForm=Inf\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Aspect=Perf\\|Mood=Ind\\|POS=VERB\\|Person=0\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc3`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc1`, `Case=Nom\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Gen\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Acc\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Aspect=Imp\\|Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc3`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc3`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc3`, `AdpType=Post\\|POS=ADP`, `Case=Loc\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc1`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc1`, `Case=Nom\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `POS=PUNCT`, `Aspect=Imp\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=PUNCT\\|PunctType=Dash`, `Case=Loc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc3`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=PUNCT\\|PunctType=Excl`, `Case=Nom\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Gen\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Case=Gen\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Aspect=Perf\\|Gender=Neut\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc3`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `POS=CCONJ`, `Aspect=Imp\\|Number=Sing\\|POS=AUX\\|Person=2\\|Variant=Short`, `Degree=Pos\\|POS=ADV`, `POS=PUNCT\\|PunctType=Qest`, `Mood=Cnd\\|POS=AUX`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Aspect=Imp\\|POS=VERB\\|VerbForm=Inf\\|Voice=Act`, `Case=Acc\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `Case=Nom\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc3`, `Mood=Ind\\|POS=AUX\\|Tense=Pres\\|VerbForm=Fin\\|VerbType=Quasi`, `Case=Nom\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Degree=Pos\\|POS=ADV\\|PronType=Dem`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Number=Plur\\|POS=AUX\\|Person=1\\|Variant=Short`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc3`, `Aspect=Imp\\|POS=AUX\\|VerbForm=Inf\\|Voice=Act`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Ins\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc1`, `Degree=Sup\\|POS=ADV`, `POS=ADV\\|PronType=Dem`, `Aspect=Imp\\|Gender=Neut\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Mood=Ind\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin\\|VerbType=Quasi`, `Aspect=Imp\\|Gender=Neut\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `POS=PART`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `POS=ADV\\|PronType=Int`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1`, `POS=PART\\|Polarity=Neg`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `POS=PART\\|PartType=Int`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc1`, `Case=Acc\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc3`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc2`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc1`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc1`, `Aspect=Imp\\|Gender=Neut\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc3`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Nom\\|Degree=Cmp\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Variant=Short`, `Degree=Cmp\\|POS=ADV`, `Case=Acc\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Case=Gen\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc1`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel\\|SubGender=Masc1`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Int\\|SubGender=Masc3`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Imp\\|Number=Sing\\|POS=AUX\\|Person=1\\|Variant=Short`, `AdpType=Prep\\|POS=ADP\\|Variant=Long`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Number=Sing\\|POS=AUX\\|Person=1\\|Variant=Long`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc1`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Dat\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc2`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `POS=ADV\\|PronType=Neg`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc1`, `POS=ADJ\\|PrepCase=Pre`, `Degree=Pos\\|POS=ADV\\|PronType=Int`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc1`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Loc\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int\\|SubGender=Masc2`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Int\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Aspect=Imp\\|Number=Sing\\|POS=AUX\\|Person=2\\|Variant=Long`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Short`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int\\|SubGender=Masc1`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Variant=Short`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Nom\\|Emphatic=Yes\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1`, `Hyph=Yes\\|POS=ADJ`, `Case=Acc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Variant=Long`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV\\|PronType=Ind`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Short`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV\\|PronType=Rel`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind\\|SubGender=Masc1`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Neg\\|SubGender=Masc1`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `POS=PUNCT\\|PunctSide=Ini\\|PunctType=Quot`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Imp\\|Gender=Fem\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Nom\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Neg\\|SubGender=Masc1`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Aspect=Perf\\|Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Mood=Imp\\|POS=AUX`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc2`, `POS=PUNCT\\|PunctSide=Ini\\|PunctType=Brck`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Short`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc2`, `POS=PUNCT\\|PunctSide=Fin\\|PunctType=Brck`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc3`, `Case=Loc\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Nom\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Fut\\|VerbForm=Fin`, `Case=Acc\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Fut\\|VerbForm=Fin`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc2`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|POS=VERB\\|Person=0\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc3`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|Polite=Depr\\|SubGender=Masc2`, `Case=Acc\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Degree=Pos\\|POS=ADV\\|PronType=Ind`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Short`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Nom\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin`, `Case=Nom\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `POS=INTJ`, `POS=ADV\\|PronType=Tot`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Variant=Long`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc1`, `Agglutination=Nagl\\|Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Fut\\|VerbForm=Fin`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc2`, `Case=Nom\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc2`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Tense=Fut\\|VerbForm=Fin`, `Aspect=Imp\\|Gender=Fem\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Aspect=Imp\\|Number=Plur\\|POS=AUX\\|Person=2\\|Variant=Short`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `Case=Ins\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Case=Acc\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Imp\\|Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin`, `Case=Ins\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc1`, `Case=Gen\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc2`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Variant=Short`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Case=Nom\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc2`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc3`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Aspect=Imp\\|Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Loc\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|Gender=Fem\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Aspect=Imp\\|Gender=Fem\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Aspect=Imp\\|Gender=Fem\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Perf\\|Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind\\|SubGender=Masc1`, `Case=Acc\\|Emphatic=Yes\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Aspect=Imp\\|Gender=Neut\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc2`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind\\|SubGender=Masc1`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Fut\\|VerbForm=Fin`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc1`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Agglutination=Agl\\|Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Short`, `Aspect=Imp\\|Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Acc\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `Case=Acc\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Aspect=Perf\\|Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Aspect=Perf\\|Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc3`, `Aspect=Perf\\|Gender=Neut\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc3`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc2`, `Case=Ins\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Aspect=Perf\\|Gender=Fem\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Aspect=Imp\\|Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Nom\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Agglutination=Nagl\\|Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Imp\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=1\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Short`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Agglutination=Nagl\\|Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Short`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Case=Loc\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Case=Gen\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Aspect=Imp\\|Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc1`, `Case=Acc\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Variant=Long`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Variant=Long`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `POS=PUNCT\\|PunctSide=Fin\\|PunctType=Quot`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc3`, `Case=Acc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Loc\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|POS=VERB\\|Tense=Pres\\|VerbForm=Conv\\|Voice=Act`, `Case=Ins\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc3`, `Case=Loc\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Acc\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Case=Ins\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Case=Loc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc3`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Case=Ins\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Ins\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc1`, `Case=Nom\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Perf\\|Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Short`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc2`, `Case=Acc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc2`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Gen\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc2`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|SubGender=Masc2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `POS=SCONJ\\|PronType=Rel`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Tot\\|SubGender=Masc1`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Gender=Fem\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Gender=Fem\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc2`, `Case=Ins\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc2`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc2`, `Aspect=Imp\\|Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc1`, `Aspect=Perf\\|Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Nom\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc2`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Short`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc3`, `Aspect=Perf\\|Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Ins\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Case=Loc\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|SubGender=Masc2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Aspect=Imp\\|Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc1`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Case=Nom\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Case=Nom\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Aspect=Imp\\|Gender=Neut\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Aspect=Imp\\|Mood=Imp\\|Number=Sing\\|POS=AUX\\|Person=2\\|VerbForm=Fin\\|Voice=Act`, `POS=PUNCT\\|PunctType=Semi`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Fut\\|VerbForm=Fin`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Tense=Fut\\|VerbForm=Fin`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Short`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin`, `Aspect=Imp\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Neg\\|SubGender=Masc3`, `Case=Gen\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Ins\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Aspect=Imp\\|Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc2`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc2`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Loc\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc2`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Case=Ins\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `POS=ADJ\\|Variant=Short`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Perf\\|Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc1`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc1`, `Aspect=Perf\\|Gender=Neut\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Ins\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Voc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int\\|SubGender=Masc1`, `Aspect=Perf\\|Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Short`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Gen\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `Case=Ins\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Act`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc3`, `Case=Voc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc2`, `Case=Ins\\|Emphatic=Yes\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc2`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Variant=Long`, `Case=Acc\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc3`, `Aspect=Perf\\|Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Neg\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Aspect=Perf\\|Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|SubGender=Masc1\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc3`, `Case=Acc\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Variant=Long`, `Aspect=Imp\\|Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc1`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc2`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc2`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Aspect=Perf\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Case=Dat\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Aspect=Perf\\|Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Ins\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc2`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Tot\\|SubGender=Masc1`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Perf\\|Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Int\\|SubGender=Masc1`, `Emphatic=Yes\\|POS=PART\\|PartType=Int`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Ins\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Nom\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Aspect=Perf\\|Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Nom\\|Emphatic=Yes\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Aspect=Perf\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Loc\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc1`, `Case=Acc\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Aspect=Perf\\|Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Gen\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Tot\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Case=Gen\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Ins\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Neg\\|SubGender=Masc1`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Aspect=Perf\\|Case=Ins\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Neg\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Voc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Nom\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=1\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Gender=Neut\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int\\|SubGender=Masc1`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel\\|SubGender=Masc3`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc3`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Aspect=Imp\\|Gender=Neut\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Ins\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc3`, `Case=Ins\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Aspect=Imp\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|SubGender=Masc2`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Loc\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Pos\\|VerbForm=Vnoun`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|POS=AUX\\|VerbForm=Inf\\|Voice=Act`, `Case=Voc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|SubGender=Masc2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc2`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Ind\\|SubGender=Masc3`, `Emphatic=Yes\\|POS=ADV\\|PronType=Int`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Aspect=Perf\\|POS=VERB\\|Tense=Past\\|VerbForm=Conv\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc2`, `Aspect=Perf\\|Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc2`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|SubGender=Masc3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|SubGender=Masc2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int\\|SubGender=Masc1`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|NumType=Frac\\|Number=Plur\\|POS=NUM\\|SubGender=Masc3`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Ins\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Acc\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Int\\|SubGender=Masc1`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Tot\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Neg\\|SubGender=Masc3`, `Agglutination=Nagl\\|Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc3\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Case=Ins\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Short`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Int\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Int\\|SubGender=Masc3`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Int\\|SubGender=Masc3`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int\\|SubGender=Masc3`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Nom\\|Emphatic=Yes\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int\\|SubGender=Masc3`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Aspect=Imp\\|Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc2`, `Case=Gen\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc2`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Perf\\|Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Neg\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Variant=Short`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `POS=PART\\|Variant=Short`, `Case=Acc\\|Gender=Fem\\|NumType=Frac\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc1`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Perf\\|Case=Ins\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Case=Dat\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Agglutination=Agl\\|Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Ins\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Acc\\|Degree=Cmp\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Case=Ins\\|Emphatic=Yes\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int\\|SubGender=Masc1`, `Aspect=Perf\\|Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Mood=Imp\\|Number=Plur\\|POS=AUX\\|Person=2\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Emphatic=Yes\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int\\|SubGender=Masc1`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|SubGender=Masc2`, `Case=Ins\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Gender=Fem\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Dat\\|Degree=Pos\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Aspect=Imp\\|Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel\\|SubGender=Masc3`, `Case=Gen\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel\\|SubGender=Masc3`, `Aspect=Perf\\|Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Ins\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Loc\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Short`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Case=Ins\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Neg\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Short`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Case=Acc\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Nom\\|Gender=Neut\\|NumType=Card\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Loc\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc2`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel\\|SubGender=Masc3`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Ins\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Perf\\|Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc3`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Neg`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=3\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Neg\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel\\|SubGender=Masc1`, `Case=Ins\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Dat\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc1`, `Case=Ins\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc2`, `Case=Gen\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc2`, `Case=Dat\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Ind\\|SubGender=Masc3`, `Case=Ins\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Short`, `Aspect=Imp\\|Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Short`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Neg`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Neg\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Tense=Fut\\|VerbForm=Fin`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Imp\\|Gender=Neut\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc3`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|SubGender=Masc2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Neg\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc1`, `Case=Gen\\|Degree=Cmp\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Ind`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Gen\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Int\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc2`, `Case=Ins\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Neg\\|VerbForm=Part\\|Voice=Pass`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Case=Loc\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel\\|SubGender=Masc1`, `Case=Acc\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Int`, `Case=Acc\\|Emphatic=Yes\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Gen\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Dat\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc1`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Aspect=Imp\\|Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Neg\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Loc\\|Degree=Sup\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Plur\\|POS=PROPN`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc2`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc2`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Aspect=Perf\\|Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Aspect=Imp\\|Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Loc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc2`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Int\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Neg\\|SubGender=Masc3`, `Agglutination=Nagl\\|Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc2\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Ins\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Aspect=Imp\\|Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Neg\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Neg\\|SubGender=Masc1`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc1`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Neg`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Aspect=Perf\\|Gender=Masc\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|SubGender=Masc1\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=PART\\|Variant=Long`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Aspect=Perf\\|Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Ins\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Ins\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc2`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Short`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Acc\\|Gender=Neut\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Acc\\|Degree=Sup\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel\\|SubGender=Masc2`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Aspect=Imp\\|Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Voc\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Aspect=Imp\\|Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Case=Gen\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc2`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc3`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc3`, `Case=Ins\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PROPN\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Gen\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Case=Loc\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Short`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc1`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc3`, `Aspect=Imp\\|Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Ins\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|Polite=Depr\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1`, `Case=Nom\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Gender=Masc\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|SubGender=Masc3\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel\\|SubGender=Masc3`, `Case=Dat\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Short`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Ind`, `Aspect=Perf\\|Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Neg\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc2`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Neg\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|SubGender=Masc2\\|Variant=Long`, `Aspect=Imp\\|Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Gender=Fem\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Loc\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Aspect=Perf\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Neg\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Gen\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc2`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc1\\|Variant=Long`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Neg`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Ins\\|Gender=Neut\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc1`, `Case=Loc\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Loc\\|Degree=Cmp\\|Gender=Neut\\|Number=Plur\\|POS=ADJ`, `Aspect=Imp\\|Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=NOUN\\|Polarity=Neg\\|VerbForm=Vnoun`, `Aspect=Imp\\|Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Variant=Short`, `Aspect=Imp\\|Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Emphatic=Yes\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Loc\\|Gender=Neut\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int\\|SubGender=Masc3`, `Case=Loc\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PrepCase=Pre\\|PronType=Prs\\|Variant=Long`, `Aspect=Imp\\|Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Neg\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|SubGender=Masc1`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Polarity=Neg\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc3`, `Aspect=Imp\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Tense=Fut\\|VerbForm=Fin`, `Aspect=Perf\\|Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|SubGender=Masc3`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc2`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|Degree=Cmp\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel\\|SubGender=Masc1`, `Case=Loc\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Aspect=Imp\\|Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Neut\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem\\|SubGender=Masc2`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|SubGender=Masc2`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc2\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Neg\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|SubGender=Masc3\\|Variant=Long`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|SubGender=Masc1`, `Aspect=Imp\\|Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc3\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|VerbForm=Part\\|Voice=Pass`, `Case=Ins\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PrepCase=Npr\\|PronType=Prs\\|Variant=Long`, `Case=Ins\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Ins\\|Emphatic=Yes\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Ins\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|SubGender=Masc2`, `Aspect=Perf\\|Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Polarity=Pos\\|SubGender=Masc1\\|VerbForm=Part\\|Voice=Pass`, `Case=Ins\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Ins\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int\\|SubGender=Masc2`, `Case=Ins\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes\\|SubGender=Masc1`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel\\|SubGender=Masc1`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs` \| \| `parser` \| `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:aglt`, `aux:mood`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `ccomp:obj`, `conj`, `cop`, `cop:locat`, `csubj`, `dep`, `det`, `discourse`, `expl:impers`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `punct`, `vocative`, `xcomp`, `xcomp:obj` \| \| `experimental_edit_tree_lemmatizer` \| `1`, `3`, `5`, `6`, `7`, `9`, `11`, `13`, `15`, `17`, `19`, `21`, `23`, `25`, `26`, `30`, `32`, `34`, `38`, `39`, `41`, `43`, `46`, `48`, `51`, `53`, `55`, `57`, `60`, `62`, `63`, `66`, `68`, `70`, `72`, `75`, `77`, `79`, `81`, `82`, `84`, `86`, `88`, `91`, `93`, `94`, `96`, `98`, `99`, `101`, `104`, `107`, `109`, `111`, `113`, `117`, `119`, `122`, `124`, `126`, `127`, `128`, `130`, `131`, `133`, `134`, `136`, `137`, `139`, `141`, `143`, `144`, `146`, `148`, `150`, `152`, `154`, `156`, `158`, `160`, `162`, `165`, `167`, `169`, `171`, `172`, `173`, `174`, `175`, `177`, `179`, `180`, `183`, `185`, `187`, `189`, `191`, `193`, `195`, `197`, `198`, `200`, `204`, `206`, `207`, `209`, `210`, `211`, `213`, `215`, `217`, `219`, `221`, `223`, `226`, `228`, `230`, `232`, `235`, `236`, `238`, `240`, `242`, `243`, `245`, `246`, `248`, `250`, `252`, `254`, `256`, `259`, `261`, `263`, `264`, `266`, `268`, `270`, `272`, `273`, `275`, `277`, `279`, `280`, `282`, `284`, `286`, `288`, `289`, `291`, `293`, `295`, `297`, `300`, `302`, `303`, `305`, `306`, `307`, `309`, `311`, `313`, `315`, `318`, `320`, `321`, `323`, `325`, `327`, `328`, `331`, `333`, `335`, `340`, `342`, `344`, `345`, `347`, `350`, `352`, `353`, `355`, `356`, `358`, `361`, `363`, `365`, `290`, `367`, `369`, `371`, `373`, `375`, `377`, `379`, `381`, `383`, `385`, `387`, `389`, `392`, `394`, `396`, `398`, `400`, `403`, `405`, `407`, `410`, `411`, `413`, `415`, `417`, `418`, `422`, `424`, `426`, `428`, `430`, `432`, `434`, `436`, `438`, `440`, `442`, `444`, `447`, `449`, `451`, `453`, `455`, `457`, `459`, `463`, `465`, `467`, `471`, `473`, `475`, `477`, `479`, `481`, `482`, `484`, `486`, `488`, `490`, `492`, `493`, `495`, `497`, `498`, `499`, `501`, `502`, `504`, `507`, `509`, `511`, `513`, `515`, `517`, `519`, `521`, `522`, `524`, `526`, `527`, `529`, `531`, `534`, `536`, `539`, `541`, `542`, `543`, `545`, `547`, `549`, `551`, `552`, `555`, `557`, `558`, `560`, `561`, `563`, `565`, `567`, `568`, `570`, `572`, `574`, `576`, `578`, `580`, `582`, `584`, `586`, `589`, `591`, `592`, `594`, `596`, `597`, `599`, `601`, `602`, `603`, `605`, `607`, `609`, `610`, `612`, `614`, `616`, `620`, `622`, `624`, `626`, `628`, `631`, `633`, `635`, `637`, `639`, `641`, `643`, `648`, `650`, `652`, `654`, `656`, `658`, `660`, `662`, `663`, `665`, `667`, `669`, `671`, `673`, `677`, `679`, `681`, `683`, `685`, `688`, `690`, `692`, `694`, `696`, `698`, `700`, `704`, `706`, `708`, `712`, `714`, `716`, `718`, `719`, `721`, `722`, `724`, `726`, `729`, `731`, `733`, `734`, `736`, `738`, `740`, `742`, `744`, `746`, `748`, `750`, `752`, `753`, `755`, `757`, `759`, `761`, `763`, `766`, `768`, `770`, `772`, `774`, `776`, `778`, `780`, `783`, `785`, `787`, `790`, `792`, `794`, `796`, `798`, `801`, `803`, `805`, `807`, `809`, `811`, `813`, `815`, `817`, `818`, `820`, `822`, `824`, `826`, `828`, `829`, `830`, `834`, `837`, `839`, `841`, `843`, `844`, `845`, `849`, `851`, `853`, `855`, `857`, `859`, `861`, `863`, `866`, `867`, `869`, `870`, `872`, `874`, `875`, `879`, `881`, `883`, `884`, `887`, `889`, `891`, `892`, `893`, `894`, `895`, `897`, `898`, `900`, `901`, `903`, `904`, `906`, `907`, `908`, `909`, `913`, `915`, `917`, `919`, `920`, `922`, `924`, `926`, `928`, `930`, `931`, `933`, `934`, `935`, `937`, `938`, `939`, `940`, `942`, `944`, `948`, `952`, `953`, `954`, `955`, `957`, `961`, `963`, `966`, `968`, `969`, `970`, `973`, `974`, `976`, `977`, `979`, `981`, `982`, `984`, `986`, `988`, `989`, `991`, `993`, `995`, `997`, `999`, `1000`, `1002`, `1004`, `1006`, `1009`, `1010`, `1012`, `1016`, `1018`, `1020`, `1023`, `1026`, `1027`, `1028`, `1029`, `1032`, `1033`, `1034`, `1035`, `1036`, `1038`, `1040`, `1042`, `1043`, `1046`, `1048`, `1049`, `1051`, `1052`, `1054`, `1055`, `1056`, `1057`, `1059`, `1060`, `1061`, `1063`, `1064`, `1065`, `1067`, `1070`, `1072`, `1074`, `1075`, `1076`, `1077`, `1079`, `1080`, `1081`, `1083`, `1086`, `1088`, `1090`, `1092`, `1093`, `1095`, `1098`, `1103`, `1106`, `1108`, `1110`, `1112`, `1114`, `1116`, `1118`, `1121`, `1122`, `1124`, `1126`, `1128`, `1130`, `1132`, `1134`, `1137`, `1138`, `1140`, `1142`, `1144`, `1148`, `1150`, `1151`, `1152`, `1154`, `1156`, `1157`, `1159`, `1160`, `1161`, `1162`, `1164`, `1167`, `1169`, `1170`, `1173`, `1174`, `1176`, `1177`, `1178`, `1180`, `1183`, `1185`, `1186`, `1188`, `1190`, `1191`, `1193`, `1196`, `1198`, `1199`, `1200`, `1202`, `1203`, `1204`, `1206`, `1208`, `1211`, `1212`, `1215`, `1216`, `1219`, `1220`, `1221`, `1222`, `1223`, `1224`, `1225`, `1227`, `1229`, `1231`, `1233`, `1235`, `1237`, `1239`, `1240`, `1241`, `1242`, `1244`, `1246`, `1248`, `1249`, `1250`, `1251`, `1254`, `1255`, `1258`, `1259`, `1262`, `1263`, `1267`, `1268`, `1269`, `1270`, `1272`, `1273`, `1275`, `1279`, `1281`, `1282`, `1284`, `1285`, `1287`, `1289`, `1290`, `1292`, `1294`, `1295`, `1296`, `1297`, `1299`, `1300`, `1302`, `1304`, `1308`, `1312`, `1314`, `1316`, `1318`, `1319`, `1320`, `1321`, `1323`, `1325`, `1326`, `1328`, `1330`, `1331`, `1333`, `1334`, `1336`, `1337`, `1339`, `1340`, `1341`, `1343`, `1344`, `1346`, `1348`, `1350`, `1351`, `1354`, `1356`, `1359`, `1361`, `1363`, `1365`, `1367`, `1368`, `1369`, `1370`, `1372`, `1374`, `1031`, `1376`, `1378`, `1380`, `1383`, `1385`, `1387`, `1389`, `1391`, `1394`, `1395`, `1397`, `1399`, `1401`, `1402`, `1404`, `1405`, `1407`, `1408`, `1410`, `1411`, `1412`, `1413`, `1415`, `1418`, `1419`, `1420`, `1421`, `1422`, `1425`, `1427`, `1430`, `1432`, `1433`, `1434`, `1436`, `1437`, `1439`, `1443`, `1445`, `1447`, `1449`, `1451`, `1453`, `1455`, `1457`, `1459`, `1463`, `1465`, `1466`, `1468`, `1469`, `1471`, `1473`, `1475`, `1476`, `1478`, `1480`, `1483`, `1486`, `1488`, `1489`, `1491`, `1493`, `1495`, `1496`, `1498`, `1500`, `1501`, `1503`, `1504`, `1505`, `1506`, `1507`, `1509`, `1510`, `1511`, `1512`, `1513`, `1514`, `827`, `1516`, `1518`, `1520`, `1522`, `1524`, `1526`, `1527`, `1528`, `1529`, `1531`, `1532`, `1534`, `1535`, `1537`, `1538`, `1539`, `1541`, `1543`, `1544`, `1546`, `1549`, `1550`, `1552`, `1554`, `1556`, `1559`, `1561`, `1563`, `1564`, `1565`, `1566`, `1568`, `1572`, `1573`, `1574`, `1576`, `1577`, `1578`, `1579`, `1580`, `1581`, `1584`, `1585`, `1587`, `1590`, `1591`, `1593`, `1595`, `1596`, `1597`, `1598`, `1600`, `1603`, `1604`, `1605`, `1607`, `1608`, `1609`, `1610`, `1612`, `1614`, `1616`, `1618`, `1620`, `1622`, `1624`, `1626`, `1628`, `1630`, `1631`, `1632`, `1634`, `1636`, `1638`, `1640`, `1642`, `1644`, `1646`, `1648`, `1650`, `1652`, `1654`, `1655`, `1657`, `1659`, `1660`, `1662`, `1664`, `1666`, `1668`, `1671`, `1673`, `1675`, `1676`, `1680`, `1681`, `1683`, `1684`, `1686`, `1688`, `1689`, `1691`, `1692`, `1693`, `1695`, `1696`, `1697`, `1698`, `1699`, `1701`, `1703`, `1705`, `1707`, `1709`, `1711`, `1713`, `1714`, `1717`, `1718`, `1719`, `1722`, `1723`, `1724`, `1726`, `1728`, `1730`, `1731`, `1733`, `1735`, `1737`, `1738`, `1739`, 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`1941`, `1942`, `1943`, `1944`, `1945`, `1947`, `1949`, `1950`, `1953`, `1954`, `1955`, `1957`, `1958`, `1960`, `1961`, `1963`, `1965`, `1967`, `1968`, `1969`, `1971`, `1972`, `1973`, `1975`, `1978`, `1979`, `1980`, `1981`, `1983`, `1984`, `1985`, `1987`, `1988`, `1989`, `1990`, `1991`, `1992`, `1994`, `1995`, `1996`, `1997`, `1999`, `2002`, `2005`, `2007`, `2008`, `2010`, `2012`, `2013`, `2014`, `2015`, `2016`, `2017`, `2019`, `2020`, `2021`, `2024`, `2025`, `2027`, `2028`, `2029`, `2031`, `2032`, `2034`, `2036`, `2038`, `2040`, `2041`, `2042`, `2044`, `2046`, `2047`, `2048`, `1450`, `2050`, `2052`, `2053`, `2054`, `2055`, `2057`, `2060`, `2062`, `2063`, `2065`, `2068`, `2070`, `2072`, `2073`, `2075`, `2076`, `2078`, `2079`, `2081`, `2082`, `2085`, `2087`, `2089`, `2090`, `2091`, `2093`, `2094`, `2095`, `2096`, `2097`, `2098`, `2100`, `2102`, `2103`, `2104`, `2105`, `2107`, `2108`, `2109`, `2111`, `2113`, `2114`, `2115`, `2116`, `2118`, `2120`, `2121`, `2122`, `2123`, `2124`, `2125`, 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explosion/xx_udv25_oldfrenchsrcmf_trf	explosion	2021-12-10T21:31:33Z	2	0	spacy	[ "spacy", "token-classification", "multilingual", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - multilingual license: cc-by-sa-4.0 model-index: - name: xx_udv25_oldfrenchsrcmf_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9640594402 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9652113812 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9773643589 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9034097454 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9021426103 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8542218638 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.8110992529 --- UD v2.5 benchmarking pipeline for UD_Old_French-SRCMF \| Feature \| Description \| \| --- \| --- \| \| Name \| `xx_udv25_oldfrenchsrcmf_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) \| ### Label Scheme <details> <summary>View label scheme (16214 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `ADJQUA`, `ADJcar`, `ADJind`, `ADJord`, `ADJpos`, `ADJqua`, `ADVgen`, `ADVgen.PROadv`, `ADVgen.PROper`, `ADVing`, `ADVint`, `ADVneg`, `ADVneg.PROper`, `ADVsub`, `CONcoo`, `CONsub`, `CONsub.PROper`, `CONsub_o`, `CONsub_pre`, `DETcar`, `DETdef`, `DETdem`, `DETind`, `DETint`, `DETndf`, `DETord`, `DETpos`, `DETrel`, `DETrel_o`, `ETR`, `INJ`, `NOMcom`, `NOMcom.PROper`, `NOMpro`, `PRE`, `PRE.DETdef`, `PRE.PROdem`, `PRE.PROper`, `PROadv`, `PROcar`, `PROdem`, `PROimp`, `PROind`, `PROint`, `PROint.PROper`, `PROint_adv`, `PROord`, `PROper`, `PROper.PROper`, `PROpos`, `PROrel`, `PROrel.ADVneg`, `PROrel.PROadv`, `PROrel.PROper`, `PROrel_adv`, `RED`, `VERcjg`, `VERinf`, `VERppa`, `VERppe` \| \| `morphologizer` \| `POS=CCONJ`, `Definite=Def\\|POS=DET\\|PronType=Art`, `POS=NOUN`, `POS=PRON\\|PronType=Prs`, `POS=VERB\\|VerbForm=Fin`, `POS=PROPN`, `POS=PRON\\|PronType=Prs,Rel`, `POS=ADV`, `POS=ADP`, `POS=ADV\\|PronType=Dem`, `POS=PRON\\|PronType=Dem`, `POS=VERB\\|Tense=Past\\|VerbForm=Part`, `POS=AUX\\|VerbForm=Fin`, `POS=DET\\|PronType=Int`, `POS=ADJ`, `POS=PRON\\|PronType=Ind`, `POS=DET\\|PronType=Ind`, `Morph=VPar\\|POS=ADJ`, `POS=DET\\|Poss=Yes`, `POS=ADV\\|Polarity=Neg`, `Definite=Def\\|POS=ADP\\|PronType=Art`, `POS=PRON\\|PronType=Int`, `POS=SCONJ`, `POS=VERB\\|VerbForm=Inf`, `NumType=Card\\|POS=PRON`, `POS=PRON`, `NumType=Card\\|POS=DET`, `POS=PRON\\|Polarity=Neg\\|PronType=Prs`, `POS=ADJ\\|Poss=Yes`, `POS=PRON\\|Poss=Yes\\|PronType=Prs`, `Definite=Ind\\|POS=DET\\|PronType=Art`, `POS=DET\\|PronType=Dem`, `POS=AUX\\|VerbForm=Inf`, `POS=ADJ\\|PronType=Ind`, `Morph=VPar\\|POS=NOUN`, `POS=VERB\\|Tense=Pres\\|VerbForm=Part`, `Morph=VPar\\|POS=PROPN`, `Morph=VInf\\|POS=NOUN`, `NumType=Ord\\|POS=PRON`, `POS=INTJ`, `POS=SCONJ\\|PronType=Prs`, `Morph=VFin\\|POS=NOUN`, `POS=DET\\|PronType=Rel`, `NumType=Card\\|POS=ADJ`, `POS=ADJ\\|PronType=Ord`, `Morph=VFin\\|POS=ADV`, `Morph=VFin\\|POS=PROPN`, `POS=DET`, `Morph=VPar\\|POS=ADP`, `Morph=VPar\\|POS=ADV`, `NumType=Ord\\|POS=DET`, `Morph=VFin\\|POS=ADP`, `Morph=VFin\\|POS=CCONJ`, `Morph=VInf\\|POS=ADJ`, `POS=ADP\\|PronType=Dem`, `POS=ADV\\|Polarity=Int`, `Morph=VFin\\|POS=INTJ` \| \| `parser` \| `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `case:det`, `cc`, `cc:nc`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `dislocated`, `expl`, `flat`, `iobj`, `mark`, `mark:advmod`, `mark:obj`, `mark:obl`, `nmod`, `nsubj`, `nsubj:obj`, `nummod`, `obj`, `obj:advmod`, `obl`, `obl:advmod`, `parataxis`, `vocative`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `0`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`, `9`, `10`, `11`, `12`, `13`, `14`, `15`, `16`, `17`, `18`, `19`, `20`, `21`, `22`, 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nateraw/rare-puppers-123	nateraw	2021-12-10T21:18:44Z	95	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: rare-puppers-123 results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9701492786407471 --- # rare-puppers-123 Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### corgi ![corgi](images/corgi.jpg) #### samoyed ![samoyed](images/samoyed.jpg) #### shiba inu ![shiba inu](images/shiba_inu.jpg)
explosion/nb_udv25_norwegiannynorsk_trf	explosion	2021-12-10T20:45:29Z	1	0	spacy	[ "spacy", "token-classification", "nb", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - nb license: cc-by-sa-4.0 model-index: - name: nb_udv25_norwegiannynorsk_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9833263993 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9833584024 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9790699907 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9828149002 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9411235308 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.9214320428 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9910005294 --- UD v2.5 benchmarking pipeline for UD_Norwegian-Nynorsk \| Feature \| Description \| \| --- \| --- \| \| Name \| `nb_udv25_norwegiannynorsk_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) \| ### Label Scheme <details> <summary>View label scheme (1400 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` \| \| `morphologizer` \| `Number=Plur\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `POS=ADP`, `Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `POS=PUNCT`, `Gender=Masc\\|POS=NOUN`, `POS=CCONJ`, `Definite=Ind\\|Number=Plur\\|POS=NOUN`, `Animacy=Hum\\|Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin`, `Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `POS=NOUN`, `POS=PROPN`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Mood=Ind\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Animacy=Hum\\|Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `POS=ADV`, `Definite=Ind\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Gender=Masc\\|Number=Sing\\|POS=PRON\\|Poss=Yes\\|PronType=Prs`, `Definite=Def\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `POS=SCONJ`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|Poss=Yes\\|PronType=Prs`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|POS=AUX\\|Tense=Pres\\|VerbForm=Fin`, `POS=PART`, `POS=VERB\\|VerbForm=Inf`, `POS=PRON\\|PronType=Rel`, `POS=VERB\\|VerbForm=Part`, `Animacy=Hum\\|Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Number=Plur\\|POS=DET\\|PronType=Dem`, `Definite=Ind\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Abbr=Yes\\|POS=NOUN`, `Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Abbr=Yes\\|POS=ADV`, `Mood=Ind\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin`, `Degree=Cmp\\|POS=ADJ`, `POS=ADJ\\|VerbForm=Part`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=DET\\|PronType=Int`, `POS=AUX\\|VerbForm=Inf`, `Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|PronType=Prs`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Gender=Fem\\|Number=Sing\\|POS=PRON\\|Poss=Yes\\|PronType=Prs`, `Abbr=Yes\\|POS=ADJ`, `POS=PART\\|Polarity=Neg`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Art`, `POS=INTJ`, `Animacy=Hum\\|Number=Sing\\|POS=PRON\\|PronType=Art,Prs`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Art`, `Case=Gen\\|POS=PROPN`, `Animacy=Hum\\|Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part`, `Definite=Ind\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part`, `Definite=Ind\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Number=Plur\\|POS=DET\\|Polarity=Neg\\|PronType=Neg`, `Animacy=Hum\\|Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Animacy=Hum\\|Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Animacy=Hum\\|Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Art`, `Case=Gen\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `POS=PRON\\|PronType=Int`, `Case=Gen\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `NumType=Card\\|POS=NUM`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Number=Plur\\|POS=DET\\|PronType=Tot`, `Definite=Ind\\|Degree=Sup\\|POS=ADJ`, `NumType=Card\\|Number=Plur\\|POS=NUM`, `Definite=Def\\|POS=DET\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Mood=Imp\\|POS=VERB\\|VerbForm=Fin`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Definite=Def\\|POS=DET\\|PronType=Dem`, `POS=X`, `Case=Gen\\|Gender=Masc\\|POS=NOUN`, `POS=AUX\\|VerbForm=Part`, `Number=Plur\\|POS=ADJ\\|VerbForm=Part`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Number=Sing\\|POS=DET\\|PronType=Tot`, `Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Gen\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Animacy=Hum\\|Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Definite=Def\\|Degree=Sup\\|POS=ADJ`, `Gender=Masc\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `POS=DET\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind,Prs`, `Definite=Ind\\|Degree=Pos\\|POS=ADJ`, `Number=Plur\\|POS=PRON\\|Poss=Yes\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `POS=SYM`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Number=Plur\\|POS=NOUN`, `Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Gender=Fem,Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Abbr=Yes\\|POS=PRON\\|PronType=Prs`, `Abbr=Yes\\|Mood=Ind\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin`, `POS=ADJ`, `Gender=Neut\\|POS=NOUN`, `Animacy=Hum\\|Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Prs`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Gender=Fem\\|POS=NOUN`, `Degree=Pos\\|POS=ADJ`, `Definite=Def\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Animacy=Hum\\|Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Animacy=Hum\\|Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Definite=Def\\|NumType=Card\\|POS=NUM`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs,Tot`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|Polarity=Neg\\|PronType=Neg`, `Gender=Fem\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Definite=Def\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part`, `Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Animacy=Hum\\|POS=PRON\\|PronType=Int`, `Mood=Imp\\|POS=AUX\\|VerbForm=Fin`, `Case=Gen\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Number=Plur\\|POS=DET\\|PronType=Prs`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Abbr=Yes\\|POS=CCONJ`, `Number=Plur\\|POS=PRON\\|Person=3\\|Polarity=Neg\\|PronType=Neg,Prs`, `Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind,Prs`, `Definite=Def\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Number=Plur\\|POS=ADJ`, `Gender=Masc\\|Number=Sing\\|POS=DET\\|Polarity=Neg\\|PronType=Neg`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|Polarity=Neg\\|PronType=Neg,Prs`, `Abbr=Yes\\|Case=Gen\\|POS=NOUN`, `Case=Gen\\|Definite=Ind\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Definite=Def\\|POS=ADV`, `Number=Sing\\|POS=PRON\\|Polarity=Neg\\|PronType=Neg`, `Case=Gen\\|Definite=Ind\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Abbr=Yes\\|Definite=Def,Ind\\|Gender=Neut\\|Number=Plur,Sing\\|POS=NOUN`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Prs`, `Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Ind,Prs`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Abbr=Yes\\|POS=ADP`, `Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Animacy=Hum\\|Case=Nom\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Gender=Neut\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Art,Prs`, `Definite=Def\\|Number=Plur\\|POS=NOUN`, `Abbr=Yes\\|Gender=Masc\\|POS=NOUN`, `Case=Gen\\|POS=NOUN`, `Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part`, `Case=Gen\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Degree=Pos\\|Number=Plur\\|POS=ADJ`, `POS=PRON\\|PronType=Prs`, `POS=ADV\\|VerbForm=Inf`, `Degree=Sup\\|POS=ADJ`, `Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|POS=ADJ`, `Definite=Ind\\|Gender=Masc\\|POS=NOUN`, `Animacy=Hum\\|Case=Nom\\|Gender=Masc\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Abbr=Yes\\|POS=PROPN`, `Case=Gen\\|Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=VERB\\|VerbForm=Part`, `Gender=Neut\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|POS=NOUN`, `Case=Gen\\|Gender=Neut\\|POS=NOUN`, `Definite=Def\\|POS=ADJ\\|VerbForm=Part`, `POS=DET\\|PronType=Dem`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Definite=Ind\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Definite=Ind\\|Degree=Pos\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part`, `Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part`, `Case=Gen\\|Definite=Def\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Definite=Ind\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part`, `NumType=Card\\|POS=NUM\\|PronType=Dem`, `Definite=Ind\\|Number=Sing\\|POS=VERB\\|VerbForm=Part` \| \| `parser` \| `ROOT`, `acl`, `acl:cleft`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `reparandum`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `2`, `4`, `5`, `7`, `9`, `11`, `13`, `17`, `19`, `21`, `23`, `25`, `29`, `31`, `35`, `38`, `40`, `42`, `44`, `45`, `47`, `49`, `51`, `53`, `55`, `56`, `58`, `62`, `65`, `67`, `70`, `72`, `75`, `77`, `79`, `80`, `82`, `84`, `86`, `88`, `90`, `92`, `95`, `98`, `100`, `102`, `104`, `106`, `107`, `108`, `110`, `112`, `114`, `119`, `121`, `123`, `126`, `128`, `130`, `132`, `134`, `136`, `138`, `141`, `143`, `145`, `146`, `148`, `150`, `152`, `154`, `156`, `158`, `160`, `162`, `164`, `165`, `167`, `170`, `171`, `172`, `174`, `175`, `177`, `178`, `180`, `182`, `185`, `186`, `189`, `191`, `193`, `196`, `198`, `202`, `203`, `207`, `209`, `212`, `214`, `217`, `220`, `222`, `224`, `225`, `227`, `228`, `231`, `233`, `235`, `238`, `239`, `241`, `245`, `247`, 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\| \| `TOKEN_ACC` \| 99.99 \| \| `SENTS_F` \| 99.10 \| \| `SENTS_P` \| 99.15 \| \| `SENTS_R` \| 99.05 \| \| `TAG_ACC` \| 98.33 \| \| `POS_ACC` \| 98.34 \| \| `MORPH_ACC` \| 97.91 \| \| `DEP_UAS` \| 94.11 \| \| `DEP_LAS` \| 92.14 \| \| `LEMMA_ACC` \| 98.28 \|
huggingtweets/deliveroo_fr	huggingtweets	2021-12-10T19:38:51Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/deliveroo_fr/1639165126235/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1460542430357381120/3QwgzK9N_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">🍕 Deliveroo France</div> <div style="text-align: center; font-size: 14px;">@deliveroo_fr</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from 🍕 Deliveroo France. \| Data \| 🍕 Deliveroo France \| \| --- \| --- \| \| Tweets downloaded \| 3250 \| \| Retweets \| 5 \| \| Short tweets \| 209 \| \| Tweets kept \| 3036 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/35lhnvsx/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @deliveroo_fr's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3544md47) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3544md47/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/deliveroo_fr') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
explosion/lt_udv25_lithuanianalksnis_trf	explosion	2021-12-10T19:13:26Z	6	0	spacy	[ "spacy", "token-classification", "lt", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - lt license: cc-by-sa-4.0 model-index: - name: lt_udv25_lithuanianalksnis_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9542839843 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9806669262 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9549759958 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9045621622 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.8811484062 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8361832383 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9565217391 --- 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) \| ### 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.`, 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`vksm.asm.tiesiog.būs.3.`, `vksm.asm.tiesiog.būs.dgs.1.`, `vksm.asm.tiesiog.būs.dgs.2.`, `vksm.asm.tiesiog.būs.dgs.3.`, `vksm.asm.tiesiog.būs.vns.1.`, `vksm.asm.tiesiog.būs.vns.2.`, `vksm.asm.tiesiog.būs.vns.3.`, `vksm.asm.tiesiog.būt-d.3.`, `vksm.asm.tiesiog.būt-d.dgs.3.`, `vksm.asm.tiesiog.būt-d.vns.1.`, `vksm.asm.tiesiog.būt-d.vns.2.`, `vksm.asm.tiesiog.būt-d.vns.3.`, `vksm.asm.tiesiog.būt-k.`, `vksm.asm.tiesiog.būt-k.3.`, `vksm.asm.tiesiog.būt-k.dgs.1.`, `vksm.asm.tiesiog.būt-k.dgs.2.`, `vksm.asm.tiesiog.būt-k.dgs.3.`, `vksm.asm.tiesiog.būt-k.vns.1.`, `vksm.asm.tiesiog.būt-k.vns.2.`, `vksm.asm.tiesiog.būt-k.vns.3.`, `vksm.asm.tiesiog.es.3.`, `vksm.asm.tiesiog.es.dgs.1.`, `vksm.asm.tiesiog.es.dgs.2.`, `vksm.asm.tiesiog.es.dgs.3.`, `vksm.asm.tiesiog.es.vns.1.`, `vksm.asm.tiesiog.es.vns.2.`, `vksm.asm.tiesiog.es.vns.3.`, `vksm.bndr.`, `vksm.bndr.neig.`, `vksm.bndr.neig.sngr.`, `vksm.bndr.sngr.`, `vksm.dlv.neig.neveik.būt.bev.`, `vksm.dlv.neig.neveik.būt.mot.dgs.G.`, `vksm.dlv.neig.neveik.būt.mot.dgs.K.`, `vksm.dlv.neig.neveik.būt.mot.dgs.V.`, `vksm.dlv.neig.neveik.būt.mot.vns.K.`, `vksm.dlv.neig.neveik.būt.mot.vns.V.`, `vksm.dlv.neig.neveik.būt.vyr.dgs.N.`, `vksm.dlv.neig.neveik.būt.vyr.dgs.V.`, `vksm.dlv.neig.neveik.būt.vyr.vns.G.`, `vksm.dlv.neig.neveik.būt.vyr.vns.N.`, `vksm.dlv.neig.neveik.būt.vyr.vns.V.`, `vksm.dlv.neig.neveik.es.bev.`, `vksm.dlv.neig.neveik.es.mot.dgs.K.`, `vksm.dlv.neig.neveik.es.mot.dgs.V.`, `vksm.dlv.neig.neveik.es.mot.vns.G.`, `vksm.dlv.neig.neveik.es.mot.vns.K.`, `vksm.dlv.neig.neveik.es.mot.vns.V.`, `vksm.dlv.neig.neveik.es.mot.vns.Įn.`, `vksm.dlv.neig.neveik.es.vyr.dgs.G.`, `vksm.dlv.neig.neveik.es.vyr.dgs.K.`, `vksm.dlv.neig.neveik.es.vyr.dgs.V.`, `vksm.dlv.neig.neveik.es.vyr.vns.V.`, `vksm.dlv.neig.neveik.es.įvardž.mot.dgs.V.`, `vksm.dlv.neig.reik.bev.`, `vksm.dlv.neig.reik.mot.dgs.K.`, `vksm.dlv.neig.reik.mot.vns.V.`, `vksm.dlv.neig.reik.vyr.vns.V.`, `vksm.dlv.neig.sngr.neveik.būt.bev.`, `vksm.dlv.neig.sngr.neveik.es.bev.`, `vksm.dlv.neig.sngr.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.neig.sngr.veik.es.vyr.vns.V.`, `vksm.dlv.neig.veik.būt-k.bev.`, `vksm.dlv.neig.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.neig.veik.būt-k.vyr.dgs.Įn.`, `vksm.dlv.neig.veik.būt-k.vyr.vns.G.`, `vksm.dlv.neig.veik.būt-k.vyr.vns.V.`, `vksm.dlv.neig.veik.es.mot.dgs.K.`, `vksm.dlv.neig.veik.es.mot.vns.N.`, `vksm.dlv.neig.veik.es.mot.vns.V.`, `vksm.dlv.neig.veik.es.mot.vns.Įn.`, `vksm.dlv.neig.veik.es.vyr.dgs.G.`, `vksm.dlv.neig.veik.es.vyr.dgs.N.`, `vksm.dlv.neig.veik.es.vyr.dgs.V.`, `vksm.dlv.neig.veik.es.vyr.dgs.Įn.`, `vksm.dlv.neig.veik.es.vyr.vns.K.`, `vksm.dlv.neig.veik.es.vyr.vns.N.`, `vksm.dlv.neig.veik.es.vyr.vns.V.`, `vksm.dlv.neig.veik.es.įvardž.vyr.dgs.V.`, `vksm.dlv.neig.veik.es.įvardž.vyr.dgs.Įn.`, `vksm.dlv.neveik.būs.vyr.vns.G.`, `vksm.dlv.neveik.būs.vyr.vns.N.`, `vksm.dlv.neveik.būt-k.vyr.dgs.V.`, `vksm.dlv.neveik.būt-k.vyr.vns.V.`, `vksm.dlv.neveik.būt.bev.`, `vksm.dlv.neveik.būt.mot.V.`, `vksm.dlv.neveik.būt.mot.dgs.G.`, `vksm.dlv.neveik.būt.mot.dgs.K`, `vksm.dlv.neveik.būt.mot.dgs.K.`, `vksm.dlv.neveik.būt.mot.dgs.N.`, `vksm.dlv.neveik.būt.mot.dgs.V.`, `vksm.dlv.neveik.būt.mot.dgs.Įn.`, `vksm.dlv.neveik.būt.mot.vns.G.`, `vksm.dlv.neveik.būt.mot.vns.K.`, `vksm.dlv.neveik.būt.mot.vns.N.`, `vksm.dlv.neveik.būt.mot.vns.V`, `vksm.dlv.neveik.būt.mot.vns.V.`, `vksm.dlv.neveik.būt.mot.vns.Vt.`, `vksm.dlv.neveik.būt.mot.vns.Įn.`, `vksm.dlv.neveik.būt.vyr.dgs.G.`, `vksm.dlv.neveik.būt.vyr.dgs.K.`, `vksm.dlv.neveik.būt.vyr.dgs.N.`, `vksm.dlv.neveik.būt.vyr.dgs.V`, `vksm.dlv.neveik.būt.vyr.dgs.V.`, `vksm.dlv.neveik.būt.vyr.dgs.Vt.`, `vksm.dlv.neveik.būt.vyr.dgs.Įn.`, `vksm.dlv.neveik.būt.vyr.vns.G.`, `vksm.dlv.neveik.būt.vyr.vns.K.`, `vksm.dlv.neveik.būt.vyr.vns.N.`, `vksm.dlv.neveik.būt.vyr.vns.V`, `vksm.dlv.neveik.būt.vyr.vns.V.`, `vksm.dlv.neveik.būt.vyr.vns.Vt.`, `vksm.dlv.neveik.būt.vyr.vns.Įn.`, `vksm.dlv.neveik.būt.įvardž.mot.dgs.G.`, `vksm.dlv.neveik.būt.įvardž.mot.dgs.K.`, `vksm.dlv.neveik.būt.įvardž.vyr.dgs.G.`, `vksm.dlv.neveik.būt.įvardž.vyr.dgs.K.`, `vksm.dlv.neveik.būt.įvardž.vyr.dgs.V.`, `vksm.dlv.neveik.būt.įvardž.vyr.vns.K.`, `vksm.dlv.neveik.būt.įvardž.vyr.vns.V.`, `vksm.dlv.neveik.būts.vyr.dgs.V.`, `vksm.dlv.neveik.es.bev.`, `vksm.dlv.neveik.es.mot.V.`, `vksm.dlv.neveik.es.mot.dgs.G.`, `vksm.dlv.neveik.es.mot.dgs.K.`, `vksm.dlv.neveik.es.mot.dgs.N.`, `vksm.dlv.neveik.es.mot.dgs.V.`, `vksm.dlv.neveik.es.mot.dgs.Vt.`, `vksm.dlv.neveik.es.mot.dgs.Įn.`, `vksm.dlv.neveik.es.mot.vns.G.`, `vksm.dlv.neveik.es.mot.vns.K.`, `vksm.dlv.neveik.es.mot.vns.N.`, `vksm.dlv.neveik.es.mot.vns.V`, `vksm.dlv.neveik.es.mot.vns.V.`, `vksm.dlv.neveik.es.mot.vns.Vt.`, `vksm.dlv.neveik.es.mot.vns.Įn.`, `vksm.dlv.neveik.es.vyr.dgs.G.`, `vksm.dlv.neveik.es.vyr.dgs.K.`, `vksm.dlv.neveik.es.vyr.dgs.N.`, `vksm.dlv.neveik.es.vyr.dgs.V.`, `vksm.dlv.neveik.es.vyr.dgs.Įn.`, `vksm.dlv.neveik.es.vyr.vns.G.`, `vksm.dlv.neveik.es.vyr.vns.K.`, `vksm.dlv.neveik.es.vyr.vns.N.`, `vksm.dlv.neveik.es.vyr.vns.V.`, `vksm.dlv.neveik.es.vyr.vns.Įn.`, `vksm.dlv.neveik.es.įvardž.mot.dgs.K.`, `vksm.dlv.neveik.es.įvardž.mot.dgs.V.`, `vksm.dlv.neveik.es.įvardž.mot.dgs.Įn.`, `vksm.dlv.neveik.es.įvardž.mot.vns.G.`, `vksm.dlv.neveik.es.įvardž.mot.vns.K.`, `vksm.dlv.neveik.es.įvardž.mot.vns.N.`, `vksm.dlv.neveik.es.įvardž.mot.vns.V.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.G.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.K.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.N.`, `vksm.dlv.neveik.es.įvardž.vyr.dgs.V.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.G.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.K.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.N.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.V.`, `vksm.dlv.neveik.es.įvardž.vyr.vns.Įn.`, `vksm.dlv.neveik.mot.vns.V.`, `vksm.dlv.neveik.vyr.dgs.K.`, `vksm.dlv.neveik.įvardž.es.mot.vns.Vt.`, `vksm.dlv.neveik.įvardž.es.vyr.dgs.K.`, `vksm.dlv.neveik.įvardž.es.vyr.vns.K.`, `vksm.dlv.reik.bev.`, `vksm.dlv.reik.mot.vns.V.`, `vksm.dlv.reik.vyr.dgs.K.`, `vksm.dlv.reik.vyr.dgs.V.`, `vksm.dlv.reik.vyr.vns.V.`, `vksm.dlv.sngr.neveik.būt.bev.`, `vksm.dlv.sngr.neveik.būt.mot.dgs.G.`, `vksm.dlv.sngr.neveik.būt.mot.dgs.V.`, `vksm.dlv.sngr.neveik.būt.mot.vns.V.`, `vksm.dlv.sngr.neveik.būt.mot.vns.Vt.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.G.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.V.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.Vt.`, `vksm.dlv.sngr.neveik.būt.vyr.dgs.Įn.`, `vksm.dlv.sngr.neveik.būt.vyr.vns.G.`, `vksm.dlv.sngr.neveik.būt.vyr.vns.K.`, `vksm.dlv.sngr.neveik.būt.vyr.vns.V.`, `vksm.dlv.sngr.neveik.es.bev.`, `vksm.dlv.sngr.neveik.es.mot.dgs.V.`, `vksm.dlv.sngr.neveik.es.mot.vns.V.`, `vksm.dlv.sngr.neveik.es.vyr.dgs.Įn.`, `vksm.dlv.sngr.neveik.es.vyr.vns.V.`, `vksm.dlv.sngr.veik.būt-k.bev.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.G.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.K.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.V.`, `vksm.dlv.sngr.veik.būt-k.mot.dgs.Įn.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.G.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.K.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.V.`, `vksm.dlv.sngr.veik.būt-k.mot.vns.Įn.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.G.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.K.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.sngr.veik.būt-k.vyr.dgs.Įn.`, `vksm.dlv.sngr.veik.būt-k.vyr.vns.G.`, `vksm.dlv.sngr.veik.būt-k.vyr.vns.K.`, `vksm.dlv.sngr.veik.būt-k.vyr.vns.V.`, `vksm.dlv.sngr.veik.es.mot.dgs.K.`, `vksm.dlv.sngr.veik.es.mot.dgs.V.`, `vksm.dlv.sngr.veik.es.mot.dgs.Įn.`, `vksm.dlv.sngr.veik.es.mot.vns.K.`, `vksm.dlv.sngr.veik.es.vyr.dgs.G.`, `vksm.dlv.sngr.veik.es.vyr.dgs.K.`, `vksm.dlv.sngr.veik.es.vyr.dgs.N.`, `vksm.dlv.sngr.veik.es.vyr.dgs.V.`, `vksm.dlv.sngr.veik.es.vyr.vns.G.`, `vksm.dlv.sngr.veik.es.vyr.vns.K.`, `vksm.dlv.sngr.veik.es.vyr.vns.N.`, `vksm.dlv.sngr.veik.es.vyr.vns.V.`, `vksm.dlv.sngr.veik.es.įvardž.mot.vns.K.`, `vksm.dlv.veik.būs.vyr.vns.V.`, `vksm.dlv.veik.būt-k.bev.`, `vksm.dlv.veik.būt-k.mot.dgs.G.`, `vksm.dlv.veik.būt-k.mot.dgs.K.`, `vksm.dlv.veik.būt-k.mot.dgs.N.`, `vksm.dlv.veik.būt-k.mot.dgs.V.`, `vksm.dlv.veik.būt-k.mot.dgs.Vt.`, `vksm.dlv.veik.būt-k.mot.vns.G.`, `vksm.dlv.veik.būt-k.mot.vns.K.`, `vksm.dlv.veik.būt-k.mot.vns.N.`, `vksm.dlv.veik.būt-k.mot.vns.V.`, `vksm.dlv.veik.būt-k.mot.vns.Įn.`, `vksm.dlv.veik.būt-k.vyr.dgs.G.`, `vksm.dlv.veik.būt-k.vyr.dgs.K.`, `vksm.dlv.veik.būt-k.vyr.dgs.N.`, `vksm.dlv.veik.būt-k.vyr.dgs.V.`, `vksm.dlv.veik.būt-k.vyr.dgs.Įn.`, `vksm.dlv.veik.būt-k.vyr.vns.G.`, `vksm.dlv.veik.būt-k.vyr.vns.K.`, `vksm.dlv.veik.būt-k.vyr.vns.N.`, `vksm.dlv.veik.būt-k.vyr.vns.V.`, `vksm.dlv.veik.būt-k.vyr.vns.Vt.`, `vksm.dlv.veik.būt-k.vyr.vns.Įn.`, `vksm.dlv.veik.būt-k.įvardž.vyr.dgs.K.`, `vksm.dlv.veik.būt-k.įvardž.vyr.dgs.V.`, `vksm.dlv.veik.būt-k.įvardž.vyr.vns.K.`, `vksm.dlv.veik.būt-k.įvardž.vyr.vns.V.`, `vksm.dlv.veik.būt-k.įvardž.vyr.vns.Įn.`, `vksm.dlv.veik.būt.k.vyr.dgs.V.`, `vksm.dlv.veik.es.mot.dgs.G.`, `vksm.dlv.veik.es.mot.dgs.K.`, `vksm.dlv.veik.es.mot.dgs.N.`, `vksm.dlv.veik.es.mot.dgs.V.`, 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`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`, `541`, `542`, `545`, `547`, `550`, `552`, `554`, `555`, `557`, `560`, `561`, `563`, `566`, `569`, `572`, `574`, `577`, `580`, `582`, `584`, `589`, `594`, `596`, `599`, `600`, `602`, `604`, `607`, `609`, `611`, `613`, `615`, `616`, `619`, `623`, `625`, `628`, `629`, `631`, `633`, `635`, `638`, `640`, `642`, `645`, `647`, 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`924`, `928`, `929`, `930`, `931`, `932`, `935`, `936`, `938`, `940`, `942`, `944`, `945`, `947`, `951`, `953`, `956`, `958`, `959`, `961`, `963`, `965`, `967`, `969`, `970`, `972`, `975`, `976`, `977`, `979`, `980`, `981`, `983`, `987`, `990`, `992`, `993`, `995`, `996`, `998`, `1000`, `1002`, `1004`, `1006`, `1008`, `1009`, `1012`, `1014`, `1015`, `1016`, `1018`, `1019`, `1022`, `1024`, `1026`, `1028`, `1029`, `1033`, `1036`, `1038`, `1040`, `1042`, `1047`, `1049`, `1051`, `1053`, `1055`, `1057`, `1060`, `1063`, `1065`, `1067`, `1069`, `1070`, `1071`, `1073`, `1075`, `1078`, `1080`, `1082`, `1084`, `1086`, `1089`, `1092`, `1093`, `1094`, `1095`, `1096`, `1098`, `1100`, `1102`, `1104`, `1105`, `1107`, `1108`, `1110`, `1113`, `1115`, `1118`, `1121`, `1123`, `1124`, `1126`, `1127`, `1129`, `1131`, `1134`, `1137`, `1141`, `1142`, `1144`, `1146`, `1148`, `1150`, `1151`, `1153`, `1154`, `1156`, `1158`, `1159`, `1162`, `1164`, `1166`, `1169`, `1173`, `1175`, `1178`, `1180`, `1183`, `1184`, 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`3473`, `3474`, `3475`, `3477`, `3478`, `3479`, `3481`, `3482`, `3483`, `3485`, `3488`, `3489`, `3490`, `3491`, `3492`, `3493`, `3495`, `3496`, `3497`, `3498`, `3499`, `3500`, `3501`, `3502`, `3503`, `3504`, `3505`, `3506`, `3509`, `3510`, `3511`, `3512`, `3514`, `3516`, `3517`, `3518`, `3519`, `3520`, `3521`, `3522`, `3523`, `3524`, `3525`, `3526`, `3527`, `3528`, `3529`, `3530`, `3531`, `3532`, `3535`, `3536`, `3537`, `3538`, `3539`, `3540`, `3542`, `3543`, `3546`, `3547`, `3548`, `3549`, `3550`, `3551`, `3552`, `3553` \| </details> ### 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 \|
aadelucia/GPT2_small_narrative_finetuned_medium	aadelucia	2021-12-10T18:48:36Z	7	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	Please visit the repo for training details. https://github.com/AADeLucia/gpt2-narrative-decoding
crabz/FERNET-CC_sk-ner	crabz	2021-12-10T18:46:02Z	4	1	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "sk", "dataset:wikiann", "license:cc-by-nc-sa-4.0", "model-index", "autotrain_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: cc-by-nc-sa-4.0 tags: - generated_from_trainer datasets: - wikiann metrics: - precision - recall - f1 - accuracy language: - sk inference: false model-index: - name: fernet-sk-ner results: - task: name: Token Classification type: token-classification dataset: name: wikiann sk type: wikiann args: sk metrics: - name: Precision type: precision value: 0.9359821760118826 - name: Recall type: recall value: 0.9472378804960541 - name: F1 type: f1 value: 0.9415763914830033 - name: Accuracy type: accuracy value: 0.9789063466534702 --- # Named Entity Recognition based on FERNET-CC_sk This model is a fine-tuned version of [fav-kky/FERNET-CC_sk](https://huggingface.co/fav-kky/FERNET-CC_sk) on the Slovak wikiann dataset. It achieves the following results on the evaluation set: - Loss: 0.1763 - Precision: 0.9360 - Recall: 0.9472 - F1: 0.9416 - Accuracy: 0.9789 ## Intended uses & limitation Supported classes: LOCATION, PERSON, ORGANIZATION ``` from transformers import pipeline ner_pipeline = pipeline(task='ner', model='crabz/slovakbert-ner') input_sentence = "Minister financií a líder mandátovo najsilnejšieho hnutia OĽaNO Igor Matovič upozorňuje, že následky tretej vlny budú na Slovensku veľmi veľké." classifications = ner_pipeline(input_sentence) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1259 \| 1.0 \| 834 \| 0.1095 \| 0.8963 \| 0.9182 \| 0.9071 \| 0.9697 \| \| 0.071 \| 2.0 \| 1668 \| 0.0974 \| 0.9270 \| 0.9357 \| 0.9313 \| 0.9762 \| \| 0.0323 \| 3.0 \| 2502 \| 0.1259 \| 0.9257 \| 0.9330 \| 0.9293 \| 0.9745 \| \| 0.0175 \| 4.0 \| 3336 \| 0.1347 \| 0.9241 \| 0.9360 \| 0.9300 \| 0.9756 \| \| 0.0156 \| 5.0 \| 4170 \| 0.1407 \| 0.9337 \| 0.9404 \| 0.9370 \| 0.9780 \| \| 0.0062 \| 6.0 \| 5004 \| 0.1522 \| 0.9267 \| 0.9410 \| 0.9338 \| 0.9774 \| \| 0.0055 \| 7.0 \| 5838 \| 0.1559 \| 0.9322 \| 0.9429 \| 0.9375 \| 0.9780 \| \| 0.0024 \| 8.0 \| 6672 \| 0.1733 \| 0.9321 \| 0.9438 \| 0.9379 \| 0.9779 \| \| 0.0009 \| 9.0 \| 7506 \| 0.1765 \| 0.9347 \| 0.9468 \| 0.9407 \| 0.9784 \| \| 0.0002 \| 10.0 \| 8340 \| 0.1763 \| 0.9360 \| 0.9472 \| 0.9416 \| 0.9789 \| ### Framework versions - Transformers 4.14.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
explosion/lv_udv25_latvianlvtb_trf	explosion	2021-12-10T18:27:19Z	1	2	spacy	[ "spacy", "token-classification", "lv", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - lv license: cc-by-sa-4.0 model-index: - name: lv_udv25_latvianlvtb_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9158590393 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9793637642 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9568769509 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.953872229 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9130165092 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8774541377 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9776570048 --- UD v2.5 benchmarking pipeline for UD_Latvian-LVTB \| Feature \| Description \| \| --- \| --- \| \| Name \| `lv_udv25_latvianlvtb_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) \| ### Label Scheme <details> <summary>View label scheme (6012 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `X`, `affpanc`, `affpanp`, `affpayc`, `affpayp`, `affpays`, `affpdnc`, `affpdnp`, `affpdyc`, `affpdyp`, `affpdys`, `affpgnp`, `affpgyc`, `affpgyp`, `affplnc`, `affplnp`, `affplyc`, `affplyp`, `affpnnc`, `affpnnp`, `affpnyc`, `affpnyp`, `affpnys`, `affsanc`, `affsanp`, `affsayc`, `affsayp`, `affsays`, `affsdnc`, `affsdnp`, `affsdyc`, `affsdyp`, `affsgnc`, `affsgnp`, `affsgyc`, `affsgyp`, `affsgys`, `affslnc`, `affslnp`, `affslyc`, `affslyp`, `affslys`, `affsnnc`, `affsnnp`, `affsnyc`, `affsnyp`, `affsnys`, `affsvyp`, `afmpanc`, `afmpanp`, `afmpayc`, `afmpayp`, `afmpays`, `afmpdnc`, `afmpdnp`, `afmpdyc`, `afmpdyp`, `afmpdys`, `afmpgnc`, `afmpgnp`, `afmpgyc`, `afmpgyp`, `afmpgys`, `afmplnc`, `afmplnp`, `afmplyc`, `afmplyp`, `afmplys`, `afmpnnc`, `afmpnnp`, `afmpnyc`, `afmpnyp`, `afmpnys`, `afmpvyp`, `afmsanc`, `afmsanp`, `afmsayc`, `afmsayp`, `afmsays`, `afmsdnc`, `afmsdnp`, `afmsdyc`, `afmsdyp`, `afmsdys`, `afmsgnc`, `afmsgnp`, `afmsgyc`, `afmsgyp`, `afmsgys`, `afmslnc`, `afmslnp`, `afmslyc`, `afmslyp`, `afmslys`, `afmsnnc`, `afmsnnp`, `afmsnyc`, `afmsnyp`, `afmsnys`, `arfpanp`, `arfpayp`, `arfpdnp`, `arfpdyc`, `arfpdyp`, `arfpgnp`, `arfpgyp`, `arfplnc`, `arfplnp`, `arfplyc`, `arfplyp`, `arfpnnc`, `arfpnnp`, `arfpnyp`, `arfpnys`, `arfsanp`, `arfsayp`, `arfsdnp`, `arfsdyp`, `arfsgnc`, `arfsgnp`, `arfsgyp`, `arfslnp`, `arfslyp`, `arfsnnc`, `arfsnnp`, `arfsnyc`, `arfsnyp`, `arfsvyp`, `armpanp`, `armpayc`, `armpayp`, `armpdnp`, `armpdyc`, `armpdyp`, `armpdys`, `armpgnp`, `armpgyp`, `armplnp`, `armplyc`, `armplyp`, `armpnnc`, `armpnnp`, `armpnyc`, `armpnyp`, `armsanp`, `armsayc`, `armsayp`, `armsdnp`, `armsdyp`, `armsgnp`, `armsgyp`, `armslnp`, `armslyp`, `armsnnp`, `armsnyp`, `armsnys`, `cc`, `cs`, `i`, `mcc0p0`, `mccfpa`, `mccmpn`, `mccmsa`, `mcs0p0`, `mcsfp0`, `mcsfpa`, `mcsfpd`, `mcsfpg`, `mcsfpl`, `mcsfpn`, `mcsfsa`, `mcsfsd`, `mcsfsg`, `mcsfsl`, `mcsfsn`, `mcsmpa`, `mcsmpd`, `mcsmpg`, `mcsmpl`, `mcsmpn`, `mcsmsa`, `mcsmsd`, `mcsmsg`, `mcsmsl`, `mcsmsn`, `mfcfsa`, `mfcfsg`, `mfcfsn`, `mfsmsg`, `mocfsg`, `mocmsg`, `mosfpa`, `mosfpd`, `mosfpg`, `mosfpl`, `mosfpn`, `mosfsa`, `mosfsd`, `mosfsg`, `mosfsl`, `mosfsn`, `mosmpa`, `mosmpd`, `mosmpg`, `mosmpl`, `mosmpn`, `mosmsa`, `mosmsd`, `mosmsg`, `mosmsl`, `mosmsn`, `n0msa1`, `nc0000`, `nc000g`, `nc00g1`, `nc00gg`, `ncfda4`, `ncfda5`, `ncfda6`, `ncfdd4`, `ncfdd5`, `ncfdd6`, `ncfdg4`, `ncfdg5`, `ncfdg6`, `ncfdgg`, `ncfdl4`, `ncfdl5`, `ncfdl6`, `ncfdn4`, `ncfdn5`, `ncfdn6`, `ncfpa4`, `ncfpa5`, `ncfpa6`, `ncfpar`, `ncfpd4`, `ncfpd5`, `ncfpd6`, `ncfpdr`, `ncfpg1`, `ncfpg2`, `ncfpg4`, `ncfpg5`, `ncfpg6`, `ncfpgg`, `ncfpl4`, `ncfpl5`, `ncfpl6`, `ncfpn1`, `ncfpn4`, `ncfpn5`, `ncfpn6`, `ncfpnr`, `ncfsa1`, `ncfsa2`, `ncfsa4`, `ncfsa5`, `ncfsa6`, `ncfsar`, `ncfsd4`, `ncfsd5`, `ncfsd6`, `ncfsg1`, `ncfsg4`, `ncfsg5`, `ncfsg6`, `ncfsgg`, `ncfsgr`, `ncfsl1`, `ncfsl4`, `ncfsl5`, `ncfsl6`, `ncfslr`, `ncfsn4`, `ncfsn5`, `ncfsn6`, `ncfsnr`, `ncfsv4`, `ncfsv5`, `ncfva4`, `ncfva5`, `ncfvd5`, `ncfvg4`, `ncfvg5`, `ncfvl4`, `ncfvl5`, `ncfvn5`, `ncm000`, `ncmda1`, `ncmda2`, `ncmda6`, `ncmdd1`, `ncmdd2`, `ncmdd3`, `ncmdd6`, `ncmdg1`, `ncmdg2`, `ncmdg3`, `ncmdg6`, `ncmdgg`, `ncmdl1`, `ncmdl2`, `ncmdn1`, `ncmdn2`, `ncmdn6`, `ncmpa1`, `ncmpa2`, `ncmpa3`, `ncmpa4`, `ncmpd1`, `ncmpd2`, `ncmpd3`, `ncmpd5`, `ncmpg1`, `ncmpg2`, `ncmpg3`, `ncmpg4`, `ncmpg5`, `ncmpg6`, `ncmpgg`, `ncmpl1`, `ncmpl2`, `ncmpl3`, `ncmpl4`, `ncmpn0`, `ncmpn1`, `ncmpn2`, `ncmpn3`, `ncmpn4`, `ncmpn5`, `ncmpv1`, `ncmpv2`, `ncmsa1`, `ncmsa2`, `ncmsa3`, `ncmsa4`, `ncmsa5`, `ncmsd1`, `ncmsd2`, `ncmsd3`, `ncmsd4`, `ncmsg0`, `ncmsg1`, `ncmsg2`, `ncmsg3`, `ncmsg4`, `ncmsgg`, `ncmsgr`, `ncmsl1`, `ncmsl2`, `ncmsl3`, `ncmsl4`, `ncmsl5`, `ncmsn1`, `ncmsn2`, `ncmsn3`, `ncmsn4`, `ncmsnr`, `ncmsv1`, `ncmsv2`, `ncmva1`, `ncmva3`, `ncmvd1`, `ncmvd3`, `ncmvg1`, `ncmvg3`, `ncmvl1`, `ncmvl3`, `ncmvn1`, `ncmvn3`, `np0000`, `npfda4`, `npfdd4`, `npfdd6`, `npfdg1`, `npfdg4`, `npfdg6`, `npfdl4`, `npfdl6`, `npfdn4`, `npfdn5`, `npfdn6`, `npfpa5`, `npfpd5`, `npfpg2`, `npfpg4`, `npfpn4`, `npfpn5`, `npfsa4`, `npfsa5`, `npfsa6`, `npfsd4`, `npfsd5`, `npfsg1`, `npfsg3`, `npfsg4`, `npfsg5`, `npfsg6`, `npfsl4`, `npfsl5`, `npfsl6`, `npfsn3`, `npfsn4`, `npfsn5`, `npfsn6`, `npfsv4`, `npfsv5`, `npmda1`, `npmda2`, `npmdd1`, `npmdd2`, `npmdg1`, `npmdg2`, `npmdl1`, `npmdl2`, `npmdn1`, `npmdn2`, `npmpa1`, `npmpd1`, `npmpd2`, `npmpg1`, `npmpg2`, `npmpgg`, `npmpl1`, `npmpl2`, `npmpn1`, `npmpn2`, `npmsa1`, `npmsa2`, `npmsa3`, `npmsa4`, `npmsa5`, `npmsd1`, `npmsd2`, `npmsd3`, `npmsd4`, `npmsd5`, `npmsg0`, `npmsg1`, `npmsg2`, `npmsg3`, `npmsg4`, `npmsg5`, `npmsl1`, `npmsl2`, `npmsn1`, `npmsn2`, `npmsn3`, `npmsn4`, `npmsn5`, `npmsv1`, `npmsv2`, `pd0fpan`, `pd0fpdn`, `pd0fpgn`, `pd0fpln`, `pd0fpnn`, `pd0fsan`, `pd0fsdn`, `pd0fsgn`, `pd0fsln`, `pd0fsnn`, `pd0mpan`, `pd0mpdn`, `pd0mpgn`, `pd0mpln`, `pd0mply`, `pd0mpnn`, `pd0msan`, `pd0msdn`, `pd0msgn`, `pd0msln`, `pd0msnn`, `pd3fpan`, `pd3fpdn`, `pd3fpgn`, `pd3fpln`, `pd3fpnn`, `pd3fsan`, `pd3fsdn`, `pd3fsgn`, `pd3fsln`, `pd3fsnn`, `pd3mpan`, `pd3mpdn`, `pd3mpgn`, `pd3mpln`, `pd3mpnn`, `pd3msan`, `pd3msdn`, `pd3msgn`, `pd3msln`, `pd3msnn`, `pg0fpan`, `pg0fpdn`, `pg0fpgn`, `pg0fpln`, `pg0fpnn`, `pg0fsan`, `pg0fsdn`, `pg0fsgn`, `pg0fsln`, `pg0fsnn`, `pg0mpan`, `pg0mpdn`, `pg0mpgn`, `pg0mpln`, `pg0mpnn`, `pg0msan`, `pg0msdn`, `pg0msgn`, `pg0msln`, `pg0msnn`, `pi000an`, `pi000ay`, `pi000dn`, `pi000dy`, `pi000gn`, `pi000gy`, `pi000nn`, `pi000ny`, `pi0fpan`, `pi0fpay`, `pi0fpdn`, `pi0fpgn`, `pi0fpgy`, `pi0fpln`, `pi0fply`, `pi0fpnn`, `pi0fpny`, `pi0fsan`, `pi0fsay`, `pi0fsdn`, `pi0fsgn`, `pi0fsgy`, `pi0fsln`, `pi0fsnn`, `pi0fsny`, `pi0mpan`, `pi0mpay`, `pi0mpdn`, `pi0mpgn`, `pi0mpgy`, `pi0mpln`, `pi0mpnn`, `pi0mpny`, `pi0msan`, `pi0msay`, `pi0msdn`, `pi0msdy`, `pi0msgn`, `pi0msgy`, `pi0msln`, `pi0msly`, `pi0msnn`, `pi0msny`, `pi3msnn`, `pp10pan`, `pp10pdn`, `pp10pgn`, `pp10pln`, `pp10pnn`, `pp10san`, `pp10sdn`, `pp10sgn`, `pp10sln`, `pp10snn`, `pp1mpgn`, `pp20pan`, `pp20pdn`, `pp20pgn`, `pp20pnn`, `pp20san`, `pp20sdn`, `pp20sgn`, `pp20sln`, `pp20snn`, `pp2fsln`, `pp3fpan`, `pp3fpdn`, `pp3fpgn`, `pp3fpnn`, `pp3fsan`, `pp3fsdn`, `pp3fsgn`, `pp3fsln`, `pp3fsnn`, `pp3mpan`, `pp3mpdn`, `pp3mpgn`, `pp3mpln`, `pp3mpnn`, `pp3msan`, `pp3msdn`, `pp3msgn`, `pp3msln`, `pp3msnn`, `pq000an`, `pq000dn`, `pq000gn`, `pq000nn`, `pq0fpan`, `pq0fpnn`, `pq0fsnn`, `pq0mpnn`, `pq0msan`, `pq0msdn`, `pq0msln`, `pq0msnn`, `pr000an`, `pr000dn`, `pr000gn`, `pr000nn`, `pr00pgn`, `pr0fpan`, `pr0fpdn`, `pr0fpgn`, `pr0fpln`, `pr0fpnn`, `pr0fsan`, `pr0fsdn`, `pr0fsgn`, `pr0fsln`, `pr0fsnn`, `pr0mpan`, `pr0mpdn`, `pr0mpgn`, `pr0mpln`, `pr0mpnn`, `pr0msan`, `pr0msdn`, `pr0msgn`, `pr0msln`, `pr0msnn`, `ps0fpan`, `ps0fpdn`, `ps0fpgn`, `ps0fpln`, `ps0fpnn`, `ps0fsan`, `ps0fsdn`, `ps0fsgn`, `ps0fsln`, `ps0fsnn`, `ps0mpan`, `ps0mpdn`, `ps0mpgn`, `ps0mpln`, `ps0mpnn`, `ps0msan`, `ps0msdn`, `ps0msgn`, `ps0msln`, `ps0msnn`, `ps10sgn`, `ps1mpnn`, `ps1msgn`, `ps1msnn`, `ps2fsnn`, `px000an`, `px000dn`, `px000gn`, `px000ln`, `q`, `r0c`, `r0m`, `r0p`, `r0q`, `r0t`, `rcc`, `rcm`, `rcp`, `rcq`, `rct`, `rpc`, `rpm`, `rpp`, `rpq`, `rpt`, `rrm`, `rrp`, `rrt`, `rsm`, `rsp`, `rsq`, `rst`, `sp00`, `sppd`, `sppg`, `spsa`, `spsd`, `spsg`, `stpg`, `stsg`, `vcnc0ii00an`, `vcnc0ii00ay`, `vcnd0ii00an`, `vcnifi130an`, `vcnifii1pan`, `vcnifii1pay`, `vcnifii1san`, `vcnifii1say`, `vcnifii2pan`, `vcnifii2pay`, `vcnifii2san`, `vcnifii2say`, `vcnifii30an`, `vcnifii30ay`, `vcnipii1pan`, `vcnipii1pay`, `vcnipii1san`, `vcnipii1say`, `vcnipii2pan`, `vcnipii2pay`, `vcnipii2san`, `vcnipii2say`, `vcnipii30an`, `vcnipii30ay`, `vcnisii1pan`, `vcnisii1pay`, `vcnisii1san`, `vcnisii1say`, `vcnisii2pay`, `vcnisii30an`, `vcnisii30ay`, `vcnist330an`, `vcnm0ii2pan`, `vcnm0ii2san`, `vcnn0ii000n`, `vcnn0ii000y`, `vcnn0ii00an`, `vcnn0t3000n`, `vcnpdfpnasnpn`, `vcnpdfsaasypn`, `vcnpdfsgapypn`, `vcnpdfsnapnpn`, `vcnpdfsnasnpn`, `vcnpdmplasypn`, `vcnpdmpnasnpn`, `vcnpdmsaasnpy`, `vcnpdmsaasypn`, `vcnpdmsnasn0n`, `vcnpdmsnasnpn`, `vcnppfsn0000n`, `vcnppmpn0000n`, `vcnppmsn0000n`, `vcnpu0000000n`, `vcnrfii00an`, `vcnrpii00an`, `vcnrpii00ay`, `venipi130an`, `venipi130ay`, `venisi130an`, `veyifii30an`, `veyipi130an`, `veyipi130ay`, `veyipi330an`, `veyipii30an`, `veyipii30ay`, `veyisi130an`, `veyisi330an`, `veyisii30an`, `veyisii30ay`, `veypdmpnasnpn`, `veypdmsnasnpn`, `vgnpdmsgapypn`, `vmnc0i100an`, `vmnc0i100ay`, `vmnc0i10say`, `vmnc0i200an`, `vmnc0i300an`, `vmnc0i300ay`, `vmnc0ii000n`, `vmnc0ii00an`, `vmnc0ii00ay`, `vmnc0t100an`, `vmnc0t100ay`, `vmnc0t200an`, `vmnc0t200ay`, `vmnc0t300an`, `vmnc0t300ay`, `vmnc0ti00an`, `vmnd0i100an`, `vmnd0i200an`, `vmnd0i300an`, `vmnd0ii00an`, `vmnd0t100an`, `vmnd0t130an`, `vmnd0t200an`, `vmnd0t300an`, `vmnd0ti00an`, `vmnd0ti00pn`, `vmnifi11pan`, `vmnifi11pay`, `vmnifi11san`, `vmnifi11say`, `vmnifi12pan`, `vmnifi12san`, `vmnifi130an`, `vmnifi130ay`, `vmnifi13san`, `vmnifi21pan`, `vmnifi21san`, `vmnifi21say`, `vmnifi22san`, `vmnifi230an`, `vmnifi230ay`, `vmnifi31pan`, `vmnifi32san`, `vmnifi32say`, `vmnifi330an`, `vmnifi330ay`, `vmnifii1san`, `vmnifii2san`, `vmnifii30an`, `vmnifii30ay`, `vmnift11pan`, `vmnift11pay`, `vmnift11san`, `vmnift11say`, `vmnift12pan`, `vmnift12san`, `vmnift12say`, `vmnift130an`, `vmnift130ay`, `vmnift21pan`, `vmnift21pay`, `vmnift21san`, `vmnift21say`, `vmnift22pan`, `vmnift22pay`, `vmnift22san`, `vmnift22say`, `vmnift230an`, `vmnift230ay`, `vmnift31pan`, `vmnift31pay`, `vmnift31san`, `vmnift31say`, `vmnift32pan`, `vmnift32san`, `vmnift32say`, `vmnift330an`, `vmnift330ay`, `vmnifti1san`, `vmnifti2san`, `vmnifti30an`, `vmnim0230an`, `vmnipi11pan`, `vmnipi11pay`, `vmnipi11san`, `vmnipi12pan`, `vmnipi12san`, `vmnipi130an`, `vmnipi130ay`, `vmnipi21pan`, `vmnipi21san`, `vmnipi22pan`, `vmnipi22pay`, `vmnipi22san`, `vmnipi230an`, `vmnipi230ay`, `vmnipi23san`, `vmnipi31pan`, `vmnipi31san`, `vmnipi31say`, `vmnipi32pan`, `vmnipi32san`, `vmnipi330an`, `vmnipi330ay`, `vmnipii1pan`, `vmnipii1san`, `vmnipii2pan`, `vmnipii2pay`, `vmnipii2san`, `vmnipii30an`, `vmnipii30ay`, `vmnipt110an`, `vmnipt11pan`, `vmnipt11pay`, `vmnipt11san`, `vmnipt11say`, `vmnipt12pan`, `vmnipt12san`, `vmnipt12say`, `vmnipt130an`, `vmnipt130ay`, `vmnipt21pan`, `vmnipt21pay`, `vmnipt21san`, `vmnipt21say`, `vmnipt22pan`, `vmnipt22san`, `vmnipt22say`, `vmnipt230an`, `vmnipt230ay`, `vmnipt23san`, `vmnipt31pan`, `vmnipt31pay`, `vmnipt31san`, `vmnipt31say`, `vmnipt32pan`, `vmnipt32san`, `vmnipt32say`, `vmnipt330an`, `vmnipt330ay`, `vmnipti1pan`, `vmnipti1san`, `vmnipti2pan`, `vmnipti30an`, `vmnipti30ay`, `vmnipti3san`, `vmnisi11pan`, `vmnisi11san`, `vmnisi11say`, `vmnisi12san`, `vmnisi130an`, `vmnisi130ay`, `vmnisi21pan`, `vmnisi21san`, `vmnisi22pan`, `vmnisi230an`, `vmnisi230ay`, `vmnisi31pan`, `vmnisi31san`, `vmnisi31say`, `vmnisi330an`, `vmnisi330ay`, `vmnisii1pan`, `vmnisii1pay`, `vmnisii1san`, `vmnisii2san`, `vmnisii30an`, `vmnisii30ay`, `vmnist11pan`, `vmnist11pay`, `vmnist11san`, `vmnist11say`, `vmnist12pan`, `vmnist12san`, `vmnist130an`, `vmnist130ay`, `vmnist21pan`, `vmnist21pay`, `vmnist21san`, `vmnist21say`, `vmnist230an`, `vmnist230ay`, `vmnist31pan`, `vmnist31pay`, `vmnist31san`, `vmnist31say`, `vmnist32pan`, `vmnist32san`, `vmnist32say`, `vmnist330an`, `vmnist330ay`, `vmnisti1san`, `vmnisti30an`, `vmnisti30ay`, `vmnm0i12pan`, `vmnm0i12pay`, `vmnm0i12san`, `vmnm0i12say`, `vmnm0i21san`, `vmnm0i22pan`, `vmnm0i22san`, `vmnm0i32pan`, `vmnm0i32san`, `vmnm0i32say`, `vmnm0ii1pan`, `vmnm0ii2pan`, `vmnm0ii2san`, `vmnm0t11san`, `vmnm0t12pan`, `vmnm0t12pay`, `vmnm0t12san`, `vmnm0t12say`, `vmnm0t130an`, `vmnm0t21san`, `vmnm0t21say`, `vmnm0t22pan`, `vmnm0t22san`, `vmnm0t22say`, `vmnm0t230an`, `vmnm0t31san`, `vmnm0t32pan`, `vmnm0t32pay`, `vmnm0t32san`, `vmnm0t32say`, `vmnm0ti2pan`, `vmnm0ti2san`, `vmnmpi130ay`, `vmnmpi32san`, `vmnmpii2pan`, `vmnmpt12pan`, `vmnmpt12say`, `vmnmpt130ay`, `vmnmpt22san`, `vmnmpt32pan`, `vmnmpt32san`, `vmnn0i1000n`, `vmnn0i1000y`, `vmnn0i100an`, `vmnn0i130an`, `vmnn0i2000n`, `vmnn0i2000y`, `vmnn0i200an`, `vmnn0i3000n`, `vmnn0i3000y`, `vmnn0i300an`, `vmnn0ii000n`, `vmnn0ii000y`, `vmnn0t1000n`, `vmnn0t1000y`, `vmnn0t100an`, `vmnn0t2000n`, `vmnn0t2000y`, `vmnn0t200an`, `vmnn0t3000n`, `vmnn0t3000y`, `vmnn0t300an`, `vmnn0ti000n`, `vmnn0ti00an`, `vmnpdfpaapnpn`, `vmnpdfpaapypn`, `vmnpdfpaasnpn`, `vmnpdfpaasypn`, `vmnpdfpappnpn`, `vmnpdfpappnpy`, `vmnpdfpappypn`, `vmnpdfpapsnpn`, `vmnpdfpapsnpy`, `vmnpdfpapsypn`, `vmnpdfpdapnpn`, `vmnpdfpdapnpy`, `vmnpdfpdapypn`, `vmnpdfpdapysn`, `vmnpdfpdasnpn`, `vmnpdfpdasypn`, `vmnpdfpdppnpn`, `vmnpdfpdppnpy`, `vmnpdfpdppypn`, `vmnpdfpdpsnpn`, `vmnpdfpdpsypn`, `vmnpdfpdpsypy`, `vmnpdfpgapncn`, `vmnpdfpgapypn`, `vmnpdfpgppnpn`, `vmnpdfpgppnpy`, `vmnpdfpgppypn`, `vmnpdfpgpsnpn`, `vmnpdfpgpsypn`, `vmnpdfplapnpn`, `vmnpdfplapypn`, `vmnpdfplasnpn`, `vmnpdfplasypn`, `vmnpdfplppnpy`, `vmnpdfplpsnpn`, `vmnpdfplpsypn`, `vmnpdfpnapn0n`, `vmnpdfpnapnpn`, `vmnpdfpnapnpy`, `vmnpdfpnapypn`, `vmnpdfpnasnpn`, `vmnpdfpnasypn`, `vmnpdfpnasypy`, `vmnpdfpnppnpn`, `vmnpdfpnppnpy`, `vmnpdfpnppypn`, `vmnpdfpnpsnpn`, `vmnpdfpnpsnpy`, `vmnpdfpnpsypn`, `vmnpdfpnpsypy`, `vmnpdfsaapn0n`, `vmnpdfsaapncn`, `vmnpdfsaapnpn`, `vmnpdfsaapnpy`, `vmnpdfsaapypn`, `vmnpdfsaasnpn`, `vmnpdfsaasypn`, `vmnpdfsappnpn`, `vmnpdfsappnpy`, `vmnpdfsappypn`, `vmnpdfsappypy`, `vmnpdfsapsncn`, `vmnpdfsapsnpn`, `vmnpdfsapsnpy`, `vmnpdfsapsypn`, `vmnpdfsdapnpn`, `vmnpdfsdapypn`, `vmnpdfsdasnpn`, `vmnpdfsdasypn`, `vmnpdfsdppnpn`, `vmnpdfsdppypn`, `vmnpdfsdpsnpn`, `vmnpdfsdpsnpy`, `vmnpdfsdpsypn`, `vmnpdfsgapnpn`, `vmnpdfsgapypn`, `vmnpdfsgasnpn`, `vmnpdfsgasypn`, `vmnpdfsgppnpn`, `vmnpdfsgppnpy`, `vmnpdfsgppypn`, `vmnpdfsgpsnpn`, `vmnpdfsgpsypn`, `vmnpdfsgpsypy`, `vmnpdfslapnpn`, `vmnpdfslapypn`, `vmnpdfslasnpn`, `vmnpdfslasypn`, `vmnpdfslppnpn`, `vmnpdfslppypn`, `vmnpdfslpsnpn`, `vmnpdfslpsypn`, `vmnpdfslpsypy`, `vmnpdfsnapnpn`, `vmnpdfsnapnpy`, `vmnpdfsnapypn`, `vmnpdfsnapysn`, `vmnpdfsnasn0n`, `vmnpdfsnasnpn`, `vmnpdfsnasnpy`, `vmnpdfsnasypn`, `vmnpdfsnppncn`, `vmnpdfsnppnpn`, `vmnpdfsnppnpy`, `vmnpdfsnppypn`, `vmnpdfsnppypy`, `vmnpdfsnpsncn`, `vmnpdfsnpsnpn`, `vmnpdfsnpsnpy`, `vmnpdfsnpsypn`, `vmnpdfsnpsypy`, `vmnpdmpaapnpn`, `vmnpdmpaapycn`, `vmnpdmpaapypn`, `vmnpdmpaasnpn`, `vmnpdmpaasypn`, `vmnpdmpappnpn`, `vmnpdmpappypn`, `vmnpdmpapsnpn`, `vmnpdmpapsnpy`, `vmnpdmpapsypn`, `vmnpdmpapsypy`, `vmnpdmpdapnpn`, `vmnpdmpdapypn`, `vmnpdmpdasnpn`, `vmnpdmpdasypn`, `vmnpdmpdppnpn`, `vmnpdmpdppycn`, `vmnpdmpdppypn`, `vmnpdmpdpsnpn`, `vmnpdmpdpsnpy`, `vmnpdmpdpsycn`, `vmnpdmpdpsypn`, `vmnpdmpdpsypy`, `vmnpdmpgapnpn`, `vmnpdmpgapypn`, `vmnpdmpgasnpn`, `vmnpdmpgasypn`, `vmnpdmpgppypn`, `vmnpdmpgpsnpn`, `vmnpdmpgpsypn`, `vmnpdmpgpsypy`, `vmnpdmplapnpn`, `vmnpdmplapypn`, `vmnpdmplpsnpn`, `vmnpdmplpsypn`, `vmnpdmpnapnpn`, `vmnpdmpnapypn`, `vmnpdmpnasnpn`, `vmnpdmpnasypn`, `vmnpdmpnppn0n`, `vmnpdmpnppnpn`, `vmnpdmpnppnpy`, `vmnpdmpnppypn`, `vmnpdmpnpsnpn`, `vmnpdmpnpsnpy`, `vmnpdmpnpsypn`, `vmnpdmpnpsypy`, `vmnpdmpvppypn`, `vmnpdmsaapnpn`, `vmnpdmsaapypn`, `vmnpdmsaasnpn`, `vmnpdmsaasypn`, `vmnpdmsappnpn`, `vmnpdmsappnpy`, `vmnpdmsappypn`, `vmnpdmsappypy`, `vmnpdmsapsnpn`, `vmnpdmsapsnpy`, `vmnpdmsapsypn`, `vmnpdmsapsypy`, `vmnpdmsdapnpn`, `vmnpdmsdapypn`, `vmnpdmsdasnpn`, `vmnpdmsdppnpn`, `vmnpdmsdppypn`, `vmnpdmsdppypy`, `vmnpdmsdpsnpn`, `vmnpdmsdpsypn`, `vmnpdmsdpsypy`, `vmnpdmsgapnpn`, `vmnpdmsgapypn`, `vmnpdmsgasnpn`, `vmnpdmsgasypn`, `vmnpdmsgppnpn`, `vmnpdmsgppy0n`, `vmnpdmsgppypn`, `vmnpdmsgppypy`, `vmnpdmsgpsnpn`, `vmnpdmsgpsycn`, `vmnpdmsgpsypn`, `vmnpdmsgpsypy`, `vmnpdmslapnpn`, `vmnpdmslapypn`, `vmnpdmslasnpn`, `vmnpdmslasypn`, `vmnpdmslppnpn`, `vmnpdmslppy0n`, `vmnpdmslppypn`, `vmnpdmslpsnpn`, `vmnpdmslpsypn`, `vmnpdmsnapnpn`, `vmnpdmsnapnpy`, `vmnpdmsnapypn`, `vmnpdmsnasn0n`, `vmnpdmsnasnpn`, `vmnpdmsnasnpy`, `vmnpdmsnasypn`, `vmnpdmsnppnpn`, `vmnpdmsnppnpy`, `vmnpdmsnppypn`, `vmnpdmsnppypy`, `vmnpdmsnpsnpn`, `vmnpdmsnpsnpy`, `vmnpdmsnpsycn`, `vmnpdmsnpsypn`, `vmnpdmsnpsypy`, `vmnppfpn0000y`, `vmnppfsn0000n`, `vmnppmpn0000n`, `vmnppmpnap00n`, `vmnppmpnap0pn`, `vmnppmpnap0py`, `vmnppmsn0000n`, `vmnpu0000000n`, `vmnpu0000000y`, `vmnpu000000pn`, `vmnpu00000n0n`, `vmnpu000apnpn`, `vmnpumpgpsnpn`, `vmnr0t100an`, `vmnr0t3000n`, `vmnrfi100an`, `vmnrft100an`, `vmnrft200an`, `vmnrft200ay`, `vmnrft300an`, `vmnrpi1000y`, `vmnrpi100an`, `vmnrpi2000n`, `vmnrpi200an`, `vmnrpi300an`, `vmnrpii00an`, `vmnrpii00ay`, `vmnrpt100an`, `vmnrpt100ay`, `vmnrpt200an`, `vmnrpt200ay`, `vmnrpt300an`, `vmnrpt300ay`, `vmyc0i100an`, `vmyc0i100ay`, `vmyc0i200an`, `vmyc0i200ay`, `vmyc0i300an`, `vmyc0i300ay`, `vmyc0t100an`, `vmyc0t200an`, `vmyc0t300an`, `vmyc0ti00an`, `vmyd0i100an`, `vmyd0i200an`, `vmyd0i300an`, `vmyd0ii00an`, `vmyd0t100an`, `vmyd0t200an`, `vmyd0t300an`, `vmyd0ti00an`, `vmyifi11pan`, `vmyifi11san`, `vmyifi11say`, `vmyifi12pan`, `vmyifi12san`, `vmyifi130an`, `vmyifi130ay`, `vmyifi21san`, `vmyifi230an`, `vmyifi230ay`, `vmyifi31pan`, `vmyifi31san`, `vmyifi31say`, `vmyifi32san`, `vmyifi330an`, `vmyifi330ay`, `vmyift11pan`, `vmyift130an`, `vmyift21san`, `vmyift31pan`, `vmyift32san`, `vmyift330an`, `vmyifti1san`, `vmyifti30an`, `vmyipi110ay`, `vmyipi11pan`, `vmyipi11san`, `vmyipi12pan`, `vmyipi12san`, `vmyipi12say`, `vmyipi130an`, `vmyipi130ay`, `vmyipi21pan`, `vmyipi21san`, `vmyipi21say`, `vmyipi22pan`, `vmyipi22san`, `vmyipi230an`, `vmyipi230ay`, `vmyipi31pan`, `vmyipi31san`, `vmyipi31say`, `vmyipi32pan`, `vmyipi32san`, `vmyipi330an`, `vmyipi330ay`, `vmyipii1pan`, `vmyipt11pan`, `vmyipt11san`, `vmyipt12san`, `vmyipt130an`, `vmyipt130ay`, `vmyipt21san`, `vmyipt22san`, `vmyipt230an`, `vmyipt31pan`, `vmyipt31san`, `vmyipt31say`, `vmyipt32pan`, `vmyipt32san`, `vmyipt32say`, `vmyipt330an`, `vmyipt330ay`, `vmyipti1pan`, `vmyipti1san`, `vmyipti2pan`, `vmyipti30an`, `vmyipti30ay`, `vmyisi11pan`, `vmyisi11san`, `vmyisi12san`, `vmyisi130an`, `vmyisi130ay`, `vmyisi13pan`, `vmyisi21pan`, `vmyisi21san`, `vmyisi22san`, `vmyisi230an`, `vmyisi230ay`, `vmyisi31pan`, `vmyisi31san`, `vmyisi31say`, `vmyisi32san`, `vmyisi330an`, `vmyisi330ay`, `vmyisii1san`, `vmyisii30an`, `vmyist11pan`, `vmyist11san`, `vmyist130an`, `vmyist21pan`, `vmyist230an`, `vmyist230ay`, `vmyist31pan`, `vmyist31san`, `vmyist32pan`, `vmyist330an`, `vmyist330ay`, `vmyisti1pan`, `vmyisti1san`, `vmyisti30an`, `vmyisti30ay`, `vmym0i11san`, `vmym0i12pan`, `vmym0i12san`, `vmym0i12say`, `vmym0i22pan`, `vmym0i22san`, `vmym0i22say`, `vmym0i32pan`, `vmym0i32pay`, `vmym0i32san`, `vmym0t22pan`, `vmym0t22san`, `vmym0t32pan`, `vmym0t32san`, `vmympi32san`, `vmympt32san`, `vmyn0i1000n`, `vmyn0i1000y`, `vmyn0i2000n`, `vmyn0i3000n`, `vmyn0i3000y`, `vmyn0ii000n`, `vmyn0ii00an`, `vmyn0t1000n`, `vmyn0t1000y`, `vmyn0t100an`, `vmyn0t2000n`, `vmyn0t3000n`, `vmyn0t3000y`, `vmyn0ti000n`, `vmypdfpaasnpn`, `vmypdfpnasnpn`, `vmypdfpnasnpy`, `vmypdfpnasypn`, `vmypdfpnppypn`, `vmypdfsaasnpn`, `vmypdfsaasnpy`, `vmypdfsnasn0n`, `vmypdfsnasnpn`, `vmypdmpaapnpn`, `vmypdmpaasypn`, `vmypdmpnasn0n`, `vmypdmpnasnpn`, `vmypdmsaapnpn`, `vmypdmsaasnpn`, `vmypdmsnasn0n`, `vmypdmsnasnpn`, `vmypdmsnasnpy`, `vmypdmsnpsnpn`, `vmyppf0n0000n`, `vmyppfsn0000n`, `vmyppfsn0000y`, `vmyppm0n0000n`, `vmyppmpn0000n`, `vmyppms00000n`, `vmyppmsn0000n`, `vmypu0000000n`, `vmypu0000000y`, `vmypu000000pn`, `vmypumsnasnpn`, `vmyrfi100an`, `vmyrpi200an`, `vmyrpi300an`, `vmyrpt100an`, `vmyrpt300an`, `vmyrpt300ay`, `vonc0i100an`, `vonc0i100ay`, `vonc0i300an`, `vonc0i300ay`, `vonc0t300ay`, `vond0i100an`, `vond0t300an`, `vondpi300an`, `vonifi11pay`, `vonifi12pay`, `vonifi130an`, `vonifi130ay`, `vonifi230an`, `vonifi31pan`, `vonifi31san`, `vonifi31say`, `vonifi32san`, `vonifi32say`, `vonifi330an`, `vonifi330ay`, `vonift31say`, `vonift32san`, `vonift330an`, `vonift330ay`, `vonipi11pan`, `vonipi11pay`, `vonipi11san`, `vonipi11say`, `vonipi12pan`, `vonipi130an`, `vonipi130ay`, `vonipi21pan`, `vonipi230an`, `vonipi230ay`, `vonipi300ay`, `vonipi31pan`, `vonipi31pay`, `vonipi31san`, `vonipi31say`, `vonipi32pan`, `vonipi32pay`, `vonipi32san`, `vonipi32say`, `vonipi330an`, `vonipi330ay`, `vonipii30an`, `vonipt130an`, `vonipt230an`, `vonipt31pan`, `vonipt31pay`, `vonipt31san`, `vonipt31say`, `vonipt32pan`, `vonipt32san`, `vonipt330an`, `vonipt330ay`, `vonisi11san`, `vonisi11say`, `vonisi130an`, `vonisi130ay`, `vonisi230an`, `vonisi31pan`, `vonisi31pay`, `vonisi31san`, `vonisi31say`, `vonisi32pan`, `vonisi330an`, `vonisi330ay`, `vonist130an`, `vonist330an`, `vonist330ay`, `vonm0i32san`, `vonmpi32san`, `vonn0i3000n`, `vonn0t3000n`, `vonpdfpn00npy`, `vonpdfpnasnpn`, `vonpdfsnasnpn`, `vonpdfsnasnpy`, `vonpdmpnasnpn`, `vonpdmsnasnpn`, `vonpdmsnpsnpn`, `vonpdmsnpsypn`, `vonppfsn0000n`, `vonppmsn0000n`, `vonppmsn0000y`, `vonpu0000000n`, `vonpu0000000y`, `vonrft300an`, `vonrpi100ay`, `vonrpi300an`, `vonrpi300ay`, `vonrpt300an`, `vonrpt300ay`, `voyc0i100an`, `voyc0i100ay`, `voyc0i300an`, `voyc0i300ay`, `voyc0t300an`, `voyd0i100an`, `voyifi12san`, `voyifi130an`, `voyifi330an`, `voyifi330ay`, `voyifii30an`, `voyipi11pan`, `voyipi11san`, `voyipi11say`, `voyipi130an`, `voyipi130ay`, `voyipi230ay`, `voyipi300ay`, `voyipi31pan`, `voyipi31san`, `voyipi31say`, `voyipi32pan`, `voyipi330an`, `voyipi330ay`, `voyipii30an`, `voyipt11pan`, `voyipt130an`, `voyipt31san`, `voyipt32san`, `voyipt330an`, `voyipt330ay`, `voyisi11pan`, `voyisi11san`, `voyisi11say`, `voyisi130an`, `voyisi230an`, `voyisi31san`, `voyisi31say`, `voyisi330an`, `voyisi330ay`, `voyist11san`, `voyist330an`, `voym0i12pay`, `voyn0i1000n`, `voyn0i3000n`, `voyn0t1000n`, `voyp0msnap00n`, `voypdfsnasnpn`, `voypdmpnasnpn`, `voypdmsnasnpn`, `voypdmsnasnpy`, `voypu0000000n`, `voyrfi100an`, `voyrpi100an`, `voyrpi300ay`, `vpnc0i100an`, `vpnc0i300an`, `vpnd0i100an`, `vpnd0t100an`, `vpnifi12san`, `vpnifi130an`, `vpnifi31pan`, `vpnifi330an`, `vpnift130an`, `vpnift31pan`, `vpnipi11pan`, `vpnipi11pay`, `vpnipi11san`, `vpnipi130an`, `vpnipi130ay`, `vpnipi330an`, `vpnipt11pan`, `vpnipt11san`, `vpnipt130an`, `vpnipt31pan`, `vpnisi11pan`, `vpnisi11san`, `vpnisi11say`, `vpnisi130an`, `vpnisi130ay`, `vpnisi230an`, `vpnisi31san`, `vpnisi330an`, `vpnist11san`, `vpnist130an`, `vpnist330an`, `vpnisti30an`, `vpnm0i12san`, `vpnm0i32san`, `vpnm0t32san`, `vpnn0i1000n`, `vpnn0i3000n`, `vpnn0t1000n`, `vpnn0t3000n`, `vpnpdfpnasnpn`, `vpnpdfsgasypn`, `vpnpdfsnasnpn`, `vpnpdmpnasnpn`, `vpnpdmsnasnpn`, `vpnpdmsnpsnpn`, `vpnppmsn0000n`, `vpnpu0000000n`, `vpyifi130an`, `vpyipi130an`, `vpyisi130an`, `vtnc0i100an`, `vtnc0i100ay`, `vtnc0t200an`, `vtnd0i100an`, `vtnifi11pay`, `vtnifi11san`, `vtnifi130an`, `vtnifi130ay`, `vtnift130an`, `vtnipi11pan`, `vtnipi11san`, `vtnipi130an`, `vtnipi130ay`, `vtnipi230an`, `vtnipii30an`, `vtnipt230an`, `vtnipt330an`, `vtnisi11san`, `vtnisi12san`, `vtnisi130an`, `vtnisi130ay`, `vtnist330an`, `vtnn0i1000n`, `vtnn0i100an`, `vtnn0t1000n`, `vtnpdfpnasnpn`, `vtnpdfsnasnpn`, `vtnpdmpnasnpn`, `vtnpdmsnasnpn`, `vtnppmsn0000n`, `vtnpu0000000n`, `vtnrpi100an`, `vtyc0i300ay`, `vtyifi330an`, `vtyipi11san`, `vtyipi130an`, `vtyipi130ay`, `vtyipi330an`, `vtyipi330ay`, `vtyipt11pay`, `vtyipt11say`, `vtyipt130an`, `vtyipt330an`, `vtyisi11san`, `vtyisi130an`, `vtyisi330an`, `vtyist11pan`, `vtyist11san`, `vtyist130an`, `vtyist330an`, `vtyn0i1000n`, `vtyn0i3000n`, `vtyn0t1000n`, `vtyn0t3000n`, `vtypdfsnasnpn`, `vtypdmsnasnpn`, `xf`, `xn`, `xo`, `xu`, `xx`, `ya`, `yd`, `yn`, `yp`, `yr`, `yv`, `z_`, `zb`, `zc`, `zd`, `zo`, `zq`, `zs`, `zx` \| \| `morphologizer` \| `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `POS=PUNCT`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=PART`, `POS=CCONJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Evident=Fh\\|Mood=Ind\\|POS=AUX\\|Person=3\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `POS=ADP`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `POS=VERB\\|Polarity=Pos\\|VerbForm=Conv`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|Typo=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Degree=Pos\\|POS=ADV`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `POS=ADV`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `NumType=Card\\|POS=NUM`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Coll\\|POS=NOUN`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `POS=ADV\\|PronType=Dem`, `POS=ADV\\|PronType=Int`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Dat\\|Gender=Fem\\|Number=Ptan\\|POS=PROPN`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Ptan\\|POS=PROPN`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|Typo=Yes`, `POS=SCONJ`, `Mood=Cnd\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|POS=PRON\\|PronType=Rel`, `POS=AUX\\|Polarity=Pos\\|VerbForm=Inf`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `POS=VERB\\|Polarity=Pos\\|VerbForm=Inf`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Nom\\|Gender=Fem\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Case=Loc\\|Gender=Fem\\|Number=Ptan\\|POS=NOUN`, `POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Conv`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Acc\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Dat\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `POS=CCONJ\\|Polarity=Neg`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Dat\\|Gender=Fem\\|Number=Coll\\|POS=NOUN`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|POS=AUX\\|Person=3\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Degree=Cmp\\|POS=ADV`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Abbr=Yes\\|POS=PROPN`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Ptan\\|POS=NOUN`, `Case=Acc\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Dat\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=NOUN`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|Reflex=Yes\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `POS=ADV\\|PronType=Neg`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Ptan\\|POS=NOUN`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Inf`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|POS=PRON\\|PronType=Ind,Neg`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Mood=Imp\\|Number=Plur\\|POS=AUX\\|Person=2\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Loc\\|Gender=Fem\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Case=Loc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Tot`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Cnd\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV\\|PronType=Ind`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Dat\\|Gender=Masc\\|Number=Ptan\\|POS=NOUN`, `Aspect=Imp\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Ptan\\|POS=NOUN`, `Case=Loc\\|Gender=Masc\\|Number=Coll\\|POS=NOUN`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Evident=Nfh\\|Mood=Qot\\|POS=AUX\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Evident=Nfh\\|Mood=Qot\\|POS=AUX\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Ptan\\|POS=NOUN`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Mood=Cnd\\|POS=AUX\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Mood=Cnd\\|POS=AUX\\|Polarity=Neg\\|VerbForm=Fin\\|Voice=Act`, `Mood=Nec\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|POS=AUX\\|Person=3\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Case=Loc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `POS=PART\\|Polarity=Neg`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|POS=PRON\\|PronType=Int`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Gen\\|Gender=Masc\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `POS=VERB\\|Polarity=Neg\\|Reflex=Yes\\|VerbForm=Conv`, `Evident=Fh\\|Mood=Ind\\|POS=AUX\\|Person=3\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Perf\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind,Neg`, `Mood=Nec\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind,Neg`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Loc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=AUX\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|POS=PRON\\|PronType=Ind`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Imp\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Ptan\\|POS=NOUN`, `Case=Acc\\|Gender=Fem\\|Number=Ptan\\|POS=NOUN`, `Case=Acc\\|POS=PRON\\|PronType=Ind`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `POS=VERB\\|Polarity=Neg\\|VerbForm=Inf`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|Gender=Fem\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Dem`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Gen\\|Gender=Fem\\|NumType=Frac\\|Number=Sing\\|POS=NUM`, `Mood=Cnd\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Aspect=Imp\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Ptan\\|POS=NOUN`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Ptan\\|POS=PROPN`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=NOUN`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel\\|Typo=Yes`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `NumType=Ord\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Case=Dat\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Dem`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `POS=PROPN`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Gender=Masc\\|POS=NOUN`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|POS=AUX\\|Person=3\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `POS=VERB\\|Polarity=Neg\\|VerbForm=Conv`, `Mood=Nec\\|POS=AUX\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Aspect=Imp\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Coll\\|POS=NOUN`, `Abbr=Yes\\|POS=NOUN`, `Case=Nom\\|Gender=Masc\\|Number=Coll\\|POS=NOUN`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Fem\\|Number=Coll\\|POS=NOUN`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `POS=SYM`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Dem`, `Case=Nom\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|Typo=Yes`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Foreign=Yes\\|POS=X\\|Typo=Yes`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `POS=CCONJ\\|Typo=Yes`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Dat\\|Gender=Masc\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Typo=Yes`, `POS=X\\|Typo=Yes`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Imp\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Abbr=Yes\\|POS=SYM`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Tot`, `Mood=Cnd\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=DET`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Foreign=Yes\\|POS=X`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel\\|Typo=Yes`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Typo=Yes`, `Case=Acc\\|Gender=Fem\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|POS=NOUN`, `Aspect=Imp\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem\\|Typo=Yes`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Tot`, `Aspect=Perf\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem\\|Typo=Yes`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|Typo=Yes`, `Case=Loc\\|Gender=Masc\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Ptan\\|POS=PROPN`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|POS=DET\\|PronType=Rel`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `POS=PART\\|Typo=Yes`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV\\|PronType=Int,Neg`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Aspect=Perf\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|POS=PRON\\|PronType=Ind,Neg`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Ind`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|POS=DET\\|PronType=Ind,Neg`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind,Neg`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Typo=Yes`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|POS=PRON\\|PronType=Int`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Conv`, `Case=Acc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind,Neg`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|POS=PRON\\|PronType=Int`, `Case=Gen\\|POS=PRON\\|PronType=Int`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `POS=ADV\\|PronType=Tot`, `Aspect=Imp\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Conv`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Tot`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind,Neg`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind,Neg`, `Aspect=Imp\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Degree=Sup\\|POS=ADV`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|POS=PRON\\|PronType=Ind,Neg`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Tot`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Conv`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Conv`, `Case=Dat\\|Gender=Masc\\|Number=Ptan\\|POS=PROPN`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Reflex=Yes\\|VerbForm=Conv`, `POS=INTJ`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Loc\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Conv`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Tot`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Tot`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind,Neg`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind,Neg`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Loc\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Aspect=Imp\\|Case=Nom\\|Definite=Ind\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=PART\\|Polarity=Pos`, `Case=Gen\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Conv`, `Case=Dat\\|Gender=Fem\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Coll\\|POS=NOUN`, `Case=Dat\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Voc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Conv`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|POS=PRON\\|PronType=Prs\\|Reflex=Yes`, `Aspect=Imp\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `POS=ADV\\|Typo=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Voc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN`, `Case=Nom\\|Gender=Fem\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Conv`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Voc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Abbr=Yes\\|POS=ADJ`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Case=Voc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `Aspect=Perf\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Aspect=Imp\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Aspect=Imp\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Mood=Nec\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|Reflex=Yes\\|VerbForm=Fin\\|Voice=Act`, `NumType=Mult\\|POS=ADV`, `Aspect=Imp\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Masc\\|NumType=Frac\\|Number=Sing\\|POS=NUM`, `Case=Loc\\|Gender=Masc\\|Number=Ptan\\|POS=NOUN`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind,Neg`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Evident=Nfh\\|Mood=Qot\\|POS=AUX\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Aspect=Perf\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|PronType=Ind`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=3\\|PronType=Dem`, `Aspect=Imp\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=NOUN\\|Typo=Yes`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|POS=PRON\\|PronType=Ind`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind\\|Typo=Yes`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `POS=ADP\\|Typo=Yes`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Conv`, `Evident=Fh\\|Mood=Ind\\|POS=AUX\\|Person=3\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|PronType=Int`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Ind,Neg`, `Case=Gen\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|POS=PRON\\|PronType=Ind`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Conv`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Dem`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Aspect=Imp\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind,Neg`, `Case=Acc\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Ind,Neg`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind,Neg`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Typo=Yes`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Poss=Yes\\|PronType=Prs`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|POS=DET\\|PronType=Rel`, `Case=Loc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem\\|Typo=Yes`, `Case=Nom\\|POS=DET\\|PronType=Ind,Neg`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Loc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Voc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Neg\\|Tense=Fut\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind,Neg`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Tot`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Number=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=AUX\\|Polarity=Pos\\|VerbForm=Conv`, `Case=Acc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|POS=DET\\|PronType=Int`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `POS=VERB\\|Polarity=Neg\\|Reflex=Yes\\|VerbForm=Inf`, `Aspect=Imp\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Nom\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Gen\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Ptan\\|POS=PROPN`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind,Neg`, `Aspect=Imp\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `POS=VERB\\|Person=3\\|Polarity=Pos\\|VerbForm=Inf\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|PronType=Dem`, `Aspect=Imp\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|POS=DET\\|PronType=Rel`, `POS=VERB\\|Polarity=Pos\\|VerbForm=Inf\\|Voice=Act`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|VerbForm=Conv`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Dem`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Abbr=Yes\\|POS=ADV`, `Aspect=Imp\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Aspect=Imp\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Dem`, `Definite=Ind\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Conv\\|Voice=Act`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|NumType=Card\\|Number=Plur\\|POS=NUM`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Coll\\|POS=NOUN\\|PronType=Int`, `POS=VERB\\|Polarity=Pos\\|Typo=Yes\\|VerbForm=Inf`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=DET`, `Case=Acc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part\\|Voice=Act`, `Mood=Nec\\|POS=AUX\\|Polarity=Pos\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Case=Loc\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Poss=Yes\\|PronType=Prs`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=DET`, `Case=Nom\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Typo=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Conv`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Fem\\|Number=Ptan\\|POS=PROPN`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PROPN`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=DET`, `Case=Loc\\|Gender=Fem\\|Number=Coll\\|POS=NOUN`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Ind,Neg`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Tot`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|Person=3\\|PronType=Dem\\|Typo=Yes`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `POS=AUX\\|Polarity=Pos\\|VerbForm=Inf\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|Typo=Yes`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Imp\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Mood=Cnd\\|POS=VERB\\|Polarity=Neg\\|Reflex=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel\\|Typo=Yes`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Case=Loc\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `POS=PUNCT\\|Typo=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|VerbForm=Conv`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|Typo=Yes`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=1\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin`, `Case=Gen\\|POS=PRON\\|PronType=Rel`, `Mood=Imp\\|POS=VERB\\|Person=3\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Rel`, `POS=SCONJ\\|Typo=Yes`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Part\\|Voice=Pass`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|POS=DET\\|PronType=Ind`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Ind,Neg`, `Aspect=Imp\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=NOUN\\|Typo=Yes`, `Case=Nom\\|Gender=Fem\\|Number=Ptan\\|POS=PROPN`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Typo=Yes`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Definite=Ind\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Dem`, `Mood=Imp\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Mood=Cnd\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Fin\\|Voice=Act`, `Degree=Pos\\|POS=ADV\\|Typo=Yes`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Mood=Imp\\|Number=Sing\\|POS=AUX\\|Person=2\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Typo=Yes`, `Abbr=Yes\\|POS=SYM\\|Typo=Yes`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=X`, `POS=ADV\\|PronType=Neg\\|Typo=Yes`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Dem`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind,Neg`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `POS=AUX\\|Polarity=Pos\\|VerbForm=Conv`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Conv\\|Voice=Act`, `Aspect=Perf\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Mood=Nec\\|POS=VERB\\|Polarity=Pos\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Tot`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Aspect=Imp\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Mood=Nec\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Pass`, `Aspect=Imp\\|Case=Acc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Imp\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Fin\\|Voice=Act`, `POS=VERB\\|Polarity=Pos\\|Typo=Yes\\|VerbForm=Inf\\|Voice=Act`, `Aspect=Imp\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind,Neg`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV\\|PronType=Ind,Neg`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=DET`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Def\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Int`, `Case=Gen\\|Definite=Ind\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Conv\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PROPN`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Ind`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Fin`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Typo=Yes`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=AUX\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Fin`, `Case=Dat\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=DET`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|NumType=Ord\\|Number=Plur\\|POS=ADJ`, `Aspect=Perf\\|Case=Loc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Aspect=Imp\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=PROPN\\|Typo=Yes`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Dat\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|VerbForm=Inf\\|Voice=Act`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|PronType=Dem`, `Aspect=Imp\\|Case=Acc\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Definite=Def\\|Degree=Sup\\|Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Conv`, `Case=Gen\\|Definite=Def\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Part`, `Evident=Nfh\\|Mood=Qot\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|PronType=Int`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Aspect=Imp\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Sing\\|POS=PROPN\\|Typo=Yes`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|PronType=Int`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Masc\\|Number=Ptan\\|POS=NOUN\\|Typo=Yes`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|Typo=Yes`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Degree=Cmp\\|POS=ADV\\|Typo=Yes`, `POS=NOUN\\|Typo=Yes`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|Typo=Yes`, `Case=Nom\\|Gender=Fem\\|Number=Plur\\|POS=PRON\\|PronType=Int`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Dem\\|Typo=Yes`, `Case=Acc\\|Gender=Masc\\|Number=Ptan\\|POS=PROPN`, `Case=Acc\\|Gender=Fem\\|Number=Ptan\\|POS=NOUN\\|Typo=Yes`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|Typo=Yes`, `Case=Acc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Dem\\|Typo=Yes`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Typo=Yes`, `Case=Dat\\|Gender=Fem\\|Number=Sing\\|POS=PROPN\\|Typo=Yes`, `POS=AUX\\|Polarity=Neg\\|VerbForm=Inf`, `Case=Gen\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=NOUN\\|Typo=Yes`, `Case=Dat\\|Gender=Masc\\|Number=Coll\\|POS=NOUN`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Imp\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Rel`, `Mood=Cnd\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Fin`, `Case=Nom\\|Gender=Fem\\|Number=Ptan\\|POS=NOUN\\|Typo=Yes`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Typo=Yes`, `Mood=Nec\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Number=Plur\\|POS=PRON\\|PronType=Rel`, `Case=Nom\\|Gender=Fem\\|NumType=Frac\\|Number=Sing\\|POS=NUM`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|NumType=Frac\\|Number=Sing\\|POS=NUM`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|NumType=Card\\|Number=Plur\\|POS=NUM\\|Typo=Yes`, `Case=Acc\\|Gender=Fem\\|Number=Ptan\\|POS=PROPN`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=DET`, `Aspect=Imp\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|VerbForm=Conv\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=AUX\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=ADJ\\|Typo=Yes`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Voice=Act`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=DET`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Rel`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Typo=Yes`, `Case=Loc\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Sing\\|POS=ADJ`, `Aspect=Perf\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Dem,Neg`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Tense=Fut\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PROPN\\|Typo=Yes`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|POS=DET\\|PronType=Ind,Neg`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Int`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|POS=AUX\\|Person=3\\|Polarity=Pos\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Aspect=Imp\\|Case=Acc\\|Definite=Def\\|Degree=Cmp\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=AUX\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Polarity=Pos\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Case=Nom\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|VerbForm=Conv\\|Voice=Act`, `Aspect=Imp\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Loc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Voc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `POS=ADV\\|PronType=Int\\|Typo=Yes`, `Case=Dat\\|POS=PRON\\|PronType=Ind,Neg`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=2\\|Polarity=Neg\\|Tense=Fut\\|VerbForm=Fin\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=AUX\\|Person=2\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=PRON\\|PronType=Tot`, `Evident=Fh\\|Mood=Ind\\|POS=VERB\\|Person=3\\|Polarity=Neg\\|Tense=Past\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=DET\\|PronType=Tot\\|Typo=Yes`, `Evident=Fh\\|Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=2\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `Case=Dat\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Typo=Yes`, `Case=Dat\\|Gender=Masc\\|Number=Sing\\|POS=DET\\|PronType=Ind,Neg`, `Case=Nom\\|Gender=Fem\\|Number=Coll\\|POS=NOUN`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Voc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=ADJ`, `Aspect=Imp\\|Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=DET\\|Poss=Yes\\|PronType=Prs\\|Typo=Yes`, `Case=Loc\\|Gender=Masc\\|Number=Plur\\|POS=NOUN\\|Typo=Yes`, `Case=Nom\\|Definite=Def\\|Degree=Sup\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Evident=Fh\\|Mood=Ind\\|Number=Plur\\|POS=VERB\\|Person=1\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `Case=Gen\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Aspect=Imp\\|Case=Gen\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Cmp\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=ADJ\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part\\|Voice=Pass`, `Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Aspect=Perf\\|Case=Nom\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Cnd\\|POS=VERB\\|Polarity=Pos\\|Typo=Yes\\|VerbForm=Fin\\|Voice=Act`, `Abbr=Yes\\|POS=VERB`, `Aspect=Imp\\|Case=Nom\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Plur\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Act`, `NumType=Ord\\|POS=ADJ\\|Typo=Yes`, `Case=Dat\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Dat\\|Definite=Ind\\|Degree=Pos\\|Gender=Masc\\|Number=Sing\\|POS=VERB\\|Polarity=Pos\\|Tense=Pres\\|VerbForm=Part\\|Voice=Pass`, `Case=Gen\\|Gender=Fem\\|Number=Sing\\|POS=NOUN\\|PronType=Neg`, `Aspect=Perf\\|Case=Acc\\|Definite=Ind\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Reflex=Yes\\|Tense=Past\\|VerbForm=Part\\|Voice=Act`, `Aspect=Perf\\|Case=Gen\\|Definite=Def\\|Degree=Pos\\|Gender=Fem\\|Number=Sing\\|POS=VERB\\|Polarity=Neg\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Case=Loc\\|Gender=Fem\\|Number=Plur\\|POS=DET\\|PronType=Ind,Neg`, `Aspect=Perf\\|Case=Loc\\|Definite=Def\\|Degree=Pos\\|Gender=Masc\\|Number=Plur\\|POS=AUX\\|Polarity=Pos\\|Tense=Past\\|VerbForm=Part\\|Voice=Act` \| \| `parser` \| `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `0`, `2`, `4`, `6`, `8`, `10`, `11`, `13`, `15`, `18`, `20`, `22`, `26`, `28`, `31`, `34`, `37`, `39`, `41`, `43`, `45`, `47`, `49`, `52`, `54`, `56`, `58`, `60`, `61`, `64`, `66`, `67`, `69`, `71`, `73`, `74`, `76`, `78`, `80`, `83`, `85`, `86`, `87`, `89`, `91`, `93`, `95`, `97`, `98`, `101`, `104`, `107`, `109`, `110`, `112`, `113`, `116`, `119`, `122`, `124`, `126`, `128`, `131`, `134`, `138`, `140`, `142`, `145`, `147`, `149`, 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`MORPH_ACC` \| 95.69 \| \| `DEP_UAS` \| 91.30 \| \| `DEP_LAS` \| 87.75 \| \| `LEMMA_ACC` \| 95.39 \|
patrickvonplaten/wav2vec2-xls-r-phoneme-300m-sv	patrickvonplaten	2021-12-10T18:14:51Z	46	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "common_voice", "generated_from_trainer", "sv", "dataset:common_voice", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: - sv tags: - automatic-speech-recognition - common_voice - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-xls-r-phoneme-300m-sv results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Wav2vec2-xls-r-phoneme-300m-sv Note: The tokenizer was created from the official Swedish phoneme vocabulary as defined here: https://github.com/microsoft/UniSpeech/blob/main/UniSpeech/examples/unispeech/data/sv/phonesMatches_reduced.json One can simply download the file, rename it to `vocab.json` and load a `Wav2Vec2PhonemeCTCTokenizer.from_pretrained("./directory/with/vocab.json/")`. This model is a fine-tuned version of [wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - SV-SE dataset. It achieves the following results on the evaluation set: - Loss: 0.9707 - PER: 0.2215 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0005 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 32 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results See Tensorboard traces ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.8.1 - Datasets 1.16.2.dev0 - Tokenizers 0.10.3
explosion/ko_udv25_koreankaist_trf	explosion	2021-12-10T17:41:25Z	2	0	spacy	[ "spacy", "token-classification", "ko", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - ko license: cc-by-sa-4.0 model-index: - name: ko_udv25_koreankaist_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.8893108632 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9652266793 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 1.0 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9450905926 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.894835346 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8717525957 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 1.0 --- UD v2.5 benchmarking pipeline for UD_Korean-Kaist \| Feature \| Description \| \| --- \| --- \| \| Name \| `ko_udv25_koreankaist_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) \| ### Label Scheme <details> <summary>View label scheme (5329 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `ecs`, `etm`, `f`, `f+f+jcj`, `f+f+jcs`, `f+f+jct`, `f+f+jxt`, `f+jca`, `f+jca+jp+ecc`, `f+jca+jp+ep+ef`, `f+jca+jxc`, `f+jca+jxc+jcm`, `f+jca+jxt`, `f+jcj`, `f+jcm`, `f+jco`, `f+jcs`, `f+jct`, `f+jct+jcm`, `f+jp+ef`, `f+jp+ep+ef`, `f+jp+etm`, `f+jxc`, `f+jxt`, `f+ncn`, `f+ncn+jcm`, `f+ncn+jcs`, `f+ncn+jp+ecc`, `f+ncn+jxt`, `f+ncpa+jcm`, `f+npp+jcs`, `f+nq`, `f+xsn`, `f+xsn+jco`, `f+xsn+jxt`, `ii`, `jca`, `jca+jcm`, `jca+jxc`, `jca+jxt`, `jcc`, `jcj`, `jcm`, `jco`, `jcr`, `jcr+jxc`, `jcs`, `jct`, `jct+jcm`, `jct+jxt`, `jp+ecc`, `jp+ecs`, `jp+ef`, `jp+ef+jcr`, `jp+ef+jcr+jxc`, `jp+ep+ecs`, `jp+ep+ef`, `jp+ep+etm`, `jp+ep+etn`, `jp+etm`, `jp+etn`, `jp+etn+jco`, `jp+etn+jxc`, `jxc`, `jxc+jca`, `jxc+jco`, `jxc+jcs`, `jxt`, `mad`, `mad+jxc`, `mad+jxt`, `mag`, `mag+jca`, `mag+jcm`, `mag+jcs`, `mag+jp+ef+jcr`, `mag+jxc`, `mag+jxc+jxc`, `mag+jxt`, `mag+xsn`, `maj`, `maj+jxc`, `maj+jxt`, `mma`, `mmd`, `nbn`, `nbn+jca`, `nbn+jca+jcj`, `nbn+jca+jcm`, `nbn+jca+jp+ef`, `nbn+jca+jxc`, `nbn+jca+jxt`, `nbn+jcc`, `nbn+jcj`, `nbn+jcm`, `nbn+jco`, `nbn+jcr`, `nbn+jcs`, `nbn+jct`, `nbn+jct+jcm`, `nbn+jct+jxt`, `nbn+jp+ecc`, `nbn+jp+ecs`, `nbn+jp+ecs+jca`, `nbn+jp+ecs+jcm`, `nbn+jp+ecs+jco`, `nbn+jp+ecs+jxc`, `nbn+jp+ecs+jxt`, `nbn+jp+ecx`, `nbn+jp+ef`, `nbn+jp+ef+jca`, `nbn+jp+ef+jco`, `nbn+jp+ef+jcr`, `nbn+jp+ef+jcr+jxc`, `nbn+jp+ef+jcr+jxt`, `nbn+jp+ef+jcs`, `nbn+jp+ef+jxc`, `nbn+jp+ef+jxc+jco`, `nbn+jp+ef+jxf`, `nbn+jp+ef+jxt`, `nbn+jp+ep+ecc`, `nbn+jp+ep+ecs`, `nbn+jp+ep+ecs+jxc`, `nbn+jp+ep+ef`, `nbn+jp+ep+ef+jcr`, `nbn+jp+ep+etm`, `nbn+jp+ep+etn`, `nbn+jp+ep+etn+jco`, `nbn+jp+ep+etn+jcs`, `nbn+jp+etm`, `nbn+jp+etn`, `nbn+jp+etn+jca`, `nbn+jp+etn+jca+jxt`, `nbn+jp+etn+jco`, `nbn+jp+etn+jcs`, `nbn+jp+etn+jxc`, `nbn+jp+etn+jxt`, `nbn+jxc`, `nbn+jxc+jca`, `nbn+jxc+jca+jxc`, `nbn+jxc+jca+jxt`, `nbn+jxc+jcc`, `nbn+jxc+jcm`, `nbn+jxc+jco`, `nbn+jxc+jcs`, `nbn+jxc+jp+ef`, `nbn+jxc+jxc`, `nbn+jxc+jxt`, `nbn+jxt`, `nbn+nbn`, `nbn+nbn+jp+ef`, `nbn+xsm+ecs`, `nbn+xsm+ef`, `nbn+xsm+ep+ef`, `nbn+xsm+ep+ef+jcr`, `nbn+xsm+etm`, `nbn+xsn`, `nbn+xsn+jca`, `nbn+xsn+jca+jp+ef+jcr`, `nbn+xsn+jca+jxc`, `nbn+xsn+jca+jxt`, `nbn+xsn+jcm`, `nbn+xsn+jco`, `nbn+xsn+jcs`, `nbn+xsn+jct`, `nbn+xsn+jp+ecc`, `nbn+xsn+jp+ecs`, `nbn+xsn+jp+ef`, `nbn+xsn+jp+ef+jcr`, `nbn+xsn+jp+ep+ef`, `nbn+xsn+jxc`, `nbn+xsn+jxt`, `nbn+xsv+etm`, `nbu`, `nbu+jca`, `nbu+jca+jxc`, `nbu+jca+jxt`, `nbu+jcc`, `nbu+jcc+jxc`, `nbu+jcj`, `nbu+jcm`, `nbu+jco`, `nbu+jcs`, `nbu+jct`, `nbu+jct+jxc`, `nbu+jp+ecc`, `nbu+jp+ecs`, `nbu+jp+ef`, `nbu+jp+ef+jcr`, `nbu+jp+ef+jxc`, `nbu+jp+ep+ecc`, `nbu+jp+ep+ecs`, `nbu+jp+ep+ef`, `nbu+jp+ep+ef+jcr`, `nbu+jp+ep+etm`, `nbu+jp+ep+etn+jco`, `nbu+jp+etm`, `nbu+jxc`, `nbu+jxc+jca`, `nbu+jxc+jcs`, `nbu+jxc+jp+ef`, `nbu+jxc+jp+ep+ef`, `nbu+jxc+jxt`, `nbu+jxt`, `nbu+ncn`, `nbu+ncn+jca`, `nbu+ncn+jcm`, `nbu+xsn`, `nbu+xsn+jca`, `nbu+xsn+jca+jxc`, `nbu+xsn+jca+jxt`, `nbu+xsn+jcm`, `nbu+xsn+jco`, `nbu+xsn+jcs`, `nbu+xsn+jp+ecs`, `nbu+xsn+jp+ep+ef`, `nbu+xsn+jxc`, `nbu+xsn+jxc+jxt`, `nbu+xsn+jxt`, `nbu+xsv+ecc`, `nbu+xsv+etm`, `ncn`, `ncn+f+ncpa+jco`, `ncn+jca`, `ncn+jca+jca`, `ncn+jca+jcc`, `ncn+jca+jcj`, `ncn+jca+jcm`, `ncn+jca+jcs`, `ncn+jca+jct`, `ncn+jca+jp+ecc`, `ncn+jca+jp+ecs`, `ncn+jca+jp+ef`, `ncn+jca+jp+ep+ef`, `ncn+jca+jp+etm`, `ncn+jca+jp+etn+jxt`, `ncn+jca+jxc`, `ncn+jca+jxc+jcc`, `ncn+jca+jxc+jcm`, `ncn+jca+jxc+jxc`, `ncn+jca+jxc+jxt`, `ncn+jca+jxt`, `ncn+jcc`, `ncn+jcc+jxc`, `ncn+jcj`, `ncn+jcj+jxt`, `ncn+jcm`, `ncn+jco`, `ncn+jcr`, `ncn+jcr+jxc`, `ncn+jcs`, `ncn+jcs+jxt`, `ncn+jct`, `ncn+jct+jcm`, `ncn+jct+jxc`, `ncn+jct+jxt`, `ncn+jcv`, `ncn+jp+ecc`, `ncn+jp+ecc+jct`, `ncn+jp+ecc+jxc`, `ncn+jp+ecs`, `ncn+jp+ecs+jcm`, `ncn+jp+ecs+jco`, `ncn+jp+ecs+jxc`, `ncn+jp+ecs+jxt`, `ncn+jp+ecx`, `ncn+jp+ef`, `ncn+jp+ef+jca`, `ncn+jp+ef+jcm`, `ncn+jp+ef+jco`, `ncn+jp+ef+jcr`, `ncn+jp+ef+jcr+jxc`, `ncn+jp+ef+jcr+jxt`, `ncn+jp+ef+jp+etm`, `ncn+jp+ef+jxc`, `ncn+jp+ef+jxf`, `ncn+jp+ef+jxt`, `ncn+jp+ep+ecc`, `ncn+jp+ep+ecs`, `ncn+jp+ep+ecs+jxc`, `ncn+jp+ep+ecx`, `ncn+jp+ep+ef`, `ncn+jp+ep+ef+jcr`, `ncn+jp+ep+ef+jcr+jxc`, `ncn+jp+ep+ef+jxc`, `ncn+jp+ep+ef+jxf`, `ncn+jp+ep+ef+jxt`, `ncn+jp+ep+ep+etm`, `ncn+jp+ep+etm`, `ncn+jp+ep+etn`, `ncn+jp+ep+etn+jca`, `ncn+jp+ep+etn+jca+jxc`, `ncn+jp+ep+etn+jco`, `ncn+jp+ep+etn+jcs`, `ncn+jp+ep+etn+jxt`, `ncn+jp+etm`, `ncn+jp+etn`, `ncn+jp+etn+jca`, `ncn+jp+etn+jca+jxc`, `ncn+jp+etn+jca+jxt`, `ncn+jp+etn+jco`, `ncn+jp+etn+jcs`, `ncn+jp+etn+jct`, `ncn+jp+etn+jxc`, `ncn+jp+etn+jxt`, `ncn+jxc`, `ncn+jxc+jca`, `ncn+jxc+jca+jxc`, `ncn+jxc+jca+jxt`, `ncn+jxc+jcc`, `ncn+jxc+jcm`, `ncn+jxc+jco`, `ncn+jxc+jcs`, `ncn+jxc+jct+jxt`, `ncn+jxc+jp+ef`, `ncn+jxc+jp+ef+jcr`, `ncn+jxc+jp+ep+ecs`, `ncn+jxc+jp+ep+ef`, `ncn+jxc+jp+etm`, `ncn+jxc+jxc`, `ncn+jxc+jxt`, `ncn+jxt`, `ncn+jxt+jcm`, `ncn+jxt+jxc`, `ncn+nbn`, `ncn+nbn+jca`, `ncn+nbn+jcm`, `ncn+nbn+jcs`, `ncn+nbn+jp+ecc`, `ncn+nbn+jp+ep+ef`, `ncn+nbn+jxc`, `ncn+nbn+jxt`, `ncn+nbu`, `ncn+nbu+jca`, `ncn+nbu+jcm`, `ncn+nbu+jco`, `ncn+nbu+jp+ef`, `ncn+nbu+jxc`, `ncn+nbu+ncn`, `ncn+ncn`, `ncn+ncn+jca`, `ncn+ncn+jca+jcc`, `ncn+ncn+jca+jcm`, `ncn+ncn+jca+jxc`, `ncn+ncn+jca+jxc+jcm`, `ncn+ncn+jca+jxc+jxc`, `ncn+ncn+jca+jxt`, `ncn+ncn+jcc`, `ncn+ncn+jcj`, `ncn+ncn+jcm`, `ncn+ncn+jco`, `ncn+ncn+jcr`, `ncn+ncn+jcs`, `ncn+ncn+jct`, `ncn+ncn+jct+jcm`, `ncn+ncn+jct+jxc`, `ncn+ncn+jct+jxt`, `ncn+ncn+jp+ecc`, `ncn+ncn+jp+ecs`, `ncn+ncn+jp+ef`, `ncn+ncn+jp+ef+jcm`, `ncn+ncn+jp+ef+jcr`, `ncn+ncn+jp+ef+jcs`, `ncn+ncn+jp+ep+ecc`, `ncn+ncn+jp+ep+ecs`, `ncn+ncn+jp+ep+ef`, `ncn+ncn+jp+ep+ef+jcr`, `ncn+ncn+jp+ep+ep+etm`, `ncn+ncn+jp+ep+etm`, `ncn+ncn+jp+ep+etn`, `ncn+ncn+jp+etm`, `ncn+ncn+jp+etn`, `ncn+ncn+jp+etn+jca`, `ncn+ncn+jp+etn+jco`, `ncn+ncn+jp+etn+jxc`, `ncn+ncn+jxc`, `ncn+ncn+jxc+jca`, `ncn+ncn+jxc+jcc`, `ncn+ncn+jxc+jcm`, `ncn+ncn+jxc+jco`, `ncn+ncn+jxc+jcs`, `ncn+ncn+jxc+jxc`, `ncn+ncn+jxt`, `ncn+ncn+nbn`, `ncn+ncn+ncn`, `ncn+ncn+ncn+jca`, `ncn+ncn+ncn+jca+jcm`, `ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+jcj`, `ncn+ncn+ncn+jcm`, `ncn+ncn+ncn+jco`, `ncn+ncn+ncn+jcs`, `ncn+ncn+ncn+jct+jxt`, `ncn+ncn+ncn+jp+etn+jxc`, `ncn+ncn+ncn+jxt`, `ncn+ncn+ncn+ncn+jca`, `ncn+ncn+ncn+ncn+jca+jxt`, `ncn+ncn+ncn+ncn+jco`, `ncn+ncn+ncn+xsn+jp+etm`, `ncn+ncn+ncpa`, `ncn+ncn+ncpa+jca`, `ncn+ncn+ncpa+jcm`, `ncn+ncn+ncpa+jco`, `ncn+ncn+ncpa+jcs`, `ncn+ncn+ncpa+jxc`, `ncn+ncn+ncpa+jxt`, `ncn+ncn+ncpa+ncn`, `ncn+ncn+ncpa+ncn+jca`, `ncn+ncn+ncpa+ncn+jcj`, `ncn+ncn+ncpa+ncn+jcm`, `ncn+ncn+ncpa+ncn+jxt`, `ncn+ncn+xsn`, `ncn+ncn+xsn+jca`, `ncn+ncn+xsn+jca+jxt`, `ncn+ncn+xsn+jcj`, `ncn+ncn+xsn+jcm`, `ncn+ncn+xsn+jco`, `ncn+ncn+xsn+jcs`, `ncn+ncn+xsn+jct`, `ncn+ncn+xsn+jp+ecs`, `ncn+ncn+xsn+jp+ep+ef`, `ncn+ncn+xsn+jp+etm`, `ncn+ncn+xsn+jxc`, `ncn+ncn+xsn+jxc+jcs`, `ncn+ncn+xsn+jxt`, `ncn+ncn+xsv+ecc`, `ncn+ncn+xsv+etm`, `ncn+ncpa`, `ncn+ncpa+jca`, `ncn+ncpa+jca+jcm`, `ncn+ncpa+jca+jxc`, `ncn+ncpa+jca+jxt`, `ncn+ncpa+jcc`, `ncn+ncpa+jcj`, `ncn+ncpa+jcm`, `ncn+ncpa+jco`, `ncn+ncpa+jcr`, `ncn+ncpa+jcs`, `ncn+ncpa+jct`, `ncn+ncpa+jct+jcm`, `ncn+ncpa+jct+jxt`, `ncn+ncpa+jp+ecc`, `ncn+ncpa+jp+ecc+jxc`, `ncn+ncpa+jp+ecs`, `ncn+ncpa+jp+ecs+jxc`, `ncn+ncpa+jp+ef`, `ncn+ncpa+jp+ef+jcr`, `ncn+ncpa+jp+ef+jcr+jxc`, `ncn+ncpa+jp+ep+ef`, `ncn+ncpa+jp+ep+etm`, `ncn+ncpa+jp+ep+etn`, `ncn+ncpa+jp+etm`, `ncn+ncpa+jxc`, `ncn+ncpa+jxc+jca+jxc`, `ncn+ncpa+jxc+jco`, `ncn+ncpa+jxc+jcs`, `ncn+ncpa+jxt`, `ncn+ncpa+nbn+jcs`, `ncn+ncpa+ncn`, `ncn+ncpa+ncn+jca`, `ncn+ncpa+ncn+jca+jcm`, `ncn+ncpa+ncn+jca+jxc`, `ncn+ncpa+ncn+jca+jxt`, `ncn+ncpa+ncn+jcj`, `ncn+ncpa+ncn+jcm`, `ncn+ncpa+ncn+jco`, `ncn+ncpa+ncn+jcs`, `ncn+ncpa+ncn+jct`, `ncn+ncpa+ncn+jct+jcm`, `ncn+ncpa+ncn+jp+ef+jcr`, `ncn+ncpa+ncn+jp+ep+etm`, `ncn+ncpa+ncn+jxc`, `ncn+ncpa+ncn+jxt`, `ncn+ncpa+ncn+xsn+jcm`, `ncn+ncpa+ncn+xsn+jxt`, `ncn+ncpa+ncpa`, `ncn+ncpa+ncpa+jca`, `ncn+ncpa+ncpa+jcj`, `ncn+ncpa+ncpa+jcm`, `ncn+ncpa+ncpa+jco`, `ncn+ncpa+ncpa+jcs`, `ncn+ncpa+ncpa+jp+ep+ef`, `ncn+ncpa+ncpa+jxt`, `ncn+ncpa+ncpa+ncn`, `ncn+ncpa+xsn`, `ncn+ncpa+xsn+jcm`, `ncn+ncpa+xsn+jco`, `ncn+ncpa+xsn+jcs`, `ncn+ncpa+xsn+jp+ecc`, `ncn+ncpa+xsn+jp+etm`, `ncn+ncpa+xsn+jxt`, `ncn+ncpa+xsv+ecc`, `ncn+ncpa+xsv+ecs`, `ncn+ncpa+xsv+ecx`, `ncn+ncpa+xsv+ecx+px+etm`, `ncn+ncpa+xsv+ef`, `ncn+ncpa+xsv+ef+jcm`, `ncn+ncpa+xsv+ef+jcr`, `ncn+ncpa+xsv+etm`, `ncn+ncpa+xsv+etn`, `ncn+ncpa+xsv+etn+jco`, `ncn+ncps`, `ncn+ncps+jca`, `ncn+ncps+jcm`, `ncn+ncps+jco`, `ncn+ncps+jcs`, `ncn+ncps+jp+ecs`, `ncn+ncps+jxt`, `ncn+ncps+ncn+jcs`, `ncn+ncps+ncpa+ncn`, `ncn+ncps+xsm+ef`, `ncn+ncps+xsm+etm`, `ncn+nnc`, `ncn+nnc+jcs`, `ncn+nnc+nnc`, `ncn+nno`, `ncn+nq`, `ncn+nq+jca`, `ncn+nq+jca+jxc`, `ncn+nq+jca+jxt`, `ncn+nq+jcm`, `ncn+nq+jcs`, `ncn+nq+jxt`, `ncn+nq+ncn+jcm`, `ncn+nq+ncn+xsn+jcs`, `ncn+nq+xsn+jxt`, `ncn+xsa`, `ncn+xsm+ecc`, `ncn+xsm+ecs`, `ncn+xsm+ecs+jxc`, `ncn+xsm+ecx`, `ncn+xsm+ecx+jcs`, `ncn+xsm+ecx+px+ep+etm`, `ncn+xsm+ef`, `ncn+xsm+ef+jcr`, `ncn+xsm+etm`, `ncn+xsm+etn+jcm`, `ncn+xsm+etn+jp+ef+jcr`, `ncn+xsn`, `ncn+xsn+jca`, `ncn+xsn+jca+jcj`, `ncn+xsn+jca+jxc`, `ncn+xsn+jca+jxc+jxc`, `ncn+xsn+jca+jxt`, `ncn+xsn+jcc`, `ncn+xsn+jcj`, `ncn+xsn+jcm`, `ncn+xsn+jco`, `ncn+xsn+jcs`, `ncn+xsn+jcs+jxt`, `ncn+xsn+jct`, `ncn+xsn+jct+jcm`, `ncn+xsn+jct+jxc`, `ncn+xsn+jct+jxt`, `ncn+xsn+jcv`, `ncn+xsn+jp+ecc`, `ncn+xsn+jp+ecc+jxc`, `ncn+xsn+jp+ecs`, `ncn+xsn+jp+ecs+jxc`, `ncn+xsn+jp+ecx`, `ncn+xsn+jp+ecx+jxt`, `ncn+xsn+jp+ef`, `ncn+xsn+jp+ef+jca`, `ncn+xsn+jp+ef+jcr`, `ncn+xsn+jp+ep+ecc`, `ncn+xsn+jp+ep+ecs`, `ncn+xsn+jp+ep+ef`, `ncn+xsn+jp+ep+ef+jcr`, `ncn+xsn+jp+ep+etm`, `ncn+xsn+jp+ep+etn`, `ncn+xsn+jp+etm`, `ncn+xsn+jp+etn`, `ncn+xsn+jp+etn+jca`, `ncn+xsn+jp+etn+jca+jxt`, `ncn+xsn+jp+etn+jxc`, `ncn+xsn+jp+etn+jxt`, `ncn+xsn+jxc`, `ncn+xsn+jxc+jcm`, `ncn+xsn+jxc+jco`, `ncn+xsn+jxc+jcs`, `ncn+xsn+jxc+jxc`, `ncn+xsn+jxt`, `ncn+xsn+ncn+jca`, `ncn+xsn+ncn+jca+jxt`, `ncn+xsn+ncn+jcs`, `ncn+xsn+ncpa+jca`, `ncn+xsn+xsn`, `ncn+xsn+xsn+jca`, `ncn+xsn+xsn+jcm`, `ncn+xsn+xsn+jp+ecs`, `ncn+xsn+xsn+jxc`, `ncn+xsn+xsn+jxc+jcc`, `ncn+xsn+xsn+jxc+jcs`, `ncn+xsn+xsv+ecc`, `ncn+xsn+xsv+etm`, `ncn+xsn+xsv+etn`, `ncn+xsv+ecc`, `ncn+xsv+ecs`, `ncn+xsv+ecx`, `ncn+xsv+ef`, `ncn+xsv+ep+ecs`, `ncn+xsv+ep+ef`, `ncn+xsv+ep+etm`, `ncn+xsv+etm`, `ncn+xsv+etn+jca`, `ncpa`, `ncpa+jca`, `ncpa+jca+jcm`, `ncpa+jca+jct`, `ncpa+jca+jp+ecs`, `ncpa+jca+jp+ef`, `ncpa+jca+jp+ep+ef`, `ncpa+jca+jxc`, `ncpa+jca+jxc+jcm`, `ncpa+jca+jxc+jxc`, `ncpa+jca+jxc+jxt`, `ncpa+jca+jxt`, `ncpa+jcc`, `ncpa+jcj`, `ncpa+jcm`, `ncpa+jco`, `ncpa+jcr`, `ncpa+jcs`, `ncpa+jct`, `ncpa+jct+jcm`, `ncpa+jct+jxc`, `ncpa+jct+jxt`, `ncpa+jp+ecc`, `ncpa+jp+ecs`, `ncpa+jp+ecs+jxc`, `ncpa+jp+ecx`, `ncpa+jp+ecx+jxc`, `ncpa+jp+ef`, `ncpa+jp+ef+jca`, `ncpa+jp+ef+jco`, `ncpa+jp+ef+jcr`, `ncpa+jp+ef+jxc`, `ncpa+jp+ef+jxf`, `ncpa+jp+ep+ecc`, `ncpa+jp+ep+ecs`, `ncpa+jp+ep+ef`, `ncpa+jp+ep+ef+jca`, `ncpa+jp+ep+ef+jcr`, `ncpa+jp+ep+ef+jxt`, `ncpa+jp+ep+etm`, `ncpa+jp+ep+etn+jca`, `ncpa+jp+ep+etn+jca+jxc`, `ncpa+jp+ep+etn+jcs`, `ncpa+jp+etm`, `ncpa+jp+etn`, `ncpa+jp+etn+jca`, `ncpa+jp+etn+jca+jxt`, `ncpa+jp+etn+jco`, `ncpa+jp+etn+jcs`, `ncpa+jp+etn+jxc`, `ncpa+jp+etn+jxt`, `ncpa+jxc`, `ncpa+jxc+jca`, `ncpa+jxc+jca+jxc`, `ncpa+jxc+jca+jxt`, `ncpa+jxc+jcc`, `ncpa+jxc+jcm`, `ncpa+jxc+jco`, `ncpa+jxc+jcs`, `ncpa+jxc+jxc`, `ncpa+jxt`, `ncpa+jxt+jxc`, `ncpa+jxt+jxt`, `ncpa+nbn+jca`, `ncpa+nbn+jct`, `ncpa+nbn+jp+ef`, `ncpa+nbn+jp+ep+ef`, `ncpa+nbn+jp+etm`, `ncpa+nbn+jxc+jcc`, `ncpa+nbu+jca`, `ncpa+ncn`, `ncpa+ncn+jca`, `ncpa+ncn+jca+jcm`, `ncpa+ncn+jca+jxc`, `ncpa+ncn+jca+jxc+jcm`, `ncpa+ncn+jca+jxt`, `ncpa+ncn+jcc`, `ncpa+ncn+jcj`, `ncpa+ncn+jcm`, `ncpa+ncn+jco`, `ncpa+ncn+jcr`, `ncpa+ncn+jcs`, `ncpa+ncn+jct`, `ncpa+ncn+jct+jcm`, `ncpa+ncn+jct+jxc`, `ncpa+ncn+jp+ecc`, `ncpa+ncn+jp+ecs`, `ncpa+ncn+jp+ef`, `ncpa+ncn+jp+ef+jcr`, `ncpa+ncn+jp+ef+jcr+jxc`, `ncpa+ncn+jp+ep+ef`, `ncpa+ncn+jp+ep+etm`, `ncpa+ncn+jp+etm`, `ncpa+ncn+jp+etn+jca+jxt`, `ncpa+ncn+jp+etn+jco`, `ncpa+ncn+jp+etn+jxc`, `ncpa+ncn+jxc`, `ncpa+ncn+jxc+jcc`, `ncpa+ncn+jxc+jco`, `ncpa+ncn+jxt`, `ncpa+ncn+nbn`, `ncpa+ncn+ncn`, `ncpa+ncn+ncn+jca`, `ncpa+ncn+ncn+jca+jxt`, `ncpa+ncn+ncn+jcm`, `ncpa+ncn+ncn+jco`, `ncpa+ncn+ncn+jcs`, `ncpa+ncn+ncn+jp+ep+ef`, `ncpa+ncn+ncn+jp+etm`, `ncpa+ncn+ncn+jxt`, `ncpa+ncn+ncn+ncn`, `ncpa+ncn+ncn+xsn+jxt`, `ncpa+ncn+ncpa`, `ncpa+ncn+ncpa+jca`, `ncpa+ncn+ncpa+jcj`, `ncpa+ncn+ncpa+jco`, `ncpa+ncn+ncpa+ncn`, `ncpa+ncn+ncpa+ncn+jco`, `ncpa+ncn+xsn`, `ncpa+ncn+xsn+jca`, `ncpa+ncn+xsn+jca+jxc`, `ncpa+ncn+xsn+jcj`, `ncpa+ncn+xsn+jcm`, `ncpa+ncn+xsn+jco`, `ncpa+ncn+xsn+jcs`, `ncpa+ncn+xsn+jct`, `ncpa+ncn+xsn+jp+ep+ef`, `ncpa+ncn+xsn+jp+etm`, `ncpa+ncn+xsn+jxt`, `ncpa+ncpa`, `ncpa+ncpa+jca`, `ncpa+ncpa+jca+jcm`, `ncpa+ncpa+jca+jxc`, `ncpa+ncpa+jca+jxt`, `ncpa+ncpa+jcj`, `ncpa+ncpa+jcm`, `ncpa+ncpa+jco`, `ncpa+ncpa+jcs`, `ncpa+ncpa+jct`, `ncpa+ncpa+jct+jxc`, `ncpa+ncpa+jct+jxt`, `ncpa+ncpa+jp+ecc`, `ncpa+ncpa+jp+ecs`, `ncpa+ncpa+jp+ecx`, `ncpa+ncpa+jp+ef`, `ncpa+ncpa+jp+ef+jca`, `ncpa+ncpa+jp+ef+jcr`, `ncpa+ncpa+jp+ef+jcr+jxc`, `ncpa+ncpa+jp+ep+ecs`, `ncpa+ncpa+jp+etm`, `ncpa+ncpa+jxc`, `ncpa+ncpa+jxt`, `ncpa+ncpa+ncn`, `ncpa+ncpa+ncn+jca`, `ncpa+ncpa+ncn+jcj`, `ncpa+ncpa+ncn+jcm`, `ncpa+ncpa+ncn+jco`, `ncpa+ncpa+ncn+jcs`, `ncpa+ncpa+ncn+jxt`, `ncpa+ncpa+ncpa+jcm`, `ncpa+ncpa+ncpa+jcs`, `ncpa+ncpa+ncpa+ncpa+jco`, `ncpa+ncpa+xsn`, `ncpa+ncpa+xsn+jca`, `ncpa+ncpa+xsn+jcj`, `ncpa+ncpa+xsn+jco`, `ncpa+ncpa+xsn+jcs`, `ncpa+ncpa+xsn+jxc`, `ncpa+ncpa+xsn+jxt`, `ncpa+ncpa+xsv+ecc`, `ncpa+ncpa+xsv+ecs`, `ncpa+ncpa+xsv+ef`, `ncpa+ncpa+xsv+ep+ef`, `ncpa+ncpa+xsv+ep+etm`, `ncpa+ncpa+xsv+etm`, `ncpa+ncpa+xsv+etn+jca`, `ncpa+ncps`, `ncpa+ncps+jca`, `ncpa+ncps+jcm`, `ncpa+ncps+jco`, `ncpa+ncps+jcs`, `ncpa+ncps+jxt`, `ncpa+ncps+xsm+etm`, `ncpa+nq+jca`, `ncpa+xsa`, `ncpa+xsn`, `ncpa+xsn+jca`, `ncpa+xsn+jca+jxc`, `ncpa+xsn+jca+jxt`, `ncpa+xsn+jcc`, `ncpa+xsn+jcj`, `ncpa+xsn+jcm`, `ncpa+xsn+jco`, `ncpa+xsn+jcs`, `ncpa+xsn+jct`, `ncpa+xsn+jp+ecc`, `ncpa+xsn+jp+ecs`, `ncpa+xsn+jp+ecs+jxc`, `ncpa+xsn+jp+ecx`, `ncpa+xsn+jp+ecx+jxt`, `ncpa+xsn+jp+ef`, `ncpa+xsn+jp+ef+jcr`, `ncpa+xsn+jp+ef+jxf`, `ncpa+xsn+jp+ep+ecc`, `ncpa+xsn+jp+ep+ef`, `ncpa+xsn+jp+ep+ef+jco`, `ncpa+xsn+jp+ep+ef+jcr`, `ncpa+xsn+jp+etm`, `ncpa+xsn+jp+etn`, `ncpa+xsn+jp+etn+jco`, `ncpa+xsn+jp+etn+jxc`, `ncpa+xsn+jxc`, `ncpa+xsn+jxt`, `ncpa+xsv+ecc`, `ncpa+xsv+ecc+jcm`, `ncpa+xsv+ecc+jxc`, `ncpa+xsv+ecc+jxt`, `ncpa+xsv+ecs`, `ncpa+xsv+ecs+jca`, `ncpa+xsv+ecs+jco`, `ncpa+xsv+ecs+jp+ef`, `ncpa+xsv+ecs+jxc`, `ncpa+xsv+ecs+jxc+jxt`, `ncpa+xsv+ecs+jxt`, `ncpa+xsv+ecx`, `ncpa+xsv+ecx+jco`, `ncpa+xsv+ecx+jxc`, `ncpa+xsv+ecx+jxt`, `ncpa+xsv+ecx+px+ecc`, `ncpa+xsv+ecx+px+ecs`, `ncpa+xsv+ecx+px+ecx`, `ncpa+xsv+ecx+px+ecx+jxc`, `ncpa+xsv+ecx+px+ecx+px+ecs`, `ncpa+xsv+ecx+px+ef`, `ncpa+xsv+ecx+px+ef+jcr`, `ncpa+xsv+ecx+px+ep+ecc`, `ncpa+xsv+ecx+px+ep+ecs`, `ncpa+xsv+ecx+px+ep+ef`, `ncpa+xsv+ecx+px+ep+ef+jcr`, `ncpa+xsv+ecx+px+ep+etm`, `ncpa+xsv+ecx+px+ep+etn+jca`, `ncpa+xsv+ecx+px+ep+etn+jco`, `ncpa+xsv+ecx+px+ep+etn+jxc`, `ncpa+xsv+ecx+px+ep+etn+jxt`, `ncpa+xsv+ecx+px+etm`, `ncpa+xsv+ecx+px+etn`, `ncpa+xsv+ecx+px+etn+jca`, `ncpa+xsv+ecx+px+etn+jco`, `ncpa+xsv+ef`, `ncpa+xsv+ef+jca`, `ncpa+xsv+ef+jcj`, `ncpa+xsv+ef+jcm`, `ncpa+xsv+ef+jco`, `ncpa+xsv+ef+jcr`, `ncpa+xsv+ef+jcr+jxt`, `ncpa+xsv+ef+jcs`, `ncpa+xsv+ef+jxc`, `ncpa+xsv+ef+jxf`, `ncpa+xsv+ef+jxt`, `ncpa+xsv+ep+ecc`, `ncpa+xsv+ep+ecs`, `ncpa+xsv+ep+ecs+jco`, `ncpa+xsv+ep+ecs+jxc`, `ncpa+xsv+ep+ecs+jxt`, `ncpa+xsv+ep+ecx`, `ncpa+xsv+ep+ecx+jxc`, `ncpa+xsv+ep+ef`, `ncpa+xsv+ep+ef+jca`, `ncpa+xsv+ep+ef+jca+jxt`, `ncpa+xsv+ep+ef+jco`, `ncpa+xsv+ep+ef+jcr`, `ncpa+xsv+ep+ef+jcr+jxc`, `ncpa+xsv+ep+ef+jcr+jxc+jxt`, `ncpa+xsv+ep+ef+jxc`, `ncpa+xsv+ep+ef+jxf`, `ncpa+xsv+ep+ef+jxt`, `ncpa+xsv+ep+ep+ecs`, `ncpa+xsv+ep+ep+ef`, `ncpa+xsv+ep+etm`, `ncpa+xsv+ep+etn`, `ncpa+xsv+ep+etn+jca`, `ncpa+xsv+ep+etn+jca+jxc`, `ncpa+xsv+ep+etn+jcj`, `ncpa+xsv+ep+etn+jco`, `ncpa+xsv+ep+etn+jcs`, `ncpa+xsv+ep+etn+jxt`, `ncpa+xsv+etm`, `ncpa+xsv+etn`, `ncpa+xsv+etn+jca`, `ncpa+xsv+etn+jca+jxc`, `ncpa+xsv+etn+jca+jxt`, `ncpa+xsv+etn+jco`, `ncpa+xsv+etn+jcs`, `ncpa+xsv+etn+jct`, `ncpa+xsv+etn+jxc`, `ncpa+xsv+etn+jxc+jcm`, `ncpa+xsv+etn+jxc+jcs`, `ncpa+xsv+etn+jxc+jxc`, `ncpa+xsv+etn+jxc+jxt`, `ncpa+xsv+etn+jxt`, `ncps`, `ncps+jca`, `ncps+jca+jcm`, `ncps+jca+jxc`, `ncps+jca+jxc+jcm`, `ncps+jcc`, `ncps+jcj`, `ncps+jcm`, `ncps+jco`, `ncps+jcs`, `ncps+jct`, `ncps+jct+jcm`, `ncps+jct+jxt`, `ncps+jp+ecc`, `ncps+jp+ecs`, `ncps+jp+ecs+jxt`, `ncps+jp+ef`, `ncps+jp+ef+jcr`, `ncps+jp+ep+ef`, `ncps+jp+ep+etn`, `ncps+jp+etm`, `ncps+jp+etn+jcs`, `ncps+jp+etn+jxt`, `ncps+jxc`, `ncps+jxc+jxc`, `ncps+jxt`, `ncps+nbn+jp+etm`, `ncps+nbn+jxc`, `ncps+ncn`, `ncps+ncn+jca`, `ncps+ncn+jca+jcm`, `ncps+ncn+jcm`, `ncps+ncn+jco`, `ncps+ncn+jcs`, `ncps+ncn+jct+jxt`, `ncps+ncn+jp+ef`, `ncps+ncn+jp+ef+jcr`, `ncps+ncn+jp+etm`, `ncps+ncn+jxc+jco`, `ncps+ncn+jxt`, `ncps+ncn+ncn`, `ncps+ncn+ncn+jca+jxc`, `ncps+ncn+ncn+jcm`, `ncps+ncn+ncn+jco`, `ncps+ncn+ncn+jxt`, `ncps+ncn+xsn`, `ncps+ncn+xsn+jca`, `ncps+ncn+xsn+jcj`, `ncps+ncn+xsn+jco`, `ncps+ncn+xsn+jp+ecc`, `ncps+ncn+xsn+jp+etm`, `ncps+ncpa`, `ncps+ncpa+jca`, `ncps+ncpa+jcc`, `ncps+ncpa+jcj`, `ncps+ncpa+jcm`, `ncps+ncpa+jco`, `ncps+ncpa+jcs`, `ncps+ncpa+jp+etm`, `ncps+ncpa+jxt`, `ncps+ncpa+xsv+etm`, `ncps+ncps+jca`, `ncps+ncps+jcm`, `ncps+ncps+xsm+ecc`, `ncps+ncps+xsm+ecs`, `ncps+ncps+xsm+etm`, `ncps+xsa`, `ncps+xsa+jxc`, `ncps+xsm+ecc`, `ncps+xsm+ecc+jxc`, `ncps+xsm+ecc+jxt`, `ncps+xsm+ecs`, `ncps+xsm+ecs+jxc`, `ncps+xsm+ecs+jxt`, `ncps+xsm+ecx`, `ncps+xsm+ecx+jcs`, `ncps+xsm+ecx+jxc`, `ncps+xsm+ecx+jxt`, `ncps+xsm+ecx+px+ecc`, `ncps+xsm+ecx+px+ecs`, `ncps+xsm+ecx+px+ecx`, `ncps+xsm+ecx+px+ecx+jxt`, `ncps+xsm+ecx+px+ef`, `ncps+xsm+ecx+px+ep+ecs`, `ncps+xsm+ecx+px+ep+ef`, `ncps+xsm+ecx+px+ep+etm`, `ncps+xsm+ecx+px+ep+etn+jco`, `ncps+xsm+ecx+px+etm`, `ncps+xsm+ecx+px+etn`, `ncps+xsm+ecx+px+etn+jca`, `ncps+xsm+ecx+px+etn+jcj`, `ncps+xsm+ecx+px+etn+jco`, `ncps+xsm+ef`, `ncps+xsm+ef+jco`, `ncps+xsm+ef+jcr`, `ncps+xsm+ef+jcr+jxc`, `ncps+xsm+ef+jcr+jxt`, `ncps+xsm+ef+jxf`, `ncps+xsm+ef+jxt`, `ncps+xsm+ep+ecc`, `ncps+xsm+ep+ecs`, `ncps+xsm+ep+ecs+etm`, `ncps+xsm+ep+ef`, `ncps+xsm+ep+ef+jco`, `ncps+xsm+ep+ef+jcr`, `ncps+xsm+ep+ef+jxf`, `ncps+xsm+ep+ep+ef`, `ncps+xsm+ep+etm`, `ncps+xsm+ep+etn`, `ncps+xsm+ep+etn+jxt`, `ncps+xsm+etm`, `ncps+xsm+etn`, `ncps+xsm+etn+jca`, `ncps+xsm+etn+jca+jxt`, `ncps+xsm+etn+jcj`, `ncps+xsm+etn+jcm`, `ncps+xsm+etn+jco`, `ncps+xsm+etn+jcs`, `ncps+xsm+etn+jct`, `ncps+xsm+etn+jct+jcm`, `ncps+xsm+etn+jp+ef+jcr`, `ncps+xsm+etn+jp+etm`, `ncps+xsm+etn+jxc`, `ncps+xsm+etn+jxc+jxt`, `ncps+xsm+etn+jxt`, `ncps+xsn`, `ncps+xsn+jca`, `ncps+xsn+jca+jxt`, `ncps+xsn+jcm`, `ncps+xsn+jco`, `ncps+xsn+jcs`, `ncps+xsn+jp+ecc`, `ncps+xsn+jp+ep+ecs`, `ncps+xsn+jp+etm`, `ncps+xsn+jxc`, `ncps+xsn+jxt`, `ncps+xsv+etm`, `nnc`, `nnc+f`, `nnc+f+jca`, `nnc+f+jp+ef`, `nnc+jca`, `nnc+jca+jxc`, `nnc+jca+jxt`, `nnc+jcc`, `nnc+jcj`, `nnc+jcm`, `nnc+jco`, `nnc+jcs`, `nnc+jp+ecc`, `nnc+jp+ecs`, `nnc+jp+ef`, `nnc+jp+ef+jcr`, `nnc+jp+ep+ef`, `nnc+jp+ep+etm`, `nnc+jp+etm`, `nnc+jp+etn+jca`, `nnc+jxc`, `nnc+jxt`, `nnc+nbn`, `nnc+nbn+jcm`, `nnc+nbn+jco`, `nnc+nbn+nbu+jcc`, `nnc+nbn+nbu+jcs`, `nnc+nbn+xsn`, `nnc+nbu`, `nnc+nbu+jca`, `nnc+nbu+jca+jxc`, `nnc+nbu+jcc`, `nnc+nbu+jcj`, `nnc+nbu+jcm`, `nnc+nbu+jco`, `nnc+nbu+jcs`, `nnc+nbu+jp+ef`, `nnc+nbu+jp+ef+jcr`, `nnc+nbu+jp+ep+ecs`, `nnc+nbu+jp+ep+ef`, `nnc+nbu+jp+etm`, `nnc+nbu+jxc`, `nnc+nbu+jxc+jcs`, `nnc+nbu+jxc+jxt`, `nnc+nbu+jxt`, `nnc+nbu+nbu`, `nnc+nbu+nbu+jcm`, `nnc+nbu+nbu+jp+ef+jcr`, `nnc+nbu+ncn`, `nnc+nbu+ncn+jca`, `nnc+nbu+ncn+jcj`, `nnc+nbu+ncn+jcm`, `nnc+nbu+ncn+jxc`, `nnc+nbu+xsn`, `nnc+nbu+xsn+jca`, `nnc+nbu+xsn+jcm`, `nnc+nbu+xsn+jco`, `nnc+nbu+xsn+jcs`, `nnc+nbu+xsn+jp+ecc`, `nnc+nbu+xsn+jp+ef`, `nnc+nbu+xsn+jxc`, `nnc+nbu+xsn+jxc+jcm`, `nnc+nbu+xsn+jxt`, `nnc+nbu+xsv+etm`, `nnc+ncn`, `nnc+ncn+jca`, `nnc+ncn+jca+jxt`, `nnc+ncn+jcj`, `nnc+ncn+jcm`, `nnc+ncn+jco`, `nnc+ncn+jcs`, `nnc+ncn+jct`, `nnc+ncn+jp+ef`, `nnc+ncn+jp+etm`, `nnc+ncn+jxc`, `nnc+ncn+jxt`, `nnc+ncn+nbu`, `nnc+ncn+nbu+xsn+jca`, `nnc+ncn+ncn+jca+jxt`, `nnc+ncn+ncn+xsn`, `nnc+ncn+nnc+nnc`, `nnc+ncn+xsn`, `nnc+ncn+xsn+jp+etm`, `nnc+ncn+xsn+jxt`, `nnc+ncpa`, `nnc+ncpa+jcs`, `nnc+nnc`, `nnc+nnc+jca`, `nnc+nnc+jca+jxt`, `nnc+nnc+jcm`, `nnc+nnc+jco`, `nnc+nnc+jp+ef`, `nnc+nnc+nbu`, `nnc+nnc+nbu+jca`, `nnc+nnc+nbu+jcc`, `nnc+nnc+nbu+jcm`, `nnc+nnc+nbu+jco`, `nnc+nnc+nbu+jcs`, `nnc+nnc+nbu+jp+ep+ef`, `nnc+nnc+nbu+jp+etm`, `nnc+nnc+nbu+jxc`, `nnc+nnc+nbu+xsn`, `nnc+nnc+nbu+xsn+jcm`, `nnc+nnc+nbu+xsn+jxc`, `nnc+nnc+ncn+jco`, `nnc+nnc+nnc`, `nnc+nnc+nnc+nnc`, `nnc+nnc+su+jp+ef`, `nnc+nnc+xsn`, `nnc+nnc+xsn+jcm`, `nnc+nnc+xsn+nbu+jca`, `nnc+nnc+xsn+nbu+jcm`, `nnc+nnc+xsn+nbu+jco`, `nnc+nnc+xsn+nbu+jcs`, `nnc+nno+nbu`, `nnc+nno+nbu+jcc`, `nnc+su`, `nnc+su+jca`, `nnc+su+jcm`, `nnc+su+jco`, `nnc+su+jcs`, `nnc+su+jxc`, `nnc+su+xsn`, `nnc+xsn`, `nnc+xsn+jca`, `nnc+xsn+jca+jxt`, `nnc+xsn+jcm`, `nnc+xsn+jco`, `nnc+xsn+jcs`, `nnc+xsn+jp+ef`, `nnc+xsn+jxc`, `nnc+xsn+nbn+jca`, `nnc+xsn+nbu`, `nnc+xsn+nbu+jca`, `nnc+xsn+nbu+jcm`, `nnc+xsn+nbu+jco`, `nnc+xsn+nbu+jcs`, `nnc+xsn+nnc+nbu`, `nnc+xsn+nnc+nbu+jcm`, `nno`, `nno+jca`, `nno+jca+jxt`, `nno+jcj`, `nno+jcm`, `nno+jco`, `nno+jcs`, `nno+jxt`, `nno+nbn`, `nno+nbn+jcm`, `nno+nbn+xsn`, `nno+nbu`, `nno+nbu+jca`, `nno+nbu+jca+jxc`, `nno+nbu+jca+jxt`, `nno+nbu+jcc`, `nno+nbu+jcj`, `nno+nbu+jcm`, `nno+nbu+jco`, `nno+nbu+jcs`, `nno+nbu+jct`, `nno+nbu+jp+ecc`, `nno+nbu+jp+ecs`, `nno+nbu+jp+ef`, `nno+nbu+jp+ep+ecc`, `nno+nbu+jp+ep+ecs`, `nno+nbu+jp+ep+ef`, `nno+nbu+jp+etm`, `nno+nbu+jxc`, `nno+nbu+jxc+jca`, `nno+nbu+jxc+jcm`, `nno+nbu+jxc+jp+ef`, `nno+nbu+jxc+jp+etm`, `nno+nbu+jxc+jxc`, `nno+nbu+jxc+jxt`, `nno+nbu+jxt`, `nno+nbu+nbu`, `nno+nbu+ncn`, `nno+nbu+ncn+jp+ep+ef`, `nno+nbu+ncn+ncn`, `nno+nbu+xsn`, `nno+nbu+xsn+jca`, `nno+nbu+xsn+jcc`, `nno+nbu+xsn+jcm`, `nno+nbu+xsn+jxc`, `nno+nbu+xsn+jxt`, `nno+ncn`, `nno+ncn+jca`, `nno+ncn+jca+jxc`, `nno+ncn+jca+jxt`, `nno+ncn+jcm`, `nno+ncn+jco`, `nno+ncn+jcs`, `nno+ncn+jct`, `nno+ncn+jp+ef`, `nno+ncn+jp+etm`, `nno+ncn+jxc`, `nno+ncn+jxc+jxt`, `nno+ncn+ncn+jp+etm`, `nno+ncn+xsn`, `nno+ncn+xsn+jca`, `nno+ncn+xsn+jp+ep+ef`, `nno+ncn+xsn+jp+etm`, `nno+ncpa+jp+ep+etn+jca+jxc`, `nno+nnc`, `nno+xsn`, `nno+xsn+jca`, `nno+xsn+jca+jxc`, `nno+xsn+jxc`, `nno+xsn+jxc+jcs`, `nno+xsn+nbu`, `nno+xsn+nbu+jcm`, `npd`, `npd+jca`, `npd+jca+jcm`, `npd+jca+jp+ef`, `npd+jca+jp+ef+jca`, `npd+jca+jxc`, `npd+jca+jxc+jcm`, `npd+jca+jxt`, `npd+jcc`, `npd+jcj`, `npd+jcm`, `npd+jco`, `npd+jcs`, `npd+jct`, `npd+jct+jcm`, `npd+jct+jxt`, `npd+jp+ecc`, `npd+jp+ecs`, `npd+jp+ecs+jco`, `npd+jp+ecs+jxt`, `npd+jp+ef`, `npd+jp+ef+jca`, `npd+jp+ef+jcm`, `npd+jp+ef+jco`, `npd+jp+ef+jcr`, `npd+jp+ef+jcs`, `npd+jp+ef+jp+ef`, `npd+jp+ef+jp+etm`, `npd+jp+ef+jxc`, `npd+jp+ef+jxt`, `npd+jp+ep+ef`, `npd+jp+etm`, `npd+jxc`, `npd+jxc+jca`, `npd+jxc+jca+jxc`, `npd+jxc+jcc`, `npd+jxc+jcr`, `npd+jxc+jp+ef`, `npd+jxc+jxc`, `npd+jxc+jxt`, `npd+jxt`, `npd+nbn`, `npd+nbn+jca`, `npd+nbn+jcs`, `npd+nbn+jxc`, `npd+nbn+jxc+jxt`, `npd+ncn`, `npd+ncn+jca`, `npd+ncn+jca+jxc`, `npd+ncn+jcm`, `npd+ncn+jco`, `npd+ncn+jcs`, `npd+ncn+jxt`, `npd+npd`, `npd+xsn`, `npd+xsn+jca`, `npd+xsn+jca+jxc`, `npd+xsn+jca+jxt`, `npd+xsn+jcm`, `npd+xsn+jco`, `npd+xsn+jcs`, `npd+xsn+jct`, `npd+xsn+jp+ef`, `npd+xsn+jxc`, `npd+xsn+jxt`, `npp`, `npp+jca`, `npp+jca+jcm`, `npp+jca+jxc`, `npp+jca+jxc+jcm`, `npp+jca+jxt`, `npp+jcc`, `npp+jcj`, `npp+jcm`, `npp+jco`, `npp+jcs`, `npp+jcs+jxt`, `npp+jct`, `npp+jct+jcm`, `npp+jct+jxc`, `npp+jct+jxt`, `npp+jp+ecs`, `npp+jp+ecs+jco`, `npp+jp+ef`, `npp+jp+ef+jcs`, `npp+jp+ef+jxc+jcs`, `npp+jp+ef+jxt`, `npp+jp+ep+ecc`, `npp+jp+ep+ef`, `npp+jp+ep+etm`, `npp+jp+etm`, `npp+jxc`, `npp+jxc+jcc`, `npp+jxc+jcm`, `npp+jxc+jco`, `npp+jxt`, `npp+nbn+jca`, `npp+nbn+jcs`, `npp+ncn`, `npp+ncn+jca`, `npp+ncn+jca+jxc`, `npp+ncn+jca+jxt`, `npp+ncn+jcm`, `npp+ncn+jco`, `npp+ncn+jcs`, `npp+ncn+jct`, `npp+ncn+jct+jxt`, `npp+ncn+jp+ecs`, `npp+ncn+jxc`, `npp+ncn+jxt`, `npp+ncn+xsn`, `npp+ncpa`, `npp+ncpa+jca`, `npp+ncpa+jca+jxc`, `npp+ncpa+jcj`, `npp+ncpa+jcm`, `npp+ncpa+jco`, `npp+ncpa+jcs`, `npp+ncpa+jxt`, `npp+ncpa+ncpa+jca`, `npp+ncpa+xsn+jp+ecc`, `npp+ncpa+xsn+jp+etm`, `npp+npp+jco`, `npp+xsn`, `npp+xsn+jca`, `npp+xsn+jca+jxc`, `npp+xsn+jca+jxc+jxc`, `npp+xsn+jca+jxt`, `npp+xsn+jcj`, `npp+xsn+jcm`, `npp+xsn+jco`, `npp+xsn+jcs`, `npp+xsn+jcs+jxt`, `npp+xsn+jct`, `npp+xsn+jct+jcm`, `npp+xsn+jct+jxt`, `npp+xsn+jp+ecs`, `npp+xsn+jp+ef`, `npp+xsn+jp+etm`, `npp+xsn+jxc`, `npp+xsn+jxc+jcs`, `npp+xsn+jxc+jxt`, `npp+xsn+jxt`, `npp+xsn+ncn`, `npp+xsn+xsn`, `npp+xsn+xsn+jca`, `npp+xsn+xsn+jca+jxt`, `nq`, `nq+jca`, `nq+jca+jca`, `nq+jca+jca+jxc`, `nq+jca+jcm`, `nq+jca+jxc`, `nq+jca+jxc+jcm`, `nq+jca+jxc+jxc`, `nq+jca+jxt`, `nq+jcc`, `nq+jcj`, `nq+jcm`, `nq+jco`, `nq+jcr`, `nq+jcs`, `nq+jcs+jca+jxc`, `nq+jcs+jxt`, `nq+jct`, `nq+jct+jcm`, `nq+jct+jxt`, `nq+jp+ecc`, `nq+jp+ecs`, `nq+jp+ef`, `nq+jp+ef+jcr`, `nq+jp+ef+jcr+jxc`, `nq+jp+ep+ecc`, `nq+jp+ep+ecs`, `nq+jp+ep+ef`, `nq+jp+ep+etm`, `nq+jp+ep+etn`, `nq+jp+etm`, `nq+jp+etn+jco`, `nq+jxc`, `nq+jxc+jca+jxt`, `nq+jxc+jcm`, `nq+jxc+jcs`, `nq+jxc+jp+ef`, `nq+jxc+jp+ef+jcr`, `nq+jxc+jxc`, `nq+jxc+jxc+jxt`, `nq+jxc+jxt`, `nq+jxt`, `nq+nbn`, `nq+nbn+jca`, `nq+nbn+jcm`, `nq+nbn+jp+ep+ef`, `nq+ncn`, `nq+ncn+jca`, `nq+ncn+jca+jcm`, `nq+ncn+jca+jxc`, `nq+ncn+jca+jxt`, `nq+ncn+jcc`, `nq+ncn+jcj`, `nq+ncn+jcm`, `nq+ncn+jco`, `nq+ncn+jcs`, `nq+ncn+jct`, `nq+ncn+jct+jcm`, `nq+ncn+jct+jxc`, `nq+ncn+jct+jxt`, `nq+ncn+jp+ef`, `nq+ncn+jp+ep+ef`, `nq+ncn+jp+ep+etm`, `nq+ncn+jp+etm`, `nq+ncn+jxc`, `nq+ncn+jxc+jxt`, `nq+ncn+jxt`, `nq+ncn+ncn`, `nq+ncn+ncn+jca`, `nq+ncn+ncn+jca+jxt`, `nq+ncn+ncn+jcm`, `nq+ncn+ncn+jco`, `nq+ncn+ncn+jp+etm`, `nq+ncn+ncn+jxc`, `nq+ncn+ncn+ncn`, `nq+ncn+ncn+ncn+jca`, `nq+ncn+ncn+ncn+jcs`, `nq+ncn+ncn+xsn+jxt`, `nq+ncn+ncpa+jca`, `nq+ncn+ncpa+jcs`, `nq+ncn+ncpa+jxt`, `nq+ncn+ncpa+ncn`, `nq+ncn+ncpa+ncn+jcm`, `nq+ncn+xsn`, `nq+ncn+xsn+jca`, `nq+ncn+xsn+jca+jxt`, `nq+ncn+xsn+jcm`, `nq+ncn+xsn+jco`, `nq+ncn+xsn+jcs`, `nq+ncn+xsn+jct`, `nq+ncn+xsn+jp+etm`, `nq+ncn+xsn+jxt`, `nq+ncpa`, `nq+ncpa+jca`, `nq+ncpa+jcm`, `nq+ncpa+jco`, `nq+ncpa+jxt`, `nq+ncpa+ncn+jcm`, `nq+ncpa+ncn+jp+ef`, `nq+ncpa+ncn+jp+etm`, `nq+nq`, `nq+nq+jca`, `nq+nq+jcj`, `nq+nq+jcm`, `nq+nq+jcs`, `nq+nq+jct`, `nq+nq+jxc+jcs`, `nq+nq+jxt`, `nq+nq+ncn`, `nq+nq+ncn+jca`, `nq+nq+nq+jxt`, `nq+nq+nq+nq+jcm`, `nq+xsm+ecs`, `nq+xsm+etm`, `nq+xsn`, `nq+xsn+jca`, `nq+xsn+jca+jxc`, `nq+xsn+jca+jxt`, `nq+xsn+jcj`, `nq+xsn+jcm`, `nq+xsn+jco`, `nq+xsn+jcs`, `nq+xsn+jcs+jxt`, `nq+xsn+jct`, `nq+xsn+jct+jcm`, `nq+xsn+jp+ef`, `nq+xsn+jp+ef+jcr`, `nq+xsn+jp+ep+ef`, `nq+xsn+jp+etm`, `nq+xsn+jp+etn+jco`, `nq+xsn+jxc`, `nq+xsn+jxt`, `nq+xsn+xsn`, `nq+xsn+xsn+jcj`, `nq+xsn+xsn+jcs`, `nq+xsn+xsv+ep+etm`, `nq+xsv+ecs`, `paa+ecc`, `paa+ecc+jxc`, `paa+ecc+jxt`, `paa+ecs`, `paa+ecs+etm`, `paa+ecs+jca`, `paa+ecs+jcm`, `paa+ecs+jco`, `paa+ecs+jct`, `paa+ecs+jp+ecc`, `paa+ecs+jp+ep+ef`, `paa+ecs+jxc`, `paa+ecs+jxc+jxt`, `paa+ecs+jxt`, `paa+ecx`, `paa+ecx+jco`, `paa+ecx+jcs`, `paa+ecx+jxc`, `paa+ecx+jxt`, `paa+ecx+px+ecc`, `paa+ecx+px+ecs`, `paa+ecx+px+ecx`, `paa+ecx+px+ecx+jxc`, `paa+ecx+px+ecx+px+ecc`, `paa+ecx+px+ecx+px+ecx`, `paa+ecx+px+ecx+px+ef`, `paa+ecx+px+ecx+px+ep+ef`, `paa+ecx+px+ecx+px+etm`, `paa+ecx+px+ef`, `paa+ecx+px+ef+jcr`, `paa+ecx+px+ep+ecc`, `paa+ecx+px+ep+ecs`, `paa+ecx+px+ep+ef`, `paa+ecx+px+ep+ef+jcr`, `paa+ecx+px+ep+etm`, `paa+ecx+px+ep+etn`, `paa+ecx+px+ep+etn+jco`, `paa+ecx+px+etm`, `paa+ecx+px+etn`, `paa+ecx+px+etn+jca`, `paa+ecx+px+etn+jco`, `paa+ecx+px+etn+jcs`, `paa+ecx+px+etn+jxc`, `paa+ecx+px+etn+jxt`, `paa+ef`, `paa+ef+ecc`, `paa+ef+ecs`, `paa+ef+ecs+jxc`, `paa+ef+jca`, `paa+ef+jcm`, `paa+ef+jco`, `paa+ef+jcr`, `paa+ef+jcr+jxc`, `paa+ef+jcr+jxt`, `paa+ef+jxf`, `paa+ep+ecc`, `paa+ep+ecs`, `paa+ep+ecs+jxc`, `paa+ep+ef`, `paa+ep+ef+jcr`, `paa+ep+ef+jxc`, `paa+ep+ef+jxf`, `paa+ep+ef+jxt`, `paa+ep+ep+ecs`, `paa+ep+ep+ef`, `paa+ep+ep+etm`, `paa+ep+etm`, `paa+ep+etn`, `paa+ep+etn+jca`, `paa+ep+etn+jca+jxc`, `paa+ep+etn+jco`, `paa+ep+etn+jcs`, `paa+ep+etn+jxt`, `paa+etm`, `paa+etn`, `paa+etn+jca`, `paa+etn+jca+jxc`, `paa+etn+jca+jxt`, `paa+etn+jcc`, `paa+etn+jcj`, `paa+etn+jcm`, `paa+etn+jco`, `paa+etn+jcs`, `paa+etn+jct`, `paa+etn+jp+ecc`, `paa+etn+jp+ef`, `paa+etn+jp+ep+ecs`, `paa+etn+jp+ep+ef`, `paa+etn+jxc`, `paa+etn+jxt`, `paa+jxt`, `pad+ecc`, `pad+ecc+jxt`, `pad+ecs`, `pad+ecs+jxc`, `pad+ecs+jxt`, `pad+ecx`, `pad+ecx+jcs`, `pad+ecx+jxc`, `pad+ecx+jxt`, `pad+ecx+px+ecs`, `pad+ecx+px+ecx+px+ecc+jxt`, `pad+ef`, `pad+ef+jcr`, `pad+ef+jcr+jxt`, `pad+ef+jxf`, `pad+ef+jxt`, `pad+ep+ecc`, `pad+ep+ecs`, `pad+ep+ef`, `pad+ep+ef+jco`, `pad+ep+etm`, `pad+etm`, `pad+etn`, `pad+etn+jxt`, `pvd+ecc+jxc`, `pvd+ecs`, `pvd+ecs+jp+ecs`, `pvd+ecs+jxc`, `pvd+ecs+jxt`, `pvd+ecx`, `pvd+ep+ef`, `pvd+ep+etm`, `pvd+etm`, `pvd+etn`, `pvd+etn+jca`, `pvd+etn+jca+jxc`, `pvg+ecc`, `pvg+ecc+jxc`, `pvg+ecc+jxt`, `pvg+ecs`, `pvg+ecs+ecs`, `pvg+ecs+jca`, `pvg+ecs+jca+jxt`, `pvg+ecs+jcc`, `pvg+ecs+jcm`, `pvg+ecs+jco`, `pvg+ecs+jcs`, `pvg+ecs+jct`, `pvg+ecs+jp+ecs`, `pvg+ecs+jp+ef`, `pvg+ecs+jp+ep+ecs`, `pvg+ecs+jp+ep+ef`, `pvg+ecs+jp+ep+ef+jcr`, `pvg+ecs+jxc`, `pvg+ecs+jxc+jcc`, `pvg+ecs+jxc+jp+ef`, `pvg+ecs+jxc+jp+ep+ef`, `pvg+ecs+jxt`, `pvg+ecx`, `pvg+ecx+jco`, `pvg+ecx+jxc`, `pvg+ecx+jxt`, `pvg+ecx+jxt+px+ep+ef`, `pvg+ecx+px+ecc`, `pvg+ecx+px+ecc+jxc`, `pvg+ecx+px+ecc+jxt`, `pvg+ecx+px+ecs`, `pvg+ecx+px+ecs+jxc`, `pvg+ecx+px+ecs+jxt`, `pvg+ecx+px+ecx`, `pvg+ecx+px+ecx+jco`, `pvg+ecx+px+ecx+jxc`, `pvg+ecx+px+ecx+jxt`, `pvg+ecx+px+ecx+px+ecc`, `pvg+ecx+px+ecx+px+ecs`, `pvg+ecx+px+ecx+px+ecs+jxt`, `pvg+ecx+px+ecx+px+ecx`, `pvg+ecx+px+ecx+px+ecx+px+ecc`, `pvg+ecx+px+ecx+px+ef`, `pvg+ecx+px+ecx+px+ep+ecc`, `pvg+ecx+px+ecx+px+ep+ef`, `pvg+ecx+px+ecx+px+ep+etm`, `pvg+ecx+px+ecx+px+ep+etn+jco`, `pvg+ecx+px+ecx+px+etm`, `pvg+ecx+px+ecx+px+etn`, `pvg+ecx+px+ecx+px+etn+jca`, `pvg+ecx+px+ef`, `pvg+ecx+px+ef+jca`, `pvg+ecx+px+ef+jcm`, `pvg+ecx+px+ef+jcr`, `pvg+ecx+px+ep+ecc`, `pvg+ecx+px+ep+ecs`, `pvg+ecx+px+ep+ecs+jxc`, `pvg+ecx+px+ep+ef`, `pvg+ecx+px+ep+ef+jcm`, `pvg+ecx+px+ep+ef+jcr`, `pvg+ecx+px+ep+ef+jxf`, `pvg+ecx+px+ep+ep+ecs`, `pvg+ecx+px+ep+etm`, `pvg+ecx+px+ep+etn`, `pvg+ecx+px+ep+etn+jca`, `pvg+ecx+px+ep+etn+jca+jxc`, `pvg+ecx+px+ep+etn+jco`, `pvg+ecx+px+etm`, `pvg+ecx+px+etn`, `pvg+ecx+px+etn+jca`, `pvg+ecx+px+etn+jca+jxc`, `pvg+ecx+px+etn+jca+jxt`, `pvg+ecx+px+etn+jco`, `pvg+ecx+px+etn+jcs`, `pvg+ecx+px+etn+jct`, `pvg+ecx+px+etn+jxc`, `pvg+ecx+px+etn+jxc+jxt`, `pvg+ecx+px+etn+jxt`, `pvg+ef`, `pvg+ef+jca`, `pvg+ef+jcm`, `pvg+ef+jco`, `pvg+ef+jcr`, `pvg+ef+jcr+jxc`, `pvg+ef+jcr+jxt`, `pvg+ef+jcs`, `pvg+ef+jp+ef+jcr`, `pvg+ef+jp+etm`, `pvg+ef+jxc`, `pvg+ef+jxf`, `pvg+ef+jxt`, `pvg+ep+ecc`, `pvg+ep+ecc+jxt`, `pvg+ep+ecs`, `pvg+ep+ecs+jca+jxt`, `pvg+ep+ecs+jco`, `pvg+ep+ecs+jxc`, `pvg+ep+ecs+jxt`, `pvg+ep+ecx`, `pvg+ep+ecx+px+ef`, `pvg+ep+ef`, `pvg+ep+ef+jca`, `pvg+ep+ef+jcm`, `pvg+ep+ef+jco`, `pvg+ep+ef+jcr`, `pvg+ep+ef+jcr+jxc`, `pvg+ep+ef+jcr+jxt`, `pvg+ep+ef+jct`, `pvg+ep+ef+jxc`, `pvg+ep+ef+jxf`, `pvg+ep+ef+jxt`, `pvg+ep+ep+ef`, `pvg+ep+ep+ef+jco`, `pvg+ep+ep+ef+jxf`, `pvg+ep+etm`, `pvg+ep+etn`, `pvg+ep+etn+jca`, `pvg+ep+etn+jca+jxc`, `pvg+ep+etn+jca+jxt`, `pvg+ep+etn+jco`, `pvg+ep+etn+jcs`, `pvg+ep+etn+jxt`, `pvg+etm`, `pvg+etn`, `pvg+etn+jca`, `pvg+etn+jca+jxc`, `pvg+etn+jca+jxt`, `pvg+etn+jcc`, `pvg+etn+jcj`, `pvg+etn+jcm`, `pvg+etn+jco`, `pvg+etn+jcr`, `pvg+etn+jcs`, `pvg+etn+jct`, `pvg+etn+jct+jxt`, `pvg+etn+jp+ecc`, `pvg+etn+jp+ecs`, `pvg+etn+jp+ef`, `pvg+etn+jp+ef+jcr`, `pvg+etn+jp+ef+jcs`, `pvg+etn+jp+ep+ef`, `pvg+etn+jp+ep+ef+jcr`, `pvg+etn+jp+etm`, `pvg+etn+jxc`, `pvg+etn+jxc+jca+jxt`, `pvg+etn+jxc+jcm`, `pvg+etn+jxc+jco`, `pvg+etn+jxc+jcs`, `pvg+etn+jxc+jxt`, `pvg+etn+jxt`, `pvg+etn+xsm+ecs`, `pvg+etn+xsn+jcm`, `px+ecc`, `px+ecc+jxc`, `px+ecc+jxc+jp+ef`, `px+ecc+jxt`, `px+ecs`, `px+ecs+jca`, `px+ecs+jcc`, `px+ecs+jcj`, `px+ecs+jcm`, `px+ecs+jco`, `px+ecs+jp+ep+ef`, `px+ecs+jxc`, `px+ecs+jxt`, `px+ecx`, `px+ecx+jxc`, `px+ecx+jxt`, `px+ecx+px+ecs`, `px+ecx+px+ecx`, `px+ecx+px+ef`, `px+ecx+px+ef+jcr`, `px+ecx+px+ep+ef`, `px+ecx+px+etm`, `px+ecx+px+etn+jca`, `px+ef`, `px+ef+etm`, `px+ef+jca`, `px+ef+jca+jxc`, `px+ef+jcj`, `px+ef+jcm`, `px+ef+jco`, `px+ef+jcr`, `px+ef+jcr+jxc`, `px+ef+jcs`, `px+ef+jp+etm`, `px+ef+jxc`, `px+ef+jxf`, `px+ef+jxt`, `px+ep+ecc`, `px+ep+ecs`, `px+ep+ecs+jxc`, `px+ep+ecs+jxt`, `px+ep+ecx`, `px+ep+ef`, `px+ep+ef+jca`, `px+ep+ef+jco`, `px+ep+ef+jcr`, `px+ep+ef+jcr+jxc`, `px+ep+ef+jxf`, `px+ep+ep+ef`, `px+ep+ep+ef+jxf`, `px+ep+etm`, `px+ep+etn`, `px+ep+etn+jca`, `px+ep+etn+jca+jxc`, `px+ep+etn+jco`, `px+ep+etn+jcs`, `px+ep+etn+jxc`, `px+ep+etn+jxt`, `px+etm`, `px+etn`, `px+etn+jca`, `px+etn+jca+jxc`, `px+etn+jca+jxt`, `px+etn+jco`, `px+etn+jcs`, `px+etn+jct`, `px+etn+jxc`, `px+etn+jxc+jxt`, `px+etn+jxt`, `sf`, `sl`, `sp`, `sr`, `su`, `su+jca`, `su+jcm`, `xp+nbn`, `xp+nbu`, `xp+ncn`, `xp+ncn+jca`, `xp+ncn+jcm`, `xp+ncn+jco`, `xp+ncn+jcs`, `xp+ncn+jp+ef`, `xp+ncn+jp+ep+ef`, `xp+ncn+jxt`, `xp+ncn+ncn+jca`, `xp+ncn+ncn+jcm`, `xp+ncn+ncn+jco`, `xp+ncn+ncpa+jco`, `xp+ncn+xsn`, `xp+ncn+xsn+jca`, `xp+ncn+xsn+jcm`, `xp+ncn+xsn+jp+ef`, `xp+ncn+xsn+jp+etm`, `xp+ncpa`, `xp+ncpa+jca`, `xp+ncpa+jcm`, `xp+ncpa+jco`, `xp+ncpa+ncn+jcm`, `xp+ncpa+ncn+jco`, `xp+ncpa+ncpa+jco`, `xp+ncpa+xsn`, `xp+ncpa+xsn+jp+etm`, `xp+ncpa+xsv+ecc`, `xp+ncpa+xsv+ecs`, `xp+ncpa+xsv+ecx`, `xp+ncpa+xsv+ef`, `xp+ncpa+xsv+ef+jcr`, `xp+ncpa+xsv+ep+ef`, `xp+ncpa+xsv+etm`, `xp+ncpa+xsv+etn+jca`, `xp+ncps`, `xp+ncps+xsm+ecs`, `xp+ncps+xsm+ecx`, `xp+ncps+xsm+ef`, `xp+ncps+xsm+ep+ef`, `xp+ncps+xsm+etm`, `xp+ncps+xsn`, `xp+nnc`, `xp+nnc+jcm`, `xp+nnc+nbn`, `xp+nnc+nbu`, `xp+nnc+nbu+jcs`, `xp+nnc+ncn`, `xp+nnc+ncn+jca`, `xp+nnc+ncn+jcm`, `xp+nnc+ncn+jcs`, `xp+nnc+ncn+jp+ef+jcr`, `xp+nno`, `xp+nno+jcm`, `xp+nno+nbn+jca`, `xp+nno+nbu`, `xp+nno+nbu+jcs`, `xp+nno+ncn`, `xp+nno+ncn+jca`, `xp+nno+ncn+jcs`, `xp+nno+ncn+jxt`, `xp+nq`, `xp+nq+ncn+jca`, `xp+nq+ncpa`, `xp+nq+ncpa+jco`, `xp+nq+ncpa+jp+etm`, `xsm+etm`, `xsn`, `xsn+jca`, `xsn+jca+jxt`, `xsn+jco`, `xsn+jcs`, `xsn+jp+ef`, `xsn+jp+ep+ef`, `xsn+jxc+jca+jxt`, `xsn+jxc+jcs`, `xsn+jxt`, `xsv+ecc`, `xsv+ecs`, `xsv+ecx+px+ep+ef`, `xsv+ep+ecx`, `xsv+etm` \| \| `morphologizer` \| `POS=CCONJ`, `POS=ADV`, `POS=SCONJ`, `POS=DET`, `POS=NOUN`, `POS=VERB`, `POS=ADJ`, `POS=PUNCT`, `POS=AUX`, `POS=PRON`, `POS=PROPN`, `POS=NUM`, `POS=INTJ`, `POS=PART`, `POS=X`, `POS=ADP`, `POS=SYM` \| \| `parser` \| `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `dislocated`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct`, `vocative`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `0`, `3`, `5`, `7`, `9`, `11`, `12`, `16`, `18`, `20`, `22`, `25`, `28`, `31`, `34`, `35`, `36`, `39`, `40`, `43`, `45`, `47`, `48`, `51`, `54`, `56`, `58`, `60`, `61`, `63`, `65`, `67`, `69`, `71`, `73`, `75`, `76`, `78`, `79`, `82`, `85`, `87`, `89`, `92`, `95`, `97`, `99`, `101`, `104`, `106`, `109`, `112`, `114`, `116`, `119`, `121`, `122`, `124`, `126`, `127`, `128`, `130`, `133`, `135`, `137`, `140`, `142`, `145`, `147`, `148`, `150`, `151`, `152`, `155`, `156`, `158`, `161`, `162`, `164`, `167`, `169`, `172`, `174`, `176`, `177`, `179`, `182`, `184`, `186`, `188`, `191`, `192`, `194`, `196`, `199`, `202`, `203`, `173`, `115`, `205`, `207`, `210`, `213`, `216`, `218`, `221`, `146`, `223`, `225`, `227`, `229`, `230`, `231`, `232`, `234`, `236`, `238`, `239`, `242`, `244`, `246`, `248`, `251`, `253`, `255`, `256`, `259`, `261`, `264`, `265`, `268`, `270`, `272`, `274`, `276`, `278`, `279`, `282`, `285`, `287`, `289`, `293`, `295`, `297`, `300`, `302`, `304`, `307`, `309`, `310`, `313`, `315`, `226`, `318`, `319`, `321`, `323`, `325`, `327`, `329`, `332`, `334`, `335`, `337`, `149`, `339`, `340`, `342`, `344`, `346`, `348`, `349`, `350`, `352`, `354`, `356`, `358`, `360`, `361`, `363`, `365`, `369`, `370`, `372`, `374`, `376`, `21`, `15`, `377`, `379`, `382`, `385`, `387`, `388`, `254`, `390`, `393`, `395`, `397`, `399`, `401`, `403`, `404`, `405`, `407`, `408`, `411`, `414`, `417`, `418`, `421`, `422`, `424`, `427`, `429`, `431`, `435`, `437`, `439`, `440`, `442`, `443`, `444`, `447`, `449`, `451`, `389`, `454`, `455`, `457`, `460`, `461`, `463`, `466`, `468`, `471`, `473`, `476`, `477`, `479`, `482`, `296`, `485`, `487`, `490`, `492`, `493`, `495`, `497`, `500`, `502`, `504`, `505`, `507`, `510`, `511`, `514`, `267`, `516`, `520`, `472`, `523`, `525`, `526`, `527`, `530`, `532`, `462`, `533`, `534`, `535`, `537`, `540`, `541`, `465`, `543`, `545`, `546`, `547`, `550`, `551`, `552`, `553`, `555`, `556`, `72`, `558`, `560`, `562`, `563`, `564`, `567`, `568`, `571`, `574`, `577`, `579`, `581`, `582`, `584`, `587`, `589`, `591`, `594`, `595`, `597`, `600`, `603`, `606`, `608`, `610`, `611`, `613`, `614`, `616`, `617`, `620`, `10`, `623`, `626`, `629`, `632`, `633`, `635`, `637`, `638`, `640`, `642`, `644`, `645`, `647`, `648`, `651`, `652`, `653`, `655`, `657`, `659`, `660`, `664`, `666`, `667`, `669`, `672`, `674`, `675`, `676`, `678`, `679`, `680`, `683`, `684`, `687`, `689`, `690`, `692`, `694`, `697`, `699`, `702`, `703`, `706`, `707`, `710`, `713`, `715`, `717`, `719`, `721`, `723`, `725`, `728`, `730`, `733`, `735`, `738`, `740`, `743`, `744`, `649`, `747`, `749`, `753`, `756`, `757`, `759`, `761`, `764`, `767`, `769`, `772`, `774`, `777`, `780`, `783`, `785`, `787`, `789`, `792`, `794`, `797`, `799`, `800`, `802`, `805`, `806`, `808`, `809`, `811`, `812`, `813`, `815`, `817`, `819`, `820`, `59`, `822`, `824`, `827`, `829`, `831`, `618`, `832`, `834`, `836`, `838`, `724`, `841`, `55`, `842`, `844`, `846`, `847`, `850`, `852`, `855`, `857`, `859`, `861`, `863`, `865`, `868`, `869`, `871`, `873`, `874`, `877`, `880`, `884`, `887`, `890`, `891`, `892`, `893`, `896`, `898`, `901`, `351`, `904`, `906`, `908`, `911`, `913`, `915`, `650`, `918`, `920`, `830`, `921`, `923`, `924`, `926`, `927`, `930`, `931`, `934`, `937`, `938`, `940`, `941`, `942`, `945`, `947`, `949`, `952`, `954`, `957`, `960`, `963`, `965`, `967`, `970`, `972`, `974`, `977`, `980`, `981`, `983`, `985`, `986`, `988`, `991`, `994`, `997`, `999`, `1000`, `1002`, `1005`, `1006`, `1007`, `1010`, `125`, `1013`, `1016`, `1017`, `1019`, `1020`, `1024`, `1026`, `1028`, `1030`, `1032`, `1034`, `1036`, `1038`, `1040`, `1041`, `1044`, `1045`, `1048`, `415`, `1051`, `1053`, `1055`, `1056`, `1058`, `1061`, `1063`, `1065`, `1067`, `1068`, `1069`, `1070`, `1074`, `946`, `1077`, `1079`, `1081`, `1083`, `1086`, `1088`, `1089`, `1092`, `936`, `1096`, `1098`, `1101`, `1104`, `1106`, `1108`, `1110`, `1112`, `1114`, `1116`, `1118`, `1119`, `1120`, `1085`, `1123`, `1125`, `1127`, `1031`, `1128`, `1131`, `1124`, `1134`, `1135`, `1137`, `1139`, `1142`, `1144`, `1145`, `1147`, `1150`, `1152`, `1156`, `1158`, `1159`, `1162`, `1164`, `1166`, `1167`, `1170`, `1172`, `1174`, `1176`, `1178`, `1180`, `1181`, `1183`, `1186`, `1187`, `1189`, `1192`, `1195`, `1198`, `1200`, `1201`, `1204`, `1206`, `1208`, `1209`, `763`, `1211`, `1212`, `1214`, `1215`, `1218`, `1220`, `1222`, `1225`, `1226`, `1227`, `1228`, `1230`, `1232`, `1234`, `1236`, `1237`, `1239`, `1241`, `181`, `1244`, `1245`, `1247`, `1249`, `1251`, `1253`, `1256`, `1257`, `1260`, `1261`, `1262`, `1264`, `1267`, `1268`, `1269`, `1272`, `1274`, `1277`, `1280`, `1283`, `1285`, `1287`, `1289`, `1290`, `1294`, `1296`, `1279`, `1298`, `1300`, `1303`, `1304`, `1306`, `1309`, `1311`, `1313`, `1314`, `1317`, `1319`, `1320`, `1324`, `1327`, `1329`, `1332`, `1334`, `1336`, `1338`, `1340`, `1342`, `1344`, `1345`, `303`, `1346`, `1349`, `1350`, `1352`, `1354`, `1356`, `1359`, `362`, `1360`, `1363`, `1365`, `1366`, `1367`, `1369`, `1370`, `1372`, `1374`, `1375`, `1378`, `1380`, `1384`, `1385`, `1389`, `1390`, `1393`, `1395`, `1398`, `1403`, `1404`, `1405`, `1407`, `1410`, `1413`, `1415`, `1418`, `1420`, `1422`, `1423`, `1425`, `1426`, `1428`, `1429`, `1431`, `1433`, `1435`, `1436`, `1438`, `1440`, `1442`, `1444`, `1447`, `1448`, `1449`, `1451`, `1452`, `105`, `1454`, `1456`, `1457`, `1459`, `1462`, `1463`, `1464`, `1466`, `1468`, `1470`, `1471`, `1475`, `810`, `1476`, `1478`, `1480`, `1483`, `1485`, `1487`, `1490`, `1493`, `450`, `1496`, `1498`, `1501`, `1504`, `1506`, `1508`, `1510`, `1513`, `1515`, `1517`, `1520`, `1523`, `1526`, `1529`, `1531`, `1535`, `1536`, `1538`, `1540`, `1542`, `1545`, `1548`, `1550`, `1554`, `1555`, `1558`, `1559`, `1561`, `1563`, `1565`, `1566`, `1568`, `1569`, `1572`, `1574`, `1576`, `1578`, `1580`, `1581`, `1582`, `1585`, `1586`, `1589`, `1591`, `1593`, `1596`, `1597`, `416`, `615`, `1599`, `1601`, `1603`, `1608`, `1611`, `840`, `1613`, `1614`, `1616`, `1618`, `1622`, `1624`, `1627`, `1630`, `1633`, `1636`, `1638`, `1642`, `1645`, `1647`, `1650`, `1653`, `1656`, `1659`, `1661`, `1664`, `1665`, `1668`, `1670`, `1671`, `1674`, `1676`, `1679`, `1680`, `1683`, `1685`, `1687`, `1689`, `1694`, `1697`, `1698`, `1699`, `1700`, `1702`, `1705`, `1706`, `1709`, `1711`, `1712`, `1714`, `1718`, `1720`, `1721`, `1723`, `1725`, `1726`, `1728`, `987`, `506`, `1730`, `1733`, `1735`, `1736`, `1738`, `1740`, `1741`, `1743`, `1745`, `1747`, `1748`, `166`, `1750`, `1752`, `1753`, `1755`, `1758`, `1761`, `1763`, `224`, `1764`, `1767`, `1768`, `1771`, `1773`, `1777`, `1779`, `1783`, `1786`, `1787`, `1791`, `1794`, `1797`, `1798`, `1799`, `1801`, `1804`, `1806`, `1807`, `1809`, `228`, `1810`, `1813`, `1814`, `1817`, `1819`, `1821`, `1824`, `1826`, `1829`, `1831`, `1833`, `1834`, `1835`, `1837`, `1839`, `1637`, `1840`, `1844`, `1846`, `905`, `1850`, `1851`, `1853`, `1855`, `1858`, `1859`, `1861`, `1862`, `1863`, `1866`, `1867`, `1869`, `1873`, `1875`, `1878`, `1879`, `1883`, `1884`, `1887`, `1890`, `1892`, `1895`, `1896`, `1899`, `1901`, `1903`, `1905`, `1907`, `1908`, `1909`, `1910`, `1912`, `1914`, `1917`, `1920`, `1922`, `1924`, `1926`, `1928`, `1929`, `1932`, `1933`, `1935`, `1936`, `1937`, `1940`, `1942`, `1944`, `1946`, `1947`, `1949`, `1952`, `1953`, `1956`, `1959`, `1960`, `1962`, `1964`, `1965`, `1966`, `1967`, `1970`, `1971`, `1972`, `1974`, `1975`, `1976`, `1977`, `1978`, `1979`, `1981`, `1982`, `1983`, `1985`, `1987`, `1991`, `673`, `1992`, `1994`, `1995`, `1997`, `1999`, `2002`, `2003`, `2005`, `2008`, `2010`, `2012`, `2013`, `2015`, `2017`, `2019`, `2020`, `2023`, `2026`, `2027`, `2030`, `2032`, `2034`, `2036`, `2038`, `2040`, `2041`, `2042`, `2045`, `2046`, `2048`, `2049`, `2051`, `2052`, `2053`, `1295`, `2054`, `536`, `2057`, `2059`, `2062`, `2064`, `2066`, `2067`, `2068`, `2072`, `2075`, `2076`, `2078`, `2081`, `2083`, `2085`, `2086`, `2088`, `2090`, `2091`, `2093`, `2096`, `2098`, `2099`, `2102`, `2104`, `2105`, `2107`, 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\| 100.00 \| \| `TOKEN_ACC` \| 100.00 \| \| `SENTS_F` \| 100.00 \| \| `SENTS_P` \| 100.00 \| \| `SENTS_R` \| 100.00 \| \| `TAG_ACC` \| 88.93 \| \| `POS_ACC` \| 96.52 \| \| `MORPH_ACC` \| 100.00 \| \| `MORPH_PER_FEAT` \| 0.00 \| \| `DEP_UAS` \| 89.48 \| \| `DEP_LAS` \| 87.18 \| \| `LEMMA_ACC` \| 94.51 \|
facebook/wav2vec2-xlsr-53-espeak-cv-ft	facebook	2021-12-10T17:18:39Z	483,555	28	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "speech", "audio", "phoneme-recognition", "dataset:common_voice", "arxiv:2109.11680", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: multi-lingual datasets: - common_voice tags: - speech - audio - automatic-speech-recognition - phoneme-recognition widget: - example_title: Librispeech sample 1 src: https://cdn-media.huggingface.co/speech_samples/sample1.flac - example_title: Librispeech sample 2 src: https://cdn-media.huggingface.co/speech_samples/sample2.flac license: apache-2.0 --- # Wav2Vec2-Large-XLSR-53 finetuned on multi-lingual Common Voice This checkpoint leverages the pretrained checkpoint [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) and is fine-tuned on [CommonVoice](https://huggingface.co/datasets/common_voice) to recognize phonetic labels in multiple languages. When using the model make sure that your speech input is sampled at 16kHz. Note that the model outputs a string of phonetic labels. A dictionary mapping phonetic labels to words has to be used to map the phonetic output labels to output words. [Paper: Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) Authors: Qiantong Xu, Alexei Baevski, Michael Auli Abstract Recent progress in self-training, self-supervised pretraining and unsupervised learning enabled well performing speech recognition systems without any labeled data. However, in many cases there is labeled data available for related languages which is not utilized by these methods. This paper extends previous work on zero-shot cross-lingual transfer learning by fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. This is done by mapping phonemes of the training languages to the target language using articulatory features. Experiments show that this simple method significantly outperforms prior work which introduced task-specific architectures and used only part of a monolingually pretrained model. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. # Usage To transcribe audio files the model can be used as a standalone acoustic model as follows: ```python from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC from datasets import load_dataset import torch # load model and processor processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft") model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-xlsr-53-espeak-cv-ft") # load dummy dataset and read soundfiles ds = load_dataset("patrickvonplaten/librispeech_asr_dummy", "clean", split="validation") # tokenize input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values # retrieve logits with torch.no_grad(): logits = model(input_values).logits # take argmax and decode predicted_ids = torch.argmax(logits, dim=-1) transcription = processor.batch_decode(predicted_ids) # => should give ['m ɪ s t ɚ k w ɪ l t ɚ ɪ z ð ɪ ɐ p ɑː s əl l ʌ v ð ə m ɪ d əl k l æ s ɪ z æ n d w iː aʊ ɡ l æ d t ə w ɛ l k ə m h ɪ z ɡ ɑː s p ə'] ```
explosion/id_udv25_indonesiangsd_trf	explosion	2021-12-10T16:10:32Z	2	0	spacy	[ "spacy", "token-classification", "id", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - id license: cc-by-sa-4.0 model-index: - name: id_udv25_indonesiangsd_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9479067555 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9316523945 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9590072946 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9804947669 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.8615977082 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.7838256704 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9297777778 --- UD v2.5 benchmarking pipeline for UD_Indonesian-GSD \| Feature \| Description \| \| --- \| --- \| \| Name \| `id_udv25_indonesiangsd_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) \| ### Label Scheme <details> <summary>View label scheme (1325 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `APP`, `ASP`, `ASP+PS2`, `ASP+PS3`, `ASP+T--`, `ASS`, `ASS+PS3`, `B--`, `B--+PS3`, `B--+T--`, `CC-`, `CC-+PS3`, `CC-+T--`, `CD-`, `CD-+PS3`, `CO-`, `CO-+PS3`, `D--`, `D--+PS2`, `D--+PS3`, `D--+T--`, `F--`, `F--+PS1`, `F--+PS2`, `F--+PS3`, `F--+T--`, `G--`, `G--+PS3`, `G--+T--`, `H--`, `H--+T--`, `I--`, `M--`, `M--+PS3`, `M--+T--`, `NOUN`, `NPD`, `NPD+PS2`, `NPD+PS3`, `NSD`, `NSD+PS1`, `NSD+PS2`, `NSD+PS3`, `NSD+T--`, `NSF`, `NSM`, `NSM+PS3`, `NUM`, `O--`, `PP1`, `PP1+T--`, `PP2`, `PP3`, `PP3+T--`, `PROPN`, `PS1`, `PS1+VSA`, `PS1+VSA+T--`, `PS2`, `PS2+VSA`, `PS3`, `PUNCT`, `R--`, `R--+PS1`, `R--+PS2`, `R--+PS3`, `S--`, `S--+PS3`, `T--`, `VERB`, `VPA`, `VSA`, `VSA+PS1`, `VSA+PS2`, `VSA+PS3`, `VSA+T--`, `VSP`, `VSP+PS3`, `VSP+T--`, `W--`, `W--+T--`, `X`, `X--`, `Z--` \| \| `morphologizer` \| `POS=PROPN`, `POS=AUX`, `POS=DET\\|PronType=Ind`, `Number=Sing\\|POS=NOUN`, `POS=PRON\\|PronType=Rel`, `Number=Sing\\|POS=VERB\\|Voice=Pass`, `POS=ADP`, `POS=PUNCT`, `Number=Sing\\|POS=PROPN`, `POS=NOUN`, `POS=ADV`, `POS=CCONJ`, `Number=Sing\\|POS=VERB\\|Voice=Act`, `POS=VERB`, `POS=DET\\|PronType=Tot`, `Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=3`, `POS=SCONJ`, `Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=DET\\|PronType=Dem`, `NumType=Card\\|POS=NUM`, `Degree=Pos\\|Number=Sing\\|POS=NOUN`, `Degree=Pos\\|Number=Sing\\|POS=ADJ`, `NumType=Card\\|POS=DET\\|PronType=Ind`, `Degree=Pos\\|Number=Sing\\|POS=ADP`, `Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=3`, `Number=Sing\\|POS=VERB`, `POS=PRON\\|PronType=Int`, `Number=Sing\\|POS=ADV\\|Voice=Act`, `Number=Sing\\|Number[psor]=Sing\\|POS=VERB\\|Person[psor]=3\\|Voice=Act`, `Number=Sing\\|POS=ADP\\|Voice=Act`, `POS=ADJ`, `Number[psor]=Sing\\|POS=ADP\\|Person[psor]=3`, `Degree=Pos\\|Number=Sing\\|POS=DET`, `Degree=Pos\\|Number=Sing\\|POS=VERB`, `POS=PRON\\|PronType=Dem`, `POS=PART\\|Polarity=Neg`, `Degree=Pos\\|Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=3`, `Number=Sing\\|POS=PRON\\|Person=1\\|Polite=Form\\|PronType=Prs`, `Number=Sing\\|POS=ADJ`, `Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=SYM`, `POS=ADV\\|PronType=Int`, `Clusivity=In\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Number=Sing\\|POS=ADJ\\|Voice=Act`, `Degree=Pos\\|Number=Sing\\|POS=PROPN`, `Degree=Pos\\|Number=Sing\\|POS=ADV`, `Number=Sing\\|Number[psor]=Sing\\|POS=VERB\\|Person[psor]=3\\|Voice=Pass`, `Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=3\\|Voice=Act`, `Number=Sing\\|POS=PROPN\\|Voice=Act`, `Number=Sing\\|POS=NOUN\\|Voice=Act`, `POS=DET`, `Number=Sing\\|POS=DET\\|Voice=Act`, `NumType=Card\\|POS=PRON\\|PronType=Ind`, `Number=Sing\\|Number[psor]=Sing\\|POS=ADV\\|Person[psor]=3`, `Number=Sing\\|POS=DET`, `Number=Sing\\|POS=ADJ\\|Voice=Pass`, `POS=CCONJ\\|PronType=Dem`, `Number=Sing\\|POS=ADP`, `Number=Sing\\|POS=ADV`, `Number=Sing\\|POS=PRON\\|Person=2\\|Polite=Infm\\|PronType=Prs`, `Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=2`, `Number=Plur\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=2`, `Number=Sing\\|POS=PRON`, `POS=PRON`, `NumType=Card\\|POS=ADV\\|PronType=Ind`, `NumType=Card\\|Number[psor]=Sing\\|POS=NUM\\|Person[psor]=3`, `Number=Sing\\|POS=PRON\\|Person=3\\|Polite=Form\\|PronType=Prs`, `POS=DET\\|PronType=Int`, `Number=Sing\\|Number[psor]=Sing\\|POS=PROPN\\|Person[psor]=3`, `Number=Sing\\|Number[psor]=Sing\\|POS=PROPN\\|Person[psor]=1`, `Degree=Pos\\|Number=Sing\\|POS=SCONJ`, `POS=PRON\\|PronType=Ind`, `Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=3\\|Voice=Pass`, `POS=VERB\\|PronType=Ind`, `Degree=Pos\\|Number=Sing\\|Number[psor]=Sing\\|POS=ADJ\\|Person[psor]=3`, `Number=Sing\\|POS=SCONJ`, `Degree=Sup\\|Number=Sing\\|Number[psor]=Sing\\|POS=ADJ\\|Person[psor]=3`, `Degree=Pos\\|Number=Sing\\|Number[psor]=Sing\\|POS=ADP\\|Person[psor]=3`, `Number=Plur\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=3`, `Number=Plur\\|POS=NOUN`, `POS=ADV\\|PronType=Dem`, `Number=Sing\\|POS=VERB\\|Person=1\\|Voice=Act`, `Degree=Sup\\|Number=Sing\\|POS=ADJ`, `Number=Sing\\|POS=ADP\\|Voice=Pass`, `Number[psor]=Sing\\|POS=PART\\|Person[psor]=3`, `Number=Sing\\|POS=NOUN\\|Voice=Pass`, `Degree=Pos\\|Number=Sing\\|Number[psor]=Sing\\|POS=CCONJ\\|Person[psor]=3`, `POS=PART`, `Number=Sing\\|Number[psor]=Sing\\|POS=PART\\|Person[psor]=3\\|Voice=Pass`, `Degree=Sup\\|Number=Sing\\|POS=ADV`, `Number=Sing\\|POS=PRON\\|Voice=Act`, `Number=Sing\\|Number[psor]=Sing\\|POS=PROPN\\|Person[psor]=3\\|Voice=Act`, `Gender=Masc\\|Number=Sing\\|POS=PROPN`, `Number[psor]=Sing\\|POS=PRON\\|Person[psor]=3\\|PronType=Tot`, `Degree=Pos\\|Number=Sing\\|POS=X`, `POS=PRON\\|PronType=Tot`, `Degree=Pos\\|Number=Sing\\|Number[psor]=Sing\\|POS=ADV\\|Person[psor]=3`, `Degree=Pos\\|Number=Sing\\|Number[psor]=Sing\\|POS=VERB\\|Person[psor]=3`, `Number=Sing\\|Number[psor]=Sing\\|POS=ADP\\|Person[psor]=3`, `Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=2`, `POS=SCONJ\\|PronType=Int`, `Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Number=Sing\\|Number[psor]=Sing\\|POS=VERB\\|Person[psor]=1\\|Voice=Act`, `Number[psor]=Sing\\|POS=DET\\|Person[psor]=3`, `Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person[psor]=3`, `Clusivity=Ex\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Number=Plur\\|POS=VERB\\|Voice=Act`, `Number=Sing\\|Number[psor]=Sing\\|POS=ADV\\|Person[psor]=3\\|Voice=Act`, `Degree=Pos\\|Number=Sing\\|POS=NOUN\\|Polarity=Neg`, `POS=X`, `Number[psor]=Sing\\|POS=ADJ\\|Person[psor]=3`, `Number=Sing\\|Number[psor]=Sing\\|POS=VERB\\|Person[psor]=3`, `Number=Sing\\|POS=PRON\\|Person=2\\|Polite=Form\\|PronType=Prs`, `Number=Sing\\|POS=PRON\\|Person=1\\|Polite=Infm\\|PronType=Prs`, `Number=Sing\\|POS=PROPN\\|Voice=Pass`, `POS=ADV\\|Polarity=Neg`, `NumType=Card\\|Number=Sing\\|POS=NUM`, `Number[psor]=Sing\\|POS=ADV\\|Person[psor]=2`, `Number[psor]=Sing\\|POS=ADV\\|Person[psor]=3`, `Degree=Sup\\|Number=Sing\\|POS=PROPN`, `POS=PROPN\\|Polarity=Neg`, `Number=Sing\\|Number[psor]=Sing\\|POS=VERB\\|Person[psor]=2\\|Voice=Act`, `Number=Sing\\|POS=PROPN\\|Person=1\\|Voice=Act`, `POS=SCONJ\\|PronType=Dem`, `Number=Sing\\|Number[psor]=Sing\\|POS=ADV\\|Person[psor]=2\\|Voice=Act`, `Number=Sing\\|POS=CCONJ`, `Degree=Sup\\|Number=Sing\\|POS=VERB`, `Number=Sing\\|Number[psor]=Sing\\|POS=ADJ\\|Person[psor]=3`, `Number=Sing\\|Number[psor]=Sing\\|POS=ADJ\\|Person[psor]=3\\|Voice=Act`, `Degree=Pos\\|Number=Sing\\|POS=PRON`, `Number=Sing\\|POS=ADV\\|Voice=Pass`, `Number[psor]=Sing\\|POS=ADP\\|Person[psor]=2`, `Number=Sing\\|POS=SYM`, `POS=ADJ\\|Polarity=Neg`, `Degree=Pos\\|NumType=Card\\|Number=Sing\\|POS=NUM`, `Number=Sing\\|Number[psor]=Sing\\|POS=SCONJ\\|Person[psor]=3`, `Degree=Pos\\|Number=Sing\\|POS=CCONJ`, `Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=1`, `Number=Sing\\|POS=CCONJ\\|Voice=Act`, `Gender=Masc\\|Number=Sing\\|POS=NOUN`, `Number=Sing\\|Number[psor]=Sing\\|POS=ADP\\|Person[psor]=3\\|Voice=Pass`, `Gender=Fem\\|Number=Sing\\|POS=PROPN`, `POS=VERB\\|PronType=Dem`, `Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Masc\\|Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=3`, `Number=Sing\\|POS=PART\\|Voice=Act`, `Degree=Sup\\|Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=3`, `POS=ADP\\|PronType=Int`, `Number[psor]=Sing\\|POS=VERB\\|Person[psor]=3`, `Number[psor]=Sing\\|POS=PRON\\|Person[psor]=3\\|PronType=Rel`, `Degree=Pos\\|Number=Sing\\|POS=AUX`, `Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=1`, `Number=Sing\\|POS=SCONJ\\|Voice=Pass`, `Degree=Sup\\|Number=Sing\\|POS=ADP`, `Number=Sing\\|POS=SCONJ\\|Voice=Act`, `NumType=Card\\|POS=DET\\|PronType=Int`, `Degree=Pos\\|Number=Sing\\|POS=PART\\|Polarity=Neg`, `Degree=Sup\\|Number=Sing\\|POS=SCONJ`, `Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=1\\|Voice=Act`, `Number=Plur\\|POS=ADJ`, `POS=VERB\\|PronType=Int`, `Number=Sing\\|POS=VERB\\|Person=2\\|Voice=Act`, `Degree=Pos\\|Number=Sing\\|Number[psor]=Sing\\|POS=ADJ\\|Person[psor]=2`, `Gender=Masc\\|Number=Sing\\|POS=ADJ`, `Number[psor]=Sing\\|POS=ADV\\|Person[psor]=3\\|PronType=Tot`, `POS=DET\\|PronType=Rel`, `Number=Sing\\|POS=NOUN\\|Polarity=Neg`, `Number=Sing\\|Number[psor]=Sing\\|POS=PROPN\\|Person[psor]=2`, `NumType=Card\\|Number=Sing\\|POS=NUM\\|Voice=Act`, `Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Number[psor]=Sing\\|POS=DET\\|Person[psor]=3\\|PronType=Tot`, `Number[psor]=Sing\\|POS=PROPN\\|Person[psor]=1`, `Gender=Fem\\|Number=Sing\\|POS=NOUN`, `Number=Sing\\|POS=VERB\\|Person=1`, `Degree=Pos\\|Number=Sing\\|Number[psor]=Sing\\|POS=PROPN\\|Person[psor]=3`, `NumType=Card\\|Number[psor]=Sing\\|POS=DET\\|Person[psor]=3\\|PronType=Ind`, `POS=ADV\\|PronType=Tot`, `Degree=Pos\\|Number=Plur\\|POS=ADV`, `Number=Plur\\|POS=ADV\\|Voice=Act`, `POS=CCONJ\\|PronType=Int`, `Degree=Pos\\|Number=Sing\\|POS=PART`, `Number[psor]=Sing\\|POS=PRON\\|Person[psor]=2`, `Number=Plur\\|POS=VERB`, `Number=Sing\\|Number[psor]=Sing\\|POS=ADJ\\|Person[psor]=3\\|Voice=Pass`, `Degree=Pos\\|Number=Sing\\|POS=PUNCT`, `Number[psor]=Sing\\|POS=ADP\\|Person[psor]=1`, `Degree=Sup\\|Number=Sing\\|POS=NOUN`, `Number[psor]=Sing\\|POS=PART\\|Person[psor]=3\\|Polarity=Neg`, `Number=Sing\\|Number[psor]=Sing\\|POS=ADP\\|Person[psor]=3\\|Voice=Act`, `POS=NOUN\\|Polarity=Neg`, `Number[psor]=Sing\\|POS=PROPN\\|Person[psor]=2`, `Number=Sing\\|Number[psor]=Sing\\|POS=NOUN\\|Person[psor]=2\\|Voice=Act` \| \| `parser` \| `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `case`, `cc`, `ccomp`, `compound`, `compound:plur`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `1`, `2`, `4`, `6`, `8`, `10`, `12`, `15`, `19`, `22`, `24`, `26`, `27`, `29`, `30`, `31`, `34`, `36`, `37`, `40`, `42`, `44`, `46`, `48`, `50`, `51`, `53`, `54`, `56`, `58`, `47`, `59`, `62`, `64`, `66`, `68`, `70`, `71`, `3`, `72`, `74`, `75`, `77`, `78`, `79`, `81`, `84`, `86`, `87`, `88`, `89`, `92`, `11`, `93`, `95`, `96`, `97`, `98`, `99`, `101`, `103`, `105`, `106`, `107`, `108`, `110`, `111`, `113`, `115`, `116`, `118`, `120`, `122`, `123`, `124`, `125`, `126`, `127`, `128`, `130`, `131`, `132`, `134`, `135`, `137`, `140`, `142`, `143`, `144`, `146`, `147`, `148`, `149`, `150`, `151`, `152`, `153`, `43`, `155`, `157`, `158`, `160`, `161`, `162`, `163`, `164`, `165`, `166`, `167`, `168`, `170`, `171`, `172`, `174`, `175`, `177`, `178`, `179`, `180`, `181`, `182`, `183`, `25`, `184`, `185`, `186`, `187`, `188`, `190`, `192`, `193`, `194`, `196`, `57`, `197`, `198`, `199`, `201`, `203`, `204`, `206`, `207`, `208`, `209`, `210`, `211`, `212`, `213`, `214`, `215`, `217`, `218`, `219`, `220`, `221`, `223`, `225`, `227`, `228`, `230`, `232`, `234`, `236`, `237`, `238`, `240`, `242`, `243`, `244`, `246`, `247`, `248`, `249`, `250`, `251`, `252`, `253`, `254`, `256`, `257`, `258`, `260`, `261`, `262`, `263`, `264`, `266`, `267`, `268`, `269`, `270`, `272`, `41`, `273`, `274`, `275`, `276`, `277`, `278`, `280`, `281`, `282`, `283`, `284`, `285`, `286`, `287`, `288`, `289`, `290`, `291`, `292`, `293`, `294`, `295`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `304`, `306`, `307`, `308`, `309`, `310`, `312`, `313`, `314`, `317`, `315`, `318`, `320`, `321`, `322`, `323`, `324`, `9`, `325`, `326`, `327`, `329`, `330`, `331`, `332`, `333`, `334`, `336`, `337`, `339`, `341`, `342`, `343`, `345`, `346`, `347`, `348`, `80`, `241`, `349`, `350`, `351`, `353`, `354`, `355`, `356`, `357`, `358`, `359`, `360`, `361`, `363`, `49`, `364`, `365`, `366`, `23`, `367`, `368`, `369`, `370`, `371`, `372`, `373`, `374`, `375`, `376`, `378`, `379`, `380`, `381`, `382`, `383`, `385`, `386`, `387`, `388`, `389`, `390`, `391`, `393`, `394`, `45`, `35`, `395`, `396`, `63`, `397`, `398`, `399`, `400`, `401`, `402`, `403`, `404`, `405`, `406`, `407`, `408`, `409`, `410`, `412`, `413`, `415`, `416`, `417`, `419`, `421`, `422`, `173`, `28`, `424`, `425`, `426`, `427`, `428`, `429`, `430`, `431`, `432`, `434`, `435`, `437`, `439`, `440`, `441`, `442`, `443`, `444`, `445`, `446`, `447`, `448`, `450`, `451`, `453`, `454`, `455`, `457`, `459`, `461`, `463`, `464`, `465`, `466`, `467`, `469`, `470`, `0`, `471`, `472`, `473`, `474`, `475`, `477`, `478`, `479`, `480`, `481`, `482`, `483`, `484`, `485`, `486`, `487`, `489`, `490`, `491`, `493`, `495`, `496`, `497`, `498`, `499`, `500`, `501`, `502`, `503`, `504`, `52`, `506`, `507`, `508`, `509`, `510`, `511`, `512`, `514`, `515`, `516`, `519`, `520`, `67`, `522`, `523`, `525`, `526`, `527`, `528`, `529`, `530`, `531`, `533`, `534`, `535`, `536`, `537`, `538`, `539`, `540`, `541`, `542`, `543`, `544`, `545`, `546`, `548`, `549`, `551`, `553`, `554`, `555`, `556`, `557`, `559`, `560`, `561`, `562`, `563`, `564`, `565`, `566`, `568`, `569`, `570`, `571`, `572`, `573`, `575`, `576`, `577`, `578`, `579`, `513`, `580`, `582`, `583`, `584`, `586`, `587`, `588`, `589`, `591`, `592`, `593`, `594`, `595`, `597`, `599`, `600`, `602`, `607`, `608`, `609`, `610`, `611`, `612`, `613`, `614`, `615`, `616`, `617`, `618`, `619`, `620`, `621`, `623`, `625`, `626`, `627`, `628`, `629`, `630`, `631`, `632`, `633`, `634`, `635`, `636`, `637`, `638`, `639`, `640`, `641`, `642`, `644`, `645`, `646`, `647`, `648`, `649`, `651`, `652`, `653`, `655`, `656`, `657`, `658`, `659`, `660`, `661`, `662`, `664`, `665`, `666`, `667`, `668`, `669`, `670`, `672`, `674`, `675`, `676`, `677`, `169`, `678`, `679`, `680`, `681`, `682`, `683`, `684`, `685`, `686`, `687`, `688`, `689`, `690`, `7`, `691`, `692`, `693`, `694`, `695`, `696`, `697`, `698`, `699`, `701`, `702`, `703`, `704`, `705`, `706`, `708`, `709`, `710`, `711`, `712`, `713`, `715`, `717`, `719`, `720`, `721`, `722`, `723`, `724`, `725`, `726`, `727`, `728`, `729`, `730`, `731`, `732`, `733`, `735`, `736`, `737`, `738`, `740`, `741`, `742`, `743`, `744`, `745`, `746`, `747`, `748`, `749`, `750`, `752`, `753`, `754`, `755`, `756`, `757`, `758`, `760`, `761`, `763`, `764`, `765`, `766`, `767`, `768`, `769`, `770`, `771`, `772`, `773`, `774`, `775`, `776`, `65`, `777`, `778`, `779`, `780`, `781`, `782`, `783`, `784`, `785`, `786`, `788`, `790`, `791`, `792`, `793`, `794`, `795`, `796`, `797`, `798`, `799`, `145`, `800`, `801`, `802`, `803`, `804`, `805`, `806`, `807`, `808`, `809`, `810`, `811`, `812`, `813`, `815`, `817`, `818`, `819`, `820`, `821`, `822`, `823`, `824`, `826`, `829`, `830`, `831`, `832`, `833`, `834`, `835`, `836`, `837`, `838`, `839`, `840`, `841`, `843`, `845`, `847`, `849`, `850`, `851`, 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`1008`, `1009`, `1012`, `1014`, `1015`, `1016`, `1019`, `1020`, `1021`, `1022`, `1023`, `1024`, `1025`, `1026`, `1027`, `1028`, `1029`, `1030`, `1031`, `1032`, `1033`, `1034`, `1035`, `1036`, `1037`, `1038`, `1039`, `1040`, `1041`, `1042`, `1043`, `1044`, `1045`, `1046`, `1047`, `1048`, `1049`, `1051`, `1052`, `1053`, `1054`, `1055`, `1056`, `1057`, `1058`, `1059`, `1060`, `1062`, `1063`, `1064`, `1065`, `1066`, `1068`, `1069`, `1070`, `1072`, `1074`, `1075`, `1076`, `1077`, `1078`, `1079`, `1080`, `1082`, `1083`, `1085`, `1086`, `1087`, `1088`, `1090`, `1091`, `1092`, `1093`, `1094`, `1095`, `1096`, `1097`, `1098`, `1099`, `1100`, `1101`, `673`, `1102`, `1103`, `1104`, `1106`, `1108`, `1109`, `1110`, `1111`, `1115`, `1116`, `1119`, `1120`, `1089`, `418`, `1121`, `1122`, `1123`, `1124`, `1125`, `1126`, `1127`, `1128`, `1129`, `1130`, `1131`, `1132`, `1134`, `1136`, `1137`, `1138`, `1139`, `1140`, `1141`, `1133`, `1142`, `1143`, `1144`, `1145`, `1146`, `1147`, `1148`, `1149`, `1150`, `1151`, `1153`, `1154`, `1156`, `1157`, `1158`, `1159`, `1160`, `1162`, `1164`, `1165`, `377`, `1166`, `1167`, `1168`, `1169`, `1170`, `1171`, `1172`, `1173`, `1174`, `1175`, `1176`, `1177`, `1179`, `1180`, `1181`, `1182`, `191`, `1183`, `1184`, `1185`, `1186`, `1187`, `1188`, `1190`, `1191`, `1192`, `1194`, `1195`, `1196`, `1197`, `1198`, `1199`, `1200`, `1201` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `TOKEN_F` \| 99.99 \| \| `TOKEN_P` \| 99.98 \| \| `TOKEN_R` \| 99.99 \| \| `TOKEN_ACC` \| 100.00 \| \| `SENTS_F` \| 92.98 \| \| `SENTS_P` \| 92.40 \| \| `SENTS_R` \| 93.56 \| \| `TAG_ACC` \| 94.79 \| \| `POS_ACC` \| 93.17 \| \| `MORPH_ACC` \| 95.90 \| \| `DEP_UAS` \| 86.16 \| \| `DEP_LAS` \| 78.38 \| \| `LEMMA_ACC` \| 98.05 \|
patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm	patrickvonplaten	2021-12-10T15:49:13Z	1,650	8	transformers	[ "transformers", "pytorch", "tf", "jax", "wav2vec2", "automatic-speech-recognition", "audio", "speech", "xlsr-fine-tuning-week", "es", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- language: es datasets: - common_voice metrics: - wer - cer tags: - audio - automatic-speech-recognition - speech - xlsr-fine-tuning-week license: apache-2.0 --- # Wav2Vec2-Large-XLSR-53-Spanish-With-LM This is a model copy of [Wav2Vec2-Large-XLSR-53-Spanish](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-spanish) that has language model support. This model card can be seen as a demo for the [pyctcdecode](https://github.com/kensho-technologies/pyctcdecode) integration with Transformers led by [this PR](https://github.com/huggingface/transformers/pull/14339). The PR explains in-detail how the integration works. In a nutshell: This PR adds a new Wav2Vec2WithLMProcessor class as drop-in replacement for Wav2Vec2Processor. The only change from the existing ASR pipeline will be: ## Changes ```diff import torch from datasets import load_dataset from transformers import AutoModelForCTC, AutoProcessor import torchaudio.functional as F model_id = "patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm" sample = next(iter(load_dataset("common_voice", "es", split="test", streaming=True))) resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy() model = AutoModelForCTC.from_pretrained(model_id) processor = AutoProcessor.from_pretrained(model_id) input_values = processor(resampled_audio, return_tensors="pt").input_values with torch.no_grad(): logits = model(input_values).logits -prediction_ids = torch.argmax(logits, dim=-1) -transcription = processor.batch_decode(prediction_ids) +transcription = processor.batch_decode(logits.numpy()).text # => 'bien y qué regalo vas a abrir primero' ``` Improvement This model has been compared on 512 speech samples from the Spanish Common Voice Test set and gives a nice 20 % performance boost: The results can be reproduced by running from this model repository: \| Model \| WER \| CER \| \| ------------- \| ------------- \| ------------- \| \| patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm \| 8.44% \| 2.93% \| \| jonatasgrosman/wav2vec2-large-xlsr-53-spanish \| 10.20% \| 3.24% \| ``` bash run_ngram_wav2vec2.py 1 512 ``` ``` bash run_ngram_wav2vec2.py 0 512 ``` with `run_ngram_wav2vec2.py` being https://huggingface.co/patrickvonplaten/wav2vec2-large-xlsr-53-spanish-with-lm/blob/main/run_ngram_wav2vec2.py
explosion/en_udv25_englishewt_trf	explosion	2021-12-10T15:24:49Z	7	1	spacy	[ "spacy", "token-classification", "en", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - en license: cc-by-sa-4.0 model-index: - name: en_udv25_englishewt_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9636175051 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9693826668 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9690635285 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9735945316 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9190022676 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8942035228 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.906218656 --- UD v2.5 benchmarking pipeline for UD_English-EWT \| Feature \| Description \| \| --- \| --- \| \| Name \| `en_udv25_englishewt_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) \| ### Label Scheme <details> <summary>View label scheme (1760 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `GW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` \| \| `morphologizer` \| `Number=Sing\\|POS=PROPN`, `POS=PUNCT`, `Degree=Pos\\|POS=ADJ`, `Number=Plur\\|POS=NOUN`, `Mood=Ind\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin`, `Definite=Def\\|POS=DET\\|PronType=Art`, `Number=Sing\\|POS=NOUN`, `POS=ADP`, `Number=Sing\\|POS=DET\\|PronType=Dem`, `Definite=Ind\\|POS=DET\\|PronType=Art`, `POS=AUX\\|VerbForm=Fin`, `POS=AUX\\|VerbForm=Inf`, `POS=VERB\\|VerbForm=Ger`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `POS=PART`, `POS=VERB\\|VerbForm=Inf`, `POS=SCONJ`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Mood=Ind\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `POS=VERB\\|Tense=Past\\|VerbForm=Part`, `NumType=Card\\|POS=NUM`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=AUX\\|VerbForm=Ger`, `POS=VERB\\|Tense=Past\\|VerbForm=Part\\|Voice=Pass`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `POS=ADV`, `Number=Sing\\|POS=PRON\\|PronType=Dem`, `Number=Plur\\|POS=PROPN`, `Degree=Pos\\|NumType=Ord\\|POS=ADJ`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Case=Nom\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Mood=Ind\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin`, `Case=Nom\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=VERB\\|Tense=Pres\\|VerbForm=Part`, `Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `POS=CCONJ`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Mood=Ind\\|POS=AUX\\|Tense=Pres\\|VerbForm=Fin`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|VerbForm=Fin`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `POS=PRON\\|PronType=Rel`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=PRON`, `Number=Plur\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `POS=AUX\\|Tense=Past\\|VerbForm=Part`, `POS=DET`, `Number=Sing\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Degree=Pos\\|POS=ADV`, `Degree=Cmp\\|POS=ADV`, `Number=Sing\\|POS=PRON`, `Degree=Cmp\\|POS=ADJ`, `Case=Nom\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=ADV\\|PronType=Dem`, `POS=ADV\\|PronType=Int`, `Number=Plur\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Number=Plur\\|POS=PRON\\|PronType=Dem`, `Mood=Imp\\|POS=VERB\\|VerbForm=Fin`, `Degree=Sup\\|POS=ADJ`, `POS=PRON\\|PronType=Int`, `NumType=Mult\\|POS=ADV`, `Case=Acc\\|Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `POS=DET\\|PronType=Int`, `POS=PRON\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Past\\|VerbForm=Fin`, `Number=Plur\\|POS=DET\\|PronType=Dem`, `POS=PRON\\|Poss=Yes\\|PronType=Int`, `Case=Acc\\|POS=PRON\\|Person=2\\|PronType=Prs`, `POS=X`, `POS=PRON\\|PronType=Dem`, `Number=Sing\\|POS=PROPN\\|Typo=Yes`, `POS=ADV\\|PronType=Rel`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Degree=Sup\\|POS=ADV`, `POS=INTJ`, `Gender=Masc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Foreign=Yes\\|POS=X`, `POS=SYM`, `Number=Sing\\|POS=ADJ`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Pres\\|VerbForm=Fin`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Person=1\\|Tense=Past\\|VerbForm=Fin`, `Mood=Imp\\|POS=AUX\\|VerbForm=Fin`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Pres\\|VerbForm=Fin`, `Abbr=Yes\\|POS=CCONJ`, `POS=SCONJ\\|Typo=Yes`, `Case=Acc\\|Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Number=Sing\\|POS=SYM`, `POS=DET\\|Typo=Yes`, `Degree=Pos\\|POS=PROPN`, `Abbr=Yes\\|POS=ADP`, `POS=ADP\\|Typo=Yes`, `Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `POS=PRON\\|Person=2\\|Poss=Yes\\|PronType=Prs\\|Typo=Yes`, `Abbr=Yes\\|POS=VERB\\|Tense=Pres\\|VerbForm=Part`, `Abbr=Yes\\|POS=PART`, `POS=AUX\\|Typo=Yes\\|VerbForm=Fin`, `Degree=Pos\\|POS=ADJ\\|Typo=Yes`, `POS=VERB\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part\\|Voice=Pass`, `Number=Sing\\|POS=NOUN\\|Typo=Yes`, `Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Reflex=Yes`, `Abbr=Yes\\|Number=Sing\\|POS=NOUN`, `Degree=Pos\\|POS=NOUN`, `POS=CCONJ\\|Typo=Yes`, `Number=Sing\\|POS=X`, `Abbr=Yes\\|POS=SCONJ`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Reflex=Yes`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin`, `Mood=Ind\\|POS=AUX\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin`, `POS=ADV\\|Typo=Yes`, `Mood=Ind\\|Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=1\\|Tense=Past\\|VerbForm=Fin`, `Number=Sing\\|POS=NUM`, `POS=PRON\\|Poss=Yes\\|PronType=Rel`, `Abbr=Yes\\|Mood=Ind\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin`, `Abbr=Yes\\|POS=INTJ`, `Abbr=Yes\\|POS=VERB\\|VerbForm=Inf`, `Abbr=Yes\\|Number=Sing\\|POS=PRON`, `Abbr=Yes\\|POS=PRON\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Abbr=Yes\\|POS=PRON\\|PronType=Int`, `Abbr=Yes\\|POS=AUX\\|VerbForm=Fin`, `Abbr=Yes\\|POS=ADV`, `Abbr=Yes\\|Number=Plur\\|POS=NOUN`, `Abbr=Yes\\|Mood=Ind\\|POS=AUX\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin`, `POS=ADJ`, `Number=Plur\\|POS=NOUN\\|Typo=Yes`, `POS=DET\\|PronType=Rel\\|Typo=Yes`, `POS=PART\\|Typo=Yes`, `Abbr=Yes\\|POS=DET`, `POS=DET\\|PronType=Dem`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Typo=Yes`, `Degree=Pos\\|NumType=Ord\\|POS=ADV`, `POS=NOUN`, `Number=Plur\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|Typo=Yes`, `POS=PRON\\|Typo=Yes`, `Number=Plur\\|POS=VERB`, `POS=VERB\\|Typo=Yes\\|VerbForm=Inf`, `Mood=Ind\\|POS=VERB\\|Tense=Past\\|Typo=Yes\\|VerbForm=Fin`, `Mood=Imp\\|POS=AUX\\|VerbForm=Inf`, `Abbr=Yes\\|Mood=Imp\\|POS=VERB\\|VerbForm=Fin`, `Abbr=Yes\\|Case=Nom\\|POS=PRON\\|Person=2\\|PronType=Prs`, `POS=VERB\\|Tense=Past\\|Typo=Yes\\|VerbForm=Part`, `Mood=Ind\\|POS=AUX\\|Tense=Past\\|Typo=Yes\\|VerbForm=Fin`, `Mood=Ind\\|POS=VERB\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin`, `Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `POS=VERB\\|Typo=Yes\\|VerbForm=Ger`, `Mood=Ind\\|Number=Sing\\|POS=VERB\\|Person=3\\|Tense=Pres\\|Typo=Yes\\|VerbForm=Fin`, `Abbr=Yes\\|POS=PRON`, `Abbr=Yes\\|Number=Plur\\|POS=NOUN\\|Typo=Yes`, `Case=Nom\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Typo=Yes`, `Abbr=Yes\\|Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Gender=Fem\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs` \| \| `parser` \| `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `cc:preconj`, `ccomp`, `compound`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:predet`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nmod:npmod`, `nmod:poss`, `nmod:tmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:npmod`, `obl:tmod`, `orphan`, `parataxis`, `punct`, `reparandum`, `vocative`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `0`, `2`, `4`, `6`, `8`, `10`, `12`, `13`, `15`, `17`, `19`, `21`, `23`, `26`, `28`, `29`, `30`, `32`, `34`, `36`, `39`, `42`, `43`, `45`, `47`, `49`, `51`, `53`, `55`, `57`, `59`, `61`, `62`, `64`, `67`, `69`, `71`, `73`, `75`, `77`, `79`, `81`, `83`, `85`, `87`, `1`, `89`, `90`, `92`, `94`, `95`, `97`, `99`, `101`, `105`, `106`, `108`, `110`, `111`, `112`, `113`, `115`, `117`, `119`, `121`, `122`, `124`, `125`, `126`, `127`, `128`, `129`, `130`, `132`, `133`, `136`, `137`, `138`, `139`, `142`, `143`, `145`, `150`, `153`, `156`, `157`, `159`, `162`, `163`, `164`, `167`, `169`, `171`, `174`, `176`, `177`, `179`, `182`, `184`, `187`, `189`, `191`, `193`, `194`, `197`, `198`, `201`, `203`, `204`, `208`, `210`, `211`, `213`, `214`, `215`, `217`, `220`, `221`, `224`, `225`, `227`, `229`, `231`, `233`, `235`, `236`, `239`, `241`, `242`, `244`, `246`, `247`, `248`, `249`, `250`, `251`, `252`, `254`, `256`, `258`, `259`, `261`, `263`, `264`, `265`, `266`, `269`, `270`, `272`, `273`, `274`, `276`, `277`, `278`, `281`, `283`, `72`, `285`, `287`, `288`, `291`, `292`, `293`, `296`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `304`, `305`, `306`, `307`, `308`, `309`, `310`, `311`, `315`, `316`, `317`, `318`, `319`, `320`, `322`, `88`, `324`, `327`, `328`, `332`, `336`, `337`, `338`, `340`, `341`, `342`, `343`, `344`, `347`, `349`, `350`, `351`, `352`, `353`, `354`, `356`, `357`, `358`, `360`, `361`, `362`, `363`, `364`, `365`, `366`, `367`, `369`, `373`, `375`, `376`, `377`, `378`, `379`, `144`, `381`, `383`, `384`, `386`, `387`, `389`, `390`, `393`, `394`, `396`, `397`, `398`, `399`, `402`, `405`, `407`, `408`, `410`, `411`, `412`, `413`, `414`, `416`, `418`, `419`, `421`, `422`, `423`, `424`, `426`, `428`, `429`, `430`, `432`, `434`, `436`, `437`, `438`, `441`, `442`, `443`, `444`, `445`, `446`, `447`, `260`, `448`, `452`, `453`, `454`, `455`, `456`, `457`, `458`, `460`, `461`, `462`, `463`, `464`, `465`, `466`, `467`, `409`, `468`, `469`, `470`, `471`, `472`, `473`, `476`, `477`, `481`, `484`, `486`, `487`, `488`, `491`, `492`, `493`, `494`, `495`, `496`, `497`, `498`, `499`, `500`, `503`, `504`, `506`, `507`, `508`, `509`, `511`, `512`, `513`, `514`, `515`, `516`, `517`, `518`, `519`, `107`, `520`, `521`, `522`, `523`, `524`, `525`, `526`, `527`, `528`, `529`, `531`, `533`, `534`, `537`, `538`, `542`, `543`, `544`, `545`, `546`, `547`, `548`, `549`, `550`, `553`, `554`, `557`, `558`, `560`, `561`, `564`, `565`, `566`, `567`, `568`, `569`, `570`, `571`, `572`, `573`, `574`, `575`, `576`, `577`, `578`, `579`, `580`, `581`, `582`, `583`, `584`, `586`, `587`, `588`, `589`, `590`, `591`, `592`, `594`, `595`, `76`, `596`, `597`, `598`, `600`, `601`, `602`, `149`, `603`, `604`, `605`, `606`, `607`, `608`, `609`, `490`, `610`, `611`, `96`, `255`, `614`, `617`, `619`, `620`, `621`, `622`, `623`, `624`, `626`, `627`, `628`, `630`, `632`, `633`, `635`, `638`, `639`, `640`, `641`, `644`, `647`, `650`, `654`, `657`, `659`, `173`, `661`, `662`, `663`, `664`, `668`, `669`, `670`, `671`, `673`, `676`, `677`, `678`, `680`, `682`, `158`, `91`, `683`, `684`, `685`, `686`, `687`, `688`, `689`, `690`, `691`, `692`, `693`, `695`, `697`, `699`, `700`, `701`, `183`, `702`, `703`, `704`, `706`, `707`, `709`, `711`, `713`, `485`, `714`, `716`, `717`, `718`, `719`, `720`, `721`, `722`, `723`, `724`, `726`, `727`, `728`, `729`, `730`, `731`, `732`, `733`, `734`, `735`, `736`, `737`, `738`, `739`, `741`, `742`, `744`, `745`, `746`, `748`, `749`, `752`, `753`, `754`, `755`, `756`, `757`, `759`, `760`, `762`, `763`, `764`, `765`, `768`, `769`, `772`, `774`, `775`, `776`, `777`, `781`, `782`, `783`, `784`, `785`, `786`, `787`, `788`, `789`, `78`, `791`, `794`, `795`, `796`, `798`, `800`, `801`, `802`, `803`, `804`, `805`, `806`, `807`, `808`, `809`, `810`, `811`, `812`, `813`, `814`, `815`, `816`, `817`, `818`, `819`, `820`, `822`, `823`, `824`, `825`, `826`, `827`, `828`, `829`, `830`, `131`, `831`, `631`, `832`, `833`, `834`, `838`, `839`, `841`, `842`, `843`, `844`, `845`, `846`, `847`, `849`, `792`, `850`, `851`, `852`, `853`, `856`, `857`, `858`, `859`, `860`, `861`, `862`, `864`, `865`, `715`, `866`, `867`, `868`, `869`, `870`, `871`, `872`, `873`, `877`, `878`, `879`, `881`, `882`, `883`, `885`, `886`, `887`, `888`, `848`, `889`, `890`, `891`, `892`, `893`, `894`, `895`, `896`, `900`, `901`, `902`, `903`, `905`, `907`, `908`, `911`, `912`, `913`, `914`, `918`, `919`, `920`, `923`, `924`, `925`, `926`, `927`, `928`, `929`, `930`, `931`, `932`, `933`, `52`, 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`1943`, `1944`, `1945`, `1946`, `1947`, `1948`, `1949`, `1950`, `1951`, `1952`, `1953`, `1954`, `1955`, `1956`, `1957`, `1958`, `1959`, `1961`, `1962`, `1963`, `1964`, `1965`, `1966`, `1967`, `1968`, `1969`, `1970`, `1971`, `1972`, `1973`, `1974`, `1975`, `1976`, `1977`, `1978`, `1979`, `1982`, `1985`, `1987`, `1988`, `1989`, `1990`, `1992`, `1994`, `1995`, `1996`, `1997`, `1998`, `1999`, `2000`, `2003`, `2006`, `152`, `2007`, `2009`, `2010`, `2011`, `2012`, `2013`, `2014`, `2015`, `2016`, `2017`, `2019`, `2020`, `2021`, `2022`, `2023`, `2024`, `2025`, `2026`, `2029`, `2030`, `2031`, `2032`, `2033`, `2034`, `2035`, `2037`, `2038`, `2039`, `2040`, `2041`, `2042`, `2043`, `2044`, `2045`, `2047` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `TOKEN_F` \| 99.15 \| \| `TOKEN_P` \| 99.18 \| \| `TOKEN_R` \| 99.11 \| \| `TOKEN_ACC` \| 99.83 \| \| `SENTS_F` \| 90.62 \| \| `SENTS_P` \| 90.99 \| \| `SENTS_R` \| 90.26 \| \| `TAG_ACC` \| 96.36 \| \| `POS_ACC` \| 96.94 \| \| `MORPH_ACC` \| 96.91 \| \| `DEP_UAS` \| 91.90 \| \| `DEP_LAS` \| 89.42 \| \| `LEMMA_ACC` \| 97.36 \|
explosion/nl_udv25_dutchlassysmall_trf	explosion	2021-12-10T14:38:34Z	4	0	spacy	[ "spacy", "token-classification", "nl", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - nl license: cc-by-sa-4.0 model-index: - name: nl_udv25_dutchlassysmall_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9592982456 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9636842105 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9772747214 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9670628481 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9022862512 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8620941643 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9183673469 --- UD v2.5 benchmarking pipeline for UD_Dutch-LassySmall \| Feature \| Description \| \| --- \| --- \| \| Name \| `nl_udv25_dutchlassysmall_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) \| ### Label Scheme <details> <summary>View label scheme (1070 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `ADJ\\|nom\\|basis\\|met-e\\|mv-n`, `ADJ\\|nom\\|basis\\|met-e\\|zonder-n\\|bijz`, `ADJ\\|nom\\|basis\\|met-e\\|zonder-n\\|stan`, `ADJ\\|nom\\|basis\\|zonder\\|mv-n`, `ADJ\\|nom\\|basis\\|zonder\\|zonder-n`, `ADJ\\|nom\\|comp\\|met-e\\|mv-n`, `ADJ\\|nom\\|sup\\|met-e\\|mv-n`, `ADJ\\|nom\\|sup\\|met-e\\|zonder-n\\|stan`, `ADJ\\|nom\\|sup\\|zonder\\|zonder-n`, `ADJ\\|postnom\\|basis\\|zonder`, `ADJ\\|prenom\\|basis\\|met-e\\|bijz`, `ADJ\\|prenom\\|basis\\|met-e\\|stan`, `ADJ\\|prenom\\|basis\\|zonder`, `ADJ\\|prenom\\|comp\\|met-e\\|stan`, `ADJ\\|prenom\\|comp\\|zonder`, `ADJ\\|prenom\\|sup\\|met-e\\|stan`, `ADJ\\|vrij\\|basis\\|zonder`, `ADJ\\|vrij\\|comp\\|zonder`, `ADJ\\|vrij\\|sup\\|zonder`, `BW`, `LET`, `LID\\|bep\\|gen\\|evmo`, `LID\\|bep\\|gen\\|rest3`, `LID\\|bep\\|stan\\|evon`, `LID\\|bep\\|stan\\|rest`, `LID\\|onbep\\|stan\\|agr`, `N\\|eigen\\|ev\\|basis\\|gen`, `N\\|eigen\\|ev\\|basis\\|genus\\|stan`, `N\\|eigen\\|ev\\|basis\\|onz\\|stan`, `N\\|eigen\\|ev\\|basis\\|zijd\\|stan`, `N\\|eigen\\|ev\\|dim\\|onz\\|stan`, `N\\|eigen\\|mv\\|basis`, `N\\|soort\\|ev\\|basis\\|dat`, `N\\|soort\\|ev\\|basis\\|gen`, `N\\|soort\\|ev\\|basis\\|genus\\|stan`, `N\\|soort\\|ev\\|basis\\|onz\\|stan`, `N\\|soort\\|ev\\|basis\\|zijd\\|stan`, `N\\|soort\\|ev\\|dim\\|onz\\|stan`, `N\\|soort\\|mv\\|basis`, `N\\|soort\\|mv\\|dim`, `SPEC\\|afgebr`, `SPEC\\|afk`, `SPEC\\|deeleigen`, `SPEC\\|enof`, `SPEC\\|symb`, `SPEC\\|vreemd`, `TSW`, `TW\\|hoofd\\|nom\\|mv-n\\|basis`, `TW\\|hoofd\\|nom\\|zonder-n\\|basis`, `TW\\|hoofd\\|nom\\|zonder-n\\|dim`, `TW\\|hoofd\\|prenom\\|stan`, `TW\\|hoofd\\|vrij`, `TW\\|rang\\|nom\\|zonder-n`, `TW\\|rang\\|prenom\\|stan`, `VG\\|neven`, `VG\\|onder`, `VNW\\|aanw\\|adv-pron\\|obl\\|vol\\|3o\\|getal`, `VNW\\|aanw\\|adv-pron\\|stan\\|red\\|3\\|getal`, `VNW\\|aanw\\|det\\|stan\\|nom\\|met-e\\|mv-n`, `VNW\\|aanw\\|det\\|stan\\|nom\\|met-e\\|zonder-n`, `VNW\\|aanw\\|det\\|stan\\|prenom\\|met-e\\|rest`, `VNW\\|aanw\\|det\\|stan\\|prenom\\|zonder\\|agr`, `VNW\\|aanw\\|det\\|stan\\|prenom\\|zonder\\|evon`, `VNW\\|aanw\\|det\\|stan\\|prenom\\|zonder\\|rest`, `VNW\\|aanw\\|pron\\|gen\\|vol\\|3m\\|ev`, `VNW\\|aanw\\|pron\\|stan\\|vol\\|3o\\|ev`, `VNW\\|aanw\\|pron\\|stan\\|vol\\|3\\|getal`, `VNW\\|betr\\|det\\|stan\\|nom\\|zonder\\|zonder-n`, `VNW\\|betr\\|pron\\|stan\\|vol\\|3\\|ev`, `VNW\\|betr\\|pron\\|stan\\|vol\\|persoon\\|getal`, `VNW\\|bez\\|det\\|stan\\|red\\|3\\|ev\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|vol\\|1\\|ev\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|vol\\|1\\|mv\\|prenom\\|met-e\\|rest`, `VNW\\|bez\\|det\\|stan\\|vol\\|1\\|mv\\|prenom\\|zonder\\|evon`, `VNW\\|bez\\|det\\|stan\\|vol\\|2\\|getal\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|vol\\|3m\\|ev\\|prenom\\|met-e\\|rest`, `VNW\\|bez\\|det\\|stan\\|vol\\|3p\\|mv\\|prenom\\|met-e\\|rest`, `VNW\\|bez\\|det\\|stan\\|vol\\|3v\\|ev\\|prenom\\|met-e\\|rest`, `VNW\\|bez\\|det\\|stan\\|vol\\|3\\|ev\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|vol\\|3\\|mv\\|prenom\\|zonder\\|agr`, `VNW\\|onbep\\|adv-pron\\|obl\\|vol\\|3o\\|getal`, `VNW\\|onbep\\|det\\|stan\\|nom\\|met-e\\|mv-n`, `VNW\\|onbep\\|det\\|stan\\|nom\\|met-e\\|zonder-n`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|met-e\\|agr`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|met-e\\|evz`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|met-e\\|mv`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|met-e\\|rest`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|zonder\\|agr`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|zonder\\|evon`, `VNW\\|onbep\\|det\\|stan\\|vrij\\|zonder`, `VNW\\|onbep\\|grad\\|gen\\|nom\\|met-e\\|mv-n\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|nom\\|met-e\\|mv-n\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|nom\\|met-e\\|zonder-n\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|nom\\|met-e\\|zonder-n\\|sup`, `VNW\\|onbep\\|grad\\|stan\\|prenom\\|met-e\\|agr\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|prenom\\|met-e\\|agr\\|sup`, `VNW\\|onbep\\|grad\\|stan\\|prenom\\|met-e\\|mv\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|prenom\\|zonder\\|agr\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|prenom\\|zonder\\|agr\\|comp`, `VNW\\|onbep\\|grad\\|stan\\|vrij\\|zonder\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|vrij\\|zonder\\|comp`, `VNW\\|onbep\\|grad\\|stan\\|vrij\\|zonder\\|sup`, `VNW\\|onbep\\|pron\\|stan\\|vol\\|3o\\|ev`, `VNW\\|onbep\\|pron\\|stan\\|vol\\|3p\\|ev`, `VNW\\|pers\\|pron\\|nomin\\|nadr\\|3v\\|ev\\|fem`, `VNW\\|pers\\|pron\\|nomin\\|red\\|1\\|mv`, `VNW\\|pers\\|pron\\|nomin\\|red\\|2v\\|ev`, `VNW\\|pers\\|pron\\|nomin\\|red\\|3p\\|ev\\|masc`, `VNW\\|pers\\|pron\\|nomin\\|vol\\|1\\|ev`, `VNW\\|pers\\|pron\\|nomin\\|vol\\|1\\|mv`, `VNW\\|pers\\|pron\\|nomin\\|vol\\|2b\\|getal`, `VNW\\|pers\\|pron\\|nomin\\|vol\\|3p\\|mv`, `VNW\\|pers\\|pron\\|nomin\\|vol\\|3v\\|ev\\|fem`, `VNW\\|pers\\|pron\\|nomin\\|vol\\|3\\|ev\\|masc`, `VNW\\|pers\\|pron\\|obl\\|nadr\\|3m\\|ev\\|masc`, `VNW\\|pers\\|pron\\|obl\\|vol\\|3p\\|mv`, `VNW\\|pers\\|pron\\|obl\\|vol\\|3\\|ev\\|masc`, `VNW\\|pers\\|pron\\|obl\\|vol\\|3\\|getal\\|fem`, `VNW\\|pers\\|pron\\|stan\\|red\\|3\\|ev\\|fem`, `VNW\\|pers\\|pron\\|stan\\|red\\|3\\|ev\\|onz`, `VNW\\|pers\\|pron\\|stan\\|red\\|3\\|mv`, `VNW\\|pr\\|pron\\|obl\\|red\\|1\\|ev`, `VNW\\|pr\\|pron\\|obl\\|red\\|2v\\|getal`, `VNW\\|pr\\|pron\\|obl\\|vol\\|1\\|ev`, `VNW\\|pr\\|pron\\|obl\\|vol\\|1\\|mv`, `VNW\\|recip\\|pron\\|obl\\|vol\\|persoon\\|mv`, `VNW\\|refl\\|pron\\|obl\\|nadr\\|3\\|getal`, `VNW\\|refl\\|pron\\|obl\\|red\\|3\\|getal`, `VNW\\|vb\\|adv-pron\\|obl\\|vol\\|3o\\|getal`, `VNW\\|vb\\|pron\\|stan\\|vol\\|3o\\|ev`, `VNW\\|vb\\|pron\\|stan\\|vol\\|3p\\|getal`, `VZ\\|fin`, `VZ\\|init`, `VZ\\|versm`, `WW\\|inf\\|nom\\|zonder\\|zonder-n`, `WW\\|inf\\|vrij\\|zonder`, `WW\\|od\\|nom\\|met-e\\|mv-n`, `WW\\|od\\|nom\\|met-e\\|zonder-n`, `WW\\|od\\|prenom\\|met-e`, `WW\\|od\\|prenom\\|zonder`, `WW\\|od\\|vrij\\|zonder`, `WW\\|pv\\|conj\\|ev`, `WW\\|pv\\|tgw\\|ev`, `WW\\|pv\\|tgw\\|met-t`, `WW\\|pv\\|tgw\\|mv`, `WW\\|pv\\|verl\\|ev`, `WW\\|pv\\|verl\\|mv`, `WW\\|vd\\|nom\\|met-e\\|mv-n`, `WW\\|vd\\|nom\\|met-e\\|zonder-n`, `WW\\|vd\\|prenom\\|met-e`, `WW\\|vd\\|prenom\\|zonder`, `WW\\|vd\\|vrij\\|zonder` \| \| `morphologizer` \| `Definite=Def\\|POS=DET`, `Degree=Pos\\|POS=ADJ`, `POS=CCONJ`, `Gender=Neut\\|Number=Sing\\|POS=NOUN`, `POS=PUNCT`, `POS=DET`, `Degree=Sup\\|POS=ADJ`, `Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Fin`, `Gender=Com\\|Number=Sing\\|POS=PROPN`, `POS=SYM`, `POS=NUM`, `POS=ADP`, `Definite=Ind\\|POS=DET`, `Gender=Com\\|Number=Sing\\|POS=NOUN`, `Degree=Cmp\\|POS=ADJ`, `Gender=Neut\\|Number=Sing\\|POS=PROPN`, `POS=ADV`, `Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `POS=PROPN`, `Number=Plur\\|POS=NOUN`, `POS=VERB\\|VerbForm=Inf`, `POS=PRON\\|PronType=Rel`, `Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin`, `POS=PRON\\|PronType=Ind`, `POS=VERB\\|VerbForm=Part`, `POS=ADJ`, `POS=X`, `Gender=Com,Neut\\|Number=Sing\\|POS=PROPN`, `Foreign=Yes\\|POS=X`, `POS=PRON\\|Person=3\\|PronType=Prs`, `POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Number=Plur\\|POS=PROPN`, `Number=Plur\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin`, `Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin`, `POS=PRON\\|Person=3\\|PronType=Rel`, `POS=AUX\\|VerbForm=Inf`, `POS=SCONJ`, `Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Fin`, `Abbr=Yes\\|POS=X`, `Case=Nom\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=PRON\\|Person=3\\|PronType=Dem`, `Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `POS=PRON\\|PronType=Dem`, `POS=PRON\\|Person=3\\|PronType=Int`, `Gender=Com,Neut\\|Number=Sing\\|POS=NOUN`, `POS=PRON\\|Person=3\\|PronType=Ind`, `Case=Nom\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Number=Sing\\|POS=NOUN`, `Case=Acc\\|POS=PRON\\|PronType=Rcp`, `POS=AUX\\|VerbForm=Part`, `Number=Sing\\|POS=PROPN`, `Case=Nom\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Acc\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=INTJ`, `POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Acc\\|POS=PRON\\|Person=1\\|PronType=Prs`, `POS=PRON\\|Person=2\\|Poss=Yes\\|PronType=Prs` \| \| `parser` \| `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `1`, `3`, `4`, `6`, `8`, `10`, `13`, `15`, `17`, `19`, `21`, `22`, `25`, `27`, `29`, `30`, `32`, `36`, `37`, `39`, `41`, `42`, `45`, `47`, `49`, `51`, `53`, `55`, `58`, `59`, `61`, `63`, `65`, `66`, `70`, `72`, `74`, `76`, `78`, `79`, `80`, `82`, `84`, `86`, `89`, `92`, `94`, `96`, `97`, `99`, `101`, `104`, `107`, `109`, `110`, `112`, `114`, `116`, `117`, `118`, `119`, `120`, `121`, `124`, `126`, `127`, `130`, `133`, `134`, `135`, `137`, `139`, `142`, `143`, `145`, `148`, `152`, `154`, `156`, `159`, `160`, `163`, `165`, `167`, `168`, `172`, `175`, `176`, `178`, `182`, `184`, `187`, `189`, `190`, `192`, `194`, `195`, `197`, `199`, `200`, `203`, `205`, `207`, `208`, `209`, `212`, `214`, `215`, `217`, `219`, `220`, `221`, `224`, `227`, `228`, `230`, `232`, `233`, `237`, `238`, `240`, `241`, `242`, `244`, `245`, `248`, `249`, `250`, `251`, `252`, `255`, `258`, `259`, `260`, `262`, `266`, `268`, `270`, 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`1476`, `1478`, `1479`, `1480`, `1481`, `1482`, `1483`, `1484` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `TOKEN_F` \| 99.91 \| \| `TOKEN_P` \| 99.88 \| \| `TOKEN_R` \| 99.94 \| \| `TOKEN_ACC` \| 100.00 \| \| `SENTS_F` \| 91.84 \| \| `SENTS_P` \| 90.52 \| \| `SENTS_R` \| 93.20 \| \| `TAG_ACC` \| 95.93 \| \| `POS_ACC` \| 96.37 \| \| `MORPH_ACC` \| 97.73 \| \| `DEP_UAS` \| 90.23 \| \| `DEP_LAS` \| 86.21 \| \| `LEMMA_ACC` \| 96.71 \|
explosion/nl_udv25_dutchalpino_trf	explosion	2021-12-10T13:52:44Z	6	1	spacy	[ "spacy", "token-classification", "nl", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - nl license: cc-by-sa-4.0 model-index: - name: nl_udv25_dutchalpino_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9559890516 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9766694183 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9678932963 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.964639444 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9465618861 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.9227973676 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9084457062 --- UD v2.5 benchmarking pipeline for UD_Dutch-Alpino \| Feature \| Description \| \| --- \| --- \| \| Name \| `nl_udv25_dutchalpino_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) \| ### Label Scheme <details> <summary>View label scheme (1712 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `ADJ\\|nom\\|basis\\|met-e\\|mv-n`, `ADJ\\|nom\\|basis\\|met-e\\|zonder-n\\|stan`, `ADJ\\|nom\\|basis\\|zonder\\|zonder-n`, `ADJ\\|nom\\|comp\\|met-e\\|mv-n`, `ADJ\\|nom\\|comp\\|met-e\\|zonder-n\\|stan`, `ADJ\\|nom\\|sup\\|met-e\\|mv-n`, `ADJ\\|nom\\|sup\\|met-e\\|zonder-n\\|stan`, `ADJ\\|nom\\|sup\\|zonder\\|zonder-n`, `ADJ\\|postnom\\|basis\\|met-s`, `ADJ\\|postnom\\|basis\\|zonder`, `ADJ\\|postnom\\|comp\\|met-s`, `ADJ\\|prenom\\|basis\\|met-e\\|stan`, `ADJ\\|prenom\\|basis\\|zonder`, `ADJ\\|prenom\\|comp\\|met-e\\|stan`, `ADJ\\|prenom\\|comp\\|zonder`, `ADJ\\|prenom\\|sup\\|met-e\\|stan`, `ADJ\\|vrij\\|basis\\|zonder`, `ADJ\\|vrij\\|comp\\|zonder`, `ADJ\\|vrij\\|dim\\|zonder`, `ADJ\\|vrij\\|sup\\|zonder`, `BW`, `LET`, `LID\\|bep\\|dat\\|evmo`, `LID\\|bep\\|gen\\|evmo`, `LID\\|bep\\|gen\\|rest3`, `LID\\|bep\\|stan\\|evon`, `LID\\|bep\\|stan\\|rest`, `LID\\|onbep\\|stan\\|agr`, `N\\|eigen\\|ev\\|basis\\|gen`, `N\\|eigen\\|ev\\|basis\\|genus\\|stan`, `N\\|eigen\\|ev\\|basis\\|onz\\|stan`, `N\\|eigen\\|ev\\|basis\\|zijd\\|stan`, `N\\|eigen\\|ev\\|dim\\|onz\\|stan`, `N\\|eigen\\|mv\\|basis`, `N\\|soort\\|ev\\|basis\\|dat`, `N\\|soort\\|ev\\|basis\\|gen`, `N\\|soort\\|ev\\|basis\\|genus\\|stan`, `N\\|soort\\|ev\\|basis\\|onz\\|stan`, `N\\|soort\\|ev\\|basis\\|zijd\\|stan`, `N\\|soort\\|ev\\|dim\\|onz\\|stan`, `N\\|soort\\|mv\\|basis`, `N\\|soort\\|mv\\|dim`, `SPEC\\|afgebr`, `SPEC\\|afk`, `SPEC\\|deeleigen`, `SPEC\\|enof`, `SPEC\\|meta`, `SPEC\\|symb`, `SPEC\\|vreemd`, `TSW`, `TW\\|hoofd\\|nom\\|mv-n\\|basis`, `TW\\|hoofd\\|nom\\|mv-n\\|dim`, `TW\\|hoofd\\|nom\\|zonder-n\\|basis`, `TW\\|hoofd\\|nom\\|zonder-n\\|dim`, `TW\\|hoofd\\|prenom\\|stan`, `TW\\|hoofd\\|vrij`, `TW\\|rang\\|nom\\|mv-n`, `TW\\|rang\\|nom\\|zonder-n`, `TW\\|rang\\|prenom\\|stan`, `VG\\|neven`, `VG\\|onder`, `VNW\\|aanw\\|adv-pron\\|obl\\|vol\\|3o\\|getal`, `VNW\\|aanw\\|adv-pron\\|stan\\|red\\|3\\|getal`, `VNW\\|aanw\\|det\\|dat\\|nom\\|met-e\\|zonder-n`, `VNW\\|aanw\\|det\\|dat\\|prenom\\|met-e\\|evmo`, `VNW\\|aanw\\|det\\|gen\\|prenom\\|met-e\\|rest3`, `VNW\\|aanw\\|det\\|stan\\|nom\\|met-e\\|mv-n`, `VNW\\|aanw\\|det\\|stan\\|nom\\|met-e\\|zonder-n`, `VNW\\|aanw\\|det\\|stan\\|prenom\\|met-e\\|rest`, `VNW\\|aanw\\|det\\|stan\\|prenom\\|zonder\\|agr`, `VNW\\|aanw\\|det\\|stan\\|prenom\\|zonder\\|evon`, `VNW\\|aanw\\|det\\|stan\\|prenom\\|zonder\\|rest`, `VNW\\|aanw\\|det\\|stan\\|vrij\\|zonder`, `VNW\\|aanw\\|pron\\|gen\\|vol\\|3m\\|ev`, `VNW\\|aanw\\|pron\\|stan\\|vol\\|3o\\|ev`, `VNW\\|aanw\\|pron\\|stan\\|vol\\|3\\|getal`, `VNW\\|betr\\|det\\|stan\\|nom\\|met-e\\|zonder-n`, `VNW\\|betr\\|det\\|stan\\|nom\\|zonder\\|zonder-n`, `VNW\\|betr\\|pron\\|stan\\|vol\\|3\\|ev`, `VNW\\|betr\\|pron\\|stan\\|vol\\|persoon\\|getal`, `VNW\\|bez\\|det\\|gen\\|vol\\|3\\|ev\\|prenom\\|met-e\\|rest3`, `VNW\\|bez\\|det\\|stan\\|nadr\\|2v\\|mv\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|red\\|1\\|ev\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|red\\|2v\\|ev\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|red\\|3\\|ev\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|vol\\|1\\|ev\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|vol\\|1\\|mv\\|prenom\\|met-e\\|rest`, `VNW\\|bez\\|det\\|stan\\|vol\\|1\\|mv\\|prenom\\|zonder\\|evon`, `VNW\\|bez\\|det\\|stan\\|vol\\|2v\\|ev\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|vol\\|2\\|getal\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|vol\\|3m\\|ev\\|nom\\|met-e\\|zonder-n`, `VNW\\|bez\\|det\\|stan\\|vol\\|3v\\|ev\\|nom\\|met-e\\|zonder-n`, `VNW\\|bez\\|det\\|stan\\|vol\\|3\\|ev\\|prenom\\|zonder\\|agr`, `VNW\\|bez\\|det\\|stan\\|vol\\|3\\|mv\\|prenom\\|zonder\\|agr`, `VNW\\|onbep\\|adv-pron\\|gen\\|red\\|3\\|getal`, `VNW\\|onbep\\|adv-pron\\|obl\\|vol\\|3o\\|getal`, `VNW\\|onbep\\|det\\|stan\\|nom\\|met-e\\|mv-n`, `VNW\\|onbep\\|det\\|stan\\|nom\\|met-e\\|zonder-n`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|met-e\\|agr`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|met-e\\|evz`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|met-e\\|mv`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|met-e\\|rest`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|zonder\\|agr`, `VNW\\|onbep\\|det\\|stan\\|prenom\\|zonder\\|evon`, `VNW\\|onbep\\|det\\|stan\\|vrij\\|zonder`, `VNW\\|onbep\\|grad\\|stan\\|nom\\|met-e\\|mv-n\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|nom\\|met-e\\|mv-n\\|sup`, `VNW\\|onbep\\|grad\\|stan\\|nom\\|met-e\\|zonder-n\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|nom\\|met-e\\|zonder-n\\|sup`, `VNW\\|onbep\\|grad\\|stan\\|prenom\\|met-e\\|agr\\|basis`, `VNW\\|onbep\\|grad\\|stan\\|prenom\\|met-e\\|agr\\|comp`, `VNW\\|onbep\\|grad\\|stan\\|prenom\\|met-e\\|agr\\|sup`, `VNW\\|onbep\\|grad\\|stan\\|prenom\\|met-e\\|mv\\|basis`, 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`VNW\\|pers\\|pron\\|nomin\\|vol\\|3p\\|mv`, `VNW\\|pers\\|pron\\|nomin\\|vol\\|3v\\|ev\\|fem`, `VNW\\|pers\\|pron\\|nomin\\|vol\\|3\\|ev\\|masc`, `VNW\\|pers\\|pron\\|obl\\|nadr\\|3m\\|ev\\|masc`, `VNW\\|pers\\|pron\\|obl\\|red\\|3\\|ev\\|masc`, `VNW\\|pers\\|pron\\|obl\\|vol\\|2v\\|ev`, `VNW\\|pers\\|pron\\|obl\\|vol\\|3p\\|mv`, `VNW\\|pers\\|pron\\|obl\\|vol\\|3\\|ev\\|masc`, `VNW\\|pers\\|pron\\|obl\\|vol\\|3\\|getal\\|fem`, `VNW\\|pers\\|pron\\|stan\\|nadr\\|2v\\|mv`, `VNW\\|pers\\|pron\\|stan\\|red\\|3\\|ev\\|fem`, `VNW\\|pers\\|pron\\|stan\\|red\\|3\\|ev\\|onz`, `VNW\\|pers\\|pron\\|stan\\|red\\|3\\|mv`, `VNW\\|pr\\|pron\\|obl\\|nadr\\|1\\|ev`, `VNW\\|pr\\|pron\\|obl\\|nadr\\|2v\\|getal`, `VNW\\|pr\\|pron\\|obl\\|nadr\\|2\\|getal`, `VNW\\|pr\\|pron\\|obl\\|red\\|1\\|ev`, `VNW\\|pr\\|pron\\|obl\\|red\\|2v\\|getal`, `VNW\\|pr\\|pron\\|obl\\|vol\\|1\\|ev`, `VNW\\|pr\\|pron\\|obl\\|vol\\|1\\|mv`, `VNW\\|pr\\|pron\\|obl\\|vol\\|2\\|getal`, `VNW\\|recip\\|pron\\|gen\\|vol\\|persoon\\|mv`, `VNW\\|recip\\|pron\\|obl\\|vol\\|persoon\\|mv`, `VNW\\|refl\\|pron\\|obl\\|nadr\\|3\\|getal`, `VNW\\|refl\\|pron\\|obl\\|red\\|3\\|getal`, `VNW\\|vb\\|adv-pron\\|obl\\|vol\\|3o\\|getal`, `VNW\\|vb\\|det\\|stan\\|nom\\|met-e\\|zonder-n`, `VNW\\|vb\\|det\\|stan\\|prenom\\|met-e\\|rest`, `VNW\\|vb\\|det\\|stan\\|prenom\\|zonder\\|evon`, `VNW\\|vb\\|pron\\|gen\\|vol\\|3m\\|ev`, `VNW\\|vb\\|pron\\|gen\\|vol\\|3p\\|mv`, `VNW\\|vb\\|pron\\|gen\\|vol\\|3v\\|ev`, `VNW\\|vb\\|pron\\|stan\\|vol\\|3o\\|ev`, `VNW\\|vb\\|pron\\|stan\\|vol\\|3p\\|getal`, `VZ\\|fin`, `VZ\\|init`, `VZ\\|versm`, `WW\\|inf\\|nom\\|zonder\\|zonder-n`, `WW\\|inf\\|prenom\\|met-e`, `WW\\|inf\\|vrij\\|zonder`, `WW\\|od\\|nom\\|met-e\\|mv-n`, `WW\\|od\\|nom\\|met-e\\|zonder-n`, `WW\\|od\\|prenom\\|met-e`, `WW\\|od\\|prenom\\|zonder`, `WW\\|od\\|vrij\\|zonder`, `WW\\|pv\\|conj\\|ev`, `WW\\|pv\\|tgw\\|ev`, `WW\\|pv\\|tgw\\|met-t`, `WW\\|pv\\|tgw\\|mv`, `WW\\|pv\\|verl\\|ev`, `WW\\|pv\\|verl\\|mv`, `WW\\|vd\\|nom\\|met-e\\|mv-n`, `WW\\|vd\\|nom\\|met-e\\|zonder-n`, `WW\\|vd\\|prenom\\|met-e`, `WW\\|vd\\|prenom\\|zonder`, `WW\\|vd\\|vrij\\|zonder` \| \| `morphologizer` \| `POS=PRON\\|Person=3\\|PronType=Dem`, `Number=Sing\\|POS=AUX\\|Tense=Pres\\|VerbForm=Fin`, `POS=ADV`, `POS=VERB\\|VerbForm=Part`, `POS=PUNCT`, `Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `POS=ADP`, `POS=NUM`, `Number=Plur\\|POS=NOUN`, `POS=VERB\\|VerbForm=Inf`, `POS=SCONJ`, `Definite=Def\\|POS=DET`, `Gender=Com\\|Number=Sing\\|POS=NOUN`, `Number=Sing\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin`, `Degree=Pos\\|POS=ADJ`, `Gender=Neut\\|Number=Sing\\|POS=PROPN`, `Gender=Com\\|Number=Sing\\|POS=PROPN`, `POS=AUX\\|VerbForm=Inf`, `Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin`, `POS=DET`, `Gender=Neut\\|Number=Sing\\|POS=NOUN`, `POS=PRON\\|Person=3\\|PronType=Prs`, `POS=CCONJ`, `Number=Plur\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin`, `POS=PRON\\|Person=3\\|PronType=Ind`, `Degree=Cmp\\|POS=ADJ`, `Case=Nom\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Definite=Ind\\|POS=DET`, `Case=Nom\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Number=Plur\\|POS=AUX\\|Tense=Pres\\|VerbForm=Fin`, `POS=PRON\\|PronType=Rel`, `Case=Acc\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin`, `Gender=Com,Neut\\|Number=Sing\\|POS=NOUN`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=PROPN`, `POS=PRON\\|PronType=Ind`, `POS=PRON\\|Person=3\\|PronType=Int`, `Case=Acc\\|POS=PRON\\|PronType=Rcp`, `Number=Plur\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin`, `Number=Sing\\|POS=NOUN`, `POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `POS=SYM`, `Abbr=Yes\\|POS=X`, `Gender=Com,Neut\\|Number=Sing\\|POS=PROPN`, `Degree=Sup\\|POS=ADJ`, `Foreign=Yes\\|POS=X`, `POS=ADJ`, `Number=Sing\\|POS=PROPN`, `POS=PRON\\|PronType=Dem`, `POS=AUX\\|VerbForm=Part`, `POS=PRON\\|Person=3\\|PronType=Rel`, `Number=Plur\\|POS=PROPN`, `POS=PRON\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Dat\\|POS=PRON\\|PronType=Dem`, `Case=Nom\\|POS=PRON\\|Person=2\\|PronType=Prs`, `POS=X`, `POS=INTJ`, `Case=Gen\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `POS=PRON\\|PronType=Int`, `Case=Acc\\|POS=PRON\\|Person=2\\|PronType=Prs`, `POS=PRON\\|Person=2\\|PronType=Prs`, `Case=Gen\\|POS=PRON\\|Person=2\\|PronType=Prs` \| \| `parser` \| `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl`, `expl:pv`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `1`, `4`, `5`, `10`, `12`, `14`, `16`, `20`, `24`, `25`, `28`, `30`, `32`, `34`, `38`, `40`, `42`, `45`, `47`, `48`, `51`, `52`, `54`, `55`, `57`, `59`, `62`, `64`, `66`, `68`, `70`, `72`, `76`, `78`, `81`, `83`, `84`, `86`, `89`, `91`, `92`, `96`, `99`, `101`, `104`, `106`, `109`, `114`, `115`, `117`, `118`, `120`, `121`, `123`, `126`, `129`, `131`, `133`, `137`, `139`, `141`, `143`, `145`, `146`, `148`, `151`, `154`, `158`, `160`, `163`, `165`, `98`, `168`, `169`, `171`, `174`, `177`, `181`, `183`, `185`, `187`, `191`, `194`, `196`, `199`, `202`, `206`, `209`, `211`, `212`, `214`, `217`, `61`, `219`, `221`, `224`, `226`, `227`, `229`, `231`, `235`, `236`, `238`, `240`, `242`, `245`, `247`, `251`, `253`, `257`, `260`, `262`, `264`, `263`, `266`, `267`, `271`, `273`, `274`, `275`, `278`, `280`, `281`, `282`, `284`, `286`, `291`, `293`, `296`, `298`, `299`, `301`, `303`, `307`, `308`, `310`, `312`, `314`, `316`, `318`, `320`, `322`, `324`, `325`, `328`, `330`, `332`, `333`, `336`, `337`, `339`, `342`, `344`, `345`, `349`, `352`, `353`, `354`, `355`, `357`, `360`, `362`, `363`, `365`, `368`, `372`, `373`, `375`, `377`, `379`, `383`, `385`, `387`, `389`, `390`, `392`, `394`, `396`, `398`, `402`, `404`, `407`, `409`, `9`, `411`, `412`, `414`, `417`, `418`, `420`, `422`, `423`, `425`, `429`, `431`, `432`, `435`, `437`, `438`, `440`, `442`, `444`, `448`, `450`, `451`, `454`, `456`, `457`, `459`, `461`, `463`, `464`, `466`, `468`, `469`, `472`, `473`, `476`, `477`, `478`, `480`, `484`, `487`, `489`, `491`, `493`, `496`, `497`, `500`, `502`, `505`, `506`, `508`, `510`, `511`, `512`, `515`, `518`, `523`, `525`, `528`, `531`, `532`, `534`, `306`, `535`, `537`, `539`, `542`, `544`, `548`, `552`, `555`, `556`, `557`, `558`, `559`, `560`, `564`, `566`, `538`, `567`, `569`, `570`, `572`, `573`, `575`, `577`, `579`, `580`, `582`, `583`, `584`, `587`, `588`, `591`, `593`, `595`, `597`, `599`, `601`, `602`, `605`, `607`, `609`, `611`, `614`, `616`, `617`, `618`, `620`, `621`, `622`, `623`, `625`, `626`, `629`, `632`, `634`, `636`, `638`, `641`, `642`, `644`, `647`, `648`, `650`, `651`, `654`, `655`, `657`, `659`, `660`, `663`, `664`, `665`, `666`, `668`, `671`, `673`, `675`, `676`, `677`, `678`, `33`, `681`, `683`, `686`, `688`, `691`, `692`, `694`, `697`, `698`, `699`, `700`, `701`, `702`, `703`, `706`, `709`, `712`, `713`, `714`, `717`, `720`, `721`, `682`, `723`, `725`, `728`, `730`, `733`, `735`, `738`, `740`, `741`, `743`, `744`, `745`, `748`, `750`, `751`, `753`, `756`, `759`, `760`, `762`, `763`, `764`, `767`, `771`, `773`, `774`, `776`, `234`, `777`, `779`, `364`, `781`, `382`, `783`, `784`, `785`, `786`, `788`, `791`, `793`, `794`, `796`, `799`, `693`, `801`, `804`, `805`, `807`, `808`, `811`, `813`, `814`, `815`, `816`, `818`, `820`, `821`, `824`, `825`, `826`, `827`, `828`, `829`, `830`, `833`, `834`, `836`, `839`, `841`, `845`, `847`, `848`, `849`, `850`, `851`, `856`, `858`, `859`, `860`, `861`, `862`, `864`, `866`, `869`, `871`, `873`, `875`, `876`, `877`, `878`, `881`, `882`, `883`, `884`, `885`, `887`, `889`, `890`, `670`, `891`, `894`, `896`, `899`, `900`, `902`, `904`, `908`, `910`, `913`, `915`, `916`, `918`, `921`, `923`, `924`, `926`, `927`, `931`, `934`, `936`, `938`, `940`, `942`, `943`, `946`, `949`, `950`, `951`, `952`, `953`, `954`, `955`, `958`, `959`, `961`, `962`, `963`, `69`, `964`, `967`, `969`, `972`, `973`, `975`, `977`, `978`, `980`, `982`, `983`, `984`, `986`, `988`, `989`, `991`, `992`, `993`, `995`, `996`, `290`, `998`, `999`, `1000`, `1001`, `1003`, `1005`, `1007`, `1008`, `1009`, `1011`, `1014`, `1015`, `1016`, `1017`, `1018`, `1019`, `1021`, `1022`, `1023`, `1024`, `1025`, `1027`, `1030`, `1031`, `1032`, `1033`, `1036`, `1038`, `1041`, `1045`, `1046`, `1048`, `1052`, `1053`, `1055`, `1056`, `1057`, `1059`, `1060`, `1062`, `1064`, `1068`, `1069`, `1070`, `1073`, `1075`, `1076`, `1077`, `1080`, `1083`, `1086`, `1087`, `1088`, `1091`, `1092`, `1095`, `1098`, `1099`, `1100`, `1101`, `1104`, `1108`, `1109`, `1111`, `1113`, `1114`, `1115`, `1116`, `1118`, `1120`, `1121`, `1122`, `1125`, `1126`, `1129`, `1132`, `1133`, `1136`, `1137`, `1138`, `1140`, `1141`, `1142`, `1143`, `1144`, `1146`, `1147`, `1148`, `1149`, `1150`, `71`, `1151`, `1154`, `1155`, `1156`, `1158`, `1160`, `1161`, `1162`, `1163`, `1164`, `1165`, `1166`, `1168`, `1171`, `1172`, `1174`, `1175`, `1176`, `1177`, `1178`, `1180`, `1183`, `1185`, `1189`, `1192`, `1194`, `1195`, `1196`, `1198`, `1199`, `1200`, `1201`, `1202`, `981`, `1203`, `1204`, `1208`, `1209`, `1210`, `1211`, `1212`, `1213`, `1215`, `1216`, `1218`, `1219`, `1221`, `1223`, `1224`, `1225`, `1227`, `1228`, `1230`, `1231`, `1232`, `1234`, `1235`, `1236`, `1237`, `1239`, `1241`, `1243`, `1245`, `1247`, `1248`, `1249`, `1250`, `1252`, `1253`, `1254`, `1255`, `1256`, `1257`, `1258`, `1259`, `1261`, `1263`, `1265`, `1266`, `1267`, `1270`, `1271`, `1272`, `1273`, `1275`, `1276`, `1277`, `1280`, `53`, `1281`, `1285`, `1286`, `1287`, `1288`, `1291`, `1292`, `1294`, `1296`, `1298`, `1300`, `1301`, `1303`, `1305`, `1306`, `1308`, `1309`, `1311`, `1312`, `1315`, `1318`, `1321`, `1322`, `1323`, `1326`, `1328`, `1330`, `1332`, `1334`, `1335`, `1337`, `1338`, `1340`, `1342`, `1343`, `1344`, `1346`, `1347`, `1348`, `1349`, `1350`, `1351`, `1353`, `1355`, `1356`, `1357`, `1359`, `1361`, `1362`, `1364`, `1365`, `1368`, `1369`, `1370`, `1371`, `1372`, `1376`, `1377`, `1380`, `1381`, `1382`, `1385`, `1386`, `1387`, `1388`, `1389`, `1390`, `1391`, `1392`, `1393`, `1394`, `1396`, `1397`, `1399`, `1398`, `1403`, `1405`, `1407`, `1411`, `1413`, `1415`, `1416`, `1417`, `1418`, `1421`, `1422`, `1424`, `1425`, `1426`, `1427`, `1428`, `1429`, `1431`, `1432`, `1434`, `803`, `1435`, `1436`, `1437`, `1439`, `1441`, `1445`, `1448`, `1449`, `1450`, `1451`, `1453`, `1454`, `1456`, `1459`, `1460`, `1461`, `1464`, `1466`, `1467`, `1470`, `1473`, `1477`, `1479`, `1481`, `1482`, `1485`, `1487`, `1488`, `1490`, `1495`, `1496`, `1497`, `1499`, `1500`, `1501`, `1503`, `1504`, `1505`, `1506`, `1508`, `1509`, `1512`, `1514`, `1515`, `1516`, `1517`, `1269`, `1518`, `1520`, `1521`, `1523`, `1524`, `1526`, `1528`, `1529`, `1531`, `1532`, `1534`, `1536`, `1537`, `1538`, `1539`, `1540`, `1541`, `294`, `1542`, `1544`, `1546`, `1548`, `1549`, `1551`, `1554`, `1555`, `1556`, `1557`, `1559`, `1560`, `1563`, `1565`, `1566`, `1567`, `1568`, `1569`, `1570`, `1571`, `1572`, `1575`, `1576`, `1577`, `1578`, `1580`, `1582`, `1583`, `1586`, `1589`, `1592`, `1593`, `1594`, `1595`, `1596`, `1597`, `1598`, `1600`, `1601`, `1602`, `1604`, `1605`, `1606`, `1607`, `1608`, `1609`, `1610`, `1611`, `1612`, `1614`, `1615`, `1617`, `1619`, `1620`, `1621`, `1622`, `1623`, `1626`, `1628`, `1629`, `1630`, `1631`, `1632`, `1634`, `1636`, `1638`, `1639`, `1641`, `1643`, `1644`, `1646`, `1647`, `1648`, `1649`, `1222`, `1650`, `1652`, `1653`, `1655`, `1656`, `1657`, `1659`, `1661`, `1662`, `1664`, `1667`, `1668`, `1670`, `1671`, `1673`, `1676`, `1677`, `1679`, `1680`, `1682`, `1685`, `1687`, `1689`, `1691`, `1692`, `1695`, `1696`, `1699`, `1701`, `1703`, `1705`, `1707`, `1708`, `1709`, `1710`, `1712`, `1714`, `1715`, `1718`, `1720`, `1721`, `1722`, `1724`, `1725`, `1726`, `1728`, `1729`, `1731`, `1732`, `1733`, `1734`, `1736`, `1739`, `1742`, `1743`, `1746`, `1748`, `1749`, `1751`, `1752`, `1753`, `1754`, `1395`, `1756`, `1759`, `1760`, `1761`, `1762`, `1764`, `1766`, `1768`, `1770`, `1772`, `1773`, `1774`, `1775`, `1776`, `1777`, `1779`, `1233`, `1781`, `1782`, `1783`, `1785`, `1786`, `1787`, `1789`, `1790`, `1791`, `1543`, `1792`, `1794`, `1795`, `1796`, `1798`, `1800`, `1801`, `1802`, `1804`, `1806`, `1807`, `1809`, `1812`, `1814`, `1817`, `1818`, `1738`, `1819`, `1822`, `1824`, `1825`, `1827`, `1828`, `0`, `1829`, `1830`, `1831`, `1833`, `1834`, `1835`, `1837`, `1839`, `1841`, `1844`, `1845`, `1846`, `1847`, `1848`, `1581`, `1849`, `1850`, `1852`, `1854`, `1855`, `1856`, `1857`, `1858`, `1859`, `1860`, `1862`, `1864`, `1866`, `1867`, `1868`, `1869`, `1788`, `1871`, `77`, `1872`, `1873`, `1875`, `1877`, `1878`, `1879`, `1883`, `674`, `1884`, `1886`, `1887`, `1888`, `1889`, `1891`, `1892`, `1894`, `1895`, `1898`, `1899`, `1901`, `1902`, `1903`, `1905`, `1908`, `1911`, `1913`, `1915`, `1916`, `1917`, `1920`, `1921`, `1922`, `1923`, `1924`, `1925`, `1926`, `1927`, `1929`, `1930`, `1931`, `1932`, `1934`, `1935`, `1938`, `1940`, `1941`, `1942`, `1944`, `1945`, `1946`, `1948`, `1949`, `1950`, `1952`, `1953`, `1954`, `1955`, `1956`, `1957`, `1958`, `1959`, `1960`, `1962`, `1963`, `1964`, `1966`, `1968`, `1970`, `1971`, `1972`, `1973`, `1976`, `1978`, `1979`, `1980`, `1981`, `1982`, `1984`, `1985`, `1986`, `1987`, `1988`, `1990`, `237`, `1992`, `1993`, `1994`, `1995`, `1996`, `1997`, `1998`, `1999`, `2000`, `2002`, `2005`, `2007`, `2009`, `2010`, `2011`, `2012`, `2013`, `2014`, `2015`, `2016`, `2017`, `2019`, `2020`, `2021`, `2023`, `2025`, `2026`, `2028`, `2029`, `2032`, `1511`, `2034`, `2036`, `2038`, `2040`, `2042`, `2043`, `2045`, `2046`, `2047`, `2048`, `2049`, `2051`, `2052`, `2053`, `2054`, `2055`, `2056`, `2057`, `2058`, `2059`, `2060`, `2062`, `2064`, `2065`, `2066`, `2067`, `2068`, `2069`, `2071`, `2072`, `2073`, `2074`, `2075`, `2077`, `2078`, `182`, `2081`, `2082`, `2083`, `2084`, `2087`, `2088`, `2089`, `2091`, `2094`, `2096`, `2098`, `1533`, `2099`, `2100`, `2101`, `2103`, `2105`, `2106`, `2107`, `2108`, `2109`, `2110`, `2111`, `2112`, `2113`, `2114`, `2115`, `2116`, `2117`, `2118`, `2120`, `2123`, `2124`, `2126`, `2128`, `2130`, `2132`, `2133`, `2136`, `2139`, `2140`, `39`, `2141`, `130`, `2142`, `2144`, `2145`, `2146`, `2149`, `2150`, `2152`, `2153`, `2154`, `2155`, `2157`, `2158`, `2159`, `2161`, `2162`, `2163`, `2164`, `2166`, `2169`, `2171`, `2173`, `2174`, `2175`, `2176`, `2178`, `2179`, `2180`, `2181`, `2182`, `2183`, `2184`, `2185`, `2186`, `2187`, `2188`, `2190`, `2191`, `2192`, `2193`, `2194`, `2196`, `2198`, `2199`, `2201`, `2204`, `2205`, `2207`, `2209`, `2212`, `2214`, `2216`, `2217`, `2218`, `2219`, `2220`, `2221`, `1730`, `2222`, `2223`, `501`, `2224`, `2225`, `2227`, `2229`, `2230`, `2232`, `2233`, `2234`, `2235`, `2237`, `2239`, `2241`, `2243`, `2244`, `2246`, `2247`, `2248`, `2249`, `2250`, `2251`, `2253`, `2254`, `2257`, `2259`, `2261`, `2264`, `2265`, `2266`, `2269`, `2270`, `2271`, `2273`, `2276`, `2278`, `2280`, `2281`, `2283`, `2285`, `2287`, `2288`, `2289`, `2290`, `2291`, `2292`, `2294`, `2297`, `2298`, `2300`, `2301`, `2302`, `2303`, `2304`, `2305`, `2307`, `2309`, `2312`, `1933`, `2313`, `2314`, `1423`, `2315`, `2316`, `2319`, `2321`, `2322`, `2323`, `2326`, `2328`, `2330`, `2331`, `2332`, `2334`, `63`, `2335`, `2336`, `2338`, `2339`, `2341`, `2343`, `2272`, `2344`, `2346`, `2347`, `2349`, `2350`, `2351`, `2353`, `2354`, `2355`, `2356`, `2357`, `2358`, `195` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `TOKEN_F` \| 98.65 \| \| `TOKEN_P` \| 98.49 \| \| `TOKEN_R` \| 98.82 \| \| `TOKEN_ACC` \| 99.87 \| \| `SENTS_F` \| 90.84 \| \| `SENTS_P` \| 92.62 \| \| `SENTS_R` \| 89.14 \| \| `TAG_ACC` \| 95.60 \| \| `POS_ACC` \| 97.67 \| \| `MORPH_ACC` \| 96.79 \| \| `DEP_UAS` \| 94.66 \| \| `DEP_LAS` \| 92.28 \| \| `LEMMA_ACC` \| 96.46 \|
explosion/da_udv25_danishddt_trf	explosion	2021-12-10T13:06:28Z	3	0	spacy	[ "spacy", "token-classification", "da", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - da license: cc-by-sa-4.0 model-index: - name: da_udv25_danishddt_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9848998161 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.98480302 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9819959346 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9755129694 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.8966826762 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8728917681 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.9688888889 --- UD v2.5 benchmarking pipeline for UD_Danish-DDT \| Feature \| Description \| \| --- \| --- \| \| Name \| `da_udv25_danishddt_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) \| ### Label Scheme <details> <summary>View label scheme (1316 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `ADJ`, `ADP`, `ADV`, `AUX`, `CCONJ`, `DET`, `INTJ`, `NOUN`, `NUM`, `PART`, `PRON`, `PROPN`, `PUNCT`, `SCONJ`, `SYM`, `VERB`, `X` \| \| `morphologizer` \| `AdpType=Prep\\|POS=ADP`, `Definite=Ind\\|Gender=Com\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|POS=AUX\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=PROPN`, `Definite=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\\|Gender=Com\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV`, `Number=Plur\\|POS=DET\\|PronType=Dem`, `Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Definite=Ind\\|Gender=Com\\|Number=Plur\\|POS=NOUN`, `POS=PUNCT`, `POS=CCONJ`, `Definite=Ind\\|Degree=Cmp\\|Number=Sing\\|POS=ADJ`, `Degree=Cmp\\|POS=ADJ`, `POS=PRON\\|PartType=Inf`, `Gender=Com\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Definite=Ind\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Definite=Def\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Degree=Pos\\|POS=ADV`, `Definite=Def\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `POS=PRON\\|PronType=Dem`, `NumType=Card\\|POS=NUM`, `Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Degree=Pos\\|Gender=Com\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `NumType=Ord\\|POS=ADJ`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Mood=Ind\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=VERB\\|VerbForm=Inf\\|Voice=Act`, `Mood=Ind\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=NOUN`, `Mood=Ind\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `POS=ADP\\|PartType=Inf`, `Degree=Pos\\|POS=ADJ`, `Definite=Def\\|Gender=Com\\|Number=Plur\\|POS=NOUN`, `Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Gender=Com\\|Number=Sing\\|POS=NOUN`, `POS=AUX\\|VerbForm=Inf\\|Voice=Act`, `Definite=Ind\\|Degree=Pos\\|Gender=Com\\|Number=Sing\\|POS=ADJ`, `Gender=Com\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Number=Plur\\|POS=DET\\|PronType=Ind`, `Gender=Com\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `POS=PART\\|PartType=Inf`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Gender=Com\\|POS=PRON\\|PronType=Ind`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Mood=Imp\\|POS=VERB`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Definite=Ind\\|Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Part`, `POS=X`, `Case=Nom\\|Gender=Com\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Gender=Com\\|Number=Plur\\|POS=NOUN`, `POS=VERB\\|Tense=Pres\\|VerbForm=Part`, `Number=Plur\\|POS=PRON\\|PronType=Int,Rel`, `POS=VERB\\|VerbForm=Inf\\|Voice=Pass`, `Case=Gen\\|Definite=Ind\\|Gender=Com\\|Number=Sing\\|POS=NOUN`, `Degree=Cmp\\|POS=ADV`, `POS=ADV\\|PartType=Inf`, `Degree=Sup\\|POS=ADV`, `Number=Plur\\|POS=PRON\\|PronType=Dem`, `Number=Plur\\|POS=PRON\\|PronType=Ind`, `Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|POS=PROPN`, `POS=ADP`, `Degree=Cmp\\|Number=Plur\\|POS=ADJ`, `Definite=Def\\|Degree=Sup\\|POS=ADJ`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Gender=Com\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|Style=Form`, `Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Case=Gen\\|Degree=Cmp\\|POS=ADJ`, `Case=Gen\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Number[psor]=Plur\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `POS=INTJ`, `Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|Style=Form`, `Case=Acc\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Definite=Ind\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Number=Sing\\|POS=PRON\\|PronType=Int,Rel`, `Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|Style=Form`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int,Rel`, `Definite=Def\\|Degree=Sup\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Ind\\|Number=Sing\\|POS=NOUN`, `Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Number=Plur\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `POS=SYM`, `Case=Nom\\|Gender=Com\\|POS=PRON\\|Person=2\\|Polite=Form\\|PronType=Prs`, `Degree=Sup\\|POS=ADJ`, `Number=Plur\\|POS=DET\\|PronType=Ind\\|Style=Arch`, `Case=Gen\\|Gender=Com\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Foreign=Yes\\|POS=X`, `POS=DET\\|Person=2\\|Polite=Form\\|Poss=Yes\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Acc\\|Gender=Com\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|POS=PRON\\|PronType=Int,Rel`, `Gender=Com\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Abbr=Yes\\|POS=X`, `Case=Gen\\|Definite=Ind\\|Gender=Com\\|Number=Plur\\|POS=NOUN`, `Definite=Def\\|Degree=Abs\\|POS=ADJ`, `Definite=Ind\\|Degree=Sup\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|POS=NOUN`, `Gender=Com\\|Number=Plur\\|POS=NOUN`, `Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Gender=Com\\|POS=PRON\\|PronType=Int,Rel`, `Case=Nom\\|Gender=Com\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Degree=Abs\\|POS=ADV`, `POS=VERB\\|VerbForm=Ger`, `POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Definite=Def\\|Degree=Sup\\|Number=Sing\\|POS=ADJ`, `Number=Plur\\|Number[psor]=Plur\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|Style=Form`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Com\\|POS=PRON\\|Person=2\\|Polite=Form\\|PronType=Prs`, `Gender=Com\\|Number=Sing\\|POS=PRON\\|PronType=Int,Rel`, `POS=VERB\\|Tense=Pres`, `Case=Gen\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `POS=PRON\\|Person=2\\|Polite=Form\\|Poss=Yes\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `POS=AUX\\|Tense=Pres\\|VerbForm=Part`, `Mood=Ind\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Degree=Sup\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Com\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Ind\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Mood=Imp\\|POS=AUX`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Number[psor]=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Definite=Def\\|Gender=Com\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Com\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|POS=NOUN`, `Number[psor]=Plur\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `POS=DET\\|PronType=Dem`, `Definite=Def\\|Number=Plur\\|POS=NOUN` \| \| `parser` \| `ROOT`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `goeswith`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:loc`, `obl:tmod`, `punct`, `vocative`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `1`, `2`, `4`, `7`, `9`, `11`, `13`, `15`, `17`, `19`, `21`, `23`, `27`, `31`, `33`, `35`, `37`, `39`, `42`, `44`, `45`, `5`, `47`, `49`, `51`, `53`, `55`, `57`, `59`, `63`, `67`, `69`, `73`, `75`, `77`, `79`, `81`, `83`, `85`, `87`, `89`, `91`, `93`, `95`, `97`, `101`, `103`, `104`, `106`, `109`, `113`, `115`, `116`, `117`, `118`, `119`, `122`, `124`, `127`, `130`, `133`, `134`, `135`, `138`, `140`, `141`, `144`, `146`, `148`, `149`, `151`, `153`, `154`, `156`, `157`, `158`, `159`, 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`1649`, `1651`, `1653`, `1656`, `1657`, `1658`, `1659`, `1660`, `1661`, `1663`, `1665`, `1666`, `1667`, `1668`, `1670`, `1673`, `1674`, `1676`, `1677`, `1678`, `1679`, `1680`, `1681`, `1684`, `1685`, `1687`, `1688`, `1689`, `1690`, `1692`, `1693`, `1643`, `1694`, `1695`, `1696`, `1697`, `1699`, `1701`, `1702`, `1704`, `1706`, `1708`, `1710`, `1711`, `1712`, `1714`, `1715`, `1717`, `1719`, `1720`, `1721`, `1722`, `1723`, `1724`, `1725`, `1726`, `1727`, `1728`, `1729`, `1730`, `1732`, `1734`, `1735`, `1737`, `1739`, `1741`, `1742`, `1743`, `1745`, `1747`, `1749`, `1750`, `1751`, `1753`, `1754`, `1756`, `1758`, `1759`, `1760`, `1761`, `1762`, `1764`, `1766`, `1768`, `1769`, `1770`, `1771`, `1772`, `1773`, `1774` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `TOKEN_F` \| 99.96 \| \| `TOKEN_P` \| 99.95 \| \| `TOKEN_R` \| 99.96 \| \| `TOKEN_ACC` \| 100.00 \| \| `SENTS_F` \| 96.89 \| \| `SENTS_P` \| 97.15 \| \| `SENTS_R` \| 96.63 \| \| `TAG_ACC` \| 98.49 \| \| `POS_ACC` \| 98.48 \| \| `MORPH_ACC` \| 98.20 \| \| `DEP_UAS` \| 89.67 \| \| `DEP_LAS` \| 87.29 \| \| `LEMMA_ACC` \| 97.55 \|
dlb/electra-base-portuguese-uncased-brwac	dlb	2021-12-10T12:33:58Z	352	6	transformers	[ "transformers", "pytorch", "electra", "pretraining", "pt", "dataset:brwac", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: pt tags: - electra - pretraining - pytorch datasets: - brwac ---
explosion/af_udv25_afrikaansafribooms_trf	explosion	2021-12-10T11:34:54Z	5	0	spacy	[ "spacy", "token-classification", "af", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - af license: cc-by-sa-4.0 model-index: - name: af_udv25_afrikaansafribooms_trf results: - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9601278917 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9852374236 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9751739703 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9786593964 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.9078427294 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.8749739963 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 1.0 --- UD v2.5 benchmarking pipeline for UD_Afrikaans-AfriBooms \| Feature \| Description \| \| --- \| --- \| \| Name \| `af_udv25_afrikaansafribooms_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) \| ### Label Scheme <details> <summary>View label scheme (455 labels for 6 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `experimental_char_ner_tokenizer` \| `TOKEN` \| \| `senter` \| `I`, `S` \| \| `tagger` \| `AOA`, `AOP`, `ASA`, `ASP`, `AVA`, `AVP`, `BO`, `BS`, `BV`, `KN`, `KO`, `LB`, `LO`, `NA`, `NEE`, `NM`, `NME`, `NSE`, `NSED`, `NSM`, `PA`, `PB`, `PDHEB`, `PDHEDP`, `PDHENP`, `PDHEW`, `PDMB`, `PDMP`, `PDMW`, `PDOENP`, `PDOEW`, `PDVEB`, `PDVEDP`, `PDVENP`, `PDVEW`, `PEEB`, `PEEDP`, `PEENP`, `PEMB`, `PEMP`, `PEMW`, `PO`, `PTEB`, `PTEDP`, `PTENP`, `PTEW`, `PTMP`, `PV`, `PW`, `RA`, `RK`, `RL`, `RO`, `RS`, `RSF`, `RV`, `RWD`, `SVS`, `THAB`, `THAO`, `THBB`, `THBO`, `THNB`, `THPB`, `THPO`, `TRAB`, `TRAO`, `TRBB`, `UPB`, `UPD`, `UPI`, `UPO`, `UPS`, `UPV`, `UPW`, `UXD`, `VTHOG`, `VTHOK`, `VTHOO`, `VTHOV`, `VTHSG`, `VTHSO`, `VTUOA`, `VTUOM`, `VTUOP`, `VUOT`, `VVHOG`, `VVHOK`, `VVHOO`, `VVUOM`, `VVUOP`, `ZE`, `ZM`, `ZPL`, `ZPR` \| \| `morphologizer` \| `Definite=Def\\|POS=DET\\|PronType=Art`, `Number=Sing\\|POS=NOUN`, `AdpType=Prep\\|POS=ADP`, `AdjType=Attr\\|Case=Nom\\|Degree=Pos\\|POS=ADJ`, `Number=Plur\\|POS=NOUN`, `POS=AUX\\|Tense=Pres\\|VerbForm=Fin,Inf\\|VerbType=Cop`, `Definite=Ind\\|POS=DET\\|PronType=Art`, `POS=NUM`, `POS=PART\\|PartType=Inf`, `POS=VERB\\|Subcat=Tran\\|Tense=Pres\\|VerbForm=Fin,Inf`, `POS=PRON\\|PronType=Rel`, `POS=AUX\\|Tense=Pres\\|VerbForm=Fin,Inf\\|VerbType=Pas`, `POS=PUNCT`, `POS=CCONJ`, `POS=SCONJ`, `POS=VERB\\|Subcat=Intr\\|Tense=Pres\\|VerbForm=Fin,Inf`, `POS=VERB\\|Subcat=Intr\\|Tense=Past\\|VerbForm=Part`, `POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|VerbType=Pas`, `Degree=Pos\\|POS=ADV`, `POS=AUX\\|Tense=Pres\\|VerbForm=Fin,Inf\\|VerbType=Mod`, `POS=DET\\|PronType=Ind`, `POS=X`, `Number=Sing\\|POS=PROPN`, `POS=PRON\\|PronType=Ind`, `POS=PART\\|PartType=Neg`, `POS=VERB\\|Subcat=Tran\\|Tense=Past\\|VerbForm=Part`, `AdjType=Pred\\|Case=Nom\\|Degree=Pos\\|POS=ADJ`, `POS=DET\\|PronType=Dem`, `Degree=Cmp\\|POS=ADV`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=SYM`, `Case=Acc,Nom\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `POS=PART\\|PartType=Gen`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs\\|Reflex=Yes`, `Degree=Sup\\|POS=ADV`, `Degree=Dim\\|Number=Sing\\|POS=NOUN`, `Number=Sing\\|POS=PRON\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `POS=PRON\\|PronType=Int`, `Number=Plur\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `Number=Plur\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `AdjType=Attr\\|Case=Nom\\|Degree=Sup\\|POS=ADJ`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `AdjType=Pred\\|Case=Nom\\|Degree=Cmp\\|POS=ADJ`, `POS=VERB\\|Subcat=Prep\\|Tense=Pres\\|VerbForm=Fin,Inf`, `POS=AUX\\|Tense=Pres\\|VerbForm=Fin,Inf\\|VerbType=Aux`, `Number=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `POS=PRON\\|PronType=Rcp`, `POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|VerbType=Mod`, `Case=Acc,Nom\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|VerbType=Cop`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Number=Sing\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Case=Acc,Nom\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `AdjType=Attr\\|Case=Nom\\|Degree=Cmp\\|POS=ADJ`, `Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs\\|Reflex=Yes`, `Case=Acc\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `AdjType=Pred\\|Case=Nom\\|Degree=Sup\\|POS=ADJ` \| \| `parser` \| `ROOT`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `punct`, `xcomp` \| \| `experimental_edit_tree_lemmatizer` \| `1`, `2`, `4`, `7`, `8`, `10`, `12`, `14`, `16`, `18`, `21`, `24`, `26`, `28`, `31`, `32`, `34`, `37`, `39`, `40`, `42`, `44`, `46`, `47`, `49`, `51`, `53`, `54`, `56`, `57`, `58`, `59`, `61`, `64`, `66`, `68`, `69`, `72`, `74`, `75`, `77`, `78`, `81`, `83`, `84`, `85`, `86`, `87`, `90`, `92`, `94`, `96`, `99`, `101`, `103`, `105`, `108`, `110`, `113`, `116`, `117`, `118`, `121`, `123`, `124`, `125`, `127`, `128`, `129`, `133`, `136`, `138`, `141`, `143`, `145`, `147`, `151`, `153`, `154`, `156`, `158`, `159`, `160`, `162`, `164`, `165`, `167`, `168`, `170`, `172`, `174`, `176`, `178`, `179`, `180`, `181`, `183`, `185`, `189`, `190`, `191`, `192`, `194`, `195`, `197`, `198`, `201`, `202`, `203`, `204`, `206`, `207`, `209`, `213`, `214`, `216`, `217`, `218`, `220`, `221`, `222`, `223`, `225`, `226`, `228`, `229`, `231`, `233`, `234`, `236`, `238`, `240`, `241`, `244`, `247`, `248`, `249`, `250`, `252`, `253`, `255`, `256`, `257`, `258`, `261`, `262`, `263`, `265`, `267`, `269`, `270`, `271`, `273`, `275`, `276`, `278`, `279`, `281`, `283`, `285`, `287`, `289`, `291`, `294`, `296`, `297`, `298`, `299`, `300`, `301`, `302`, `303`, `305`, `306`, `307`, `309`, `310`, `311`, `313`, `314`, `315`, `317`, `320`, `321`, `323`, `325`, `326`, `327`, `328`, `329`, `330`, `332`, `333`, `335`, `336`, `337`, `338`, `339`, `340`, `341`, `343`, `344`, `347`, `348`, `349`, `351`, `353`, `355`, `357`, `359`, `360`, `361`, `362`, `365`, `366`, `367`, `369`, `371`, `373`, `374`, `375`, `377`, `379`, `381`, `383`, `386`, `388`, `390`, `392`, `393`, `395`, `397`, `398`, `400`, `401`, `402`, `403`, `405`, `406`, `408`, `409`, `411`, `412`, `414`, `417`, `215`, `418`, `419`, `420`, `421`, `422`, `424`, `425`, `426`, `427`, `429`, `431`, `432`, `433`, `434`, `436`, `438`, `439`, `440`, `442`, `443`, `444`, `447`, `449`, `450`, `452` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `TOKEN_F` \| 99.92 \| \| `TOKEN_P` \| 99.89 \| \| `TOKEN_R` \| 99.94 \| \| `TOKEN_ACC` \| 100.00 \| \| `SENTS_F` \| 100.00 \| \| `SENTS_P` \| 100.00 \| \| `SENTS_R` \| 100.00 \| \| `TAG_ACC` \| 96.01 \| \| `POS_ACC` \| 98.52 \| \| `MORPH_ACC` \| 97.52 \| \| `DEP_UAS` \| 90.78 \| \| `DEP_LAS` \| 87.50 \| \| `LEMMA_ACC` \| 97.87 \|
huggingtweets/googleai	huggingtweets	2021-12-10T09:50:14Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/googleai/1639129810325/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/993649592422907904/yD7LkqU2_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Google AI</div> <div style="text-align: center; font-size: 14px;">@googleai</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Google AI. \| Data \| Google AI \| \| --- \| --- \| \| Tweets downloaded \| 1754 \| \| Retweets \| 51 \| \| Short tweets \| 20 \| \| Tweets kept \| 1683 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/176c02iv/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @googleai's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3cg366zk) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3cg366zk/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/googleai') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
TJMUCH/transcriptome-iseeek	TJMUCH	2021-12-10T09:32:52Z	23	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	# iSEEEK A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings ## An simple pipeline for single-cell analysis ```python import torch import gzip import re from tqdm import tqdm import numpy as np import scanpy as sc from torch.utils.data import DataLoader, Dataset from transformers import PreTrainedTokenizerFast, BertForMaskedLM class LineDataset(Dataset): def __init__(self, lines): self.lines = lines self.regex = re.compile(r'\-\|\.') def __getitem__(self, i): return self.regex.sub('_', self.lines[i]) def __len__(self): return len(self.lines) device = "cuda" if torch.cuda.is_available() else "cpu" torch.set_num_threads(2) tokenizer = PreTrainedTokenizerFast.from_pretrained("TJMUCH/transcriptome-iseeek") model = BertForMaskedLM.from_pretrained("TJMUCH/transcriptome-iseeek").bert model = model.to(device) model.eval() ## Data desposited in https://huggingface.co/TJMUCH/transcriptome-iseeek/tree/main lines = [s.strip().decode() for s in gzip.open("pbmc_ranking.txt.gz")] labels = [s.strip().decode() for s in gzip.open("pbmc_label.txt.gz")] labels = np.asarray(labels) ds = LineDataset(lines) dl = DataLoader(ds, batch_size=80) features = [] for a in tqdm(dl, total=len(dl)): batch = tokenizer(a, max_length=128, truncation=True, padding=True, return_tensors="pt") for k, v in batch.items(): batch[k] = v.to(device) with torch.no_grad(): out = model(batch) f = out.last_hidden_state[:,0,:] features.extend(f.tolist()) features = np.stack(features) adata = sc.AnnData(features) adata.obs['celltype'] = labels adata.obs.celltype = adata.obs.celltype.astype("category") sc.pp.neighbors(adata, use_rep='X') sc.tl.umap(adata) sc.tl.leiden(adata) sc.pl.umap(adata, color=['celltype','leiden'],save= "UMAP") ``` ## Extract token representations ```python cell_counts = len(lines) x = np.zeros((cell_counts, len(tokenizer)), dtype=np.float16) for a in tqdm(dl, total=len(dl)): batch = tokenizer(a, max_length=128, truncation=True, padding=True, return_tensors="pt") for k, v in batch.items(): batch[k] = v.to(device) with torch.no_grad(): out = model(batch) eos_idxs = batch.attention_mask.sum(dim=1) - 1 f = out.last_hidden_state batch_size = f.shape[0] input_ids = batch.input_ids for i in range(batch_size): ##genes = tokenizer.batch_decode(input_ids[i]) token_norms = [f[i][j].norm().item() for j in range(1, eos_idxs[i])] idxs = input_ids[i].tolist()[1:eos_idxs[i]] x[counter, idxs] = token_norms counter = counter + 1 ```
Jeska/BertjeWDialDataALLQonly05	Jeska	2021-12-10T07:54:00Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer model-index: - name: BertjeWDialDataALLQonly05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BertjeWDialDataALLQonly05 This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.3921 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.9349 \| 1.0 \| 871 \| 2.9642 \| \| 2.9261 \| 2.0 \| 1742 \| 2.9243 \| \| 2.8409 \| 3.0 \| 2613 \| 2.8895 \| \| 2.7308 \| 4.0 \| 3484 \| 2.8394 \| \| 2.6042 \| 5.0 \| 4355 \| 2.7703 \| \| 2.4671 \| 6.0 \| 5226 \| 2.7522 \| \| 2.3481 \| 7.0 \| 6097 \| 2.6339 \| \| 2.2493 \| 8.0 \| 6968 \| 2.6224 \| \| 2.1233 \| 9.0 \| 7839 \| 2.5637 \| \| 2.0194 \| 10.0 \| 8710 \| 2.4896 \| \| 1.9178 \| 11.0 \| 9581 \| 2.4689 \| \| 1.8588 \| 12.0 \| 10452 \| 2.4663 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
masoudmzb/wav2vec2-xlsr-multilingual-53-fa	masoudmzb	2021-12-10T07:10:05Z	105	6	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "arxiv:2006.13979", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	# wav2vec 2.0 multilingual ( Finetued ) The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. Note that this model should be fine-tuned on a downstream task, like Automatic Speech Recognition. Check out [this blog](https://huggingface.co/blog/fine-tune-wav2vec2-english) for more information. [Paper](https://arxiv.org/abs/2006.13979) Authors: Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli Abstract This paper presents XLSR which learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations and jointly learns a quantization of the latents shared across languages. The resulting model is fine-tuned on labeled data and experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining. On the CommonVoice benchmark, XLSR shows a relative phoneme error rate reduction of 72% compared to the best known results. On BABEL, our approach improves word error rate by 16% relative compared to a comparable system. Our approach enables a single multilingual speech recognition model which is competitive to strong individual models. Analysis shows that the latent discrete speech representations are shared across languages with increased sharing for related languages. We hope to catalyze research in low-resource speech understanding by releasing XLSR-53, a large model pretrained in 53 languages. The original model can be found under https://github.com/pytorch/fairseq/tree/master/examples/wav2vec#wav2vec-20. Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Persian (Farsi) using [Common Voice](https://huggingface.co/datasets/common_voice) plus Our own created Dataset(1/3 of total dataset). When using this model, make sure that your speech input is sampled at 16kHz. ## Evaluation: 🌡️ We have evaluated the model on private dataset with different type of audios (unfortunately the dataset for testing and validation is not publicly available but to see a sample of the dataset [check this link)](https://github.com/shenasa-ai/speech2text#part-of-our-dataset-v01--) : \| Name \| test dataset (wer) \| \| :----------------------------------------------------------: \| :-----------------: \| \| [m3hrdadfi/wav2vec2-large-xlsr-persian-v3](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3) \| 0.56754 \| \| [This New Model](https://huggingface.co/masoudmzb/wav2vec2-xlsr-multilingual-53-fa) \| 0.40815 \| \| Base Multilingual Model \| 0.69746 \| - This Table show if we add more data we will have much better result ## How to use❓ ### Use FineTuned Model This model is finetuned on [m3hrdadfi/wav2vec2-large-xlsr-persian-v3](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3) , so the process for train or evaluation is same > ```bash > # requirement packages > !pip install git+https://github.com/huggingface/datasets.git > !pip install git+https://github.com/huggingface/transformers.git > !pip install torchaudio > !pip install librosa > !pip install jiwer > !pip install parsivar > !pip install num2fawords > ``` Normalizer ```bash # Normalizer !wget -O normalizer.py https://huggingface.co/m3hrdadfi/"wav2vec2-large-xlsr-persian-v3/raw/main/dictionary.py !wget -O normalizer.py https://huggingface.co/m3hrdadfi/"wav2vec2-large-xlsr-persian-v3/raw/main/normalizer.py ``` If you are not sure your transcriptions are clean or not (having weird characters or any other alphabete chars ) use this code provided by [m3hrdadfi/wav2vec2-large-xlsr-persian-v3](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3) Cleaning (Fill the data part with your own data dir) ```python from normalizer import normalizer def cleaning(text): if not isinstance(text, str): return None return normalizer({"sentence": text}, return_dict=False) # edit these parts with your own data directory data_dir = "data" test = pd.read_csv(f"{data_dir}/yourtest.tsv", sep=" ") test["path"] = data_dir + "/clips/" + test["path"] print(f"Step 0: {len(test)}") test["status"] = test["path"].apply(lambda path: True if os.path.exists(path) else None) test = test.dropna(subset=["path"]) test = test.drop("status", 1) print(f"Step 1: {len(test)}") test["sentence"] = test["sentence"].apply(lambda t: cleaning(t)) test = test.dropna(subset=["sentence"]) print(f"Step 2: {len(test)}") test = test.reset_index(drop=True) print(test.head()) test = test[["path", "sentence"]] test.to_csv("/content/test.csv", sep=" ", encoding="utf-8", index=False) ``` Prediction ```python import numpy as np import pandas as pd import librosa import torch import torchaudio from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor from datasets import load_dataset, load_metric import IPython.display as ipd model_name_or_path = "masoudmzb/wav2vec2-xlsr-multilingual-53-fa" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(model_name_or_path, device) processor = Wav2Vec2Processor.from_pretrained(model_name_or_path) model = Wav2Vec2ForCTC.from_pretrained(model_name_or_path).to(device) def speech_file_to_array_fn(batch): speech_array, sampling_rate = torchaudio.load(batch["path"]) speech_array = speech_array.squeeze().numpy() speech_array = librosa.resample(np.asarray(speech_array), sampling_rate, processor.feature_extractor.sampling_rate) batch["speech"] = speech_array return batch def predict(batch): features = processor( batch["speech"], sampling_rate=processor.feature_extractor.sampling_rate, return_tensors="pt", padding=True ) input_values = features.input_values.to(device) attention_mask = features.attention_mask.to(device) with torch.no_grad(): logits = model(input_values, attention_mask=attention_mask).logits pred_ids = torch.argmax(logits, dim=-1) batch["predicted"] = processor.batch_decode(pred_ids) return batch # edit these parts with your own data directory dataset = load_dataset("csv", data_files={"test": "/path_to/your_test.csv"}, delimiter=" ")["test"] dataset = dataset.map(speech_file_to_array_fn) result = dataset.map(predict, batched=True, batch_size=4) ``` WER Score ```python wer = load_metric("wer") print("WER: {:.2f}".format(100 * wer.compute(predictions=result["predicted"], references=result["sentence"]))) ``` Output ```python max_items = np.random.randint(0, len(result), 20).tolist() for i in max_items: reference, predicted = result["sentence"][i], result["predicted"][i] print("reference:", reference) print("predicted:", predicted) print('---') ``` ## training details: 🔭 One model was trained on Persian Mozilla dataset before So we Decided to continue from that one. Model is warm started from `mehrdadfa`’s [checkpoint](https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3) - For more details, you can take a look at config.json at the model card in 🤗 Model Hub - The model trained 84000 steps, equal to 12.42 Epochs. - The base model to finetune was https://huggingface.co/m3hrdadfi/wav2vec2-large-xlsr-persian-v3/tree/main ## Fine Tuning Recommendations: 🐤 For fine tuning you can check the link below. but be aware some Tips. you may need gradient_accumulation because you need more batch size. the are many hyperparameters make sure you set them properly : - learning_rate - attention_dropout - hidden_dropout - feat_proj_dropout - mask_time_prob - layer_drop ### Fine Tuning Examples 👷‍♂️👷‍♀️ \| Dataset \| Fine Tuning Example \| \| ------------------------------------------------ \| ------------------------------------------------------------ \| \| Fine Tune on Mozilla Turkish Dataset \| <a href="https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLSR_Wav2Vec2_on_Turkish_ASR_with_%F0%9F%A4%97_Transformers.ipynb"><img src="https://img.shields.io/static/v1?label=Colab&message=Fine-tuning Example&logo=Google%20Colab&color=f9ab00"></a> \| \| Sample Code for Other Dataset And other Language \| [github_link](https://github.com/m3hrdadfi/notebooks/) \| ## Contact us: 🤝 If you have a technical question regarding the model, pretraining, code or publication, please create an issue in the repository. This is the fastest way to reach us. ## Citation: ↩️ we didn't publish any papers on the work. However, if you did, please cite us properly with an entry like one below. ```bibtex @misc{wav2vec2-xlsr-multilingual-53-fa, author = {Paparnchi, Seyyed Mohammad Masoud}, title = {wav2vec2-xlsr-multilingual-53-fa}, year = 2021, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/Hamtech-ai/wav2vec2-fa}}, } ```
kco4776/kogpt-chat	kco4776	2021-12-10T06:24:09Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-03-02T23:29:05Z	## References - [koGPT2](https://github.com/SKT-AI/KoGPT2) - [koGPT2-chatbot](https://github.com/haven-jeon/KoGPT2-chatbot)
huggingtweets/moncleryear	huggingtweets	2021-12-10T06:00:52Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/moncleryear/1639116048575/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1455663704146579459/y1Vb5Ur2_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Ɠucci</div> <div style="text-align: center; font-size: 14px;">@moncleryear</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Ɠucci. \| Data \| Ɠucci \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 47 \| \| Short tweets \| 716 \| \| Tweets kept \| 2481 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/133g5roi/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @moncleryear's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/klif92y8) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/klif92y8/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/moncleryear') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/krankergeist1	huggingtweets	2021-12-10T05:50:24Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/krankergeist1/1639115420252/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1437721566108917762/bzn0HN29_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Kranker Geist</div> <div style="text-align: center; font-size: 14px;">@krankergeist1</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Kranker Geist. \| Data \| Kranker Geist \| \| --- \| --- \| \| Tweets downloaded \| 755 \| \| Retweets \| 75 \| \| Short tweets \| 336 \| \| Tweets kept \| 344 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/zpv7kt6r/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @krankergeist1's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1jipf5v9) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1jipf5v9/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/krankergeist1') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/notcrypticno	huggingtweets	2021-12-10T05:41:54Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/notcrypticno/1639114910066/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1462813846746406916/M_72p8Tr_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">incognito tab #3</div> <div style="text-align: center; font-size: 14px;">@notcrypticno</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from incognito tab #3. \| Data \| incognito tab #3 \| \| --- \| --- \| \| Tweets downloaded \| 3248 \| \| Retweets \| 30 \| \| Short tweets \| 1015 \| \| Tweets kept \| 2203 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2c09ohhz/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @notcrypticno's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lmw2qufb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lmw2qufb/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/notcrypticno') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
luoren000/Token_Classification	luoren000	2021-12-09T20:47:25Z	0	0	null	[ "tensorboard", "region:us" ]	null	2022-03-02T23:29:05Z	This is Contract Token Classification Model
yongzx/gpt2-finetuned-oscar-de	yongzx	2021-12-09T16:44:10Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "feature-extraction", "text-generation", "de", "dataset:oscar", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: - de tags: - text-generation license: mit datasets: - oscar widget: - text: "Mein Name ist Anna. Ich komme aus Österreich und " --- # GPT-2 finetuned on German Dataset ### Tokenizer We first trained a tokenizer on OSCAR's `unshuffled_original_de` German data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the [Python file](https://github.com/bigscience-workshop/multilingual-modeling/blob/gpt2-ko/experiments/exp-001/train_tokenizer_gpt2.py) for the training. ### Model We finetuned the `wte` and `wpe` layers of GPT-2 (while freezing the parameters of all other layers) on OSCAR's `unshuffled_original_de` German data subset. We used [Huggingface's code](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py) for fine-tuning the causal language model GPT-2, but with the following parameters changed ``` - preprocessing_num_workers: 8 - per_device_train_batch_size: 2 - gradient_accumulation_steps: 4 - per_device_eval_batch_size: 2 - eval_accumulation_steps: 4 - eval_steps: 1000 - evaluation_strategy: "steps" - max_eval_samples: 5000 ``` Training details: total training steps: 457000, effective train batch size per step: 32, max tokens per batch: 1024) Final checkpoint: checkpoint-457000
Katsiaryna/distilbert-base-uncased-finetuned_9th	Katsiaryna	2021-12-09T13:46:21Z	9	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned_9th results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned_9th This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2826 - Accuracy: 0.4462 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.2357 \| 1.0 \| 569 \| 0.2277 \| 0.3474 \| \| 0.2237 \| 2.0 \| 1138 \| 0.2316 \| 0.3474 \| \| 0.1847 \| 3.0 \| 1707 \| 0.2456 \| 0.3712 \| \| 0.1302 \| 4.0 \| 2276 \| 0.2763 \| 0.4602 \| \| 0.0863 \| 5.0 \| 2845 \| 0.2826 \| 0.4462 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
ncduy/distilbert-base-cased-distilled-squad-finetuned-squad-small	ncduy	2021-12-09T12:41:47Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-cased-distilled-squad-finetuned-squad-small results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-cased-distilled-squad-finetuned-squad-small This model is a fine-tuned version of [distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert-base-cased-distilled-squad) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
InfoCoV/Cro-CoV-cseBERT	InfoCoV	2021-12-09T12:39:35Z	13	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	## Usage: ``` from sentence_transformers import models from sentence_transformers import SentenceTransformer word_embedding_model = models.Transformer('Cro-CoV-cseBERT') pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode_mean_tokens=True, pooling_mode_cls_token=False, pooling_mode_max_tokens=False) model = SentenceTransformer(modules=[word_embedding_model, pooling_model], device='') ## device = 'gpu' or 'cpu' texts_emb = model.encode(texts) ``` ## Datasets: https://github.com/InfoCoV/InfoCoV ## Paper: Please cite https://www.mdpi.com/2076-3417/11/21/10442
ncduy/bert-base-cased-finetuned-emotion	ncduy	2021-12-09T10:30:48Z	5	1	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - f1 model-index: - name: bert-base-cased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: F1 type: f1 value: 0.9365323747830425 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-emotion This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1342 - F1: 0.9365 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.7357 \| 1.0 \| 250 \| 0.2318 \| 0.9224 \| \| 0.1758 \| 2.0 \| 500 \| 0.1679 \| 0.9349 \| \| 0.1228 \| 3.0 \| 750 \| 0.1385 \| 0.9382 \| \| 0.0961 \| 4.0 \| 1000 \| 0.1452 \| 0.9340 \| \| 0.0805 \| 5.0 \| 1250 \| 0.1342 \| 0.9365 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
yellowback/gpt-neo-japanese-1.3B	yellowback	2021-12-09T08:59:05Z	23	9	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "text generation", "causal-lm", "japanese", "ja", "dataset:oscar", "dataset:cc100", "dataset:wikipedia", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: - ja tags: - text generation - pytorch - causal-lm - japanese license: apache-2.0 datasets: - oscar - cc100 - wikipedia --- # GPT-Neo 1.3B pre-trained model for Japanese ## Model Description GPT2/GPT3 like model trained on Japanese.corpus. ## Training data - cc100 ja - oscar ja - wikipedia ja ## How to use ``` from transformers import pipeline >>> generator = pipeline('text-generation', model='yellowback/gpt-neo-japanese-1.3B') >>> generator("こんばんは、徳川家康です。", do_sample=True, max_length=50, num_return_sequences=3) [{'generated_text': 'こんばんは、徳川家康です。世の中を見渡してみても、残念なことだけれども、まぎれもなく「世のなか...\n5月になりました、家康です。ゴールデンウィークも終ってしまい、世間では'}, {'generated_text': 'こんばんは、徳川家康です。さあ今日は昨晩から降り続いた雨は上がりましたが、まだまだ雨脚は強いですが、晴れるところは晴れて欲しいですね。昨日の夜は仕事だったので、今日の夕'}, {'generated_text': 'こんばんは、徳川家康です。今回は、『世界史再考──日本史再考』という本を書いたあと、『世界史再考──日本史再考』の6~8章'}] ```
yongzx/gpt2-finetuned-oscar-ko	yongzx	2021-12-09T06:53:05Z	98	0	transformers	[ "transformers", "pytorch", "gpt2", "feature-extraction", "text-generation", "ko", "dataset:oscar", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: - ko tags: - text-generation license: mit datasets: - oscar widget: - text: "모든사람은교육을 " --- # GPT-2 finetuned on Korean Dataset ### Tokenizer We first trained a tokenizer on OSCAR's `unshuffled_original_ko` Korean data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the [Python file](https://github.com/bigscience-workshop/multilingual-modeling/blob/gpt2-ko/experiments/exp-001/train_tokenizer_gpt2.py) for the training. ### Model We finetuned the `wte` and `wpe` layers of GPT-2 (while freezing the parameters of all other layers) on OSCAR's `unshuffled_original_ko` Korean data subset. We used [Huggingface's code](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py) for fine-tuning the causal language model GPT-2, but with the following parameters changed ``` - preprocessing_num_workers: 8 - per_device_train_batch_size: 2 - gradient_accumulation_steps: 4 - per_device_eval_batch_size: 2 - eval_accumulation_steps: 4 - eval_steps: 1000 - evaluation_strategy: "steps" - max_eval_samples: 5000 ``` Training details: total training steps: 688000, effective train batch size per step: 32, max tokens per batch: 1024)
yongzx/gpt2-finetuned-oscar-fr	yongzx	2021-12-09T06:28:11Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "feature-extraction", "text-generation", "fr", "dataset:oscar", "license:mit", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: - fr tags: - text-generation license: mit datasets: - oscar widget: - text: "Je suis ravi de vous " --- # GPT-2 finetuned on French Dataset ### Tokenizer We first trained a tokenizer on OSCAR's `unshuffled_original_fr` French data subset by following the training of GPT2 tokenizer (same vocab size of 50,257). Here's the [Python file](https://github.com/bigscience-workshop/multilingual-modeling/blob/gpt2-fr/experiments/exp-001/train_tokenizer_gpt2.py) for the training. ### Model We finetuned the `wte` and `wpe` layers of GPT-2 (while freezing the parameters of all other layers) on OSCAR's `unshuffled_original_fr` French data subset. We used [Huggingface's code](https://github.com/huggingface/transformers/blob/master/examples/pytorch/language-modeling/run_clm.py) for fine-tuning the causal language model GPT-2, but with the following parameters changed ``` - preprocessing_num_workers: 8 - per_device_train_batch_size: 2 - gradient_accumulation_steps: 4 - per_device_eval_batch_size: 2 - eval_accumulation_steps: 4 - eval_steps: 1000 - evaluation_strategy: "steps" - max_eval_samples: 5000 ``` Setup: 8 RTX-3090 GPUs, trained for seven days (total training steps: 110500, effective train batch size: 64, tokens per batch: 1024) Final checkpoint: checkpoint-111500
addy88/hindi-wav2vec2-stt	addy88	2021-12-09T03:55:47Z	30	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	## Usage The model can be used directly (without a language model) as follows: ```python import soundfile as sf import torch from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import argparse def parse_transcription(wav_file): # load pretrained model processor = Wav2Vec2Processor.from_pretrained("addy88/hindi-wav2vec2-stt") model = Wav2Vec2ForCTC.from_pretrained("addy88/hindi-wav2vec2-stt") # load audio audio_input, sample_rate = sf.read(wav_file) # pad input values and return pt tensor input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values # INFERENCE # retrieve logits & take argmax logits = model(input_values).logits predicted_ids = torch.argmax(logits, dim=-1) # transcribe transcription = processor.decode(predicted_ids[0], skip_special_tokens=True) print(transcription) ```
Katsiaryna/distilbert-base-uncased-finetuned	Katsiaryna	2021-12-09T00:20:03Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8229 - Accuracy: 0.54 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 7 \| 0.7709 \| 0.74 \| \| No log \| 2.0 \| 14 \| 0.7048 \| 0.72 \| \| No log \| 3.0 \| 21 \| 0.8728 \| 0.46 \| \| No log \| 4.0 \| 28 \| 0.7849 \| 0.64 \| \| No log \| 5.0 \| 35 \| 0.8229 \| 0.54 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
Jeska/BertjeWDialDataALLQonly02	Jeska	2021-12-08T21:40:27Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer model-index: - name: BertjeWDialDataALLQonly02 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # BertjeWDialDataALLQonly02 This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.9043 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.2438 \| 1.0 \| 871 \| 2.1122 \| \| 2.1235 \| 2.0 \| 1742 \| 2.0784 \| \| 2.0712 \| 3.0 \| 2613 \| 2.0679 \| \| 2.0034 \| 4.0 \| 3484 \| 2.0546 \| \| 1.9375 \| 5.0 \| 4355 \| 2.0277 \| \| 1.8911 \| 6.0 \| 5226 \| 2.0364 \| \| 1.8454 \| 7.0 \| 6097 \| 1.9812 \| \| 1.808 \| 8.0 \| 6968 \| 2.0175 \| \| 1.7716 \| 9.0 \| 7839 \| 2.0286 \| \| 1.7519 \| 10.0 \| 8710 \| 1.9653 \| \| 1.7358 \| 11.0 \| 9581 \| 1.9817 \| \| 1.7084 \| 12.0 \| 10452 \| 1.9633 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
oo/distilbert-base-uncased-finetuned-squad	oo	2021-12-08T18:56:24Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
patrickvonplaten/phoneme_test_5_sv	patrickvonplaten	2021-12-08T17:13:24Z	28	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "multilingual_librispeech", "generated_from_trainer", "dataset:multilingual_librispeech", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - automatic-speech-recognition - multilingual_librispeech - generated_from_trainer datasets: - multilingual_librispeech model-index: - name: wav2vec2-300m-mls-german-ft results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-300m-mls-german-ft This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MULTILINGUAL_LIBRISPEECH - GERMAN 10h dataset. It achieves the following results on the evaluation set: - Loss: 0.2398 - Wer: 0.1520 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 200.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\| \| 3.0132 \| 7.25 \| 500 \| 2.9393 \| 1.0 \| \| 2.9241 \| 14.49 \| 1000 \| 2.8734 \| 1.0 \| \| 1.0766 \| 21.74 \| 1500 \| 0.2773 \| 0.2488 \| \| 0.8416 \| 28.99 \| 2000 \| 0.2224 \| 0.1990 \| \| 0.8048 \| 36.23 \| 2500 \| 0.2063 \| 0.1792 \| \| 0.7664 \| 43.48 \| 3000 \| 0.2088 \| 0.1748 \| \| 0.6571 \| 50.72 \| 3500 \| 0.2042 \| 0.1668 \| \| 0.7014 \| 57.97 \| 4000 \| 0.2136 \| 0.1649 \| \| 0.6171 \| 65.22 \| 4500 \| 0.2139 \| 0.1641 \| \| 0.6609 \| 72.46 \| 5000 \| 0.2144 \| 0.1621 \| \| 0.6318 \| 79.71 \| 5500 \| 0.2129 \| 0.1600 \| \| 0.6222 \| 86.96 \| 6000 \| 0.2124 \| 0.1582 \| \| 0.608 \| 94.2 \| 6500 \| 0.2255 \| 0.1639 \| \| 0.6099 \| 101.45 \| 7000 \| 0.2265 \| 0.1622 \| \| 0.6069 \| 108.7 \| 7500 \| 0.2246 \| 0.1593 \| \| 0.5929 \| 115.94 \| 8000 \| 0.2323 \| 0.1617 \| \| 0.6218 \| 123.19 \| 8500 \| 0.2287 \| 0.1566 \| \| 0.5751 \| 130.43 \| 9000 \| 0.2275 \| 0.1563 \| \| 0.5181 \| 137.68 \| 9500 \| 0.2316 \| 0.1579 \| \| 0.6306 \| 144.93 \| 10000 \| 0.2372 \| 0.1556 \| \| 0.5874 \| 152.17 \| 10500 \| 0.2362 \| 0.1533 \| \| 0.5546 \| 159.42 \| 11000 \| 0.2342 \| 0.1543 \| \| 0.6294 \| 166.67 \| 11500 \| 0.2381 \| 0.1536 \| \| 0.5989 \| 173.91 \| 12000 \| 0.2360 \| 0.1527 \| \| 0.5697 \| 181.16 \| 12500 \| 0.2399 \| 0.1526 \| \| 0.5379 \| 188.41 \| 13000 \| 0.2375 \| 0.1523 \| \| 0.5022 \| 195.65 \| 13500 \| 0.2395 \| 0.1519 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.15.2.dev0 - Tokenizers 0.10.3
NbAiLabArchive/test_NCC_small_flax_stream_100	NbAiLabArchive	2021-12-08T13:44:15Z	5	0	transformers	[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
NbAiLabArchive/test_w8	NbAiLabArchive	2021-12-08T10:57:06Z	4	0	transformers	[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
mchochowski/test-model	mchochowski	2021-12-08T10:04:42Z	78	0	transformers	[ "transformers", "image-classification", "resnet", "dataset:imagenet", "arxiv:1512.03385", "arxiv:1502.01852", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - image-classification - resnet datasets: - imagenet widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace --- ### Model Description The *ResNet50 v1.5* model is a modified version of the [original ResNet50 v1 model](https://arxiv.org/abs/1512.03385). The difference between v1 and v1.5 is that, in the bottleneck blocks which requires downsampling, v1 has stride = 2 in the first 1x1 convolution, whereas v1.5 has stride = 2 in the 3x3 convolution. This difference makes ResNet50 v1.5 slightly more accurate (\~0.5% top1) than v1, but comes with a smallperformance drawback (\~5% imgs/sec). The model is initialized as described in [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf) This model is trained with mixed precision using Tensor Cores on Volta, Turing, and the NVIDIA Ampere GPU architectures. Therefore, researchers can get results over 2x faster than training without Tensor Cores, while experiencing the benefits of mixed precision training. This model is tested against each NGC monthly container release to ensure consistent accuracy and performance over time. Note that the ResNet50 v1.5 model can be deployed for inference on the [NVIDIA Triton Inference Server](https://github.com/NVIDIA/trtis-inference-server) using TorchScript, ONNX Runtime or TensorRT as an execution backend. For details check [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_for_triton_from_pytorch) ### Example In the example below we will use the pretrained *ResNet50 v1.5* model to perform inference on *image* and present the result. To run the example you need some extra python packages installed. These are needed for preprocessing images and visualization. ```python !pip install validators matplotlib ``` ```python import torch from PIL import Image import torchvision.transforms as transforms import numpy as np import json import requests import matplotlib.pyplot as plt import warnings warnings.filterwarnings('ignore') %matplotlib inline device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") print(f'Using {device} for inference') ``` Load the model pretrained on IMAGENET dataset. ```python resnet50 = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_resnet50', pretrained=True) utils = torch.hub.load('NVIDIA/DeepLearningExamples:torchhub', 'nvidia_convnets_processing_utils') resnet50.eval().to(device) ``` Prepare sample input data. ```python uris = [ 'http://images.cocodataset.org/test-stuff2017/000000024309.jpg', 'http://images.cocodataset.org/test-stuff2017/000000028117.jpg', 'http://images.cocodataset.org/test-stuff2017/000000006149.jpg', 'http://images.cocodataset.org/test-stuff2017/000000004954.jpg', ] batch = torch.cat( [utils.prepare_input_from_uri(uri) for uri in uris] ).to(device) ``` Run inference. Use `pick_n_best(predictions=output, n=topN)` helepr function to pick N most probably hypothesis according to the model. ```python with torch.no_grad(): output = torch.nn.functional.softmax(resnet50(batch), dim=1) results = utils.pick_n_best(predictions=output, n=5) ``` Display the result. ```python for uri, result in zip(uris, results): img = Image.open(requests.get(uri, stream=True).raw) img.thumbnail((256,256), Image.ANTIALIAS) plt.imshow(img) plt.show() print(result) ``` ### Details For detailed information on model input and output, training recipies, inference and performance visit: [github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnet50v1.5) and/or [NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_50_v1_5_for_pytorch) ### References - [Original ResNet50 v1 paper](https://arxiv.org/abs/1512.03385) - [Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification](https://arxiv.org/pdf/1502.01852.pdf) - [model on github](https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Classification/ConvNets/resnet50v1.5) - [model on NGC](https://ngc.nvidia.com/catalog/resources/nvidia:resnet_50_v1_5_for_pytorch) - [pretrained model on NGC](https://ngc.nvidia.com/catalog/models/nvidia:resnet50_pyt_amp) ```python ```
rafiulrumy/wav2vec2-large-xlsr-hindi-demo-colab_2	rafiulrumy	2021-12-08T09:51:42Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xlsr-hindi-demo-colab_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-hindi-demo-colab_2 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: 3.8793 - Wer: 1.1357 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 22.381 \| 1.11 \| 20 \| 22.1964 \| 1.0 \| \| 7.6212 \| 2.22 \| 40 \| 4.0591 \| 1.0 \| \| 3.6951 \| 3.32 \| 60 \| 3.6782 \| 1.0 \| \| 3.5574 \| 4.43 \| 80 \| 3.6776 \| 1.0 \| \| 3.5374 \| 5.54 \| 100 \| 3.5649 \| 1.0 \| \| 3.5512 \| 6.65 \| 120 \| 3.5266 \| 1.0 \| \| 3.5075 \| 7.76 \| 140 \| 3.6860 \| 1.0 \| \| 3.5097 \| 8.86 \| 160 \| 3.4941 \| 1.0 \| \| 3.481 \| 9.97 \| 180 \| 3.4659 \| 1.0 \| \| 3.5623 \| 11.11 \| 200 \| 3.7254 \| 1.0 \| \| 3.4404 \| 12.22 \| 220 \| 3.5225 \| 1.0 \| \| 3.432 \| 13.32 \| 240 \| 3.5706 \| 1.0 \| \| 3.4177 \| 14.43 \| 260 \| 3.3833 \| 1.0 \| \| 3.3735 \| 15.54 \| 280 \| 3.4140 \| 1.0 \| \| 3.31 \| 16.65 \| 300 \| 3.2702 \| 1.0 \| \| 3.2256 \| 17.76 \| 320 \| 3.2405 \| 1.0 \| \| 3.0546 \| 18.86 \| 340 \| 3.1644 \| 1.0 \| \| 2.7233 \| 19.97 \| 360 \| 2.9753 \| 1.0 \| \| 2.2822 \| 21.11 \| 380 \| 3.1119 \| 1.1183 \| \| 1.8027 \| 22.22 \| 400 \| 3.0035 \| 1.2378 \| \| 1.5274 \| 23.32 \| 420 \| 2.8536 \| 1.2227 \| \| 1.2313 \| 24.43 \| 440 \| 2.9544 \| 1.0951 \| \| 1.0956 \| 25.54 \| 460 \| 2.8814 \| 1.0661 \| \| 0.9456 \| 26.65 \| 480 \| 3.1192 \| 1.1589 \| \| 0.7893 \| 27.76 \| 500 \| 3.2919 \| 1.1833 \| \| 0.7256 \| 28.86 \| 520 \| 3.0864 \| 1.0951 \| \| 0.6051 \| 29.97 \| 540 \| 3.5888 \| 1.1821 \| \| 0.6087 \| 31.11 \| 560 \| 3.4579 \| 1.1392 \| \| 0.5529 \| 32.22 \| 580 \| 3.1998 \| 1.0708 \| \| 0.5211 \| 33.32 \| 600 \| 3.4655 \| 1.1311 \| \| 0.4506 \| 34.43 \| 620 \| 3.4338 \| 1.1694 \| \| 0.4101 \| 35.54 \| 640 \| 3.5189 \| 1.1450 \| \| 0.4484 \| 36.65 \| 660 \| 3.6585 \| 1.1601 \| \| 0.4038 \| 37.76 \| 680 \| 3.6314 \| 1.1497 \| \| 0.3539 \| 38.86 \| 700 \| 3.6955 \| 1.1485 \| \| 0.3898 \| 39.97 \| 720 \| 3.5738 \| 1.1148 \| \| 0.35 \| 41.11 \| 740 \| 3.6594 \| 1.1195 \| \| 0.3328 \| 42.22 \| 760 \| 3.6894 \| 1.1299 \| \| 0.3264 \| 43.32 \| 780 \| 3.7290 \| 1.1021 \| \| 0.3364 \| 44.43 \| 800 \| 3.7256 \| 1.1543 \| \| 0.3071 \| 45.54 \| 820 \| 3.8834 \| 1.1415 \| \| 0.3074 \| 46.65 \| 840 \| 3.8077 \| 1.1450 \| \| 0.3064 \| 47.76 \| 860 \| 3.8733 \| 1.1346 \| \| 0.3223 \| 48.86 \| 880 \| 3.8780 \| 1.1323 \| \| 0.275 \| 49.97 \| 900 \| 3.8793 \| 1.1357 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.13.3 - Tokenizers 0.10.3
tngo/DialoGPT-small-HankHill	tngo	2021-12-08T08:37:47Z	5	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- tags: - conversational --- # Hank Hill ChatBot This is an instance of microsoft/DialoGPT-small trained on a tv show character, Hank Hill from King of The Hill. The data comes from a csv file that contains character lines from the first 5 seasons of the show. Updated some portion of the data to accurately show Hank's famous pronunciation of the word "what" with "hwhat". Chat with the model: ## Issues Occasionally the chatbot just responds with just multiple '!' characters. The chatbot frequently responds with "I'm not your buddy, pal" to uncomfortable/strange prompts/messages. Still working on a fix for those known issues. ```Python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("tngo/DialoGPT-small-HankHill") model = AutoModelWithLMHead.from_pretrained("tngo/DialoGPT-small-HankHill") # Let's chat for 4 lines 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("Hank Hill Bot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) ```
NbAiLabArchive/test_w4	NbAiLabArchive	2021-12-08T08:27:23Z	4	0	transformers	[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
NbAiLabArchive/test_w6	NbAiLabArchive	2021-12-08T08:05:56Z	4	0	transformers	[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
huggingtweets/whatsylviaate	huggingtweets	2021-12-08T06:59:48Z	6	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/whatsylviaate/1638946783606/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1417983394235965444/fooJopVZ_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Sylvia Plath's Food Diary</div> <div style="text-align: center; font-size: 14px;">@whatsylviaate</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Sylvia Plath's Food Diary. \| Data \| Sylvia Plath's Food Diary \| \| --- \| --- \| \| Tweets downloaded \| 717 \| \| Retweets \| 18 \| \| Short tweets \| 2 \| \| Tweets kept \| 697 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/29xzctsj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @whatsylviaate's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2jwd8u1b) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2jwd8u1b/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/whatsylviaate') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/sadfaceone	huggingtweets	2021-12-08T03:05:38Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/sadfaceone/1638932633342/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1461421488330870790/uqHRnPLI_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">MelancholyAK</div> <div style="text-align: center; font-size: 14px;">@sadfaceone</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from MelancholyAK. \| Data \| MelancholyAK \| \| --- \| --- \| \| Tweets downloaded \| 3235 \| \| Retweets \| 202 \| \| Short tweets \| 466 \| \| Tweets kept \| 2567 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2aeiomu7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @sadfaceone's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2loki1ml) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2loki1ml/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/sadfaceone') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/foxehhyz	huggingtweets	2021-12-08T01:50:36Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/foxehhyz/1638928181616/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1445910806420344839/Rm_oWBH0_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Pakun/Foxe</div> <div style="text-align: center; font-size: 14px;">@foxehhyz</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Pakun/Foxe. \| Data \| Pakun/Foxe \| \| --- \| --- \| \| Tweets downloaded \| 3243 \| \| Retweets \| 413 \| \| Short tweets \| 192 \| \| Tweets kept \| 2638 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/urqo8vqu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @foxehhyz's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/38j8w9y5) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/38j8w9y5/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/foxehhyz') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
huggingtweets/chrisrgun	huggingtweets	2021-12-08T01:19:04Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/chrisrgun/1638926316305/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1438283137692446720/2Xc5tmwD_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Chris Ray Gun 🇵🇷</div> <div style="text-align: center; font-size: 14px;">@chrisrgun</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Chris Ray Gun 🇵🇷. \| Data \| Chris Ray Gun 🇵🇷 \| \| --- \| --- \| \| Tweets downloaded \| 3230 \| \| Retweets \| 364 \| \| Short tweets \| 484 \| \| Tweets kept \| 2382 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/245uz5wp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @chrisrgun's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/fjh34bsj) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/fjh34bsj/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/chrisrgun') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
austin/Austin-MeDeBERTa	austin	2021-12-08T00:30:40Z	7	2	transformers	[ "transformers", "pytorch", "deberta", "fill-mask", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer model-index: - name: deberta-pretrained-large results: [] --- # Austin MeDeBERTa This model was developed using further MLM pre-training on [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base), using a dataset of 1.1M clinical notes from the Austin Health EMR. The notes span discharge summaries, inpatient notes, radiology reports and histopathology reports. ## Model description This is the base version of the original DeBERTa model. The architecture and tokenizer are unchanged. ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 9 - eval_batch_size: 9 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:------:\|:---------------:\| \| 0.9756 \| 0.51 \| 40000 \| 0.9127 \| \| 0.8876 \| 1.01 \| 80000 \| 0.8221 \| \| 0.818 \| 1.52 \| 120000 \| 0.7786 \| \| 0.7836 \| 2.03 \| 160000 \| 0.7438 \| \| 0.7672 \| 2.54 \| 200000 \| 0.7165 \| \| 0.734 \| 3.04 \| 240000 \| 0.6948 \| \| 0.7079 \| 3.55 \| 280000 \| 0.6749 \| \| 0.6987 \| 4.06 \| 320000 \| 0.6598 \| \| 0.6771 \| 4.57 \| 360000 \| 0.6471 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu113 - Datasets 1.15.1 - Tokenizers 0.10.3
mrm8488/deberta-v3-small-finetuned-mnli	mrm8488	2021-12-07T17:45:59Z	9	3	transformers	[ "transformers", "pytorch", "tensorboard", "deberta-v2", "text-classification", "generated_from_trainer", "deberta-v3", "en", "dataset:glue", "arxiv:2006.03654", "arxiv:2111.09543", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - en license: mit tags: - generated_from_trainer - deberta-v3 datasets: - glue metrics: - accuracy model-index: - name: ds_results results: - task: name: Text Classification type: text-classification dataset: name: GLUE MNLI type: glue args: mnli metrics: - name: Accuracy type: accuracy value: 0.874593165174939 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # DeBERTa v3 (small) fine-tuned on MNLI This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE MNLI dataset. It achieves the following results on the evaluation set: - Loss: 0.4985 - Accuracy: 0.8746 ## Model description [DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data. Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates. In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we replaced the MLM objective with the RTD(Replaced Token Detection) objective introduced by ELECTRA for pre-training, as well as some innovations to be introduced in our upcoming paper. Compared to DeBERTa-V2, our V3 version significantly improves the model performance in downstream tasks. You can find a simple introduction about the model from the appendix A11 in our original [paper](https://arxiv.org/abs/2006.03654), but we will provide more details in a separate write-up. The DeBERTa V3 small model comes with 6 layers and a hidden size of 768. Its total parameter number is 143M since we use a vocabulary containing 128K tokens which introduce 98M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2. ## Intended uses & limitations More information needed ## Training and evaluation data The Multi-Genre Natural Language Inference Corpus is a crowdsourced collection of sentence pairs with textual entailment annotations. Given a premise sentence and a hypothesis sentence, the task is to predict whether the premise entails the hypothesis (entailment), contradicts the hypothesis (contradiction), or neither (neutral). The premise sentences are gathered from ten different sources, including transcribed speech, fiction, and government reports. The authors of the benchmark use the standard test set, for which they obtained private labels from the RTE authors, and evaluate on both the matched (in-domain) and mismatched (cross-domain) section. They also uses and recommend the SNLI corpus as 550k examples of auxiliary training data. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - 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: 1000 - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.7773 \| 0.04 \| 1000 \| 0.5241 \| 0.7984 \| \| 0.546 \| 0.08 \| 2000 \| 0.4629 \| 0.8194 \| \| 0.5032 \| 0.12 \| 3000 \| 0.4704 \| 0.8274 \| \| 0.4711 \| 0.16 \| 4000 \| 0.4383 \| 0.8355 \| \| 0.473 \| 0.2 \| 5000 \| 0.4652 \| 0.8305 \| \| 0.4619 \| 0.24 \| 6000 \| 0.4234 \| 0.8386 \| \| 0.4542 \| 0.29 \| 7000 \| 0.4825 \| 0.8349 \| \| 0.4468 \| 0.33 \| 8000 \| 0.3985 \| 0.8513 \| \| 0.4288 \| 0.37 \| 9000 \| 0.4084 \| 0.8493 \| \| 0.4354 \| 0.41 \| 10000 \| 0.3850 \| 0.8533 \| \| 0.423 \| 0.45 \| 11000 \| 0.3855 \| 0.8509 \| \| 0.4167 \| 0.49 \| 12000 \| 0.4122 \| 0.8513 \| \| 0.4129 \| 0.53 \| 13000 \| 0.4009 \| 0.8550 \| \| 0.4135 \| 0.57 \| 14000 \| 0.4136 \| 0.8544 \| \| 0.4074 \| 0.61 \| 15000 \| 0.3869 \| 0.8595 \| \| 0.415 \| 0.65 \| 16000 \| 0.3911 \| 0.8517 \| \| 0.4095 \| 0.69 \| 17000 \| 0.3880 \| 0.8593 \| \| 0.4001 \| 0.73 \| 18000 \| 0.3907 \| 0.8587 \| \| 0.4069 \| 0.77 \| 19000 \| 0.3686 \| 0.8630 \| \| 0.3927 \| 0.81 \| 20000 \| 0.4008 \| 0.8593 \| \| 0.3958 \| 0.86 \| 21000 \| 0.3716 \| 0.8639 \| \| 0.4016 \| 0.9 \| 22000 \| 0.3594 \| 0.8679 \| \| 0.3945 \| 0.94 \| 23000 \| 0.3595 \| 0.8679 \| \| 0.3932 \| 0.98 \| 24000 \| 0.3577 \| 0.8645 \| \| 0.345 \| 1.02 \| 25000 \| 0.4080 \| 0.8699 \| \| 0.2885 \| 1.06 \| 26000 \| 0.3919 \| 0.8674 \| \| 0.2858 \| 1.1 \| 27000 \| 0.4346 \| 0.8651 \| \| 0.2872 \| 1.14 \| 28000 \| 0.4105 \| 0.8674 \| \| 0.3002 \| 1.18 \| 29000 \| 0.4133 \| 0.8708 \| \| 0.2954 \| 1.22 \| 30000 \| 0.4062 \| 0.8667 \| \| 0.2912 \| 1.26 \| 31000 \| 0.3972 \| 0.8708 \| \| 0.2958 \| 1.3 \| 32000 \| 0.3713 \| 0.8732 \| \| 0.293 \| 1.34 \| 33000 \| 0.3717 \| 0.8715 \| \| 0.3001 \| 1.39 \| 34000 \| 0.3826 \| 0.8716 \| \| 0.2864 \| 1.43 \| 35000 \| 0.4155 \| 0.8694 \| \| 0.2827 \| 1.47 \| 36000 \| 0.4224 \| 0.8666 \| \| 0.2836 \| 1.51 \| 37000 \| 0.3832 \| 0.8744 \| \| 0.2844 \| 1.55 \| 38000 \| 0.4179 \| 0.8699 \| \| 0.2866 \| 1.59 \| 39000 \| 0.3969 \| 0.8681 \| \| 0.2883 \| 1.63 \| 40000 \| 0.4000 \| 0.8683 \| \| 0.2832 \| 1.67 \| 41000 \| 0.3853 \| 0.8688 \| \| 0.2876 \| 1.71 \| 42000 \| 0.3924 \| 0.8677 \| \| 0.2855 \| 1.75 \| 43000 \| 0.4177 \| 0.8719 \| \| 0.2845 \| 1.79 \| 44000 \| 0.3877 \| 0.8724 \| \| 0.2882 \| 1.83 \| 45000 \| 0.3961 \| 0.8713 \| \| 0.2773 \| 1.87 \| 46000 \| 0.3791 \| 0.8740 \| \| 0.2767 \| 1.91 \| 47000 \| 0.3877 \| 0.8779 \| \| 0.2772 \| 1.96 \| 48000 \| 0.4022 \| 0.8690 \| \| 0.2816 \| 2.0 \| 49000 \| 0.3837 \| 0.8732 \| \| 0.2068 \| 2.04 \| 50000 \| 0.4644 \| 0.8720 \| \| 0.1914 \| 2.08 \| 51000 \| 0.4919 \| 0.8744 \| \| 0.2 \| 2.12 \| 52000 \| 0.4870 \| 0.8702 \| \| 0.1904 \| 2.16 \| 53000 \| 0.5038 \| 0.8737 \| \| 0.1915 \| 2.2 \| 54000 \| 0.5232 \| 0.8711 \| \| 0.1956 \| 2.24 \| 55000 \| 0.5192 \| 0.8747 \| \| 0.1911 \| 2.28 \| 56000 \| 0.5215 \| 0.8761 \| \| 0.2053 \| 2.32 \| 57000 \| 0.4604 \| 0.8738 \| \| 0.2008 \| 2.36 \| 58000 \| 0.5162 \| 0.8715 \| \| 0.1971 \| 2.4 \| 59000 \| 0.4886 \| 0.8754 \| \| 0.192 \| 2.44 \| 60000 \| 0.4921 \| 0.8725 \| \| 0.1937 \| 2.49 \| 61000 \| 0.4917 \| 0.8763 \| \| 0.1931 \| 2.53 \| 62000 \| 0.4789 \| 0.8778 \| \| 0.1964 \| 2.57 \| 63000 \| 0.4997 \| 0.8721 \| \| 0.2008 \| 2.61 \| 64000 \| 0.4748 \| 0.8756 \| \| 0.1962 \| 2.65 \| 65000 \| 0.4840 \| 0.8764 \| \| 0.2029 \| 2.69 \| 66000 \| 0.4889 \| 0.8767 \| \| 0.1927 \| 2.73 \| 67000 \| 0.4820 \| 0.8758 \| \| 0.1926 \| 2.77 \| 68000 \| 0.4857 \| 0.8762 \| \| 0.1919 \| 2.81 \| 69000 \| 0.4836 \| 0.8749 \| \| 0.1911 \| 2.85 \| 70000 \| 0.4859 \| 0.8742 \| \| 0.1897 \| 2.89 \| 71000 \| 0.4853 \| 0.8766 \| \| 0.186 \| 2.93 \| 72000 \| 0.4946 \| 0.8768 \| \| 0.2011 \| 2.97 \| 73000 \| 0.4851 \| 0.8767 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0+cu111 - Datasets 1.15.1 - Tokenizers 0.10.3
huggingtweets/zemmoureric	huggingtweets	2021-12-07T17:28:50Z	6	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/zemmoureric/1638897923015/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1465637600060919808/8nQIBTEI_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Eric Zemmour</div> <div style="text-align: center; font-size: 14px;">@zemmoureric</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from Eric Zemmour. \| Data \| Eric Zemmour \| \| --- \| --- \| \| Tweets downloaded \| 3244 \| \| Retweets \| 546 \| \| Short tweets \| 175 \| \| Tweets kept \| 2523 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/24afk8k1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zemmoureric's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2dz80r2t) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2dz80r2t/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/zemmoureric') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly	Jeska	2021-12-07T15:55:12Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # VaccinChatSentenceClassifierDutch_fromBERTje2_DAdialogQonly This model is a fine-tuned version of [outputDAQonly/checkpoint-8710](https://huggingface.co/outputDAQonly/checkpoint-8710) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5008 - Accuracy: 0.9068 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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-06 - lr_scheduler_type: linear - num_epochs: 15.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 4.0751 \| 1.0 \| 1320 \| 3.1674 \| 0.4086 \| \| 2.5619 \| 2.0 \| 2640 \| 2.0335 \| 0.6426 \| \| 1.8549 \| 3.0 \| 3960 \| 1.3537 \| 0.7861 \| \| 1.106 \| 4.0 \| 5280 \| 0.9515 \| 0.8519 \| \| 0.6698 \| 5.0 \| 6600 \| 0.7152 \| 0.8757 \| \| 0.4497 \| 6.0 \| 7920 \| 0.5838 \| 0.8921 \| \| 0.2626 \| 7.0 \| 9240 \| 0.5300 \| 0.8940 \| \| 0.1762 \| 8.0 \| 10560 \| 0.4984 \| 0.8958 \| \| 0.119 \| 9.0 \| 11880 \| 0.4906 \| 0.9059 \| \| 0.0919 \| 10.0 \| 13200 \| 0.4896 \| 0.8995 \| \| 0.0722 \| 11.0 \| 14520 \| 0.5012 \| 0.9022 \| \| 0.0517 \| 12.0 \| 15840 \| 0.4951 \| 0.9040 \| \| 0.0353 \| 13.0 \| 17160 \| 0.4988 \| 0.9040 \| \| 0.0334 \| 14.0 \| 18480 \| 0.5035 \| 0.9049 \| \| 0.0304 \| 15.0 \| 19800 \| 0.5008 \| 0.9068 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
Jeska/VaccinChatSentenceClassifierDutch_fromBERTje2	Jeska	2021-12-07T13:46:05Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer metrics: - accuracy model-index: - name: VaccinChatSentenceClassifierDutch_fromBERTje2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # VaccinChatSentenceClassifierDutch_fromBERTje2 This model is a fine-tuned version of [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5112 - Accuracy: 0.9004 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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-06 - lr_scheduler_type: linear - num_epochs: 15.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 4.1505 \| 1.0 \| 1320 \| 3.3293 \| 0.3793 \| \| 2.7333 \| 2.0 \| 2640 \| 2.2295 \| 0.6133 \| \| 2.0189 \| 3.0 \| 3960 \| 1.5134 \| 0.7587 \| \| 1.2504 \| 4.0 \| 5280 \| 1.0765 \| 0.8282 \| \| 0.7733 \| 5.0 \| 6600 \| 0.7937 \| 0.8629 \| \| 0.5217 \| 6.0 \| 7920 \| 0.6438 \| 0.8784 \| \| 0.3148 \| 7.0 \| 9240 \| 0.5733 \| 0.8857 \| \| 0.2067 \| 8.0 \| 10560 \| 0.5362 \| 0.8912 \| \| 0.1507 \| 9.0 \| 11880 \| 0.5098 \| 0.8903 \| \| 0.1024 \| 10.0 \| 13200 \| 0.5078 \| 0.8976 \| \| 0.0837 \| 11.0 \| 14520 \| 0.5054 \| 0.8967 \| \| 0.0608 \| 12.0 \| 15840 \| 0.5062 \| 0.8958 \| \| 0.0426 \| 13.0 \| 17160 \| 0.5072 \| 0.9013 \| \| 0.0374 \| 14.0 \| 18480 \| 0.5110 \| 0.9040 \| \| 0.0346 \| 15.0 \| 19800 \| 0.5112 \| 0.9004 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.10.0 - Datasets 1.16.1 - Tokenizers 0.10.3
castorini/duot5-base-msmarco	castorini	2021-12-07T12:53:29Z	265	0	transformers	[ "transformers", "pytorch", "jax", "t5", "text2text-generation", "arxiv:2101.05667", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	This model is a T5-base pairwise reranker fine-tuned on MS MARCO passage dataset for 50k steps (or 5 epochs). For more details on how to use it, check [pygaggle.ai](pygaggle.ai) Paper describing the model: [The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models](https://arxiv.org/pdf/2101.05667.pdf)
Seongkyu/bert-base-cased-finetuned-squad	Seongkyu	2021-12-07T09:52:54Z	25	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: bert-base-cased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-finetuned-squad This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.0458 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.0179 \| 1.0 \| 6194 \| 0.9548 \| \| 0.7277 \| 2.0 \| 12388 \| 0.9717 \| \| 0.507 \| 3.0 \| 18582 \| 1.0458 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
akahana/indonesia-sentiment-roberta	akahana	2021-12-07T04:26:11Z	14	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "id", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: "id" widget: - text: "dia orang yang baik ya bunds." --- ## how to use ```python from transformers import pipeline, set_seed path = "akahana/indonesia-sentiment-roberta" emotion = pipeline('text-classification', model=path,device=0) set_seed(42) kalimat = "dia orang yang baik ya bunds." preds = emotion(kalimat) preds ```
NbAiLabArchive/test_NCC_OSCAR_style	NbAiLabArchive	2021-12-07T01:55:12Z	3	0	transformers	[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
NbAiLabArchive/test_NCC_OSCAR_style_98w	NbAiLabArchive	2021-12-07T01:53:59Z	4	0	transformers	[ "transformers", "jax", "tensorboard", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
SaulLu/test-push-to-hub	SaulLu	2021-12-06T23:21:03Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:04Z	test readme test 2 test 3 test 4 test 5 test 6 test 7 test 8 test 9 test 10 test 11
philschmid/MiniLMv2-L6-H384-emotion	philschmid	2021-12-06T19:59:04Z	5	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:emotion", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - emotion metrics: - accuracy model-index: - name: MiniLMv2-L6-H384-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9215 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # MiniLMv2-L6-H384-emotion This model is a fine-tuned version of [nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2140 - Accuracy: 0.9215 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - 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: 8 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.432 \| 1.0 \| 500 \| 0.9992 \| 0.6805 \| \| 0.8073 \| 2.0 \| 1000 \| 0.5437 \| 0.846 \| \| 0.4483 \| 3.0 \| 1500 \| 0.3018 \| 0.909 \| \| 0.2833 \| 4.0 \| 2000 \| 0.2412 \| 0.915 \| \| 0.2169 \| 5.0 \| 2500 \| 0.2140 \| 0.9215 \| \| 0.1821 \| 6.0 \| 3000 \| 0.2159 \| 0.917 \| \| 0.154 \| 7.0 \| 3500 \| 0.2084 \| 0.919 \| \| 0.1461 \| 8.0 \| 4000 \| 0.2047 \| 0.92 \| ### Framework versions - Transformers 4.12.3 - Pytorch 1.9.1 - Datasets 1.15.1 - Tokenizers 0.10.3
gokulkarthik/xlm-roberta-qa-chaii	gokulkarthik	2021-12-06T15:50:08Z	16	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "question-answering", "en", "ta", "hi", "dataset:squad", "dataset:chaii", "endpoints_compatible", "region:us" ]	question-answering	2022-03-02T23:29:05Z	--- language: - en - ta - hi datasets: - squad - chaii widget: - text: "அலுமினியத்தின் அணு எண் என்ன?" context: "அலுமினியம் (ஆங்கிலம்: அலுமினியம்; வட அமெரிக்க ஆங்கிலம்: Aluminum) ஒரு வேதியியல் தனிமம் ஆகும். இதனுடைய அணு எண் 13 ஆகும். இது பூமியில் அதிகம் கிடைக்கும் உலோகங்களுள் ஒன்று. இது மின்சாரத்தையும் வெப்பத்தையும் கடத்த வல்லது. பாக்ஸைட் என்ற தாதுவில் இருந்து அலுமினியம் தயாரிக்கப்படுகிறது. இதன் வேதிக்குறியீடு Al ஆகும்." - text: "ज्वाला गुट्टा की माँ का नाम क्या है?" context: "ज्वाला गुट्टा (जन्म: 7 सितंबर 1983; वर्धा, महाराष्ट्र) एक भारतीय बैडमिंटन खिलाडी हैं। प्रारंभिक जीवन ज्वाला गुट्टा का जन्म 7 सितंबर 1983 को वर्धा, महाराष्ट्र में हुआ था। उनके पिता एम. क्रांति तेलुगु और मां येलन चीन से हैं। उनकी मां येलन गुट्टा पहली बार 1977 में अपने दादा जी के साथ भारत आई थीं। ज्वाला गुट्टा की प्रारंभिक पढ़ाई हैदराबाद से हुई और यहीं से उन्होंने बैडमिंटन खेलना भी शुरू किया। कॅरियर 10 साल की उम्र से ही ज्वाला गुट्टा ने एस.एम. आरिफ से ट्रेनिंग लेना शुरू कर दिया था। एस.एम. आरिफ भारत के जाने माने खेल प्रशिक्षक हैं जिन्हें द्रोणाचार्य अवार्ड से सम्मानित किया गया है। पहली बार 13 साल की उम्र में उन्होंने मिनी नेशनल बैडमिंटन चैंपियनशिप जीती थी। साल 2000 में ज्वाला गुट्टा ने 17 साल की उम्र में जूनियर नेशनल बैडमिंटन चैंपियनशिप जीती। इसी साल उन्होंने श्रुति कुरियन के साथ डबल्स में जोड़ी बनाते हुए महिलाओं के डबल्स जूनियर नेशनल बैडमिंटन चैंपियनशिप और सीनियर नेशनल बैडमिंटन चैंपियनशिप में जीत हासिल की। श्रुति कुरियन के साथ उनकी जोड़ी काफी लंबे समय तक चली। 2002 से 2008 तक लगातार सात बार ज्वाला गुट्टा ने महिलाओं के नेशनल युगल प्रतियोगिता में जीत हासिल की।" - text: "How many bones do you have in your body?" context: "A normal adult human skeleton consists of the following 206 (208 if the breast is thought to be three parts). This number can vary depending on the physiological differences. For example, in a very small number of humans, an extra rib (neck) or an extra lower spinal cord is found. There are 22 bones in the human skull (excluding the ear tendons), which are divided into eight cranium bones and 14 facial bones. (Thick numbers indicate the numbers seen in the nearby picture.) Bones (8) 1 frontal bone (2) 3 temporal bone (2) 4 occipital bone (4) Sphinoid bone (14) 7 mandible (6) maxilla (2) palatine bone (2) 5 zygotic bone (9) 9 nasal bone (2) The sacral vertebrae (4 or 5), in adults, form the sacral vertebrae (3 to 5), in adults they form the valve." --- # XLM-RoBERTa for question answering in Indian languages pre-trained XLM-Roberta with intermediate pre-training on SQUAD dataset (English) and fine tuning on Chaii dataset (Tamil, Hindi) # How to use from the 🤗/transformers library ``` from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("gokulkarthik/xlm-roberta-qa-chaii") model = AutoModelForQuestionAnswering.from_pretrained("gokulkarthik/xlm-roberta-qa-chaii") ```
Ayham/xlnetgpt2_xsum7	Ayham	2021-12-06T13:13:12Z	7	0	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer model-index: - name: xlnetgpt2_xsum7 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnetgpt2_xsum7 This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
DebateLabKIT/argument-analyst	DebateLabKIT	2021-12-06T12:23:43Z	37	4	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "en", "dataset:debatelab/aaac", "arxiv:2110.01509", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- language: - "en" license: "cc-by-sa-4.0" datasets: - debatelab/aaac widget: - text: "reason_statements: argument_source: If Peter likes fish, Peter has been to New York. So, Peter has been to New York." example_title: "Premise identification" - text: "argdown_reconstruction: argument_source: If Peter likes fish, Peter has been to New York. So, Peter has been to New York." example_title: "Argdown reconstruction" - text: "premises_formalized: reason_statements: If Peter likes fish, Peter has been to New York. (ref: (1))" example_title: "Formalization" inference: parameters: max_length: 80 --- Pretraining Dataset: [AAAC01](https://huggingface.co/datasets/debatelab/aaac) Demo: [DeepA2 Demo](https://huggingface.co/spaces/debatelab/deepa2-demo) Paper: [DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models](https://arxiv.org/abs/2110.01509) Authors: Gregor Betz, Kyle Richardson ## Abstract In this paper, we present and implement a multi-dimensional, modular framework for performing deep argument analysis (DeepA2) using current pre-trained language models (PTLMs). ArgumentAnalyst -- a T5 model (Raffel et al. 2020) set up and trained within DeepA2 -- reconstructs argumentative texts, which advance an informal argumentation, as valid arguments: It inserts, e.g., missing premises and conclusions, formalizes inferences, and coherently links the logical reconstruction to the source text. We create a synthetic corpus for deep argument analysis, and evaluate ArgumentAnalyst on this new dataset as well as on existing data, specifically EntailmentBank (Dalvi et al. 2021). Our empirical findings vindicate the overall framework and highlight the advantages of a modular design, in particular its ability to emulate established heuristics (such as hermeneutic cycles), to explore the model's uncertainty, to cope with the plurality of correct solutions (underdetermination), and to exploit higher-order evidence.
emfa/l-lectra-danish-finetuned-hatespeech	emfa	2021-12-06T11:14:45Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- license: mit tags: - generated_from_trainer model-index: - name: l-lectra-danish-finetuned-hatespeech results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # l-lectra-danish-finetuned-hatespeech This model is for a university project and is uploaded for sharing between students. It is training on a danish hate speech labeled training set. Feel free to use it, but as of now, we don't promise any good results ;-) This model is a fine-tuned version of [Maltehb/-l-ctra-danish-electra-small-uncased](https://huggingface.co/Maltehb/-l-ctra-danish-electra-small-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2608 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-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: 4.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 315 \| 0.2561 \| \| 0.291 \| 2.0 \| 630 \| 0.2491 \| \| 0.291 \| 3.0 \| 945 \| 0.2434 \| \| 0.2089 \| 4.0 \| 1260 \| 0.2608 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
ncduy/marian-finetuned-kde4-en-to-fr	ncduy	2021-12-06T08:46:30Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 args: en-fr metrics: - name: Bleu type: bleu value: 52.8691179414982 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 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 the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8558 - Bleu: 52.8691 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0a0+0aef44c - Datasets 1.16.1 - Tokenizers 0.10.3
tyoyo/t5-base-TEDxJP-1body-2context	tyoyo	2021-12-06T08:37:39Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:te_dx_jp", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: cc-by-sa-4.0 tags: - generated_from_trainer datasets: - te_dx_jp model-index: - name: t5-base-TEDxJP-1body-2context results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-TEDxJP-1body-2context This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.4968 - Wer: 0.1969 - Mer: 0.1895 - Wil: 0.2801 - Wip: 0.7199 - Hits: 55902 - Substitutions: 6899 - Deletions: 3570 - Insertions: 2599 - Cer: 0.1727 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Mer \| Wil \| Wip \| Hits \| Substitutions \| Deletions \| Insertions \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:------:\|:-----:\|:-------------:\|:---------:\|:----------:\|:------:\| \| 0.7136 \| 1.0 \| 746 \| 0.5716 \| 0.2512 \| 0.2345 \| 0.3279 \| 0.6721 \| 54430 \| 7249 \| 4692 \| 4731 \| 0.2344 \| \| 0.6267 \| 2.0 \| 1492 \| 0.5152 \| 0.2088 \| 0.2005 \| 0.2917 \| 0.7083 \| 55245 \| 6949 \| 4177 \| 2732 \| 0.2009 \| \| 0.5416 \| 3.0 \| 2238 \| 0.4969 \| 0.2025 \| 0.1948 \| 0.2851 \| 0.7149 \| 55575 \| 6871 \| 3925 \| 2646 \| 0.1802 \| \| 0.5223 \| 4.0 \| 2984 \| 0.4915 \| 0.1989 \| 0.1917 \| 0.2816 \| 0.7184 \| 55652 \| 6826 \| 3893 \| 2481 \| 0.1754 \| \| 0.4985 \| 5.0 \| 3730 \| 0.4929 \| 0.1991 \| 0.1916 \| 0.2814 \| 0.7186 \| 55759 \| 6828 \| 3784 \| 2603 \| 0.1753 \| \| 0.4675 \| 6.0 \| 4476 \| 0.4910 \| 0.1969 \| 0.1897 \| 0.2799 \| 0.7201 \| 55834 \| 6859 \| 3678 \| 2534 \| 0.1756 \| \| 0.445 \| 7.0 \| 5222 \| 0.4940 \| 0.1955 \| 0.1884 \| 0.2782 \| 0.7218 \| 55881 \| 6821 \| 3669 \| 2485 \| 0.1712 \| \| 0.4404 \| 8.0 \| 5968 \| 0.4932 \| 0.1979 \| 0.1903 \| 0.2801 \| 0.7199 \| 55881 \| 6828 \| 3662 \| 2643 \| 0.1742 \| \| 0.4525 \| 9.0 \| 6714 \| 0.4951 \| 0.1968 \| 0.1893 \| 0.2799 \| 0.7201 \| 55939 \| 6897 \| 3535 \| 2632 \| 0.1740 \| \| 0.4077 \| 10.0 \| 7460 \| 0.4968 \| 0.1969 \| 0.1895 \| 0.2801 \| 0.7199 \| 55902 \| 6899 \| 3570 \| 2599 \| 0.1727 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3
ncduy/bert-finetuned-ner	ncduy	2021-12-06T06:21:38Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 args: conll2003 metrics: - name: Precision type: precision value: 0.9310572323932047 - name: Recall type: recall value: 0.9500168293503871 - name: F1 type: f1 value: 0.9404414827155352 - name: Accuracy type: accuracy value: 0.9865191028433508 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0590 - Precision: 0.9311 - Recall: 0.9500 - F1: 0.9404 - Accuracy: 0.9865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0874 \| 1.0 \| 1756 \| 0.0635 \| 0.9211 \| 0.9369 \| 0.9289 \| 0.9835 \| \| 0.0376 \| 2.0 \| 3512 \| 0.0618 \| 0.9342 \| 0.9485 \| 0.9413 \| 0.9858 \| \| 0.0226 \| 3.0 \| 5268 \| 0.0590 \| 0.9311 \| 0.9500 \| 0.9404 \| 0.9865 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
prithivida/cnn-lstm-probwordnoise	prithivida	2021-12-06T05:56:30Z	0	0	null	[ "pytorch", "CNN", "LSTM", "en", "region:us" ]	null	2022-03-02T23:29:05Z	--- language: - en tags: - CNN - LSTM license: "MIT" --- # NeuSpell: A Neural Spelling Correction Toolkit This model checkpoint belongs to the Original Neuspell python library and is ported to HuggingFace Hub to be used as a part of NeuSpell-Demo spaces. - [Refer to the Fork of the library (with HF hub support) in GitHub:](https://github.com/PrithivirajDamodaran/neuspell) - [Refer to the original library in GitHub:](https://github.com/neuspell/neuspell)
ingridnc/t5-small-finetuned-fi-to-en	ingridnc	2021-12-06T01:11:33Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt19", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt19 metrics: - bleu model-index: - name: t5-small-finetuned-fi-to-en results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt19 type: wmt19 args: fi-en metrics: - name: Bleu type: bleu value: 1.618 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-fi-to-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt19 dataset. It achieves the following results on the evaluation set: - Loss: 3.3598 - Bleu: 1.618 - Gen Len: 17.3223 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\| \| 3.3627 \| 1.0 \| 6250 \| 3.5122 \| 1.2882 \| 17.1803 \| \| 3.2162 \| 2.0 \| 12500 \| 3.4442 \| 1.4329 \| 17.2617 \| \| 3.1304 \| 3.0 \| 18750 \| 3.3872 \| 1.4862 \| 17.296 \| \| 3.0832 \| 4.0 \| 25000 \| 3.3648 \| 1.5795 \| 17.3047 \| \| 3.0623 \| 5.0 \| 31250 \| 3.3598 \| 1.618 \| 17.3223 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
diegor2/t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetu-truncated-41f800	diegor2	2021-12-06T00:23:37Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt16_en_ro_pre_processed", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- tags: - generated_from_trainer datasets: - wmt16_en_ro_pre_processed metrics: - bleu model-index: - name: t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro-TRAIN_EPOCHS-1 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16_en_ro_pre_processed type: wmt16_en_ro_pre_processed args: enro metrics: - name: Bleu type: bleu value: 0.0002 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-tiny-random-length-96-learning_rate-2e-05-weight_decay-0.005-finetuned-en-to-ro-TRAIN_EPOCHS-1 This model is a fine-tuned version of [patrickvonplaten/t5-tiny-random](https://huggingface.co/patrickvonplaten/t5-tiny-random) on the wmt16_en_ro_pre_processed dataset. It achieves the following results on the evaluation set: - Loss: 6.4897 - Bleu: 0.0002 - Gen Len: 9.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:-------:\| \| 6.2585 \| 1.0 \| 76290 \| 6.4897 \| 0.0002 \| 9.0 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
usc-isi/sbert-roberta-large-anli-mnli-snli	usc-isi	2021-12-05T21:04:27Z	8	1	sentence-transformers	[ "sentence-transformers", "pytorch", "roberta", "feature-extraction", "sentence-similarity", "transformers", "en", "dataset:anli", "dataset:multi_nli", "dataset:snli", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- language: - en pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers datasets: - anli - multi_nli - snli --- # sbert-roberta-large-anli-mnli-snli This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. The model is weight initialized by RoBERTa-large and trained on ANLI (Nie et al., 2020), MNLI (Williams et al., 2018), and SNLI (Bowman et al., 2015) using the [`training_nli.py`](https://github.com/UKPLab/sentence-transformers/blob/v0.3.5/examples/training/nli/training_nli.py) example script. Training Details: - Learning rate: 2e-5 - Batch size: 8 - Pooling: Mean - Training time: ~20 hours on one [NVIDIA GeForce RTX 2080 Ti](https://www.nvidia.com/en-us/geforce/graphics-cards/rtx-2080-ti/) ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ```bash pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer("usc-isi/sbert-roberta-large-anli-mnli-snli") embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (Hugging Face Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: first, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python import torch from transformers import AutoModel, AutoTokenizer # Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] # First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ["This is an example sentence", "Each sentence is converted"] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained("usc-isi/sbert-roberta-large-anli-mnli-snli") model = AutoModel.from_pretrained("usc-isi/sbert-roberta-large-anli-mnli-snli") # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors="pt") # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, max pooling. sentence_embeddings = mean_pooling(model_output, encoded_input["attention_mask"]) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results See section 4.1 of our paper for evaluation results. ## Full Model Architecture ```text SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors For more information about the project, see our paper: > Ciosici, Manuel, et al. "Machine-Assisted Script Curation." _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstrations_, Association for Computational Linguistics, 2021, pp. 8–17. _ACLWeb_, <https://www.aclweb.org/anthology/2021.naacl-demos.2>. ## References - Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. [A large annotated corpus for learning natural language inference](https://doi.org/10.18653/v1/D15-1075). In _Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing_, pages 632–642, Lisbon, Portugal. Association for Computational Linguistics. - Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. [AdversarialNLI: A new benchmark for natural language understanding](https://doi.org/10.18653/v1/2020.acl-main.441). In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_, pages 4885–4901, Online. Association for Computational Linguistics. - Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. [A broad-coverage challenge corpus for sentence understanding through inference](https://doi.org/10.18653/v1/N18-1101). In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)_, pages 1112–1122, New Orleans, Louisiana. Association for Computational Linguistics.
Ayham/xlnet_gpt_xsum	Ayham	2021-12-05T21:00:24Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "encoder-decoder", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- tags: - generated_from_trainer model-index: - name: xlnet_gpt_xsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlnet_gpt_xsum This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
chandank/bart-base-finetuned-kaggglenews-fact-corrector-II	chandank	2021-12-05T20:22:09Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-base-finetuned-kaggglenews-fact-corrector-II results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-kaggglenews-fact-corrector-II This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 305 \| 1.5749 \| 27.9313 \| 15.1004 \| 23.3282 \| 25.2336 \| 20.0 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
tyoyo/t5-base-TEDxJP-1body-1context	tyoyo	2021-12-05T20:01:50Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:te_dx_jp", "license:cc-by-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: cc-by-sa-4.0 tags: - generated_from_trainer datasets: - te_dx_jp model-index: - name: t5-base-TEDxJP-1body-1context results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-TEDxJP-1body-1context This model is a fine-tuned version of [sonoisa/t5-base-japanese](https://huggingface.co/sonoisa/t5-base-japanese) on the te_dx_jp dataset. It achieves the following results on the evaluation set: - Loss: 0.5061 - Wer: 0.1990 - Mer: 0.1913 - Wil: 0.2823 - Wip: 0.7177 - Hits: 55830 - Substitutions: 6943 - Deletions: 3598 - Insertions: 2664 - Cer: 0.1763 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - 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_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Mer \| Wil \| Wip \| Hits \| Substitutions \| Deletions \| Insertions \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:------:\|:-----:\|:-------------:\|:---------:\|:----------:\|:------:\| \| 0.7277 \| 1.0 \| 746 \| 0.5799 \| 0.2384 \| 0.2256 \| 0.3188 \| 0.6812 \| 54323 \| 7170 \| 4878 \| 3777 \| 0.2371 \| \| 0.6278 \| 2.0 \| 1492 \| 0.5254 \| 0.2070 \| 0.1997 \| 0.2905 \| 0.7095 \| 55045 \| 6885 \| 4441 \| 2412 \| 0.1962 \| \| 0.5411 \| 3.0 \| 2238 \| 0.5076 \| 0.2022 \| 0.1950 \| 0.2858 \| 0.7142 \| 55413 \| 6902 \| 4056 \| 2463 \| 0.1805 \| \| 0.53 \| 4.0 \| 2984 \| 0.5020 \| 0.1979 \| 0.1911 \| 0.2814 \| 0.7186 \| 55599 \| 6849 \| 3923 \| 2362 \| 0.1761 \| \| 0.5094 \| 5.0 \| 3730 \| 0.4999 \| 0.1987 \| 0.1915 \| 0.2828 \| 0.7172 \| 55651 \| 6944 \| 3776 \| 2465 \| 0.1742 \| \| 0.4783 \| 6.0 \| 4476 \| 0.5016 \| 0.1985 \| 0.1914 \| 0.2826 \| 0.7174 \| 55684 \| 6947 \| 3740 \| 2490 \| 0.1753 \| \| 0.4479 \| 7.0 \| 5222 \| 0.5035 \| 0.1976 \| 0.1905 \| 0.2819 \| 0.7181 \| 55726 \| 6961 \| 3684 \| 2468 \| 0.1733 \| \| 0.4539 \| 8.0 \| 5968 \| 0.5022 \| 0.1967 \| 0.1896 \| 0.2807 \| 0.7193 \| 55795 \| 6938 \| 3638 \| 2477 \| 0.1729 \| \| 0.4632 \| 9.0 \| 6714 \| 0.5034 \| 0.1991 \| 0.1913 \| 0.2824 \| 0.7176 \| 55844 \| 6942 \| 3585 \| 2687 \| 0.1758 \| \| 0.4201 \| 10.0 \| 7460 \| 0.5061 \| 0.1990 \| 0.1913 \| 0.2823 \| 0.7177 \| 55830 \| 6943 \| 3598 \| 2664 \| 0.1763 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.15.1 - Tokenizers 0.10.3
chandank/bart-base-finetuned-kaggglenews-baseline-final	chandank	2021-12-05T18:45:24Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-base-finetuned-kaggglenews-baseline-final results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-kaggglenews-baseline-final This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.6942 - Rouge1: 28.581 - Rouge2: 16.3417 - Rougel: 24.1277 - Rougelsum: 25.9797 - Gen Len: 20.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 495 \| 1.7514 \| 27.911 \| 15.7038 \| 23.6466 \| 25.2111 \| 20.0 \| \| 2.0585 \| 2.0 \| 990 \| 1.6655 \| 28.7581 \| 16.4875 \| 24.2669 \| 26.1676 \| 20.0 \| \| 1.4173 \| 3.0 \| 1485 \| 1.6942 \| 28.581 \| 16.3417 \| 24.1277 \| 25.9797 \| 20.0 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
danielbispov/t5-small-finetuned-fi-to-en	danielbispov	2021-12-05T16:40:52Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt19", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt19 metrics: - bleu model-index: - name: t5-small-finetuned-fi-to-en results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt19 type: wmt19 args: fi-en metrics: - name: Bleu type: bleu value: 1.129 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-fi-to-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt19 dataset. It achieves the following results on the evaluation set: - Loss: 3.5235 - Bleu: 1.129 - Gen Len: 17.088 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----:\|:-------:\| \| 3.414 \| 1.0 \| 6250 \| 3.5235 \| 1.129 \| 17.088 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.9.1 - Datasets 1.16.1 - Tokenizers 0.10.3
asyou20/1234	asyou20	2021-12-05T15:06:56Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	git clone https://github.com/saic-mdal/lama.git
Kithogue/T5_Question_Generation	Kithogue	2021-12-05T15:05:13Z	13	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	T5-base fine-tuned on SQuAD and CoQA datasets for question generation language: - en-us tags: - question-generation license: - MIT datasets: - SQuAD 2.0 - CoQA
NbAiLabArchive/test_NCC_OSCAR_16w_noada	NbAiLabArchive	2021-12-04T22:33:03Z	4	0	transformers	[ "transformers", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:04Z	Just for performing some experiments. Do not use.
felipetanios/opus-mt-de-en-finetuned-de-to-en-second	felipetanios	2021-12-04T18:48:17Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: opus-mt-de-en-finetuned-de-to-en-second results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: de-en metrics: - name: Bleu type: bleu value: 37.9762 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-de-en-finetuned-de-to-en-second This model is a fine-tuned version of [Helsinki-NLP/opus-mt-de-en](https://huggingface.co/Helsinki-NLP/opus-mt-de-en) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.2282 - Bleu: 37.9762 - Gen Len: 25.3696 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 157 \| 1.1837 \| 38.8278 \| 25.22 \| \| No log \| 2.0 \| 314 \| 1.2057 \| 38.3047 \| 25.2908 \| \| No log \| 3.0 \| 471 \| 1.2167 \| 38.231 \| 25.316 \| \| 1.4808 \| 4.0 \| 628 \| 1.2256 \| 37.9871 \| 25.3556 \| \| 1.4808 \| 5.0 \| 785 \| 1.2282 \| 37.9762 \| 25.3696 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
dee4hf/deeBERT	dee4hf	2021-12-04T18:44:11Z	0	0	null	[ "region:us" ]	null	2022-03-02T23:29:05Z	trying to create my first BERT model
rossanez/t5-small-finetuned-de-en-final	rossanez	2021-12-04T14:59:44Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt14", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt14 metrics: - bleu model-index: - name: t5-small-finetuned-de-en-final results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt14 type: wmt14 args: de-en metrics: - name: Bleu type: bleu value: 9.8394 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-en-final This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. It achieves the following results on the evaluation set: - Loss: 2.3285 - Bleu: 9.8394 - Gen Len: 17.325 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 188 \| 2.3867 \| 9.7928 \| 17.2581 \| \| No log \| 2.0 \| 376 \| 2.3942 \| 9.7222 \| 17.4186 \| \| 0.7948 \| 3.0 \| 564 \| 2.3909 \| 9.6495 \| 17.3513 \| \| 0.7948 \| 4.0 \| 752 \| 2.3496 \| 9.7376 \| 17.3417 \| \| 0.7948 \| 5.0 \| 940 \| 2.3285 \| 9.8394 \| 17.325 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
rossanez/t5-small-finetuned-de-en-batch8	rossanez	2021-12-04T14:31:59Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt14", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt14 metrics: - bleu model-index: - name: t5-small-finetuned-de-en-batch8 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt14 type: wmt14 args: de-en metrics: - name: Bleu type: bleu value: 10.039 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-en-batch8 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. It achieves the following results on the evaluation set: - Loss: 2.1282 - Bleu: 10.039 - Gen Len: 17.3839 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 375 \| 2.0912 \| 9.9147 \| 17.3084 \| \| 1.5593 \| 2.0 \| 750 \| 2.0858 \| 9.9386 \| 17.4299 \| \| 1.4383 \| 3.0 \| 1125 \| 2.1137 \| 9.9804 \| 17.34 \| \| 1.3562 \| 4.0 \| 1500 \| 2.1198 \| 9.9685 \| 17.367 \| \| 1.3562 \| 5.0 \| 1875 \| 2.1282 \| 10.039 \| 17.3839 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
rossanez/t5-small-finetuned-de-en-nofp16	rossanez	2021-12-04T13:59:26Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt14", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt14 metrics: - bleu model-index: - name: t5-small-finetuned-de-en-nofp16 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt14 type: wmt14 args: de-en metrics: - name: Bleu type: bleu value: 9.5801 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-en-nofp16 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. It achieves the following results on the evaluation set: - Loss: 2.1460 - Bleu: 9.5801 - Gen Len: 17.333 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 188 \| 2.1899 \| 9.4821 \| 17.312 \| \| No log \| 2.0 \| 376 \| 2.1986 \| 9.5705 \| 17.3853 \| \| 1.2118 \| 3.0 \| 564 \| 2.1933 \| 9.448 \| 17.3293 \| \| 1.2118 \| 4.0 \| 752 \| 2.1607 \| 9.563 \| 17.336 \| \| 1.2118 \| 5.0 \| 940 \| 2.1460 \| 9.5801 \| 17.333 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
rossanez/t5-small-finetuned-de-en-lr2e-4	rossanez	2021-12-04T13:15:11Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt14", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt14 metrics: - bleu model-index: - name: t5-small-finetuned-de-en-lr2e-4 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt14 type: wmt14 args: de-en metrics: - name: Bleu type: bleu value: 9.12 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-en-lr2e-4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt14 dataset. It achieves the following results on the evaluation set: - Loss: 2.0115 - Bleu: 9.12 - Gen Len: 17.4026 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 188 \| 2.0701 \| 8.1225 \| 17.4542 \| \| No log \| 2.0 \| 376 \| 2.0316 \| 8.5741 \| 17.4229 \| \| 2.2224 \| 3.0 \| 564 \| 2.0229 \| 8.9227 \| 17.3703 \| \| 2.2224 \| 4.0 \| 752 \| 2.0105 \| 9.0764 \| 17.4053 \| \| 2.2224 \| 5.0 \| 940 \| 2.0115 \| 9.12 \| 17.4026 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
chandank/bart-base-finetuned-kaggglenews-batch8-LR2E6	chandank	2021-12-04T12:07:12Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-base-finetuned-kaggglenews-batch8-LR2E6 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-kaggglenews-batch8-LR2E6 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-06 - 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 495 \| 1.7971 \| 26.6141 \| 13.9957 \| 22.3012 \| 23.7509 \| 20.0 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
chandank/bart-base-finetuned-kaggglenews-batch8-LR4	chandank	2021-12-04T11:53:34Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-base-finetuned-kaggglenews-batch8-LR4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-kaggglenews-batch8-LR4 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 495 \| 1.6037 \| 28.1247 \| 15.9399 \| 23.8676 \| 25.3739 \| 20.0 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
chandank/bart-base-finetuned-kaggglenews-batch8-LR1	chandank	2021-12-04T11:37:31Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bart-base-finetuned-kaggglenews-batch8-LR1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-kaggglenews-batch8-LR1 This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### 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 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 495 \| 1.6826 \| 27.5191 \| 15.0672 \| 23.3065 \| 24.7163 \| 20.0 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
Edomonndo/opus-mt-ja-en-finetuned-ja-to-en_xml	Edomonndo	2021-12-04T10:23:03Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model_index: - name: opus-mt-ja-en-finetuned-ja-to-en_xml results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation metric: name: Bleu type: bleu value: 73.8646 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opus-mt-ja-en-finetuned-ja-to-en_xml This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ja-en](https://huggingface.co/Helsinki-NLP/opus-mt-ja-en) on an unkown dataset. It achieves the following results on the evaluation set: - Loss: 0.7520 - Bleu: 73.8646 - Gen Len: 27.0884 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - 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: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| 1.0512 \| 1.0 \| 748 \| 0.8333 \| 59.8234 \| 27.905 \| \| 0.6076 \| 2.0 \| 1496 \| 0.7817 \| 62.5606 \| 26.1834 \| \| 0.4174 \| 3.0 \| 2244 \| 0.7817 \| 64.8346 \| 28.2918 \| \| 0.2971 \| 4.0 \| 2992 \| 0.7653 \| 67.6013 \| 27.2222 \| \| 0.2172 \| 5.0 \| 3740 \| 0.7295 \| 69.4017 \| 27.0174 \| \| 0.1447 \| 6.0 \| 4488 \| 0.7522 \| 68.8355 \| 28.2865 \| \| 0.0953 \| 7.0 \| 5236 \| 0.7596 \| 71.4743 \| 27.1861 \| \| 0.0577 \| 8.0 \| 5984 \| 0.7469 \| 72.0684 \| 26.921 \| \| 0.04 \| 9.0 \| 6732 \| 0.7526 \| 73.2821 \| 27.1365 \| \| 0.0213 \| 10.0 \| 7480 \| 0.7520 \| 73.8646 \| 27.0884 \| ### Framework versions - Transformers 4.9.1 - Pytorch 1.10.0+cu111 - Datasets 1.10.2 - Tokenizers 0.10.3
limjiayi/bert-hateful-memes-expanded	limjiayi	2021-12-04T04:38:38Z	14	3	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-hateful-memes-expanded results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-hateful-memes-expanded This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on texts from the following datasets: - [Hateful Memes](https://hatefulmemeschallenge.com/), `train`, `dev_seen` and `dev_unseen` - [HarMeme](https://github.com/di-dimitrov/harmeme), `train`, `val` and `test` - [MultiOFF](https://github.com/bharathichezhiyan/Multimodal-Meme-Classification-Identifying-Offensive-Content-in-Image-and-Text), `Training`, `Validation` and `Testing` It achieves the following results on the evaluation set: - Loss: 3.7600 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.11.0 - Pytorch 1.8.1+cu102 - Datasets 1.8.0 - Tokenizers 0.10.2
marciovbarbosa/t5-small-finetuned-de-to-en-fp16	marciovbarbosa	2021-12-04T04:27:50Z	4	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: t5-small-finetuned-de-to-en-fp16 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: de-en metrics: - name: Bleu type: bleu value: 9.2226 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-to-en-fp16 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.9416 - Bleu: 9.2226 - Gen Len: 17.3311 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 272 \| 2.1671 \| 3.8489 \| 17.6382 \| \| 2.6715 \| 2.0 \| 544 \| 2.0660 \| 6.4354 \| 17.4905 \| \| 2.6715 \| 3.0 \| 816 \| 2.0206 \| 7.4092 \| 17.3708 \| \| 2.4325 \| 4.0 \| 1088 \| 1.9926 \| 8.1453 \| 17.3685 \| \| 2.4325 \| 5.0 \| 1360 \| 1.9739 \| 8.6739 \| 17.3521 \| \| 2.3312 \| 6.0 \| 1632 \| 1.9602 \| 8.8808 \| 17.3681 \| \| 2.3312 \| 7.0 \| 1904 \| 1.9509 \| 9.1173 \| 17.3491 \| \| 2.2946 \| 8.0 \| 2176 \| 1.9465 \| 9.1504 \| 17.3414 \| \| 2.2946 \| 9.0 \| 2448 \| 1.9426 \| 9.2372 \| 17.3398 \| \| 2.2665 \| 10.0 \| 2720 \| 1.9416 \| 9.2226 \| 17.3311 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
marciovbarbosa/t5-small-finetuned-de-to-en-lr3e-4	marciovbarbosa	2021-12-04T03:33:12Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: t5-small-finetuned-de-to-en-lr3e-4 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: de-en metrics: - name: Bleu type: bleu value: 11.9094 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-to-en-lr3e-4 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.9059 - Bleu: 11.9094 - Gen Len: 17.2257 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\| \| No log \| 1.0 \| 272 \| 1.8814 \| 10.3468 \| 17.2244 \| \| 2.2309 \| 2.0 \| 544 \| 1.8320 \| 10.9949 \| 17.2768 \| \| 2.2309 \| 3.0 \| 816 \| 1.8273 \| 11.4299 \| 17.2147 \| \| 1.7515 \| 4.0 \| 1088 \| 1.8321 \| 11.5576 \| 17.3191 \| \| 1.7515 \| 5.0 \| 1360 \| 1.8377 \| 11.8255 \| 17.2244 \| \| 1.488 \| 6.0 \| 1632 \| 1.8562 \| 11.6741 \| 17.2427 \| \| 1.488 \| 7.0 \| 1904 \| 1.8653 \| 11.7363 \| 17.2331 \| \| 1.3301 \| 8.0 \| 2176 \| 1.8938 \| 12.0458 \| 17.2044 \| \| 1.3301 \| 9.0 \| 2448 \| 1.9005 \| 11.8676 \| 17.2437 \| \| 1.2241 \| 10.0 \| 2720 \| 1.9059 \| 11.9094 \| 17.2257 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3
marciovbarbosa/t5-small-finetuned-de-to-en	marciovbarbosa	2021-12-04T00:56:09Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:wmt16", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - wmt16 metrics: - bleu model-index: - name: t5-small-finetuned-de-to-en results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: wmt16 type: wmt16 args: de-en metrics: - name: Bleu type: bleu value: 9.2166 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-de-to-en This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the wmt16 dataset. It achieves the following results on the evaluation set: - Loss: 1.9417 - Bleu: 9.2166 - Gen Len: 17.3404 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Bleu \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:-------:\| \| No log \| 1.0 \| 272 \| 2.1660 \| 3.8515 \| 17.6289 \| \| 2.6678 \| 2.0 \| 544 \| 2.0656 \| 6.4422 \| 17.4842 \| \| 2.6678 \| 3.0 \| 816 \| 2.0203 \| 7.4348 \| 17.3741 \| \| 2.4316 \| 4.0 \| 1088 \| 1.9926 \| 8.0914 \| 17.3658 \| \| 2.4316 \| 5.0 \| 1360 \| 1.9739 \| 8.6535 \| 17.3461 \| \| 2.3307 \| 6.0 \| 1632 \| 1.9603 \| 8.8757 \| 17.3768 \| \| 2.3307 \| 7.0 \| 1904 \| 1.9509 \| 9.0744 \| 17.3511 \| \| 2.2945 \| 8.0 \| 2176 \| 1.9466 \| 9.1111 \| 17.3418 \| \| 2.2945 \| 9.0 \| 2448 \| 1.9427 \| 9.1969 \| 17.3351 \| \| 2.2666 \| 10.0 \| 2720 \| 1.9417 \| 9.2166 \| 17.3404 \| ### Framework versions - Transformers 4.12.5 - Pytorch 1.10.0+cu111 - Datasets 1.16.1 - Tokenizers 0.10.3

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