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timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-27 12:31:29
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| library_name
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kimnt93/chat-7b-v1.1
|
kimnt93
| 2023-10-10T06:39:46Z | 0 | 0 |
peft
|
[
"peft",
"pytorch",
"mistral",
"region:us"
] | null | 2023-10-09T14:08:02Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
|
spacy/pt_core_news_sm
|
spacy
| 2023-10-10T06:39:16Z | 103 | 2 |
spacy
|
[
"spacy",
"token-classification",
"pt",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- pt
license: cc-by-sa-4.0
model-index:
- name: pt_core_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8794147723
- name: NER Recall
type: recall
value: 0.8800897408
- name: NER F Score
type: f_score
value: 0.8797521271
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.8880083214
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9624052702
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.947102526
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9675598039
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8891065154
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8444034795
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9277043528
---
### Details: https://spacy.io/models/pt#pt_core_news_sm
Portuguese pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `pt_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD Portuguese Bosque v2.8](https://github.com/UniversalDependencies/UD_Portuguese-Bosque) (Rademaker, Alexandre; Freitas, Cláudia; de Souza, Elvis; Silveira, Aline; Cavalcanti, Tatiana; Evelyn, Wograine; Rocha, Luisa; Soares-Bastos, Isabela; Bick, Eckhard; Chalub, Fabricio; Paulino-Passos, Guilherme; Real, Livy; de Paiva, Valeria; Zeman, Daniel; Popel, Martin; Mareček, David; Silveira, Natalia; Martins, André)<br />[WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (590 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Def\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `POS=PUNCT`, `NumType=Card\|POS=NUM`, `POS=ADV`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=ADP`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=ADV\|Polarity=Neg`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=X`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=CCONJ`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `POS=AUX\|VerbForm=Inf`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `POS=VERB\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Number=Plur\|POS=AUX\|Person=3\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Art`, `POS=VERB\|VerbForm=Part`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `NumType=Ord\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Gender=Masc\|NumType=Mult\|Number=Sing\|POS=NUM`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Sing\|POS=PROPN\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `POS=AUX\|VerbForm=Part`, `POS=SPACE`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Number=Sing\|POS=DET\|PronType=Art`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|NumType=Frac\|Number=Sing\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Number=Plur\|POS=VERB\|Person=3\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Definite=Def\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Acc\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=AUX`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Art`, `POS=INTJ`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Emp`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=NOUN\|Voice=Pass`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=ADJ`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Number=Sing\|POS=AUX\|Person=3\|VerbForm=Inf`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=NUM`, `Number=Sing\|POS=NOUN`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Dem`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PART`, `Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=ADV`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Card\|POS=DET`, `Number=Plur\|POS=VERB\|Person=1\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Gender=Masc\|POS=ADJ`, `POS=NOUN`, `POS=AUX\|VerbForm=Ger`, `Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Emp`, `POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=DET`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|POS=PRON\|PronType=Prs`, `POS=VERB\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=PROPN\|PronType=Art`, `Case=Dat\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pqp\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=ADJ\|PronType=Dem`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=AUX\|Person=1\|Tense=Past`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Past\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADV\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=X`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=SCONJ`, `Gender=Masc\|Number=Sing\|POS=PRON`, `Gender=Fem\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `POS=ADP\|PronType=Dem`, `Definite=Def\|Gender=Fem\|POS=ADP\|PronType=Art`, `POS=ADP\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=ADP\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `POS=DET`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Emp`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Art`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=AUX\|Person=1\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Degree=Cmp\|POS=ADJ`, `Gender=Fem\|Number=Plur\|POS=ADP\|PronType=Ind`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=3\|VerbForm=Inf\|Voice=Pass`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=2\|PronType=Prs\|VerbForm=Inf`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem,Masc\|Number=Sing\|POS=PROPN`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=NUM`, `POS=PRON\|PronType=Neg`, `Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Dem`, `POS=SYM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pqp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=X`, `Case=Dat\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|NumType=Sets\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|POS=AUX\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Ind`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Prs`, `Number=Sing\|POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADP\|PronType=Ind`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Art`, `Number=Sing\|POS=VERB`, `Number=Sing\|POS=DET`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin\|Voice=Pass`, `NumType=Mult\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Neg`, `Mood=Ind\|POS=VERB\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=SCONJ\|PronType=Rel`, `Case=Acc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Sing\|POS=ADV\|Polarity=Neg`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf`, `Definite=Def\|Gender=Masc\|POS=ADP\|PronType=Art`, `Gender=Masc\|POS=NOUN`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=NOUN`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=SCONJ\|PronType=Art`, `POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=PRON\|PronType=Tot`, `Number=Sing\|POS=DET\|PronType=Rel`, `Gender=Fem\|Number=Plur\|POS=VERB`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|PronType=Prs\|VerbForm=Inf`, `Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `NumType=Range\|POS=NUM`, `Case=Dat\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Person=1\|VerbForm=Inf\|Voice=Pass`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=SCONJ\|PronType=Dem`, `NumType=Frac\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADP\|PronType=Ind`, `Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADV\|PronType=Rel`, `Mood=Cnd\|POS=VERB\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Ind`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf\|Voice=Pass`, `POS=VERB\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Number=Sing\|POS=X`, `POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Acc\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Gender=Masc\|Number=Sing\|POS=ADV\|PronType=Int`, `Case=Dat\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=1,3\|PronType=Prs\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|VerbForm=Inf`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Int`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pqp\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|POS=AUX\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Fem\|POS=AUX\|PronType=Prs\|VerbForm=Ger`, `Case=Acc\|Gender=Fem\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=VERB\|Person=1\|PronType=Prs\|VerbForm=Inf`, `Case=Acc\|Gender=Masc\|Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=AUX\|Person=3\|PronType=Prs\|VerbForm=Ger`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Mood=Ind\|Number=Plur,Sing\|POS=VERB\|Person=1,3\|PronType=Prs\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=X`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Fem\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Pqp\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Sing\|POS=NUM`, `Number=Plur\|POS=PROPN`, `Case=Dat\|POS=PRON\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=VERB`, `Case=Acc\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|PronType=Prs\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, _(truncated: full list in pipeline meta)_ |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `parataxis`, `punct`, `xcomp` |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 100.00 |
| `TOKEN_P` | 99.88 |
| `TOKEN_R` | 99.95 |
| `TOKEN_F` | 99.92 |
| `POS_ACC` | 96.24 |
| `MORPH_ACC` | 94.71 |
| `MORPH_MICRO_P` | 97.38 |
| `MORPH_MICRO_R` | 96.78 |
| `MORPH_MICRO_F` | 97.08 |
| `SENTS_P` | 92.75 |
| `SENTS_R` | 94.91 |
| `SENTS_F` | 92.77 |
| `DEP_UAS` | 88.91 |
| `DEP_LAS` | 84.44 |
| `LEMMA_ACC` | 96.76 |
| `TAG_ACC` | 88.80 |
| `ENTS_P` | 87.94 |
| `ENTS_R` | 88.01 |
| `ENTS_F` | 87.98 |
|
spacy/ro_core_news_lg
|
spacy
| 2023-10-10T06:39:13Z | 17 | 2 |
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_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7502799552
- name: NER Recall
type: recall
value: 0.7721859393
- name: NER F Score
type: f_score
value: 0.7610753502
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9657255109
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9395242502
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9499516228
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9575746914
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8875784191
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8359473024
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9699799867
---
### Details: https://spacy.io/models/ro#ro_core_news_lg
Romanian pipeline optimized for CPU. Components: tok2vec, tagger, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler.
| Feature | Description |
| --- | --- |
| **Name** | `ro_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Components** | `tok2vec`, `tagger`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
| **Vectors** | 500000 keys, 500000 unique vectors (300 dimensions) |
| **Sources** | [UD Romanian RRT v2.8](https://github.com/UniversalDependencies/UD_Romanian-RRT) (Barbu Mititelu, Verginica; Irimia, Elena; Perez, Cenel-Augusto; Ion, Radu; Simionescu, Radu; Popel, Martin)<br />[RONEC - the Romanian Named Entity Corpus (ca9ce460)](https://github.com/dumitrescustefan/ronec) (Dumitrescu, Stefan Daniel; Avram, Andrei-Marius; Morogan, Luciana; Toma; Stefan)<br />[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (540 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `ARROW`, `Af`, `Afcfp-n`, `Afcfson`, `Afcfsrn`, `Afcmpoy`, `Afcms-n`, `Afp`, `Afp-p-n`, `Afp-poy`, `Afp-srn`, `Afpf--n`, `Afpfp-n`, `Afpfp-ny`, `Afpfpoy`, `Afpfpry`, `Afpfson`, `Afpfsoy`, `Afpfsrn`, `Afpfsry`, `Afpm--n`, `Afpmp-n`, `Afpmpoy`, `Afpmpry`, `Afpms-n`, `Afpmsoy`, `Afpmsry`, `Afsfp-n`, `Afsfsrn`, `BULLET`, `COLON`, `COMMA`, `Ccssp`, `Ccsspy`, `Crssp`, `Csssp`, `Cssspy`, `DASH`, `DBLQ`, `Dd3-po---e`, `Dd3-po---o`, `Dd3fpo`, `Dd3fpr`, `Dd3fpr---e`, `Dd3fpr---o`, `Dd3fpr--y`, `Dd3fso`, `Dd3fso---e`, `Dd3fsr`, `Dd3fsr---e`, `Dd3fsr---o`, `Dd3fsr--yo`, `Dd3mpo`, `Dd3mpr`, `Dd3mpr---e`, `Dd3mpr---o`, `Dd3mso---e`, `Dd3msr`, `Dd3msr---e`, `Dd3msr---o`, `Dh1ms`, `Dh3fp`, `Dh3fso`, `Dh3fsr`, `Dh3mp`, `Dh3ms`, `Di3`, `Di3-----y`, `Di3--r---e`, `Di3-po`, `Di3-po---e`, `Di3-sr`, `Di3-sr---e`, `Di3-sr--y`, `Di3fp`, `Di3fpr`, `Di3fpr---e`, `Di3fso`, `Di3fso---e`, `Di3fsr`, `Di3fsr---e`, `Di3mp`, `Di3mpr`, `Di3mpr---e`, `Di3ms`, `Di3ms----e`, `Di3mso---e`, `Di3msr`, `Di3msr---e`, `Ds1fp-p`, `Ds1fp-s`, `Ds1fsop`, `Ds1fsos`, `Ds1fsrp`, `Ds1fsrs`, `Ds1fsrs-y`, `Ds1mp-p`, `Ds1mp-s`, `Ds1ms-p`, `Ds1ms-s`, `Ds1msrs-y`, `Ds2---s`, `Ds2fp-p`, `Ds2fp-s`, `Ds2fsrp`, `Ds2fsrs`, `Ds2mp-p`, `Ds2mp-s`, `Ds2ms-p`, `Ds2ms-s`, `Ds3---p`, `Ds3---s`, `Ds3---sy`, `Ds3fp-s`, `Ds3fsos`, `Ds3fsrs`, `Ds3mp-s`, `Ds3ms-s`, `Dw3--r---e`, `Dw3-po---e`, `Dw3fpr`, `Dw3fso---e`, `Dw3fsr`, `Dw3mpr`, `Dw3mso---e`, `Dw3msr`, `Dz3fsr---e`, `Dz3mso---e`, `Dz3msr---e`, `EQUAL`, `EXCL`, `EXCLHELLIP`, `GE`, `GT`, `HELLIP`, `I`, `LCURL`, `LPAR`, `LSQR`, `LT`, `M`, `Mc-p-d`, `Mc-p-l`, `Mc-s-b`, `Mc-s-d`, `Mc-s-l`, `Mcfp-l`, `Mcfp-ln`, `Mcfprln`, `Mcfprly`, `Mcfsoln`, `Mcfsrl`, `Mcfsrln`, `Mcfsrly`, `Mcmp-l`, `Mcms-ln`, `Mcmsrl`, `Mcmsrln`, `Mcmsrly`, `Mffprln`, `Mffsrln`, `Mlfpo`, `Mlfpr`, `Mlmpr`, `Mo---l`, `Mo---ln`, `Mo-s-r`, `Mofp-ln`, `Mofpoly`, `Mofprly`, `Mofs-l`, `Mofsoln`, `Mofsoly`, `Mofsrln`, `Mofsrly`, `Mompoly`, `Momprly`, `Moms-l`, `Moms-ln`, `Momsoly`, `Momsrly`, `Nc`, `Nc---n`, `Ncf--n`, `Ncfp-n`, `Ncfpoy`, `Ncfpry`, `Ncfs-n`, `Ncfson`, `Ncfsoy`, `Ncfsrn`, `Ncfsry`, `Ncfsryy`, `Ncfsvy`, `Ncm--n`, `Ncmp-n`, `Ncmpoy`, `Ncmpry`, `Ncms-n`, `Ncms-ny`, `Ncms-y`, `Ncmsoy`, `Ncmsrn`, `Ncmsry`, `Ncmsryy`, `Ncmsvn`, `Ncmsvy`, `Np`, `Npfson`, `Npfsoy`, `Npfsrn`, `Npfsry`, `Npmpoy`, `Npmpry`, `Npms-n`, `Npmsoy`, `Npmsry`, `PERCENT`, `PERIOD`, `PLUS`, `PLUSMINUS`, `Pd3-po`, `Pd3fpr`, `Pd3fso`, `Pd3fsr`, `Pd3mpo`, `Pd3mpr`, `Pd3mpr--y`, `Pd3mso`, `Pd3msr`, `Pi3--r`, `Pi3-po`, `Pi3-so`, `Pi3-sr`, `Pi3fpr`, `Pi3fso`, `Pi3fsr`, `Pi3mpr`, `Pi3mso`, `Pi3msr`, `Pi3msr--y`, `Pp1-pa--------w`, `Pp1-pa--y-----w`, `Pp1-pd--------s`, `Pp1-pd--------w`, `Pp1-pd--y-----w`, `Pp1-pr--------s`, `Pp1-sa--------s`, `Pp1-sa--------w`, `Pp1-sa--y-----w`, `Pp1-sd--------s`, `Pp1-sd--------w`, `Pp1-sd--y-----w`, `Pp1-sn--------s`, `Pp2-----------s`, `Pp2-pa--------w`, `Pp2-pa--y-----w`, `Pp2-pd--------w`, `Pp2-pd--y-----w`, `Pp2-pr--------s`, `Pp2-sa--------s`, `Pp2-sa--------w`, `Pp2-sa--y-----w`, `Pp2-sd--------s`, `Pp2-sd--------w`, `Pp2-sd--y-----w`, `Pp2-sn--------s`, `Pp2-so--------s`, `Pp2-sr--------s`, `Pp3-p---------s`, `Pp3-pd--------w`, `Pp3-pd--y-----w`, `Pp3-po--------s`, `Pp3-sd--------w`, `Pp3-sd--y-----w`, `Pp3-so--------s`, `Pp3fpa--------w`, `Pp3fpa--y-----w`, `Pp3fpr--------s`, `Pp3fs---------s`, `Pp3fsa--------w`, `Pp3fsa--y-----w`, `Pp3fso--------s`, `Pp3fsr--------s`, `Pp3fsr--y-----s`, `Pp3mpa--------w`, `Pp3mpa--y-----w`, `Pp3mpr--------s`, `Pp3ms---------s`, `Pp3msa--------w`, `Pp3msa--y-----w`, `Pp3mso--------s`, `Pp3msr--------s`, `Pp3msr--y-----s`, `Ps1fp-s`, `Ps1fsrp`, `Ps1fsrs`, `Ps1mp-p`, `Ps1ms-p`, `Ps2fp-s`, `Ps2fsrp`, `Ps2fsrs`, `Ps3---p`, `Ps3---s`, `Ps3fp-s`, `Ps3fsrs`, `Ps3mp-s`, `Ps3ms-s`, `Pw3--r`, `Pw3-po`, `Pw3-so`, `Pw3fpr`, `Pw3fso`, `Pw3mpr`, `Pw3mso`, `Px3--a--------s`, `Px3--a--------w`, `Px3--a--y-----w`, `Px3--d--------w`, `Px3--d--y-----w`, `Pz3-sr`, `Pz3fsr`, `QUEST`, `QUOT`, `Qf`, `Qn`, `Qs`, `Qs-y`, `Qz`, `Qz-y`, `RCURL`, `RPAR`, `RSQR`, `Rc`, `Rgp`, `Rgpy`, `Rgs`, `Rp`, `Rw`, `Rw-y`, `Rz`, `SCOLON`, `SLASH`, `STAR`, `Sp`, `Spsa`, `Spsay`, `Spsd`, `Spsg`, `Td-po`, `Tdfpr`, `Tdfso`, `Tdfsr`, `Tdmpr`, `Tdmso`, `Tdmsr`, `Tf-so`, `Tffpoy`, `Tffpry`, `Tffs-y`, `Tfmpoy`, `Tfms-y`, `Tfmsoy`, `Tfmsry`, `Ti-po`, `Tifp-y`, `Tifso`, `Tifsr`, `Timso`, `Timsr`, `Tsfp`, `Tsfs`, `Tsmp`, `Tsms`, `UNDERSC`, `Va--1`, `Va--1-----y`, `Va--1p`, `Va--1s`, `Va--1s----y`, `Va--2p`, `Va--2p----y`, `Va--2s`, `Va--2s----y`, `Va--3`, `Va--3-----y`, `Va--3p`, `Va--3p----y`, `Va--3s`, `Va--3s----y`, `Vag`, `Vag-------y`, `Vaii1`, `Vaii2s`, `Vaii3p`, `Vaii3s`, `Vail3p`, `Vail3s`, `Vaip1p`, `Vaip1s`, `Vaip2p`, `Vaip2s`, `Vaip3p`, `Vaip3p----y`, `Vaip3s`, `Vaip3s----y`, `Vais3p`, `Vais3s`, `Vam-2s`, `Vanp`, `Vap--sm`, `Vasp1p`, `Vasp1s`, `Vasp2p`, `Vasp2s`, `Vasp3`, `Vmg`, `Vmg-------y`, `Vmii1`, `Vmii1-----y`, `Vmii2p`, `Vmii2s`, `Vmii3p`, `Vmii3p----y`, `Vmii3s`, `Vmii3s----y`, `Vmil1`, `Vmil1p`, `Vmil2s`, `Vmil3p`, `Vmil3p----y`, `Vmil3s`, `Vmil3s----y`, `Vmip1p`, `Vmip1p----y`, `Vmip1s`, `Vmip1s----y`, `Vmip2p`, `Vmip2s`, `Vmip2s----y`, `Vmip3`, `Vmip3-----y`, `Vmip3p`, `Vmip3s`, `Vmip3s----y`, `Vmis1p`, `Vmis1s`, `Vmis3p`, `Vmis3p----y`, `Vmis3s`, `Vmis3s----y`, `Vmm-2p`, `Vmm-2s`, `Vmnp`, `Vmnp------y`, `Vmp--pf`, `Vmp--pm`, `Vmp--sf`, `Vmp--sm`, `Vmp--sm---y`, `Vmsp1p`, `Vmsp2p`, `Vmsp2s`, `Vmsp3`, `Vmsp3-----y`, `X`, `Y`, `Ya`, `Yn`, `Ynfsoy`, `Ynfsry`, `Ynmsoy`, `Ynmsry`, `Yp`, `Yp,Yn`, `Yp-sr`, `Yr`, `_SP` |
| **`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`, `expl`, `expl:impers`, `expl:pass`, `expl:poss`, `expl:pv`, `fixed`, `flat`, `goeswith`, `iobj`, `mark`, `nmod`, `nmod:tmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `obl:pmod`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`ner`** | `DATETIME`, `EVENT`, `FACILITY`, `GPE`, `LANGUAGE`, `LOC`, `MONEY`, `NAT_REL_POL`, `NUMERIC_VALUE`, `ORDINAL`, `ORGANIZATION`, `PERIOD`, `PERSON`, `PRODUCT`, `QUANTITY`, `WORK_OF_ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.80 |
| `TOKEN_P` | 99.67 |
| `TOKEN_R` | 99.57 |
| `TOKEN_F` | 99.59 |
| `TAG_ACC` | 96.57 |
| `SENTS_P` | 97.32 |
| `SENTS_R` | 96.68 |
| `SENTS_F` | 97.00 |
| `DEP_UAS` | 88.76 |
| `DEP_LAS` | 83.59 |
| `LEMMA_ACC` | 95.76 |
| `POS_ACC` | 93.95 |
| `MORPH_ACC` | 95.00 |
| `MORPH_MICRO_P` | 99.05 |
| `MORPH_MICRO_R` | 95.76 |
| `MORPH_MICRO_F` | 97.04 |
| `ENTS_P` | 75.03 |
| `ENTS_R` | 77.22 |
| `ENTS_F` | 76.11 |
|
spacy/ru_core_news_lg
|
spacy
| 2023-10-10T06:38:03Z | 70 | 8 |
spacy
|
[
"spacy",
"token-classification",
"ru",
"license:mit",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- ru
license: mit
model-index:
- name: ru_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9524209818
- name: NER Recall
type: recall
value: 0.9535431745
- name: NER F Score
type: f_score
value: 0.9529817478
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.989280677
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.989280677
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9749177029
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 2.15295e-05
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.962198055
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9511948091
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9985729236
---
### Details: https://spacy.io/models/ru#ru_core_news_lg
Russian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `ru_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 500002 keys, 500002 unique vectors (300 dimensions) |
| **Sources** | [Nerus](https://github.com/natasha/nerus) (Alexander Kukushkin)<br />[Navec](https://github.com/natasha/navec) (Alexander Kukushkin) |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (900 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Acc\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Degree=Pos\|POS=ADV`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=Third\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=PROPN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=NUM`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=Third`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=DET`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=SCONJ`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=Third`, `Case=Acc\|POS=NUM`, `Case=Ins\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=CCONJ`, `Case=Nom\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|StyleVariant=Short\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=Third\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=Third\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Number=Plur\|POS=ADJ\|StyleVariant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Number=Plur\|POS=VERB\|StyleVariant=Short\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Cnd\|POS=SCONJ`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=Third`, `POS=PART\|Polarity=Neg`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf\|Voice=Mid`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=SPACE`, `Case=Nom\|Number=Plur\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=PRON`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=INTJ`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=Third`, `Case=Nom\|Number=Plur\|POS=PRON`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|StyleVariant=Short`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=Third`, `Case=Gen\|POS=PRON`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf\|Voice=Act`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=Third`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET`, `Case=Nom\|POS=PRON`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=Third\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=Third`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=First`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=First\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Mood=Cnd\|POS=AUX`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=First`, `Case=Gen\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf\|Voice=Mid`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET`, `POS=PART`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=Third\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=Third\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|StyleVariant=Short`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=Third`, `Aspect=Perf\|Gender=Neut\|Number=Sing\|POS=VERB\|StyleVariant=Short\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=Third\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Perf\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Conv\|Voice=Act`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|POS=NUM`, `Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=Third\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NUM`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=Third\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=PRON\|Person=Third`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Dat\|POS=PRON`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=Third\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=Third`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|POS=AUX\|VerbForm=Inf\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=Third\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|StyleVariant=Short`, `Degree=Cmp\|POS=ADV`, `Aspect=Perf\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=First\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Ins\|Number=Plur\|POS=DET`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=First\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|POS=NUM`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Gender=Fem\|Number=Sing\|POS=VERB\|StyleVariant=Short\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Conv\|Voice=Act`, `Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=Second`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET`, `POS=ADV`, `Case=Acc\|POS=PRON`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Ins\|POS=NUM`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=First\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=Third`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Perf\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=PRON`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Number=Plur\|POS=DET`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=Third`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON`, `Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=Second\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=Second`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=Second\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `POS=SYM`, `Degree=Cmp\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Masc\|POS=NUM`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Fem\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Degree=Pos\|POS=ADJ`, `Case=Ins\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=Third`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=PRON`, `Animacy=Anim\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=PUNCT\|StyleVariant=Short`, `Case=Ins\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=SCONJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Imp\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Mid`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=First\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=First`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=Second\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `POS=NOUN`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=Third`, `Degree=Cmp\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=Third`, `Animacy=Anim\|Case=Voc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=Third`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=NUM`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|POS=NUM`, `Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Animacy=Inan\|Case=Par\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON`, `Case=Gen\|Number=Plur\|POS=DET\|Person=Third`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADV`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NUM`, `Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Masc\|POS=NUM`, `Aspect=Imp\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=Third`, `Animacy=Anim\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|POS=NUM`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `POS=ADV\|Polarity=Neg`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Perf\|Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Acc\|Gender=Neut\|POS=NUM`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=Second\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Number=Sing\|POS=PRON\|Person=First`, `Case=Nom\|Gender=Neut\|POS=NUM`, `Case=Gen\|POS=VERB\|Polarity=Neg`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=Second\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON`, `Aspect=Imp\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Number=Plur\|POS=PRON`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=Third`, `Case=Gen\|Number=Plur\|POS=PRON`, `Aspect=Perf\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `POS=CCONJ\|Polarity=Neg`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=Third`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=PRON\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Conv\|Voice=Mid`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=Second`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=Second\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=Third\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET`, `Animacy=Anim\|Case=Acc\|POS=NUM`, `Aspect=Imp\|Number=Plur\|POS=VERB\|StyleVariant=Short\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|StyleVariant=Short\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=Third`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NUM`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NUM`, `Aspect=Imp\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Loc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=First`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=First`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=Second`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=Second\|VerbForm=Fin\|Voice=Act`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=First`, `Foreign=Yes\|POS=PUNCT`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=PRON\|Person=Third\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=First\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=Third`, `Case=Dat\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|POS=NUM`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=First\|Tense=Pres\|VerbForm=Fin\|Voice=Mid`, `Case=Dat\|Number=Plur\|POS=DET`, `Aspect=Imp\|POS=AUX\|Tense=Pres\|VerbForm=Conv\|Voice=Act`, `Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|POS=PRON`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Perf\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=Third`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=Second\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `POS=PROPN`, `Aspect=Perf\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=Second\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=Second`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=Third`, `Animacy=Anim\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Dat\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=First`, `Aspect=Imp\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Person=Third`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NUM`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PUNCT`, `Animacy=Anim\|Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=PRON\|Person=First`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Imp\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=Second`, `Aspect=Perf\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET`, `Animacy=Anim\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON`, `Aspect=Perf\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Number=Plur\|POS=PRON`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=PROPN`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Gen\|Number=Plur\|POS=DET`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Ins\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Dat\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADV`, `Foreign=Yes\|POS=PART`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=First\|Tense=Fut\|VerbForm=Fin\|Voice=Act`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=DET`, `Case=Loc\|Gender=Fem\|POS=NUM`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Conv\|Voice=Mid`, `Aspect=Imp\|Case=Loc\|Number=Plur\|POS=VERB\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PUNCT`, `Animacy=Anim\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Aspect=Perf\|Case=Ins\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET`, `Animacy=Anim\|Aspect=Imp\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=Third`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=PUNCT`, `Aspect=Imp\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Anim\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=ADV`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=DET`, `Aspect=Imp\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Act`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=First\|Tense=Fut\|VerbForm=Fin\|Voice=Mid`, `Case=Nom\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=Second\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=DET`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=First`, _(truncated: full list in pipeline meta)_ |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `csubj:pass`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `nummod:entity`, `nummod:gov`, `obj`, `obl`, `obl:agent`, `orphan`, `parataxis`, `punct`, `xcomp` |
| **`ner`** | `LOC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.68 |
| `TOKEN_P` | 97.28 |
| `TOKEN_R` | 98.31 |
| `TOKEN_F` | 97.79 |
| `POS_ACC` | 98.93 |
| `MORPH_ACC` | 97.49 |
| `MORPH_MICRO_P` | 98.97 |
| `MORPH_MICRO_R` | 98.30 |
| `MORPH_MICRO_F` | 98.64 |
| `SENTS_P` | 99.87 |
| `SENTS_R` | 99.85 |
| `SENTS_F` | 99.86 |
| `DEP_UAS` | 96.22 |
| `DEP_LAS` | 95.12 |
| `TAG_ACC` | 98.93 |
| `LEMMA_ACC` | 0.00 |
| `ENTS_P` | 95.24 |
| `ENTS_R` | 95.35 |
| `ENTS_F` | 95.30 |
|
spacy/sl_core_news_lg
|
spacy
| 2023-10-10T06:36:35Z | 7 | 0 |
spacy
|
[
"spacy",
"token-classification",
"sl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2023-07-07T09:07:23Z |
---
tags:
- spacy
- token-classification
language:
- sl
license: cc-by-sa-4.0
model-index:
- name: sl_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7710843373
- name: NER Recall
type: recall
value: 0.8101265823
- name: NER F Score
type: f_score
value: 0.7901234568
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9249278769
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9768137622
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9278341703
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9581792927
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8735384615
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8428571429
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.8977497039
---
### Details: https://spacy.io/models/sl#sl_core_news_lg
Slovenian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), attribute_ruler, senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `sl_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
| **Vectors** | floret (200000, 300) |
| **Sources** | [UD Slovenian SSJ v2.11](https://github.com/UniversalDependencies/UD_Slovenian-SSJ) (Dobrovoljc, Kaja; Erjavec, Tomaž; Krek, Simon)<br />[Training corpus SUK 1.0](https://www.clarin.si/repository/xmlui/handle/11356/1747) (Arhar Holdt, Špela; Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž; Gantar, Polona; Čibej, Jaka; Pori, Eva; Terčon, Luka; Munda, Tina; Žitnik, Slavko; Robida, Nejc; Blagus, Neli; Može, Sara; Ledinek, Nina; Holz, Nanika; Zupan, Katja; Kuzman, Taja; Kavčič, Teja; Škrjanec, Iza; Marko, Dafne; Jezeršek, Lucija; Zajc, Anja)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (2401 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `Agcfdn`, `Agcfpa`, `Agcfpd`, `Agcfpg`, `Agcfpi`, `Agcfpl`, `Agcfpn`, `Agcfsa`, `Agcfsd`, `Agcfsg`, `Agcfsi`, `Agcfsl`, `Agcfsn`, `Agcmda`, `Agcmdn`, `Agcmpa`, `Agcmpd`, `Agcmpg`, `Agcmpi`, `Agcmpl`, `Agcmpn`, `Agcmsay`, `Agcmsd`, `Agcmsg`, `Agcmsi`, `Agcmsl`, `Agcmsny`, `Agcndn`, `Agcnpa`, `Agcnpd`, `Agcnpg`, `Agcnpi`, `Agcnpl`, `Agcnpn`, `Agcnsa`, `Agcnsd`, `Agcnsg`, `Agcnsi`, `Agcnsl`, `Agcnsn`, `Agpfda`, `Agpfdg`, `Agpfdi`, `Agpfdl`, `Agpfdn`, `Agpfpa`, `Agpfpd`, `Agpfpg`, `Agpfpi`, `Agpfpl`, `Agpfpn`, `Agpfsa`, `Agpfsd`, `Agpfsg`, `Agpfsi`, `Agpfsl`, `Agpfsn`, `Agpmda`, `Agpmdd`, `Agpmdg`, `Agpmdi`, `Agpmdl`, `Agpmdn`, `Agpmpa`, `Agpmpd`, `Agpmpg`, `Agpmpi`, `Agpmpl`, `Agpmpn`, `Agpmsa`, `Agpmsan`, `Agpmsay`, `Agpmsd`, `Agpmsg`, `Agpmsi`, `Agpmsl`, `Agpmsnn`, `Agpmsny`, `Agpnda`, `Agpndg`, `Agpndi`, `Agpndn`, `Agpnpa`, `Agpnpd`, `Agpnpg`, `Agpnpi`, `Agpnpl`, `Agpnpn`, `Agpnsa`, `Agpnsd`, `Agpnsg`, `Agpnsi`, `Agpnsl`, `Agpnsn`, `Agsfda`, `Agsfpa`, `Agsfpg`, `Agsfpi`, `Agsfpl`, `Agsfpn`, `Agsfsa`, `Agsfsd`, `Agsfsg`, `Agsfsi`, `Agsfsl`, `Agsfsn`, `Agsmdn`, `Agsmpa`, `Agsmpd`, `Agsmpg`, `Agsmpi`, `Agsmpl`, `Agsmpn`, `Agsmsa`, `Agsmsay`, `Agsmsg`, `Agsmsi`, `Agsmsl`, `Agsmsny`, `Agsnpa`, `Agsnpg`, `Agsnpn`, `Agsnsa`, `Agsnsg`, `Agsnsi`, `Agsnsl`, `Agsnsn`, `Appfda`, `Appfdg`, `Appfdi`, `Appfdn`, `Appfpa`, `Appfpd`, `Appfpg`, `Appfpi`, `Appfpl`, `Appfpn`, `Appfsa`, `Appfsd`, `Appfsg`, `Appfsi`, `Appfsl`, `Appfsn`, `Appmda`, `Appmdg`, `Appmdl`, `Appmdn`, `Appmpa`, `Appmpd`, `Appmpg`, `Appmpi`, `Appmpl`, `Appmpn`, `Appmsa`, `Appmsan`, `Appmsay`, `Appmsd`, `Appmsg`, `Appmsi`, `Appmsl`, `Appmsnn`, `Appmsny`, `Appndg`, `Appndn`, `Appnpa`, `Appnpg`, `Appnpi`, `Appnpl`, `Appnpn`, `Appnsa`, `Appnsd`, `Appnsg`, `Appnsi`, `Appnsl`, `Appnsn`, `Aspfdi`, `Aspfdn`, `Aspfpa`, `Aspfpg`, `Aspfpl`, `Aspfpn`, `Aspfsa`, `Aspfsd`, `Aspfsg`, `Aspfsi`, `Aspfsl`, `Aspfsn`, `Aspmdl`, `Aspmdn`, `Aspmpa`, `Aspmpd`, `Aspmpg`, `Aspmpl`, `Aspmpn`, `Aspmsa`, `Aspmsan`, `Aspmsd`, `Aspmsg`, `Aspmsi`, `Aspmsl`, `Aspmsnn`, `Aspnpa`, `Aspnpg`, `Aspnpi`, `Aspnpl`, `Aspnpn`, `Aspnsa`, `Aspnsg`, `Aspnsi`, `Aspnsl`, `Aspnsn`, `Cc`, `Cs`, `I`, `Mdc`, `Mdo`, `Mlc-pa`, `Mlc-pd`, `Mlc-pg`, `Mlc-pi`, `Mlc-pl`, `Mlc-pn`, `Mlcfda`, `Mlcfdg`, `Mlcfdi`, `Mlcfdl`, `Mlcfdn`, `Mlcfpa`, `Mlcfpd`, `Mlcfpg`, `Mlcfpi`, `Mlcfpl`, `Mlcfpn`, `Mlcmda`, `Mlcmdg`, `Mlcmdi`, `Mlcmdl`, `Mlcmdn`, `Mlcmpa`, `Mlcmpd`, `Mlcmpg`, `Mlcmpi`, `Mlcmpl`, `Mlcmpn`, `Mlcnda`, `Mlcndg`, `Mlcndi`, `Mlcndl`, `Mlcndn`, `Mlcnpa`, `Mlcnpg`, `Mlcnpi`, `Mlcnpl`, `Mlcnpn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsa`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlonda`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mlpfdl`, `Mlpfdn`, `Mlpfpa`, `Mlpfpg`, `Mlpfpi`, `Mlpfpl`, `Mlpfpn`, `Mlpfsa`, `Mlpfsd`, `Mlpfsg`, `Mlpfsi`, `Mlpfsl`, `Mlpfsn`, `Mlpmdl`, `Mlpmpa`, `Mlpmpd`, `Mlpmpg`, `Mlpmpi`, `Mlpmpl`, `Mlpmpn`, `Mlpmsa`, `Mlpmsan`, `Mlpmsay`, `Mlpmsd`, `Mlpmsg`, `Mlpmsi`, `Mlpmsl`, `Mlpmsn`, `Mlpmsnn`, `Mlpmsny`, `Mlpnpa`, `Mlpnpg`, `Mlpnpi`, `Mlpnpl`, `Mlpnpn`, `Mlpnsa`, `Mlpnsg`, `Mlpnsi`, `Mlpnsl`, `Mlpnsn`, `Mlsfpa`, `Mlsfsg`, `Mlsfsi`, `Mlsfsn`, `Mlsmpi`, `Mlsmsg`, `Mlsmsi`, `Mlsnsa`, `Mlsnsi`, `Mlsnsn`, `Mrc`, `Mro`, `Ncfda`, `Ncfdd`, `Ncfdg`, `Ncfdi`, `Ncfdl`, `Ncfdn`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncmda`, `Ncmdd`, `Ncmdg`, `Ncmdi`, `Ncmdl`, `Ncmdn`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncnda`, `Ncndd`, `Ncndg`, `Ncndi`, `Ncndl`, `Ncndn`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Npfpa`, `Npfpd`, `Npfpg`, `Npfpi`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmda`, `Npmdg`, `Npmdn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fda`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mda`, `Pd-mdg`, `Pd-mdi`, `Pd-mdl`, `Pd-mdn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpi`, `Pd-mpl`, `Pd-mpn`, `Pd-msa`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npd`, `Pd-npg`, `Pd-npi`, `Pd-npl`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pg-fda`, `Pg-fdg`, `Pg-fdi`, `Pg-fdl`, `Pg-fdn`, `Pg-fpa`, `Pg-fpd`, `Pg-fpg`, `Pg-fpi`, `Pg-fpl`, `Pg-fpn`, `Pg-fsa`, `Pg-fsd`, `Pg-fsg`, `Pg-fsi`, `Pg-fsl`, `Pg-fsn`, `Pg-mda`, `Pg-mdd`, `Pg-mdg`, `Pg-mdi`, `Pg-mdl`, `Pg-mdn`, `Pg-mpa`, `Pg-mpd`, `Pg-mpg`, `Pg-mpi`, `Pg-mpl`, `Pg-mpn`, `Pg-msa`, _(truncated: full list in pipeline meta)_ |
| **`morphologizer`** | `POS=PUNCT`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Ind`, `Aspect=Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Aspect=Perf\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=PRON\|PronType=Prs\|Reflex=Yes\|Variant=Short`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=SCONJ`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Short`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=VERB\|VerbForm=Inf`, `Mood=Cnd\|POS=AUX\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Aspect=Perf\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=PART`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=ADP`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|POS=ADP`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Degree=Pos\|POS=ADV`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Neut\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Degree=Cmp\|POS=ADV`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|POS=ADP`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=AUX\|VerbForm=Inf`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Bound`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `NumForm=Roman\|NumType=Ord\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=X`, `POS=SYM`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Aspect=Perf\|Gender=Masc\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|POS=X`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Dual\|POS=AUX\|VerbForm=Part`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes\|Variant=Short`, `Aspect=Imp\|Gender=Masc\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|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=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `NumForm=Digit\|NumType=Ord\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Bound`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=SPACE`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|Number[psor]=Dual\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Dual\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|POS=VERB\|VerbForm=Sup`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Neut\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Neut\|Number=Plur\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Bound`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Bound`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes\|Variant=Bound`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=INTJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|VerbForm=Sup`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Foreign=Yes\|POS=X`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=AUX\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `NumForm=Roman\|NumType=Card\|POS=NUM`, `Case=Loc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Aspect=Imp\|Gender=Neut\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Ins\|Gender=Neut\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Ins\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Neut\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Ins\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Dual\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Dual\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=PROPN`, _(truncated: full list in pipeline meta)_ |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `cc:preconj`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`ner`** | `DERIV_PER`, `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.81 |
| `TOKEN_P` | 99.81 |
| `TOKEN_R` | 99.57 |
| `TOKEN_F` | 99.69 |
| `TAG_ACC` | 92.49 |
| `POS_ACC` | 97.68 |
| `MORPH_ACC` | 92.78 |
| `MORPH_MICRO_P` | 95.99 |
| `MORPH_MICRO_R` | 95.72 |
| `MORPH_MICRO_F` | 95.86 |
| `SENTS_P` | 88.62 |
| `SENTS_R` | 90.96 |
| `SENTS_F` | 89.77 |
| `DEP_UAS` | 87.35 |
| `DEP_LAS` | 84.29 |
| `LEMMA_ACC` | 95.82 |
| `ENTS_P` | 77.11 |
| `ENTS_R` | 81.01 |
| `ENTS_F` | 79.01 |
|
spacy/sl_core_news_sm
|
spacy
| 2023-10-10T06:36:08Z | 3 | 0 |
spacy
|
[
"spacy",
"token-classification",
"sl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2023-07-07T09:07:07Z |
---
tags:
- spacy
- token-classification
language:
- sl
license: cc-by-sa-4.0
model-index:
- name: sl_core_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.625
- name: NER Recall
type: recall
value: 0.6329113924
- name: NER F Score
type: f_score
value: 0.6289308176
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9027032803
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9686932365
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9054813548
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9461694625
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8577080311
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8212668689
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9077141505
---
### Details: https://spacy.io/models/sl#sl_core_news_sm
Slovenian pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), attribute_ruler, senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `sl_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD Slovenian SSJ v2.11](https://github.com/UniversalDependencies/UD_Slovenian-SSJ) (Dobrovoljc, Kaja; Erjavec, Tomaž; Krek, Simon)<br />[Training corpus SUK 1.0](https://www.clarin.si/repository/xmlui/handle/11356/1747) (Arhar Holdt, Špela; Krek, Simon; Dobrovoljc, Kaja; Erjavec, Tomaž; Gantar, Polona; Čibej, Jaka; Pori, Eva; Terčon, Luka; Munda, Tina; Žitnik, Slavko; Robida, Nejc; Blagus, Neli; Može, Sara; Ledinek, Nina; Holz, Nanika; Zupan, Katja; Kuzman, Taja; Kavčič, Teja; Škrjanec, Iza; Marko, Dafne; Jezeršek, Lucija; Zajc, Anja) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (2401 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `Agcfdn`, `Agcfpa`, `Agcfpd`, `Agcfpg`, `Agcfpi`, `Agcfpl`, `Agcfpn`, `Agcfsa`, `Agcfsd`, `Agcfsg`, `Agcfsi`, `Agcfsl`, `Agcfsn`, `Agcmda`, `Agcmdn`, `Agcmpa`, `Agcmpd`, `Agcmpg`, `Agcmpi`, `Agcmpl`, `Agcmpn`, `Agcmsay`, `Agcmsd`, `Agcmsg`, `Agcmsi`, `Agcmsl`, `Agcmsny`, `Agcndn`, `Agcnpa`, `Agcnpd`, `Agcnpg`, `Agcnpi`, `Agcnpl`, `Agcnpn`, `Agcnsa`, `Agcnsd`, `Agcnsg`, `Agcnsi`, `Agcnsl`, `Agcnsn`, `Agpfda`, `Agpfdg`, `Agpfdi`, `Agpfdl`, `Agpfdn`, `Agpfpa`, `Agpfpd`, `Agpfpg`, `Agpfpi`, `Agpfpl`, `Agpfpn`, `Agpfsa`, `Agpfsd`, `Agpfsg`, `Agpfsi`, `Agpfsl`, `Agpfsn`, `Agpmda`, `Agpmdd`, `Agpmdg`, `Agpmdi`, `Agpmdl`, `Agpmdn`, `Agpmpa`, `Agpmpd`, `Agpmpg`, `Agpmpi`, `Agpmpl`, `Agpmpn`, `Agpmsa`, `Agpmsan`, `Agpmsay`, `Agpmsd`, `Agpmsg`, `Agpmsi`, `Agpmsl`, `Agpmsnn`, `Agpmsny`, `Agpnda`, `Agpndg`, `Agpndi`, `Agpndn`, `Agpnpa`, `Agpnpd`, `Agpnpg`, `Agpnpi`, `Agpnpl`, `Agpnpn`, `Agpnsa`, `Agpnsd`, `Agpnsg`, `Agpnsi`, `Agpnsl`, `Agpnsn`, `Agsfda`, `Agsfpa`, `Agsfpg`, `Agsfpi`, `Agsfpl`, `Agsfpn`, `Agsfsa`, `Agsfsd`, `Agsfsg`, `Agsfsi`, `Agsfsl`, `Agsfsn`, `Agsmdn`, `Agsmpa`, `Agsmpd`, `Agsmpg`, `Agsmpi`, `Agsmpl`, `Agsmpn`, `Agsmsa`, `Agsmsay`, `Agsmsg`, `Agsmsi`, `Agsmsl`, `Agsmsny`, `Agsnpa`, `Agsnpg`, `Agsnpn`, `Agsnsa`, `Agsnsg`, `Agsnsi`, `Agsnsl`, `Agsnsn`, `Appfda`, `Appfdg`, `Appfdi`, `Appfdn`, `Appfpa`, `Appfpd`, `Appfpg`, `Appfpi`, `Appfpl`, `Appfpn`, `Appfsa`, `Appfsd`, `Appfsg`, `Appfsi`, `Appfsl`, `Appfsn`, `Appmda`, `Appmdg`, `Appmdl`, `Appmdn`, `Appmpa`, `Appmpd`, `Appmpg`, `Appmpi`, `Appmpl`, `Appmpn`, `Appmsa`, `Appmsan`, `Appmsay`, `Appmsd`, `Appmsg`, `Appmsi`, `Appmsl`, `Appmsnn`, `Appmsny`, `Appndg`, `Appndn`, `Appnpa`, `Appnpg`, `Appnpi`, `Appnpl`, `Appnpn`, `Appnsa`, `Appnsd`, `Appnsg`, `Appnsi`, `Appnsl`, `Appnsn`, `Aspfdi`, `Aspfdn`, `Aspfpa`, `Aspfpg`, `Aspfpl`, `Aspfpn`, `Aspfsa`, `Aspfsd`, `Aspfsg`, `Aspfsi`, `Aspfsl`, `Aspfsn`, `Aspmdl`, `Aspmdn`, `Aspmpa`, `Aspmpd`, `Aspmpg`, `Aspmpl`, `Aspmpn`, `Aspmsa`, `Aspmsan`, `Aspmsd`, `Aspmsg`, `Aspmsi`, `Aspmsl`, `Aspmsnn`, `Aspnpa`, `Aspnpg`, `Aspnpi`, `Aspnpl`, `Aspnpn`, `Aspnsa`, `Aspnsg`, `Aspnsi`, `Aspnsl`, `Aspnsn`, `Cc`, `Cs`, `I`, `Mdc`, `Mdo`, `Mlc-pa`, `Mlc-pd`, `Mlc-pg`, `Mlc-pi`, `Mlc-pl`, `Mlc-pn`, `Mlcfda`, `Mlcfdg`, `Mlcfdi`, `Mlcfdl`, `Mlcfdn`, `Mlcfpa`, `Mlcfpd`, `Mlcfpg`, `Mlcfpi`, `Mlcfpl`, `Mlcfpn`, `Mlcmda`, `Mlcmdg`, `Mlcmdi`, `Mlcmdl`, `Mlcmdn`, `Mlcmpa`, `Mlcmpd`, `Mlcmpg`, `Mlcmpi`, `Mlcmpl`, `Mlcmpn`, `Mlcnda`, `Mlcndg`, `Mlcndi`, `Mlcndl`, `Mlcndn`, `Mlcnpa`, `Mlcnpg`, `Mlcnpi`, `Mlcnpl`, `Mlcnpn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsa`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlonda`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mlpfdl`, `Mlpfdn`, `Mlpfpa`, `Mlpfpg`, `Mlpfpi`, `Mlpfpl`, `Mlpfpn`, `Mlpfsa`, `Mlpfsd`, `Mlpfsg`, `Mlpfsi`, `Mlpfsl`, `Mlpfsn`, `Mlpmdl`, `Mlpmpa`, `Mlpmpd`, `Mlpmpg`, `Mlpmpi`, `Mlpmpl`, `Mlpmpn`, `Mlpmsa`, `Mlpmsan`, `Mlpmsay`, `Mlpmsd`, `Mlpmsg`, `Mlpmsi`, `Mlpmsl`, `Mlpmsn`, `Mlpmsnn`, `Mlpmsny`, `Mlpnpa`, `Mlpnpg`, `Mlpnpi`, `Mlpnpl`, `Mlpnpn`, `Mlpnsa`, `Mlpnsg`, `Mlpnsi`, `Mlpnsl`, `Mlpnsn`, `Mlsfpa`, `Mlsfsg`, `Mlsfsi`, `Mlsfsn`, `Mlsmpi`, `Mlsmsg`, `Mlsmsi`, `Mlsnsa`, `Mlsnsi`, `Mlsnsn`, `Mrc`, `Mro`, `Ncfda`, `Ncfdd`, `Ncfdg`, `Ncfdi`, `Ncfdl`, `Ncfdn`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncmda`, `Ncmdd`, `Ncmdg`, `Ncmdi`, `Ncmdl`, `Ncmdn`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncnda`, `Ncndd`, `Ncndg`, `Ncndi`, `Ncndl`, `Ncndn`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Npfpa`, `Npfpd`, `Npfpg`, `Npfpi`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmda`, `Npmdg`, `Npmdn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npnpn`, `Npnsa`, `Npnsd`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fda`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mda`, `Pd-mdg`, `Pd-mdi`, `Pd-mdl`, `Pd-mdn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpi`, `Pd-mpl`, `Pd-mpn`, `Pd-msa`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npd`, `Pd-npg`, `Pd-npi`, `Pd-npl`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pg-fda`, `Pg-fdg`, `Pg-fdi`, `Pg-fdl`, `Pg-fdn`, `Pg-fpa`, `Pg-fpd`, `Pg-fpg`, `Pg-fpi`, `Pg-fpl`, `Pg-fpn`, `Pg-fsa`, `Pg-fsd`, `Pg-fsg`, `Pg-fsi`, `Pg-fsl`, `Pg-fsn`, `Pg-mda`, `Pg-mdd`, `Pg-mdg`, `Pg-mdi`, `Pg-mdl`, `Pg-mdn`, `Pg-mpa`, `Pg-mpd`, `Pg-mpg`, `Pg-mpi`, `Pg-mpl`, `Pg-mpn`, `Pg-msa`, _(truncated: full list in pipeline meta)_ |
| **`morphologizer`** | `POS=PUNCT`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Ind`, `Aspect=Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Aspect=Perf\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=PRON\|PronType=Prs\|Reflex=Yes\|Variant=Short`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=SCONJ`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Short`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=VERB\|VerbForm=Inf`, `Mood=Cnd\|POS=AUX\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Aspect=Perf\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=PART`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=ADP`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|POS=ADP`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Degree=Pos\|POS=ADV`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Neut\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Degree=Cmp\|POS=ADV`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|POS=ADP`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=AUX\|VerbForm=Inf`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Bound`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `NumForm=Roman\|NumType=Ord\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=X`, `POS=SYM`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Aspect=Perf\|Gender=Masc\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|POS=X`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Dual\|POS=AUX\|VerbForm=Part`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes\|Variant=Short`, `Aspect=Imp\|Gender=Masc\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|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=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `NumForm=Digit\|NumType=Ord\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Bound`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=SPACE`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|Number[psor]=Dual\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Dual\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|POS=VERB\|VerbForm=Sup`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Neut\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Neut\|Number=Plur\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Bound`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Bound`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes\|Variant=Bound`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=INTJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|VerbForm=Sup`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Foreign=Yes\|POS=X`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=AUX\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `NumForm=Roman\|NumType=Card\|POS=NUM`, `Case=Loc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Aspect=Imp\|Gender=Neut\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Ins\|Gender=Neut\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Ins\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Neut\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Ins\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Dual\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Dual\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=PROPN`, _(truncated: full list in pipeline meta)_ |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `cc:preconj`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`ner`** | `DERIV_PER`, `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.81 |
| `TOKEN_P` | 99.81 |
| `TOKEN_R` | 99.57 |
| `TOKEN_F` | 99.69 |
| `TAG_ACC` | 90.27 |
| `POS_ACC` | 96.87 |
| `MORPH_ACC` | 90.55 |
| `MORPH_MICRO_P` | 94.35 |
| `MORPH_MICRO_R` | 94.07 |
| `MORPH_MICRO_F` | 94.21 |
| `SENTS_P` | 89.96 |
| `SENTS_R` | 91.60 |
| `SENTS_F` | 90.77 |
| `DEP_UAS` | 85.77 |
| `DEP_LAS` | 82.13 |
| `LEMMA_ACC` | 94.62 |
| `ENTS_P` | 62.50 |
| `ENTS_R` | 63.29 |
| `ENTS_F` | 62.89 |
|
spacy/sv_core_news_lg
|
spacy
| 2023-10-10T06:36:05Z | 11 | 0 |
spacy
|
[
"spacy",
"token-classification",
"sv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-05-02T07:56:46Z |
---
tags:
- spacy
- token-classification
language:
- sv
license: cc-by-sa-4.0
model-index:
- name: sv_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8605993951
- name: NER Recall
type: recall
value: 0.7615571776
- name: NER F Score
type: f_score
value: 0.8080547309
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9534551393
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9635602736
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9586608145
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9559048688
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8310026609
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.7857102417
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9398058252
---
### Details: https://spacy.io/models/sv#sv_core_news_lg
Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `sv_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
| **Vectors** | floret (200000, 300) |
| **Sources** | [UD Swedish Talbanken v2.8](https://github.com/UniversalDependencies/UD_Swedish-Talbanken) (Nivre, Joakim; Smith, Aaron)<br />[Stockholm-Umeå Corpus (SUC) v3.0](https://huggingface.co/datasets/KBLab/sucx3_ner) (Språkbanken)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (381 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `AB`, `AB\|AN`, `AB\|KOM`, `AB\|POS`, `AB\|SMS`, `AB\|SUV`, `DT\|NEU\|SIN\|DEF`, `DT\|NEU\|SIN\|IND`, `DT\|NEU\|SIN\|IND/DEF`, `DT\|UTR/NEU\|PLU\|DEF`, `DT\|UTR/NEU\|PLU\|IND`, `DT\|UTR/NEU\|PLU\|IND/DEF`, `DT\|UTR/NEU\|SIN/PLU\|IND`, `DT\|UTR/NEU\|SIN\|DEF`, `DT\|UTR/NEU\|SIN\|IND`, `DT\|UTR\|SIN\|DEF`, `DT\|UTR\|SIN\|IND`, `DT\|UTR\|SIN\|IND/DEF`, `HA`, `HD\|NEU\|SIN\|IND`, `HD\|UTR/NEU\|PLU\|IND`, `HD\|UTR\|SIN\|IND`, `HP\|-\|-\|-`, `HP\|NEU\|SIN\|IND`, `HP\|UTR/NEU\|PLU\|IND`, `HP\|UTR\|SIN\|IND`, `HS\|DEF`, `IE`, `IN`, `JJ`, `JJ\|AN`, `JJ\|KOM\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|MAS\|SIN\|DEF\|GEN`, `JJ\|POS\|MAS\|SIN\|DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|SIN\|DEF\|NOM`, `JJ\|POS\|UTR\|-\|-\|SMS`, `JJ\|POS\|UTR\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|UTR\|SIN\|IND\|GEN`, `JJ\|POS\|UTR\|SIN\|IND\|NOM`, `JJ\|SUV\|MAS\|SIN\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|IND\|NOM`, `KN`, `MAD`, `MID`, `NN`, `NN\|-\|-\|-\|-`, `NN\|AN`, `NN\|NEU\|-\|-\|SMS`, `NN\|NEU\|PLU\|DEF\|GEN`, `NN\|NEU\|PLU\|DEF\|NOM`, `NN\|NEU\|PLU\|IND\|GEN`, `NN\|NEU\|PLU\|IND\|NOM`, `NN\|NEU\|SIN\|DEF\|GEN`, `NN\|NEU\|SIN\|DEF\|NOM`, `NN\|NEU\|SIN\|IND`, `NN\|NEU\|SIN\|IND\|GEN`, `NN\|NEU\|SIN\|IND\|NOM`, `NN\|SMS`, `NN\|UTR\|-\|-\|-`, `NN\|UTR\|-\|-\|SMS`, `NN\|UTR\|PLU\|DEF\|GEN`, `NN\|UTR\|PLU\|DEF\|NOM`, `NN\|UTR\|PLU\|IND\|GEN`, `NN\|UTR\|PLU\|IND\|NOM`, `NN\|UTR\|SIN\|DEF\|GEN`, `NN\|UTR\|SIN\|DEF\|NOM`, `NN\|UTR\|SIN\|IND\|GEN`, `NN\|UTR\|SIN\|IND\|NOM`, `PAD`, `PC\|PRF\|NEU\|SIN\|IND\|NOM`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `PC\|PRF\|UTR/NEU\|SIN\|DEF\|NOM`, `PC\|PRF\|UTR\|SIN\|IND\|NOM`, `PC\|PRS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `PL`, `PM`, `PM\|GEN`, `PM\|NOM`, `PM\|SMS`, `PN\|MAS\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|DEF`, `PN\|NEU\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|SUB`, `PN\|UTR/NEU\|PLU\|DEF\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|SIN/PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|NOM`, `PN\|UTR\|SIN\|DEF\|OBJ`, `PN\|UTR\|SIN\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|SUB/OBJ`, `PN\|UTR\|SIN\|IND\|NOM`, `PN\|UTR\|SIN\|IND\|SUB`, `PN\|UTR\|SIN\|IND\|SUB/OBJ`, `PP`, `PS\|NEU\|SIN\|DEF`, `PS\|UTR/NEU\|PLU\|DEF`, `PS\|UTR/NEU\|SIN/PLU\|DEF`, `PS\|UTR\|SIN\|DEF`, `RG\|NEU\|SIN\|IND\|NOM`, `RG\|NOM`, `RG\|SMS`, `RG\|UTR\|SIN\|IND\|NOM`, `RO\|MAS\|SIN\|IND/DEF\|NOM`, `RO\|NOM`, `SN`, `UO`, `VB\|AN`, `VB\|IMP\|AKT`, `VB\|IMP\|SFO`, `VB\|INF\|AKT`, `VB\|INF\|SFO`, `VB\|KON\|PRS\|AKT`, `VB\|KON\|PRT\|AKT`, `VB\|PRS\|AKT`, `VB\|PRS\|SFO`, `VB\|PRT\|AKT`, `VB\|PRT\|SFO`, `VB\|SUP\|AKT`, `VB\|SUP\|SFO`, `_SP` |
| **`morphologizer`** | `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `POS=PUNCT`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|POS=ADV`, `POS=SCONJ`, `POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Com\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=CCONJ`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Pos\|POS=ADV`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=VERB\|VerbForm=Sup\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Degree=Pos\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Case=Nom\|NumType=Card\|POS=NUM`, `Abbr=Yes\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Sup\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `POS=AUX\|VerbForm=Sup\|Voice=Act`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=SPACE`, `POS=VERB\|VerbForm=Sup\|Voice=Pass`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|POS=ADJ\|Tense=Pres\|VerbForm=Part`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Dem`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=NOUN`, `Case=Nom\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Int`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|POS=PROPN`, `POS=PROPN`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=VERB\|VerbForm=Sup`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=SYM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Degree=Sup\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Neg`, `Mood=Sub\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|Gender=Com\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|POS=DET\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Abbr=Yes\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `NumType=Card\|POS=NUM`, `POS=INTJ`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int`, `Degree=Sup\|POS=ADV\|Polarity=Neg`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Int`, `POS=ADV\|Polarity=Neg`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Ind`, `Foreign=Yes\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Dem`, `Abbr=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `Foreign=Yes\|POS=CCONJ`, `POS=DET\|PronType=Art`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Degree=Pos\|POS=ADV\|Polarity=Neg`, `Mood=Sub\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PRON\|PronType=Ind`, `Definite=Ind\|POS=DET\|PronType=Neg`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Neg`, `POS=CCONJ\|Polarity=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Imp\|POS=AUX\|VerbForm=Fin\|Voice=Act`, `Foreign=Yes\|POS=ADV`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rcp`, `Case=Acc\|Definite=Def\|POS=PRON\|Polarity=Neg\|PronType=Ind` |
| **`parser`** | `ROOT`, `acl`, `acl:cleft`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `expl`, `fixed`, `flat:name`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `parataxis`, `punct`, `xcomp` |
| **`ner`** | `EVN`, `LOC`, `MSR`, `OBJ`, `ORG`, `PRS`, `TME`, `WRK` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.99 |
| `TOKEN_P` | 99.95 |
| `TOKEN_R` | 99.96 |
| `TOKEN_F` | 99.95 |
| `TAG_ACC` | 95.35 |
| `POS_ACC` | 96.36 |
| `MORPH_ACC` | 95.87 |
| `MORPH_MICRO_P` | 97.83 |
| `MORPH_MICRO_R` | 97.45 |
| `MORPH_MICRO_F` | 97.64 |
| `SENTS_P` | 92.02 |
| `SENTS_R` | 96.03 |
| `SENTS_F` | 93.98 |
| `DEP_UAS` | 83.10 |
| `DEP_LAS` | 78.57 |
| `LEMMA_ACC` | 95.59 |
| `ENTS_P` | 86.06 |
| `ENTS_R` | 76.16 |
| `ENTS_F` | 80.81 |
|
spacy/sv_core_news_md
|
spacy
| 2023-10-10T06:35:45Z | 2 | 1 |
spacy
|
[
"spacy",
"token-classification",
"sv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-05-02T07:56:23Z |
---
tags:
- spacy
- token-classification
language:
- sv
license: cc-by-sa-4.0
model-index:
- name: sv_core_news_md
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8421624559
- name: NER Recall
type: recall
value: 0.7542579075
- name: NER F Score
type: f_score
value: 0.7957900141
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9514136981
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9628457691
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9560069409
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9550882923
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8323804132
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.783130484
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9269717624
---
### Details: https://spacy.io/models/sv#sv_core_news_md
Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `sv_core_news_md` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
| **Vectors** | floret (50000, 300) |
| **Sources** | [UD Swedish Talbanken v2.8](https://github.com/UniversalDependencies/UD_Swedish-Talbanken) (Nivre, Joakim; Smith, Aaron)<br />[Stockholm-Umeå Corpus (SUC) v3.0](https://huggingface.co/datasets/KBLab/sucx3_ner) (Språkbanken)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (381 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `AB`, `AB\|AN`, `AB\|KOM`, `AB\|POS`, `AB\|SMS`, `AB\|SUV`, `DT\|NEU\|SIN\|DEF`, `DT\|NEU\|SIN\|IND`, `DT\|NEU\|SIN\|IND/DEF`, `DT\|UTR/NEU\|PLU\|DEF`, `DT\|UTR/NEU\|PLU\|IND`, `DT\|UTR/NEU\|PLU\|IND/DEF`, `DT\|UTR/NEU\|SIN/PLU\|IND`, `DT\|UTR/NEU\|SIN\|DEF`, `DT\|UTR/NEU\|SIN\|IND`, `DT\|UTR\|SIN\|DEF`, `DT\|UTR\|SIN\|IND`, `DT\|UTR\|SIN\|IND/DEF`, `HA`, `HD\|NEU\|SIN\|IND`, `HD\|UTR/NEU\|PLU\|IND`, `HD\|UTR\|SIN\|IND`, `HP\|-\|-\|-`, `HP\|NEU\|SIN\|IND`, `HP\|UTR/NEU\|PLU\|IND`, `HP\|UTR\|SIN\|IND`, `HS\|DEF`, `IE`, `IN`, `JJ`, `JJ\|AN`, `JJ\|KOM\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|MAS\|SIN\|DEF\|GEN`, `JJ\|POS\|MAS\|SIN\|DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|SIN\|DEF\|NOM`, `JJ\|POS\|UTR\|-\|-\|SMS`, `JJ\|POS\|UTR\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|UTR\|SIN\|IND\|GEN`, `JJ\|POS\|UTR\|SIN\|IND\|NOM`, `JJ\|SUV\|MAS\|SIN\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|IND\|NOM`, `KN`, `MAD`, `MID`, `NN`, `NN\|-\|-\|-\|-`, `NN\|AN`, `NN\|NEU\|-\|-\|SMS`, `NN\|NEU\|PLU\|DEF\|GEN`, `NN\|NEU\|PLU\|DEF\|NOM`, `NN\|NEU\|PLU\|IND\|GEN`, `NN\|NEU\|PLU\|IND\|NOM`, `NN\|NEU\|SIN\|DEF\|GEN`, `NN\|NEU\|SIN\|DEF\|NOM`, `NN\|NEU\|SIN\|IND`, `NN\|NEU\|SIN\|IND\|GEN`, `NN\|NEU\|SIN\|IND\|NOM`, `NN\|SMS`, `NN\|UTR\|-\|-\|-`, `NN\|UTR\|-\|-\|SMS`, `NN\|UTR\|PLU\|DEF\|GEN`, `NN\|UTR\|PLU\|DEF\|NOM`, `NN\|UTR\|PLU\|IND\|GEN`, `NN\|UTR\|PLU\|IND\|NOM`, `NN\|UTR\|SIN\|DEF\|GEN`, `NN\|UTR\|SIN\|DEF\|NOM`, `NN\|UTR\|SIN\|IND\|GEN`, `NN\|UTR\|SIN\|IND\|NOM`, `PAD`, `PC\|PRF\|NEU\|SIN\|IND\|NOM`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `PC\|PRF\|UTR/NEU\|SIN\|DEF\|NOM`, `PC\|PRF\|UTR\|SIN\|IND\|NOM`, `PC\|PRS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `PL`, `PM`, `PM\|GEN`, `PM\|NOM`, `PM\|SMS`, `PN\|MAS\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|DEF`, `PN\|NEU\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|SUB`, `PN\|UTR/NEU\|PLU\|DEF\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|SIN/PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|NOM`, `PN\|UTR\|SIN\|DEF\|OBJ`, `PN\|UTR\|SIN\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|SUB/OBJ`, `PN\|UTR\|SIN\|IND\|NOM`, `PN\|UTR\|SIN\|IND\|SUB`, `PN\|UTR\|SIN\|IND\|SUB/OBJ`, `PP`, `PS\|NEU\|SIN\|DEF`, `PS\|UTR/NEU\|PLU\|DEF`, `PS\|UTR/NEU\|SIN/PLU\|DEF`, `PS\|UTR\|SIN\|DEF`, `RG\|NEU\|SIN\|IND\|NOM`, `RG\|NOM`, `RG\|SMS`, `RG\|UTR\|SIN\|IND\|NOM`, `RO\|MAS\|SIN\|IND/DEF\|NOM`, `RO\|NOM`, `SN`, `UO`, `VB\|AN`, `VB\|IMP\|AKT`, `VB\|IMP\|SFO`, `VB\|INF\|AKT`, `VB\|INF\|SFO`, `VB\|KON\|PRS\|AKT`, `VB\|KON\|PRT\|AKT`, `VB\|PRS\|AKT`, `VB\|PRS\|SFO`, `VB\|PRT\|AKT`, `VB\|PRT\|SFO`, `VB\|SUP\|AKT`, `VB\|SUP\|SFO`, `_SP` |
| **`morphologizer`** | `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `POS=PUNCT`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|POS=ADV`, `POS=SCONJ`, `POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Com\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=CCONJ`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Pos\|POS=ADV`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=VERB\|VerbForm=Sup\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Degree=Pos\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Case=Nom\|NumType=Card\|POS=NUM`, `Abbr=Yes\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Sup\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `POS=AUX\|VerbForm=Sup\|Voice=Act`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=SPACE`, `POS=VERB\|VerbForm=Sup\|Voice=Pass`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|POS=ADJ\|Tense=Pres\|VerbForm=Part`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Dem`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=NOUN`, `Case=Nom\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Int`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|POS=PROPN`, `POS=PROPN`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=VERB\|VerbForm=Sup`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=SYM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Degree=Sup\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Neg`, `Mood=Sub\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|Gender=Com\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|POS=DET\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Abbr=Yes\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `NumType=Card\|POS=NUM`, `POS=INTJ`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int`, `Degree=Sup\|POS=ADV\|Polarity=Neg`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Int`, `POS=ADV\|Polarity=Neg`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Ind`, `Foreign=Yes\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Dem`, `Abbr=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `Foreign=Yes\|POS=CCONJ`, `POS=DET\|PronType=Art`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Degree=Pos\|POS=ADV\|Polarity=Neg`, `Mood=Sub\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PRON\|PronType=Ind`, `Definite=Ind\|POS=DET\|PronType=Neg`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Neg`, `POS=CCONJ\|Polarity=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Imp\|POS=AUX\|VerbForm=Fin\|Voice=Act`, `Foreign=Yes\|POS=ADV`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rcp`, `Case=Acc\|Definite=Def\|POS=PRON\|Polarity=Neg\|PronType=Ind` |
| **`parser`** | `ROOT`, `acl`, `acl:cleft`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `expl`, `fixed`, `flat:name`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `parataxis`, `punct`, `xcomp` |
| **`ner`** | `EVN`, `LOC`, `MSR`, `OBJ`, `ORG`, `PRS`, `TME`, `WRK` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.99 |
| `TOKEN_P` | 99.95 |
| `TOKEN_R` | 99.96 |
| `TOKEN_F` | 99.95 |
| `TAG_ACC` | 95.14 |
| `POS_ACC` | 96.28 |
| `MORPH_ACC` | 95.60 |
| `MORPH_MICRO_P` | 97.44 |
| `MORPH_MICRO_R` | 97.40 |
| `MORPH_MICRO_F` | 97.42 |
| `SENTS_P` | 91.01 |
| `SENTS_R` | 94.44 |
| `SENTS_F` | 92.70 |
| `DEP_UAS` | 83.24 |
| `DEP_LAS` | 78.31 |
| `LEMMA_ACC` | 95.51 |
| `ENTS_P` | 84.22 |
| `ENTS_R` | 75.43 |
| `ENTS_F` | 79.58 |
|
spacy/sv_core_news_sm
|
spacy
| 2023-10-10T06:35:38Z | 97 | 1 |
spacy
|
[
"spacy",
"token-classification",
"sv",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2022-05-02T07:56:09Z |
---
tags:
- spacy
- token-classification
language:
- sv
license: cc-by-sa-4.0
model-index:
- name: sv_core_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8002766252
- name: NER Recall
type: recall
value: 0.703892944
- name: NER F Score
type: f_score
value: 0.7489967638
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9351842401
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9511074819
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9406961315
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9489639686
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.818598856
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.7672877612
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9368318756
---
### Details: https://spacy.io/models/sv#sv_core_news_sm
Swedish pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
| Feature | Description |
| --- | --- |
| **Name** | `sv_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD Swedish Talbanken v2.8](https://github.com/UniversalDependencies/UD_Swedish-Talbanken) (Nivre, Joakim; Smith, Aaron)<br />[Stockholm-Umeå Corpus (SUC) v3.0](https://huggingface.co/datasets/KBLab/sucx3_ner) (Språkbanken) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (381 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `AB`, `AB\|AN`, `AB\|KOM`, `AB\|POS`, `AB\|SMS`, `AB\|SUV`, `DT\|NEU\|SIN\|DEF`, `DT\|NEU\|SIN\|IND`, `DT\|NEU\|SIN\|IND/DEF`, `DT\|UTR/NEU\|PLU\|DEF`, `DT\|UTR/NEU\|PLU\|IND`, `DT\|UTR/NEU\|PLU\|IND/DEF`, `DT\|UTR/NEU\|SIN/PLU\|IND`, `DT\|UTR/NEU\|SIN\|DEF`, `DT\|UTR/NEU\|SIN\|IND`, `DT\|UTR\|SIN\|DEF`, `DT\|UTR\|SIN\|IND`, `DT\|UTR\|SIN\|IND/DEF`, `HA`, `HD\|NEU\|SIN\|IND`, `HD\|UTR/NEU\|PLU\|IND`, `HD\|UTR\|SIN\|IND`, `HP\|-\|-\|-`, `HP\|NEU\|SIN\|IND`, `HP\|UTR/NEU\|PLU\|IND`, `HP\|UTR\|SIN\|IND`, `HS\|DEF`, `IE`, `IN`, `JJ`, `JJ\|AN`, `JJ\|KOM\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|MAS\|SIN\|DEF\|GEN`, `JJ\|POS\|MAS\|SIN\|DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|NEU\|SIN\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `JJ\|POS\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|PLU\|IND\|NOM`, `JJ\|POS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `JJ\|POS\|UTR/NEU\|SIN\|DEF\|NOM`, `JJ\|POS\|UTR\|-\|-\|SMS`, `JJ\|POS\|UTR\|SIN\|IND/DEF\|NOM`, `JJ\|POS\|UTR\|SIN\|IND\|GEN`, `JJ\|POS\|UTR\|SIN\|IND\|NOM`, `JJ\|SUV\|MAS\|SIN\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|DEF\|NOM`, `JJ\|SUV\|UTR/NEU\|SIN/PLU\|IND\|NOM`, `KN`, `MAD`, `MID`, `NN`, `NN\|-\|-\|-\|-`, `NN\|AN`, `NN\|NEU\|-\|-\|SMS`, `NN\|NEU\|PLU\|DEF\|GEN`, `NN\|NEU\|PLU\|DEF\|NOM`, `NN\|NEU\|PLU\|IND\|GEN`, `NN\|NEU\|PLU\|IND\|NOM`, `NN\|NEU\|SIN\|DEF\|GEN`, `NN\|NEU\|SIN\|DEF\|NOM`, `NN\|NEU\|SIN\|IND`, `NN\|NEU\|SIN\|IND\|GEN`, `NN\|NEU\|SIN\|IND\|NOM`, `NN\|SMS`, `NN\|UTR\|-\|-\|-`, `NN\|UTR\|-\|-\|SMS`, `NN\|UTR\|PLU\|DEF\|GEN`, `NN\|UTR\|PLU\|DEF\|NOM`, `NN\|UTR\|PLU\|IND\|GEN`, `NN\|UTR\|PLU\|IND\|NOM`, `NN\|UTR\|SIN\|DEF\|GEN`, `NN\|UTR\|SIN\|DEF\|NOM`, `NN\|UTR\|SIN\|IND\|GEN`, `NN\|UTR\|SIN\|IND\|NOM`, `PAD`, `PC\|PRF\|NEU\|SIN\|IND\|NOM`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|GEN`, `PC\|PRF\|UTR/NEU\|PLU\|IND/DEF\|NOM`, `PC\|PRF\|UTR/NEU\|SIN\|DEF\|NOM`, `PC\|PRF\|UTR\|SIN\|IND\|NOM`, `PC\|PRS\|UTR/NEU\|SIN/PLU\|IND/DEF\|NOM`, `PL`, `PM`, `PM\|GEN`, `PM\|NOM`, `PM\|SMS`, `PN\|MAS\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|DEF`, `PN\|NEU\|SIN\|DEF\|SUB/OBJ`, `PN\|NEU\|SIN\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|OBJ`, `PN\|UTR/NEU\|PLU\|DEF\|SUB`, `PN\|UTR/NEU\|PLU\|DEF\|SUB/OBJ`, `PN\|UTR/NEU\|PLU\|IND\|SUB/OBJ`, `PN\|UTR/NEU\|SIN/PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|OBJ`, `PN\|UTR\|PLU\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|NOM`, `PN\|UTR\|SIN\|DEF\|OBJ`, `PN\|UTR\|SIN\|DEF\|SUB`, `PN\|UTR\|SIN\|DEF\|SUB/OBJ`, `PN\|UTR\|SIN\|IND\|NOM`, `PN\|UTR\|SIN\|IND\|SUB`, `PN\|UTR\|SIN\|IND\|SUB/OBJ`, `PP`, `PS\|NEU\|SIN\|DEF`, `PS\|UTR/NEU\|PLU\|DEF`, `PS\|UTR/NEU\|SIN/PLU\|DEF`, `PS\|UTR\|SIN\|DEF`, `RG\|NEU\|SIN\|IND\|NOM`, `RG\|NOM`, `RG\|SMS`, `RG\|UTR\|SIN\|IND\|NOM`, `RO\|MAS\|SIN\|IND/DEF\|NOM`, `RO\|NOM`, `SN`, `UO`, `VB\|AN`, `VB\|IMP\|AKT`, `VB\|IMP\|SFO`, `VB\|INF\|AKT`, `VB\|INF\|SFO`, `VB\|KON\|PRS\|AKT`, `VB\|KON\|PRT\|AKT`, `VB\|PRS\|AKT`, `VB\|PRS\|SFO`, `VB\|PRT\|AKT`, `VB\|PRT\|SFO`, `VB\|SUP\|AKT`, `VB\|SUP\|SFO`, `_SP` |
| **`morphologizer`** | `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `POS=PUNCT`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Abbr=Yes\|POS=ADV`, `POS=SCONJ`, `POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Com\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PART`, `POS=VERB\|VerbForm=Inf`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=CCONJ`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Degree=Pos\|POS=ADV`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `POS=VERB\|VerbForm=Sup\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Degree=Pos\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Case=Nom\|NumType=Card\|POS=NUM`, `Abbr=Yes\|POS=NOUN`, `Case=Nom\|Definite=Def\|Degree=Sup\|POS=ADJ`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `POS=AUX\|VerbForm=Sup\|Voice=Act`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=SPACE`, `POS=VERB\|VerbForm=Sup\|Voice=Pass`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Sing\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Nom\|POS=ADJ\|Tense=Pres\|VerbForm=Part`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Dem`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Nom\|Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `POS=NOUN`, `Case=Nom\|POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Prs`, `Case=Nom\|POS=PROPN`, `Case=Nom\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=ADJ\|Tense=Past\|VerbForm=Part`, `Case=Acc\|Definite=Def\|Gender=Com\|Number=Plur\|POS=PRON\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Int`, `Number=Plur\|POS=DET\|PronType=Ind`, `Case=Gen\|POS=PROPN`, `POS=PROPN`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Int`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Neg`, `POS=VERB\|VerbForm=Sup`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Ind\|Gender=Neut\|NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|POS=ADJ`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Ind`, `POS=SYM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Definite=Ind\|Degree=Sup\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Neg`, `Mood=Sub\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Degree=Pos\|Gender=Com\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Ind\|POS=DET\|PronType=Prs`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|Poss=Yes\|PronType=Ind`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Abbr=Yes\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `NumType=Card\|POS=NUM`, `POS=INTJ`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int`, `Degree=Sup\|POS=ADV\|Polarity=Neg`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Int`, `POS=ADV\|Polarity=Neg`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Ind`, `POS=ADJ`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Tot`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Neg`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Neg`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Pass`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Def\|POS=PRON\|PronType=Ind`, `Foreign=Yes\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Dem`, `Abbr=Yes\|Mood=Imp\|POS=VERB\|VerbForm=Fin\|Voice=Act`, `Mood=Sub\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `Case=Nom\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Rel`, `Foreign=Yes\|POS=CCONJ`, `POS=DET\|PronType=Art`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Rel`, `Case=Acc\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Prs`, `Case=Gen\|Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Degree=Pos\|POS=ADV\|Polarity=Neg`, `Mood=Sub\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=PRON\|PronType=Ind`, `Definite=Ind\|POS=DET\|PronType=Neg`, `Definite=Ind\|Number=Plur\|POS=PRON\|PronType=Neg`, `POS=CCONJ\|Polarity=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Definite=Def\|Number=Plur\|POS=PRON\|PronType=Tot`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Imp\|POS=AUX\|VerbForm=Fin\|Voice=Act`, `Foreign=Yes\|POS=ADV`, `Definite=Def\|POS=PRON\|Poss=Yes\|PronType=Rcp`, `Case=Acc\|Definite=Def\|POS=PRON\|Polarity=Neg\|PronType=Ind` |
| **`parser`** | `ROOT`, `acl`, `acl:cleft`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `aux:pass`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `expl`, `fixed`, `flat:name`, `iobj`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl`, `obl:agent`, `parataxis`, `punct`, `xcomp` |
| **`ner`** | `EVN`, `LOC`, `MSR`, `OBJ`, `ORG`, `PRS`, `TME`, `WRK` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.99 |
| `TOKEN_P` | 99.95 |
| `TOKEN_R` | 99.96 |
| `TOKEN_F` | 99.95 |
| `TAG_ACC` | 93.52 |
| `POS_ACC` | 95.11 |
| `MORPH_ACC` | 94.07 |
| `MORPH_MICRO_P` | 96.02 |
| `MORPH_MICRO_R` | 95.75 |
| `MORPH_MICRO_F` | 95.88 |
| `SENTS_P` | 91.81 |
| `SENTS_R` | 95.63 |
| `SENTS_F` | 93.68 |
| `DEP_UAS` | 81.86 |
| `DEP_LAS` | 76.73 |
| `LEMMA_ACC` | 94.90 |
| `ENTS_P` | 80.03 |
| `ENTS_R` | 70.39 |
| `ENTS_F` | 74.90 |
|
spacy/uk_core_news_lg
|
spacy
| 2023-10-10T06:35:35Z | 8 | 0 |
spacy
|
[
"spacy",
"token-classification",
"uk",
"license:mit",
"model-index",
"region:us"
] |
token-classification
| 2023-01-23T13:47:04Z |
---
tags:
- spacy
- token-classification
language:
- uk
license: mit
model-index:
- name: uk_core_news_lg
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8768624014
- name: NER Recall
type: recall
value: 0.8813036776
- name: NER F Score
type: f_score
value: 0.87907743
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9817440564
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9817440564
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9520345072
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.0
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9379837528
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9169280929
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9264398403
---
### Details: https://spacy.io/models/uk#uk_core_news_lg
Ukrainian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `uk_core_news_lg` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | floret (200000, 300) |
| **Sources** | [Ukr-Synth (e5d9eaf3)](https://huggingface.co/datasets/ukr-models/Ukr-Synth) (Volodymyr Kurnosov)<br />[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1211 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `POS=CCONJ`, `Degree=Cmp\|POS=ADV`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=ADV\|PronType=Rel`, `POS=PART`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `POS=ADV`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=ADP`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Loc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Nom\|NumType=Card\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Loc\|Number=Plur\|POS=ADJ`, `POS=SCONJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf`, `Degree=Pos\|POS=ADV`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=0\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Tot`, `POS=PART\|Polarity=Neg`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PUNCT\|PunctType=Quot`, `POS=PUNCT\|PunctType=Dash`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `POS=ADV\|PronType=Dem`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|POS=ADP`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|POS=ADP`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Number=Ptan\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|POS=PRON\|PronType=Neg`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=SPACE`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Conv`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|NumType=Card\|POS=DET\|PronType=Dem`, `Animacy=Anim\|Case=Gen\|Number=Ptan\|POS=NOUN`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=NOUN`, `Abbr=Yes\|Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Animacy=Anim\|Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Loc\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Number=Plur\|POS=ADJ`, `Case=Gen\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Loc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Acc\|NumType=Card\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Abbr=Yes\|Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Degree=Abs\|POS=ADV`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|Variant=Short`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Hyph=Yes\|POS=ADJ\|Variant=Short`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Degree=Sup\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Rel`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|POS=AUX\|VerbForm=Inf`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Number=Plur\|POS=PROPN\|Uninflect=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=INTJ`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=X\|Uninflect=Yes`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Case=Loc\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Aspect=Perf\|Case=Loc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Animacy=Anim\|Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Loc\|Number=Ptan\|POS=NOUN`, `Case=Gen\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Case=Nom\|NumType=Card\|POS=NUM`, `POS=SYM`, `Case=Loc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ins\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|NumType=Card\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Gen\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Case=Ins\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Conv`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Nom\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Gen\|NumType=Card\|POS=NUM`, `Case=Ins\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Loc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Abbr=Yes\|Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Hyph=Yes\|POS=ADJ`, `POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Voc\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=ADV\|PronType=Neg`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Rel`, `Animacy=Anim\|Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Variant=Short`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Animacy=Anim\|Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=PART\|PartType=Conseq`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Ins\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|NumType=Card\|POS=DET\|PronType=Ind`, `Mood=Cnd\|POS=AUX`, `Abbr=Yes\|Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Gen\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Dem`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Nom\|Number=Ptan\|POS=NOUN`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Case=Dat\|POS=ADP`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Case=Loc\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|POS=PRON\|PronType=Ind`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=NOUN\|Uninflect=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg\|Variant=Short`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=X`, `Case=Nom\|Gender=Masc\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Animacy=Inan\|Case=Ins\|Number=Ptan\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=NOUN\|Uninflect=Yes`, `POS=ADV\|PronType=Int`, `Aspect=Imp\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Conv`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Acc\|NumType=Card\|Number=Plur\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=PROPN\|Uninflect=Yes`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Number=Ptan\|POS=PROPN\|Uninflect=Yes`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Loc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Nom\|POS=PRON\|PronType=Neg`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Number=Ptan\|POS=PROPN\|Uninflect=Yes`, `Aspect=Imp\|Case=Ins\|Number=Plur\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Animacy=Anim\|Case=Acc\|Number=Ptan\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|NumType=Card\|POS=NUM`, `Case=Ins\|Gender=Masc\|NumType=Card\|POS=NUM`, `Case=Acc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Ins\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Abbr=Yes\|Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Anim\|Animacy[gram]=Inan\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Animacy=Inan\|Case=Loc\|Number=Ptan\|POS=PROPN`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Neg`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Case=Nom\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Gen\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, _(truncated: full list in pipeline meta)_ |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advcl:sp`, `advcl:svc`, `advmod`, `advmod:det`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:numgov`, `discourse`, `expl`, `fixed`, `flat:abs`, `flat:foreign`, `flat:name`, `flat:range`, `flat:repeat`, `flat:sibl`, `flat:title`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `nummod:gov`, `obj`, `obl`, `orphan`, `parataxis`, `parataxis:discourse`, `punct`, `vocative`, `xcomp`, `xcomp:sp` |
| **`ner`** | `LOC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.99 |
| `TOKEN_P` | 99.99 |
| `TOKEN_R` | 99.97 |
| `TOKEN_F` | 99.98 |
| `POS_ACC` | 98.17 |
| `MORPH_ACC` | 95.20 |
| `MORPH_MICRO_P` | 97.88 |
| `MORPH_MICRO_R` | 97.16 |
| `MORPH_MICRO_F` | 97.52 |
| `SENTS_P` | 94.48 |
| `SENTS_R` | 90.88 |
| `SENTS_F` | 92.64 |
| `DEP_UAS` | 93.80 |
| `DEP_LAS` | 91.69 |
| `TAG_ACC` | 98.17 |
| `LEMMA_ACC` | 0.00 |
| `ENTS_P` | 87.69 |
| `ENTS_R` | 88.13 |
| `ENTS_F` | 87.91 |
|
spacy/uk_core_news_sm
|
spacy
| 2023-10-10T06:35:08Z | 2 | 0 |
spacy
|
[
"spacy",
"token-classification",
"uk",
"license:mit",
"model-index",
"region:us"
] |
token-classification
| 2023-01-23T13:45:45Z |
---
tags:
- spacy
- token-classification
language:
- uk
license: mit
model-index:
- name: uk_core_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8597027972
- name: NER Recall
type: recall
value: 0.8663290024
- name: NER F Score
type: f_score
value: 0.8630031809
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.981259784
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.981259784
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9465385785
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.0
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9343844605
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9120012055
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.922608861
---
### Details: https://spacy.io/models/uk#uk_core_news_sm
Ukrainian pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `uk_core_news_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Ukr-Synth (e5d9eaf3)](https://huggingface.co/datasets/ukr-models/Ukr-Synth) (Volodymyr Kurnosov) |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (1211 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `POS=CCONJ`, `Degree=Cmp\|POS=ADV`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `POS=ADV\|PronType=Rel`, `POS=PART`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `POS=ADV`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=ADP`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Loc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Nom\|NumType=Card\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Loc\|Number=Plur\|POS=ADJ`, `POS=SCONJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf`, `Degree=Pos\|POS=ADV`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Nom\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|POS=VERB\|Person=0\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Tot`, `POS=PART\|Polarity=Neg`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=PUNCT\|PunctType=Quot`, `POS=PUNCT\|PunctType=Dash`, `Aspect=Perf\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `POS=ADV\|PronType=Dem`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|POS=ADP`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|POS=ADP`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Acc\|Number=Ptan\|POS=NOUN`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Aspect=Perf\|Case=Nom\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Gender=Masc\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|POS=PRON\|PronType=Neg`, `Case=Nom\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `POS=SPACE`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|POS=VERB\|Tense=Past\|VerbForm=Conv`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Nom\|NumType=Card\|POS=DET\|PronType=Dem`, `Animacy=Anim\|Case=Gen\|Number=Ptan\|POS=NOUN`, `Animacy=Inan\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Case=Gen\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=NOUN`, `Abbr=Yes\|Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Animacy=Inan\|Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Animacy=Anim\|Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Loc\|Number=Plur\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Number=Plur\|POS=ADJ`, `Case=Gen\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Perf\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Loc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Acc\|NumType=Card\|POS=DET\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Abbr=Yes\|Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Dem`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Degree=Abs\|POS=ADV`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot\|Variant=Short`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Hyph=Yes\|POS=ADJ\|Variant=Short`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Degree=Sup\|POS=ADV`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Rel`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=PROPN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|POS=AUX\|VerbForm=Inf`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Nom\|Number=Plur\|POS=PROPN\|Uninflect=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=INTJ`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Case=Dat\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=X\|Uninflect=Yes`, `Aspect=Imp\|Gender=Neut\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Case=Loc\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Aspect=Perf\|Case=Loc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Animacy=Anim\|Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Animacy=Anim\|Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Loc\|Number=Ptan\|POS=NOUN`, `Case=Gen\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Case=Nom\|NumType=Card\|POS=NUM`, `POS=SYM`, `Case=Loc\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ins\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Acc\|NumType=Card\|POS=NUM`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Gen\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Case=Ins\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Conv`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Tot`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Nom\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Gen\|NumType=Card\|POS=NUM`, `Case=Ins\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Inan\|Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Tot`, `Animacy=Anim\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Case=Ins\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Perf\|Case=Nom\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Animacy=Inan\|Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Rel`, `Case=Loc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Ins\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Anim\|Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Abbr=Yes\|Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Hyph=Yes\|POS=ADJ`, `POS=ADV\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Voc\|Gender=Fem\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=ADV\|PronType=Neg`, `Case=Nom\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Rel`, `Animacy=Anim\|Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Variant=Short`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Masc\|NumType=Card\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Animacy=Anim\|Case=Acc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Fin`, `Animacy=Anim\|Case=Gen\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=PART\|PartType=Conseq`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Anim\|Case=Ins\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Gen\|NumType=Card\|POS=DET\|PronType=Ind`, `Mood=Cnd\|POS=AUX`, `Abbr=Yes\|Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Case=Gen\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Aspect=Perf\|Case=Loc\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Dem`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Inan\|Case=Nom\|Number=Ptan\|POS=NOUN`, `Aspect=Perf\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Case=Dat\|POS=ADP`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Gender=Neut\|NumType=Card\|POS=NUM`, `Animacy=Anim\|Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ\|Uninflect=Yes`, `Case=Loc\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Gen\|Gender=Fem\|NumType=Card\|Number=Plur\|POS=NOUN`, `Animacy=Anim\|Case=Nom\|POS=PRON\|PronType=Ind`, `Abbr=Yes\|Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=NOUN\|Uninflect=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg\|Variant=Short`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `POS=X`, `Case=Nom\|Gender=Masc\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Aspect=Imp\|Gender=Fem\|Mood=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Fin`, `Animacy=Inan\|Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Animacy=Inan\|Case=Ins\|Number=Ptan\|POS=NOUN`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=NOUN\|Uninflect=Yes`, `POS=ADV\|PronType=Int`, `Aspect=Imp\|POS=VERB\|Polarity=Neg\|Tense=Pres\|VerbForm=Conv`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Acc\|NumType=Card\|Number=Plur\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Number=Ptan\|POS=PROPN\|Uninflect=Yes`, `Case=Nom\|Degree=Sup\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Act`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Animacy=Inan\|Case=Nom\|Number=Ptan\|POS=PROPN\|Uninflect=Yes`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Loc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|Gender=Masc\|NumType=Card\|POS=NUM`, `Animacy=Inan\|Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Animacy=Inan\|Aspect=Perf\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Nom\|POS=PRON\|PronType=Neg`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Ins\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Animacy=Inan\|Case=Loc\|Number=Ptan\|POS=PROPN\|Uninflect=Yes`, `Aspect=Imp\|Case=Ins\|Number=Plur\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Animacy=Anim\|Case=Acc\|Number=Ptan\|POS=NOUN`, `Animacy=Anim\|Case=Gen\|Gender=Fem\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|NumType=Card\|POS=NUM`, `Case=Ins\|Gender=Masc\|NumType=Card\|POS=NUM`, `Case=Acc\|Gender=Masc\|NumType=Card\|POS=NUM`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Degree=Pos\|Gender=Neut\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Case=Ins\|Degree=Pos\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Ins\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Abbr=Yes\|Animacy=Inan\|Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Animacy=Anim\|Animacy[gram]=Inan\|Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Pos\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Gen\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Rel`, `Animacy=Inan\|Case=Loc\|Number=Ptan\|POS=PROPN`, `Aspect=Imp\|Case=Nom\|Gender=Masc\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Neg`, `Animacy=Inan\|Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Case=Nom\|Number=Plur\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Dat\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN\|Uninflect=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Abbr=Yes\|Animacy=Inan\|Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN\|Uninflect=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Aspect=Perf\|Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part\|Voice=Pass`, `Case=Gen\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Gen\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Animacy=Inan\|Case=Acc\|Number=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|NumType=Card\|POS=NUM\|Uninflect=Yes`, `Animacy=Inan\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Aspect=Imp\|Case=Acc\|Gender=Fem\|Number=Sing\|POS=ADJ\|Tense=Pres\|VerbForm=Part\|Voice=Act`, `Abbr=Yes\|Animacy=Anim\|Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN\|Uninflect=Yes`, `Case=Dat\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Ins\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Uninflect=Yes`, `Case=Gen\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NameType=Giv\|Number=Sing\|POS=PROPN`, `Animacy=Anim\|Case=Dat\|Gender=Masc\|NameType=Sur\|Number=Sing\|POS=PROPN`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, _(truncated: full list in pipeline meta)_ |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advcl:sp`, `advcl:svc`, `advmod`, `advmod:det`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `det:numgov`, `discourse`, `expl`, `fixed`, `flat:abs`, `flat:foreign`, `flat:name`, `flat:range`, `flat:repeat`, `flat:sibl`, `flat:title`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `nummod:gov`, `obj`, `obl`, `orphan`, `parataxis`, `parataxis:discourse`, `punct`, `vocative`, `xcomp`, `xcomp:sp` |
| **`ner`** | `LOC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.99 |
| `TOKEN_P` | 99.99 |
| `TOKEN_R` | 99.97 |
| `TOKEN_F` | 99.98 |
| `POS_ACC` | 98.13 |
| `MORPH_ACC` | 94.65 |
| `MORPH_MICRO_P` | 97.59 |
| `MORPH_MICRO_R` | 96.79 |
| `MORPH_MICRO_F` | 97.19 |
| `SENTS_P` | 94.12 |
| `SENTS_R` | 90.47 |
| `SENTS_F` | 92.26 |
| `DEP_UAS` | 93.44 |
| `DEP_LAS` | 91.20 |
| `TAG_ACC` | 98.13 |
| `LEMMA_ACC` | 0.00 |
| `ENTS_P` | 85.97 |
| `ENTS_R` | 86.63 |
| `ENTS_F` | 86.30 |
|
spacy/xx_ent_wiki_sm
|
spacy
| 2023-10-10T06:35:05Z | 150 | 8 |
spacy
|
[
"spacy",
"token-classification",
"multilingual",
"license:mit",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- multilingual
license: mit
model-index:
- name: xx_ent_wiki_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8352564288
- name: NER Recall
type: recall
value: 0.8264712666
- name: NER F Score
type: f_score
value: 0.8308406251
---
### Details: https://spacy.io/models/xx#xx_ent_wiki_sm
Multi-language pipeline optimized for CPU. Components: ner.
| Feature | Description |
| --- | --- |
| **Name** | `xx_ent_wiki_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `ner` |
| **Components** | `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [WikiNER](https://figshare.com/articles/Learning_multilingual_named_entity_recognition_from_Wikipedia/5462500) (Joel Nothman, Nicky Ringland, Will Radford, Tara Murphy, James R Curran) |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (4 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_P` | 83.53 |
| `ENTS_R` | 82.65 |
| `ENTS_F` | 83.08 |
|
spacy/xx_sent_ud_sm
|
spacy
| 2023-10-10T06:35:02Z | 906 | 2 |
spacy
|
[
"spacy",
"multilingual",
"license:cc-by-sa-3.0",
"model-index",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
tags:
- spacy
language:
- multilingual
license: cc-by-sa-3.0
model-index:
- name: xx_sent_ud_sm
results:
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.8588338112
---
### Details: https://spacy.io/models/xx#xx_sent_ud_sm
Multi-language pipeline optimized for CPU. Components: senter.
| Feature | Description |
| --- | --- |
| **Name** | `xx_sent_ud_sm` |
| **Version** | `3.7.0` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `senter` |
| **Components** | `senter` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [Universal Dependencies v2.8 (UD_Afrikaans-AfriBooms, UD_Croatian-SET, UD_Czech-CAC, UD_Czech-CLTT, UD_Danish-DDT, UD_Dutch-Alpino, UD_Dutch-LassySmall, UD_English-EWT, UD_Finnish-FTB, UD_Finnish-TDT, UD_French-GSD, UD_French-Spoken, UD_German-GSD, UD_Indonesian-GSD, UD_Irish-IDT, UD_Italian-TWITTIRO, UD_Korean-GSD, UD_Korean-Kaist, UD_Latvian-LVTB, UD_Lithuanian-ALKSNIS, UD_Lithuanian-HSE, UD_Marathi-UFAL, UD_Norwegian-Bokmaal, UD_Norwegian-Nynorsk, UD_Norwegian-NynorskLIA, UD_Persian-Seraji, UD_Portuguese-Bosque, UD_Portuguese-GSD, UD_Romanian-Nonstandard, UD_Romanian-RRT, UD_Russian-GSD, UD_Russian-Taiga, UD_Serbian-SET, UD_Slovak-SNK, UD_Spanish-GSD, UD_Swedish-Talbanken, UD_Telugu-MTG, UD_Vietnamese-VTB)](https://universaldependencies.org/) (Zeman, Daniel; Nivre, Joakim; Abrams, Mitchell; et al.) |
| **License** | `CC BY-SA 3.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 98.59 |
| `TOKEN_P` | 95.31 |
| `TOKEN_R` | 95.72 |
| `TOKEN_F` | 95.52 |
| `SENTS_P` | 90.66 |
| `SENTS_R` | 81.58 |
| `SENTS_F` | 85.88 |
|
manojkumarvohra/llama2-7B-Chat-8bit-guanaco-pico-adapter-hf
|
manojkumarvohra
| 2023-10-10T06:32:37Z | 5 | 0 |
peft
|
[
"peft",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-08-01T17:02:58Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# LLAMA2 7B Guanaco Pico Adapter
This is a 8Bit Quantized adapter over llama2-7b-chat-hf checkpoint.
To use the merged version of this model refer: manojkumarvohra/llama2-7B-Chat-hf-8bit-guanaco-pico-finetuned => https://huggingface.co/manojkumarvohra/llama2-7B-Chat-hf-8bit-guanaco-pico-finetuned
This is only meant for learning purpose and is not recommended to be used for any business purpose.
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
Joy23064/qlora-koalpaca-polyglot-12.8b-50step
|
Joy23064
| 2023-10-10T06:30:24Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | null | 2023-10-10T06:30:18Z |
---
library_name: peft
base_model: beomi/polyglot-ko-12.8b-safetensors
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
Gayathri142214002/t5_Comp_Question_Generation_3
|
Gayathri142214002
| 2023-10-10T06:29:16Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-10T06:15:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5_Comp_Question_Generation_3
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_Comp_Question_Generation_3
This model is a fine-tuned version of [Gayathri142214002/t5_Comp_Question_Generation_2](https://huggingface.co/Gayathri142214002/t5_Comp_Question_Generation_2) 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Superxixixi/LoCoNet_ASD
|
Superxixixi
| 2023-10-10T06:28:40Z | 106 | 2 |
transformers
|
[
"transformers",
"pytorch",
"loconet",
"feature-extraction",
"custom_code",
"arxiv:2301.08237",
"region:us"
] |
feature-extraction
| 2023-09-11T18:57:51Z |
## LoCoNet: Long-Short Context Network for Active Speaker Detection
### Dependencies
Start from building the environment
```
conda env create -f requirements.yml
conda activate loconet
```
export PYTHONPATH=**project_dir**/dlhammer:$PYTHONPATH
and replace **project_dir** with your code base location
### Data preparation
We follow TalkNet's data preparation script to download and prepare the AVA dataset.
```
python train.py --dataPathAVA AVADataPath --download
```
`AVADataPath` is the folder you want to save the AVA dataset and its preprocessing outputs, the details can be found in [here](https://github.com/TaoRuijie/TalkNet_ASD/blob/main/utils/tools.py#L34) . Please read them carefully.
After AVA dataset is downloaded, please change the DATA.dataPathAVA entry in the config file.
#### Training script
```
python -W ignore::UserWarning train.py --cfg configs/multi.yaml OUTPUT_DIR <output directory>
```
#### Pretrained model
Please download the LoCoNet trained weights on AVA dataset [here](https://drive.google.com/file/d/1EX-V464jCD6S-wg68yGuAa-UcsMrw8mK/view?usp=sharing).
```
python -W ignore::UserWarning test_multicard.py --cfg configs/multi.yaml RESUME_PATH {model download path}
```
### Citation
Please cite the following if our paper or code is helpful to your research.
```
@article{wang2023loconet,
title={LoCoNet: Long-Short Context Network for Active Speaker Detection},
author={Wang, Xizi and Cheng, Feng and Bertasius, Gedas and Crandall, David},
journal={arXiv preprint arXiv:2301.08237},
year={2023}
}
```
### Acknowledge
The code base of this project is studied from [TalkNet](https://github.com/TaoRuijie/TalkNet-ASD) which is a very easy-to-use ASD pipeline.
|
spacy/fr_dep_news_trf
|
spacy
| 2023-10-10T06:27:49Z | 10 | 0 |
spacy
|
[
"spacy",
"token-classification",
"fr",
"license:lgpl-lr",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- fr
license: lgpl-lr
model-index:
- name: fr_dep_news_trf
results:
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9595339966
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9886076602
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9816475925
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9172831895
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9483856035
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9265922369
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9269461078
---
### Details: https://spacy.io/models/fr#fr_dep_news_trf
French transformer pipeline (Transformer(name='camembert-base', piece_encoder='camembert-sentencepiece', stride=128, type='camembert', width=768, window=168, vocab_size=32005)). Components: transformer, morphologizer, parser, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `fr_dep_news_trf` |
| **Version** | `3.7.2` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `transformer`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer` |
| **Components** | `transformer`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD French Sequoia v2.8](https://github.com/UniversalDependencies/UD_French-Sequoia) (Candito, Marie; Seddah, Djamé; Perrier, Guy; Guillaume, Bruno)<br />[spaCy lookups data](https://github.com/explosion/spacy-lookups-data) (Explosion)<br />[camembert-base](https://huggingface.co/camembert-base) (Martin, Louis and Muller, Benjamin and Suarez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, Eric Villemonte and Seddah, Djame and Sagot, Benoit}) |
| **License** | `LGPL-LR` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (232 labels for 2 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `Number=Plur\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=SCONJ`, `POS=ADP`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Ord\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=PUNCT`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `POS=ADV`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `NumType=Card\|POS=NUM`, `Definite=Def\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=CCONJ`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=PRON\|PronType=Rel`, `Number=Sing\|POS=DET\|Poss=Yes`, `Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADP\|PronType=Art`, `Definite=Def\|Number=Plur\|POS=ADP\|PronType=Art`, `Definite=Ind\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Inf`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3`, `Number=Plur\|POS=DET`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADV\|PronType=Int`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Plur\|POS=DET\|Poss=Yes`, `POS=AUX\|VerbForm=Inf`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=ADV\|Polarity=Neg`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3`, `POS=PRON\|Person=3\|Reflex=Yes`, `Gender=Masc\|POS=NOUN`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=PRON\|Person=3`, `Number=Plur\|POS=NOUN`, `NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=AUX\|Tense=Pres\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|Person=3`, `Number=Sing\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=PROPN`, `Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET`, `Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes`, `Gender=Masc\|POS=PRON`, `POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Number=Sing\|POS=PRON`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|VerbForm=Fin`, `Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Dem`, `Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `POS=PRON`, `POS=NUM`, `Gender=Fem\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=PRON`, `Number=Plur\|POS=PRON\|Person=3`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Number=Sing\|POS=PRON\|Person=1`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `POS=INTJ`, `Number=Plur\|POS=PRON\|Person=2`, `NumType=Card\|POS=PRON`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `NumType=Card\|POS=NOUN`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3`, `Gender=Fem\|Number=Sing\|POS=DET`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=DET`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Definite=Ind\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=DET`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Ind\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|POS=PRON`, `Gender=Masc\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=X`, `POS=SYM`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `POS=DET`, `Gender=Masc\|Number=Plur\|POS=PRON`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|POS=VERB\|Person=3\|VerbForm=Fin`, `Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Gender=Masc\|Number=Plur\|POS=DET`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Rel`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Rel`, `POS=VERB\|Tense=Past\|VerbForm=Part\|Voice=Pass`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Imp\|POS=VERB\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|Reflex=Yes`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|Reflex=Yes`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|Person=1\|Reflex=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|POS=PROPN`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Plur\|POS=PROPN`, `Gender=Masc\|NumType=Card\|POS=NUM` |
| **`parser`** | `ROOT`, `acl`, `acl:relcl`, `advcl`, `advmod`, `amod`, `appos`, `aux:pass`, `aux:tense`, `case`, `cc`, `ccomp`, `conj`, `cop`, `dep`, `det`, `expl:comp`, `expl:pass`, `expl:subj`, `fixed`, `flat:foreign`, `flat:name`, `iobj`, `mark`, `nmod`, `nsubj`, `nsubj:pass`, `nummod`, `obj`, `obl:agent`, `obl:arg`, `obl:mod`, `parataxis`, `punct`, `vocative`, `xcomp` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.80 |
| `TOKEN_P` | 98.44 |
| `TOKEN_R` | 98.96 |
| `TOKEN_F` | 98.70 |
| `POS_ACC` | 98.86 |
| `MORPH_ACC` | 98.16 |
| `MORPH_MICRO_P` | 99.45 |
| `MORPH_MICRO_R` | 99.26 |
| `MORPH_MICRO_F` | 99.36 |
| `SENTS_P` | 91.49 |
| `SENTS_R` | 93.93 |
| `SENTS_F` | 92.69 |
| `DEP_UAS` | 94.84 |
| `DEP_LAS` | 92.66 |
| `TAG_ACC` | 95.95 |
| `LEMMA_ACC` | 91.73 |
|
spacy/ja_core_news_trf
|
spacy
| 2023-10-10T06:27:03Z | 9 | 1 |
spacy
|
[
"spacy",
"token-classification",
"ja",
"license:cc-by-sa-3.0",
"model-index",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- spacy
- token-classification
language:
- ja
license: cc-by-sa-3.0
model-index:
- name: ja_core_news_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8227383863
- name: NER Recall
type: recall
value: 0.8465408805
- name: NER F Score
type: f_score
value: 0.8344699318
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9713282143
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.979409718
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.0
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9670499959
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9304880245
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9178365731
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9507246377
---
### Details: https://spacy.io/models/ja#ja_core_news_trf
Japanese transformer pipeline (Transformer(name='cl-tohoku/bert-base-japanese-char-v2', piece_encoder='char', stride=160, type='bert', width=768, window=216, vocab_size=6144)). Components: transformer, morphologizer, parser, ner.
| Feature | Description |
| --- | --- |
| **Name** | `ja_core_news_trf` |
| **Version** | `3.7.2` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `transformer`, `morphologizer`, `parser`, `attribute_ruler`, `ner` |
| **Components** | `transformer`, `morphologizer`, `parser`, `attribute_ruler`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD Japanese GSD v2.8](https://github.com/UniversalDependencies/UD_Japanese-GSD) (Omura, Mai; Miyao, Yusuke; Kanayama, Hiroshi; Matsuda, Hiroshi; Wakasa, Aya; Yamashita, Kayo; Asahara, Masayuki; Tanaka, Takaaki; Murawaki, Yugo; Matsumoto, Yuji; Mori, Shinsuke; Uematsu, Sumire; McDonald, Ryan; Nivre, Joakim; Zeman, Daniel)<br />[UD Japanese GSD v2.8 NER](https://github.com/megagonlabs/UD_Japanese-GSD) (Megagon Labs Tokyo)<br />[cl-tohoku/bert-base-japanese-char-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-char-v2) (Inui Laboratory, Tohoku University) |
| **License** | `CC BY-SA 3.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (64 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `POS=NOUN`, `POS=ADP`, `POS=VERB`, `POS=SCONJ`, `POS=AUX`, `POS=PUNCT`, `POS=PART`, `POS=DET`, `POS=NUM`, `POS=ADV`, `POS=PRON`, `POS=ADJ`, `POS=PROPN`, `POS=CCONJ`, `POS=SYM`, `POS=NOUN\|Polarity=Neg`, `POS=AUX\|Polarity=Neg`, `POS=INTJ`, `POS=SCONJ\|Polarity=Neg` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `aux`, `case`, `cc`, `ccomp`, `compound`, `cop`, `csubj`, `dep`, `det`, `dislocated`, `fixed`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `punct` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `MOVEMENT`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PET_NAME`, `PHONE`, `PRODUCT`, `QUANTITY`, `TIME`, `TITLE_AFFIX`, `WORK_OF_ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.37 |
| `TOKEN_P` | 97.64 |
| `TOKEN_R` | 97.88 |
| `TOKEN_F` | 97.76 |
| `POS_ACC` | 97.94 |
| `MORPH_ACC` | 0.00 |
| `MORPH_MICRO_P` | 34.01 |
| `MORPH_MICRO_R` | 98.04 |
| `MORPH_MICRO_F` | 50.51 |
| `SENTS_P` | 93.18 |
| `SENTS_R` | 97.04 |
| `SENTS_F` | 95.07 |
| `DEP_UAS` | 93.05 |
| `DEP_LAS` | 91.78 |
| `TAG_ACC` | 97.13 |
| `LEMMA_ACC` | 96.70 |
| `ENTS_P` | 82.27 |
| `ENTS_R` | 84.65 |
| `ENTS_F` | 83.45 |
|
spacy/sl_core_news_trf
|
spacy
| 2023-10-10T06:26:28Z | 0 | 0 |
spacy
|
[
"spacy",
"token-classification",
"sl",
"license:cc-by-sa-4.0",
"model-index",
"region:us"
] |
token-classification
| 2023-07-07T09:26:08Z |
---
tags:
- spacy
- token-classification
language:
- sl
license: cc-by-sa-4.0
model-index:
- name: sl_core_news_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9316770186
- name: NER Recall
type: recall
value: 0.9493670886
- name: NER F Score
type: f_score
value: 0.9404388715
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9802329309
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9908323539
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.981857036
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9694198098
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9361807921
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.9223081322
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9325789722
---
### Details: https://spacy.io/models/sl#sl_core_news_trf
Slovenian transformer pipeline (Transformer(name='EMBEDDIA/sloberta', piece_encoder='camembert-sentencepiece', stride=128, type='camembert', width=768, window=168, vocab_size=32005)). Components: transformer, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), ner.
| Feature | Description |
| --- | --- |
| **Name** | `sl_core_news_trf` |
| **Version** | `3.7.2` |
| **spaCy** | `>=3.7.0,<3.8.0` |
| **Default Pipeline** | `transformer`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Components** | `transformer`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD Slovenian SSJ v2.11](https://github.com/UniversalDependencies/UD_Slovenian-SSJ) (Dobrovoljc, Kaja; Erjavec, Tomaž; Krek, Simon)<br />[SloBERTa](https://huggingface.co/EMBEDDIA/sloberta) (EMBEDDIA Project) |
| **License** | `CC BY-SA 4.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (2362 labels for 4 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `Agcfdn`, `Agcfpa`, `Agcfpd`, `Agcfpg`, `Agcfpi`, `Agcfpl`, `Agcfpn`, `Agcfsa`, `Agcfsd`, `Agcfsg`, `Agcfsi`, `Agcfsl`, `Agcfsn`, `Agcmdn`, `Agcmpa`, `Agcmpd`, `Agcmpg`, `Agcmpi`, `Agcmpl`, `Agcmpn`, `Agcmsay`, `Agcmsd`, `Agcmsg`, `Agcmsi`, `Agcmsl`, `Agcmsny`, `Agcnpa`, `Agcnpd`, `Agcnpg`, `Agcnpi`, `Agcnpl`, `Agcnpn`, `Agcnsa`, `Agcnsd`, `Agcnsg`, `Agcnsi`, `Agcnsl`, `Agcnsn`, `Agpfda`, `Agpfdg`, `Agpfdi`, `Agpfdl`, `Agpfdn`, `Agpfpa`, `Agpfpd`, `Agpfpg`, `Agpfpi`, `Agpfpl`, `Agpfpn`, `Agpfsa`, `Agpfsd`, `Agpfsg`, `Agpfsi`, `Agpfsl`, `Agpfsn`, `Agpmda`, `Agpmdg`, `Agpmdi`, `Agpmdl`, `Agpmdn`, `Agpmpa`, `Agpmpd`, `Agpmpg`, `Agpmpi`, `Agpmpl`, `Agpmpn`, `Agpmsa`, `Agpmsan`, `Agpmsay`, `Agpmsd`, `Agpmsg`, `Agpmsi`, `Agpmsl`, `Agpmsnn`, `Agpmsny`, `Agpnda`, `Agpndg`, `Agpndi`, `Agpndn`, `Agpnpa`, `Agpnpd`, `Agpnpg`, `Agpnpi`, `Agpnpl`, `Agpnpn`, `Agpnsa`, `Agpnsd`, `Agpnsg`, `Agpnsi`, `Agpnsl`, `Agpnsn`, `Agsfda`, `Agsfpa`, `Agsfpg`, `Agsfpi`, `Agsfpl`, `Agsfpn`, `Agsfsa`, `Agsfsd`, `Agsfsg`, `Agsfsi`, `Agsfsl`, `Agsfsn`, `Agsmdn`, `Agsmpa`, `Agsmpd`, `Agsmpg`, `Agsmpi`, `Agsmpl`, `Agsmpn`, `Agsmsa`, `Agsmsay`, `Agsmsg`, `Agsmsi`, `Agsmsl`, `Agsmsny`, `Agsnpa`, `Agsnpg`, `Agsnpn`, `Agsnsa`, `Agsnsi`, `Agsnsl`, `Agsnsn`, `Appfda`, `Appfdg`, `Appfdi`, `Appfdn`, `Appfpa`, `Appfpd`, `Appfpg`, `Appfpi`, `Appfpl`, `Appfpn`, `Appfsa`, `Appfsd`, `Appfsg`, `Appfsi`, `Appfsl`, `Appfsn`, `Appmda`, `Appmdg`, `Appmdl`, `Appmdn`, `Appmpa`, `Appmpd`, `Appmpg`, `Appmpi`, `Appmpl`, `Appmpn`, `Appmsa`, `Appmsan`, `Appmsay`, `Appmsd`, `Appmsg`, `Appmsi`, `Appmsl`, `Appmsnn`, `Appmsny`, `Appndg`, `Appndn`, `Appnpa`, `Appnpg`, `Appnpi`, `Appnpl`, `Appnpn`, `Appnsa`, `Appnsd`, `Appnsg`, `Appnsi`, `Appnsl`, `Appnsn`, `Aspfdn`, `Aspfpa`, `Aspfpg`, `Aspfpl`, `Aspfpn`, `Aspfsa`, `Aspfsd`, `Aspfsg`, `Aspfsi`, `Aspfsl`, `Aspfsn`, `Aspmdl`, `Aspmdn`, `Aspmpa`, `Aspmpd`, `Aspmpg`, `Aspmpl`, `Aspmpn`, `Aspmsa`, `Aspmsan`, `Aspmsd`, `Aspmsg`, `Aspmsi`, `Aspmsl`, `Aspmsnn`, `Aspnpa`, `Aspnpg`, `Aspnpi`, `Aspnpl`, `Aspnpn`, `Aspnsa`, `Aspnsg`, `Aspnsi`, `Aspnsl`, `Aspnsn`, `Cc`, `Cs`, `I`, `Mdc`, `Mdo`, `Mlc-pa`, `Mlc-pd`, `Mlc-pg`, `Mlc-pi`, `Mlc-pl`, `Mlc-pn`, `Mlcfda`, `Mlcfdg`, `Mlcfdi`, `Mlcfdl`, `Mlcfdn`, `Mlcfpa`, `Mlcfpd`, `Mlcfpg`, `Mlcfpi`, `Mlcfpl`, `Mlcfpn`, `Mlcmda`, `Mlcmdg`, `Mlcmdi`, `Mlcmdl`, `Mlcmdn`, `Mlcmpa`, `Mlcmpd`, `Mlcmpg`, `Mlcmpi`, `Mlcmpl`, `Mlcmpn`, `Mlcnda`, `Mlcndg`, `Mlcndi`, `Mlcndl`, `Mlcndn`, `Mlcnpa`, `Mlcnpg`, `Mlcnpi`, `Mlcnpl`, `Mlcnpn`, `Mlofpa`, `Mlofpd`, `Mlofpg`, `Mlofpi`, `Mlofpl`, `Mlofpn`, `Mlofsa`, `Mlofsd`, `Mlofsg`, `Mlofsi`, `Mlofsl`, `Mlofsn`, `Mlompa`, `Mlompg`, `Mlompi`, `Mlompl`, `Mlompn`, `Mlomsa`, `Mlomsd`, `Mlomsg`, `Mlomsi`, `Mlomsl`, `Mlomsn`, `Mlonda`, `Mlonpg`, `Mlonpl`, `Mlonpn`, `Mlonsa`, `Mlonsg`, `Mlonsi`, `Mlonsl`, `Mlonsn`, `Mlpfdl`, `Mlpfpa`, `Mlpfpg`, `Mlpfpi`, `Mlpfpl`, `Mlpfpn`, `Mlpfsa`, `Mlpfsd`, `Mlpfsg`, `Mlpfsi`, `Mlpfsl`, `Mlpfsn`, `Mlpmdl`, `Mlpmpa`, `Mlpmpd`, `Mlpmpg`, `Mlpmpi`, `Mlpmpl`, `Mlpmpn`, `Mlpmsa`, `Mlpmsan`, `Mlpmsay`, `Mlpmsd`, `Mlpmsg`, `Mlpmsi`, `Mlpmsl`, `Mlpmsn`, `Mlpmsnn`, `Mlpmsny`, `Mlpnpa`, `Mlpnpg`, `Mlpnpi`, `Mlpnpl`, `Mlpnpn`, `Mlpnsa`, `Mlpnsg`, `Mlpnsi`, `Mlpnsl`, `Mlpnsn`, `Mlsfpa`, `Mlsfsg`, `Mlsfsi`, `Mlsfsn`, `Mlsmpi`, `Mlsmsg`, `Mlsmsi`, `Mlsnsa`, `Mlsnsi`, `Mlsnsn`, `Mrc`, `Mro`, `Ncfda`, `Ncfdd`, `Ncfdg`, `Ncfdi`, `Ncfdl`, `Ncfdn`, `Ncfpa`, `Ncfpd`, `Ncfpg`, `Ncfpi`, `Ncfpl`, `Ncfpn`, `Ncfsa`, `Ncfsd`, `Ncfsg`, `Ncfsi`, `Ncfsl`, `Ncfsn`, `Ncmda`, `Ncmdd`, `Ncmdg`, `Ncmdi`, `Ncmdl`, `Ncmdn`, `Ncmpa`, `Ncmpd`, `Ncmpg`, `Ncmpi`, `Ncmpl`, `Ncmpn`, `Ncmsan`, `Ncmsay`, `Ncmsd`, `Ncmsg`, `Ncmsi`, `Ncmsl`, `Ncmsn`, `Ncnda`, `Ncndd`, `Ncndg`, `Ncndi`, `Ncndl`, `Ncndn`, `Ncnpa`, `Ncnpd`, `Ncnpg`, `Ncnpi`, `Ncnpl`, `Ncnpn`, `Ncnsa`, `Ncnsd`, `Ncnsg`, `Ncnsi`, `Ncnsl`, `Ncnsn`, `Npfpa`, `Npfpd`, `Npfpg`, `Npfpi`, `Npfpl`, `Npfpn`, `Npfsa`, `Npfsd`, `Npfsg`, `Npfsi`, `Npfsl`, `Npfsn`, `Npmda`, `Npmdg`, `Npmdn`, `Npmpa`, `Npmpd`, `Npmpg`, `Npmpi`, `Npmpl`, `Npmpn`, `Npmsan`, `Npmsay`, `Npmsd`, `Npmsg`, `Npmsi`, `Npmsl`, `Npmsn`, `Npnpn`, `Npnsa`, `Npnsg`, `Npnsi`, `Npnsl`, `Npnsn`, `Pd-fda`, `Pd-fpa`, `Pd-fpd`, `Pd-fpg`, `Pd-fpi`, `Pd-fpl`, `Pd-fpn`, `Pd-fsa`, `Pd-fsd`, `Pd-fsg`, `Pd-fsi`, `Pd-fsl`, `Pd-fsn`, `Pd-mda`, `Pd-mdg`, `Pd-mdl`, `Pd-mdn`, `Pd-mpa`, `Pd-mpd`, `Pd-mpg`, `Pd-mpi`, `Pd-mpl`, `Pd-mpn`, `Pd-msa`, `Pd-msd`, `Pd-msg`, `Pd-msi`, `Pd-msl`, `Pd-msn`, `Pd-npa`, `Pd-npd`, `Pd-npg`, `Pd-npl`, `Pd-npn`, `Pd-nsa`, `Pd-nsd`, `Pd-nsg`, `Pd-nsi`, `Pd-nsl`, `Pd-nsn`, `Pg-fda`, `Pg-fdg`, `Pg-fdi`, `Pg-fdl`, `Pg-fdn`, `Pg-fpa`, `Pg-fpd`, `Pg-fpg`, `Pg-fpi`, `Pg-fpl`, `Pg-fpn`, `Pg-fsa`, `Pg-fsd`, `Pg-fsg`, `Pg-fsi`, `Pg-fsl`, `Pg-fsn`, `Pg-mda`, `Pg-mdd`, `Pg-mdg`, `Pg-mdi`, `Pg-mdl`, `Pg-mdn`, `Pg-mpa`, `Pg-mpd`, `Pg-mpg`, `Pg-mpi`, `Pg-mpl`, `Pg-mpn`, `Pg-msa`, `Pg-msd`, `Pg-msg`, `Pg-msi`, `Pg-msl`, `Pg-msn`, `Pg-nda`, `Pg-ndd`, `Pg-ndn`, `Pg-npa`, _(truncated: full list in pipeline meta)_ |
| **`morphologizer`** | `POS=PUNCT`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Ind`, `Aspect=Perf\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Aspect=Perf\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=PRON\|PronType=Prs\|Reflex=Yes\|Variant=Short`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Loc\|POS=ADP`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=SCONJ`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Short`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Gender=Fem\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=VERB\|VerbForm=Inf`, `Mood=Cnd\|POS=AUX\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Gen\|POS=ADP`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=CCONJ`, `Aspect=Perf\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=PART`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|POS=ADP`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|POS=ADP`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Aspect=Imp\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Degree=Pos\|POS=ADV`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Perf\|POS=VERB\|VerbForm=Inf`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Neut\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Degree=Cmp\|POS=ADV`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Degree=Sup\|POS=ADV`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|POS=ADP`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Ins\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Aspect=Imp\|Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `POS=AUX\|VerbForm=Inf`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `POS=PART\|Polarity=Neg`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Imp\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Bound`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Loc\|POS=PRON\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Gen\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Ins\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Ins\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Nom\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Aspect=Imp\|Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `NumForm=Roman\|NumType=Ord\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=X`, `POS=SYM`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Aspect=Perf\|Gender=Masc\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Animacy=Anim\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Abbr=Yes\|POS=X`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Dual\|POS=AUX\|VerbForm=Part`, `Case=Dat\|POS=PRON\|PronType=Prs\|Reflex=Yes\|Variant=Short`, `Aspect=Imp\|Gender=Masc\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Case=Nom\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Ins\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Gender=Masc\|Number=Sing\|POS=AUX\|VerbForm=Part`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Short`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Sing\|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=Gen\|Gender=Neut\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Definite=Def\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Loc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `NumForm=Digit\|NumType=Ord\|POS=NUM`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Bound`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|POS=VERB\|VerbForm=Inf`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Loc\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Nom\|Degree=Sup\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Neut\|Number=Sing\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Int`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Nom\|Gender=Neut\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|Number[psor]=Dual\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Dual\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Gender[psor]=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Aspect=Imp\|POS=VERB\|VerbForm=Sup`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Int`, `Case=Acc\|Gender=Neut\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Neut\|Number=Plur\|POS=AUX\|VerbForm=Part`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Bound`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs\|Variant=Bound`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Acc\|POS=PRON\|PronType=Prs\|Reflex=Yes\|Variant=Bound`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Ins\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Aspect=Imp\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=INTJ`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Aspect=Perf\|POS=VERB\|VerbForm=Sup`, `Case=Nom\|Definite=Ind\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Foreign=Yes\|POS=X`, `Case=Nom\|Gender=Fem\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Sup\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Loc\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Aspect=Perf\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Aspect=Perf\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Plur\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Aspect=Imp\|Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Loc\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Neg`, `Case=Nom\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ\|VerbForm=Part`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Aspect=Perf\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Tot`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Animacy=Inan\|Case=Acc\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=AUX\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `NumForm=Roman\|NumType=Card\|POS=NUM`, `Case=Loc\|Gender=Fem\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Rel`, `Case=Acc\|Gender=Masc\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Acc\|Degree=Pos\|Gender=Masc\|Number=Dual\|POS=ADJ`, `Case=Acc\|Gender=Masc\|Number=Dual\|POS=NOUN`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `Case=Gen\|Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Gender[psor]=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Loc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Loc\|Gender=Masc\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Ins\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Gen\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Ins\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Aspect=Perf\|Gender=Neut\|Number=Plur\|POS=VERB\|VerbForm=Part`, `Case=Gen\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Fem\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=PROPN`, `Case=Dat\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Loc\|Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Neut\|Gender[psor]=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Tot`, `Aspect=Imp\|Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Degree=Cmp\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Masc\|Gender[psor]=Fem\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Case=Nom\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Nom\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Definite=Def\|Degree=Sup\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Plur\|POS=ADJ`, `Case=Dat\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Gen\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Neut\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Tot`, `Case=Loc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Gen\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Dat\|Degree=Pos\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Case=Dat\|Degree=Cmp\|Gender=Fem\|Number=Plur\|POS=ADJ`, `Aspect=Perf\|Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Case=Nom\|Degree=Pos\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Aspect=Imp\|Gender=Neut\|Number=Dual\|POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PROPN`, `Case=Ins\|Gender=Neut\|NumForm=Word\|NumType=Card\|Number=Dual\|POS=NUM`, `Case=Ins\|Gender=Neut\|Number=Dual\|POS=NOUN`, `Case=Nom\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Ins\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Case=Acc\|Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Gen\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ\|Poss=Yes`, `Case=Nom\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Tot`, `Case=Acc\|Degree=Cmp\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Loc\|Gender=Neut\|NumForm=Word\|NumType=Card\|Number=Sing\|POS=NUM`, `Case=Gen\|Degree=Sup\|Gender=Masc\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs\|Variant=Short`, `Case=Nom\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Ind`, `Case=Dat\|Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Dat\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Fem\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Dual\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Gender=Neut\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Loc\|Degree=Cmp\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Gen\|Gender=Masc\|Gender[psor]=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=DET\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Dat\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Loc\|Gender=Neut\|Number=Plur\|POS=DET\|PronType=Dem`, `Case=Acc\|Definite=Def\|Degree=Cmp\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Acc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Aspect=Perf\|Mood=Ind\|Number=Dual\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Dem`, `Case=Loc\|Gender=Neut\|Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=DET\|PronType=Tot`, `Case=Gen\|Degree=Pos\|Gender=Fem\|Number=Dual\|POS=ADJ`, `Case=Gen\|Gender=Fem\|Number=Dual\|POS=NOUN`, `Case=Ins\|Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Polarity=Pos\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Sing\|Number[psor]=Dual\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Aspect=Imp\|Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Polarity=Neg\|Tense=Pres\|VerbForm=Fin`, `Case=Gen\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Neg`, `Mood=Ind\|Number=Dual\|POS=AUX\|Person=1\|Polarity=Pos\|Tense=Fut\|VerbForm=Fin`, `Case=Nom\|Gender=Masc\|Number=Plur\|Number[psor]=Dual\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Gender=Masc\|Number=Dual\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Ins\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Gen\|Gender=Masc\|Number=Dual\|POS=PROPN`, `Case=Loc\|Gender=Masc\|Number=Dual\|POS=DET\|PronType=Tot`, _(truncated: full list in pipeline meta)_ |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `cc:preconj`, `ccomp`, `conj`, `cop`, `csubj`, `dep`, `det`, `discourse`, `expl`, `fixed`, `flat`, `flat:foreign`, `flat:name`, `iobj`, `list`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `orphan`, `parataxis`, `punct`, `vocative`, `xcomp` |
| **`ner`** | `DERIV_PER`, `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.81 |
| `TOKEN_P` | 99.81 |
| `TOKEN_R` | 99.57 |
| `TOKEN_F` | 99.69 |
| `TAG_ACC` | 98.02 |
| `POS_ACC` | 99.08 |
| `MORPH_ACC` | 98.19 |
| `MORPH_MICRO_P` | 99.15 |
| `MORPH_MICRO_R` | 99.04 |
| `MORPH_MICRO_F` | 99.09 |
| `SENTS_P` | 92.09 |
| `SENTS_R` | 94.46 |
| `SENTS_F` | 93.26 |
| `DEP_UAS` | 93.62 |
| `DEP_LAS` | 92.23 |
| `LEMMA_ACC` | 96.94 |
| `ENTS_P` | 93.17 |
| `ENTS_R` | 94.94 |
| `ENTS_F` | 94.04 |
|
pfnet/plamo-13b
|
pfnet
| 2023-10-10T06:24:54Z | 7,458 | 82 |
transformers
|
[
"transformers",
"safetensors",
"plamo",
"text-generation",
"custom_code",
"en",
"ja",
"arxiv:2302.13971",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-09-25T12:47:05Z |
---
language:
- en
- ja
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
---
# PLaMo-13B
## Model Description
PLaMo-13B is a LLaMA-based 13B model pre-trained on English and Japanese open datasets, developed by Preferred Networks, Inc.
PLaMo-13B is released under Apache v2.0 license.
[PLaMo-13B Release blog (Japanese)](https://tech.preferred.jp/ja/blog/llm-plamo/)
## Usage
### Requirements
- numpy
- sentencepiece
- torch
- transformers
### Use a pipeline as a high-level helper
```python
import transformers
pipeline = transformers.pipeline("text-generation", model="pfnet/plamo-13b", trust_remote_code=True)
print(pipeline("The future of artificial intelligence technology is ", max_new_tokens=32))
```
### Load model directly
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("pfnet/plamo-13b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("pfnet/plamo-13b", trust_remote_code=True)
text = "これからの人工知能技術は"
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(
inputs=input_ids,
max_new_tokens=32,
do_sample=True,
top_k=50,
top_p=0.95,
temperature=1.0,
)[0]
generated_text = tokenizer.decode(generated_tokens)
print(generated_text)
```
## Model Details
- Model size: 13B
- Trained tokens: 1.5T tokens (English: 1.32T tokens, Japanese: 0.18T tokens)
- Context length: 4096
- Developed by: Preferred Networks, Inc
- Model type: Causal decoder-only
- Language(s): English, Japanese
- License: Apache v2.0
## Training Dataset
### English
- C4 - English
- Project Gutenberg
- RedPajama - Arxiv
- RedPajama - CommonCrawl - English
- RedPajama - Github
- RedPajama - StackExchange
- RedPajama - Wikipedia
### Japanese
- mC4 - Japanese
- Wikipedia - Japanese
## Tokenizer
PLaMo-13B uses sentencepiece tokenizer which is trained on a subset of the datasets for model pre-training.
## Bias, Risks, and Limitations
PLaMo-13B is a new technology that carries risks with use. Testing conducted to date has been in English and Japanese, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, PLaMo-13B’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of PLaMo-13B, developers should perform safety testing and tuning tailored to their specific applications of the model.
## How to cite
```tex
@online{PLaMo2023Introducing,
author = {Preferred Networks, Inc},
title = {PLaMo-13B},
year = {2023},
url = {https://huggingface.co/pfnet/plamo-13b},
urldate = {2023-09-28}
}
```
## Citations
```tex
@article{touvron2023llama,
title={LLaMA: Open and Efficient Foundation Language Models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
|
ChangeIsKey/bert-base-swedish-cased-kubhist2
|
ChangeIsKey
| 2023-10-10T06:11:53Z | 119 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:ChangeIsKey/kubhist2",
"base_model:KBLab/bert-base-swedish-cased",
"base_model:finetune:KBLab/bert-base-swedish-cased",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-10-09T09:04:43Z |
---
widget:
- text: Felix dog i [MASK] i går.
base_model: KBLab/bert-base-swedish-cased
tags:
- generated_from_trainer
datasets:
- ChangeIsKey/kubhist2
metrics:
- accuracy
model-index:
- name: test-mlm
results:
- task:
name: Masked Language Modeling
type: fill-mask
dataset:
name: ChangeIsKey/kubhist2
type: ChangeIsKey/kubhist2
config: all
split: train[:5%]
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.49963541184895327
---
<!-- 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. -->
# test-mlm
This model is a fine-tuned version of [KBLab/bert-base-swedish-cased](https://huggingface.co/KBLab/bert-base-swedish-cased) on the ChangeIsKey/kubhist2 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8883
- Accuracy: 0.4996
## 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: 128
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- 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 4.33.1
- Pytorch 1.12.0
- Datasets 2.14.5
- Tokenizers 0.13.3
|
LilyNgo/lora_Galaxiga_gemstone-trained-xl-colab
|
LilyNgo
| 2023-10-10T06:07:20Z | 2 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-10-10T05:36:58Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of Falcon Galaxiga style
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - LilyNgo/lora_Galaxiga_gemstone-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of Falcon Galaxiga style using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
Alnusjaponica/toxicity-classification-multi-class
|
Alnusjaponica
| 2023-10-10T05:55:26Z | 33 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"generated_from_trainer",
"base_model:line-corporation/line-distilbert-base-japanese",
"base_model:finetune:line-corporation/line-distilbert-base-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-10-10T05:47:10Z |
---
license: apache-2.0
base_model: line-corporation/line-distilbert-base-japanese
tags:
- generated_from_trainer
model-index:
- name: toxicity-classification-multi-class
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. -->
# toxicity-classification-multi-class
This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the [Japanese toxicity dataset](https://github.com/inspection-ai/japanese-toxic-dataset/tree/main).
It achieves the following results on the evaluation set:
- Loss: 0.2642
- Roc Auc: 0.8447
## 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: 8.133692392125703e-06
- 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
- num_epochs: 20
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.0
|
Alnusjaponica/toxicity-score-multi-classification
|
Alnusjaponica
| 2023-10-10T05:46:13Z | 132 | 2 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"generated_from_trainer",
"base_model:line-corporation/line-distilbert-base-japanese",
"base_model:finetune:line-corporation/line-distilbert-base-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-10-04T13:45:13Z |
---
license: apache-2.0
base_model: line-corporation/line-distilbert-base-japanese
tags:
- generated_from_trainer
model-index:
- name: toxicity-score-multi-classification
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. -->
# toxicity-score-multi-classification
This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on a [Japanese toxicity dataset](https://github.com/inspection-ai/japanese-toxic-dataset/tree/main).
It achieves the following results on the evaluation set:
- Loss: 0.2649
- Roc Auc: 0.7992
## 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: 8.133692392125703e-06
- 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
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 20 | 0.6213 | 0.5148 |
| No log | 2.0 | 40 | 0.4762 | 0.4616 |
| No log | 3.0 | 60 | 0.3754 | 0.5830 |
| No log | 4.0 | 80 | 0.3314 | 0.5706 |
| No log | 5.0 | 100 | 0.3140 | 0.5740 |
| No log | 6.0 | 120 | 0.3067 | 0.6238 |
| No log | 7.0 | 140 | 0.3010 | 0.6645 |
| No log | 8.0 | 160 | 0.2975 | 0.7177 |
| No log | 9.0 | 180 | 0.2949 | 0.7392 |
| No log | 10.0 | 200 | 0.2892 | 0.7731 |
| No log | 11.0 | 220 | 0.2828 | 0.7954 |
| No log | 12.0 | 240 | 0.2827 | 0.7932 |
| No log | 13.0 | 260 | 0.2756 | 0.7984 |
| No log | 14.0 | 280 | 0.2715 | 0.8052 |
| No log | 15.0 | 300 | 0.2733 | 0.8100 |
| No log | 16.0 | 320 | 0.2754 | 0.8142 |
| No log | 17.0 | 340 | 0.2668 | 0.8130 |
| No log | 18.0 | 360 | 0.2642 | 0.8138 |
| No log | 19.0 | 380 | 0.2639 | 0.8117 |
| No log | 20.0 | 400 | 0.2659 | 0.8052 |
| No log | 21.0 | 420 | 0.2646 | 0.8082 |
| No log | 22.0 | 440 | 0.2643 | 0.8039 |
| No log | 23.0 | 460 | 0.2646 | 0.8022 |
| No log | 24.0 | 480 | 0.2644 | 0.8044 |
| 0.2305 | 25.0 | 500 | 0.2639 | 0.8035 |
| 0.2305 | 26.0 | 520 | 0.2639 | 0.8027 |
| 0.2305 | 27.0 | 540 | 0.2647 | 0.8001 |
| 0.2305 | 28.0 | 560 | 0.2643 | 0.8005 |
| 0.2305 | 29.0 | 580 | 0.2649 | 0.8001 |
| 0.2305 | 30.0 | 600 | 0.2649 | 0.7992 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.0
|
m-aliabbas1/mva_ner_2
|
m-aliabbas1
| 2023-10-10T05:39:00Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:prajjwal1/bert-tiny",
"base_model:finetune:prajjwal1/bert-tiny",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-10T05:38:53Z |
---
license: mit
base_model: prajjwal1/bert-tiny
tags:
- generated_from_trainer
model-index:
- name: mva_ner_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. -->
# mva_ner_2
This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Overall Precision: 0.9873
- Overall Recall: 0.9873
- Overall F1: 0.9873
- Overall Accuracy: 0.9987
- Year F1: 1.0
- Years Ago F1: 0.9844
## 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: 0.001
- 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
- num_epochs: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Year F1 | Years Ago F1 |
|:-------------:|:------:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:-------:|:------------:|
| 0.0099 | 35.71 | 1000 | 0.0225 | 0.9625 | 0.9747 | 0.9686 | 0.9960 | 1.0 | 0.9612 |
| 0.0078 | 71.43 | 2000 | 0.0157 | 0.9625 | 0.9747 | 0.9686 | 0.9960 | 1.0 | 0.9612 |
| 0.0078 | 107.14 | 3000 | 0.0075 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
| 0.0061 | 142.86 | 4000 | 0.0062 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
| 0.0053 | 178.57 | 5000 | 0.0032 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
| 0.0049 | 214.29 | 6000 | 0.0179 | 0.9747 | 0.9747 | 0.9747 | 0.9973 | 1.0 | 0.9688 |
| 0.0049 | 250.0 | 7000 | 0.0011 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0034 | 285.71 | 8000 | 0.0064 | 0.9747 | 0.9747 | 0.9747 | 0.9973 | 1.0 | 0.9688 |
| 0.0037 | 321.43 | 9000 | 0.0148 | 0.9875 | 1.0 | 0.9937 | 0.9987 | 1.0 | 0.9922 |
| 0.0035 | 357.14 | 10000 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.003 | 392.86 | 11000 | 0.0007 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0028 | 428.57 | 12000 | 0.0032 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
| 0.0025 | 464.29 | 13000 | 0.0006 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.0024 | 500.0 | 14000 | 0.0026 | 0.9873 | 0.9873 | 0.9873 | 0.9987 | 1.0 | 0.9844 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
lmsys/vicuna-7b-v1.5-16k
|
lmsys
| 2023-10-10T05:31:20Z | 3,501 | 85 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2307.09288",
"arxiv:2306.05685",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-31T22:03:06Z |
---
inference: false
license: llama2
---
# Vicuna Model Card
## Model Details
Vicuna is a chat assistant trained by fine-tuning Llama 2 on user-shared conversations collected from ShareGPT.
- **Developed by:** [LMSYS](https://lmsys.org/)
- **Model type:** An auto-regressive language model based on the transformer architecture
- **License:** Llama 2 Community License Agreement
- **Finetuned from model:** [Llama 2](https://arxiv.org/abs/2307.09288)
### Model Sources
- **Repository:** https://github.com/lm-sys/FastChat
- **Blog:** https://lmsys.org/blog/2023-03-30-vicuna/
- **Paper:** https://arxiv.org/abs/2306.05685
- **Demo:** https://chat.lmsys.org/
## Uses
The primary use of Vicuna is research on large language models and chatbots.
The primary intended users of the model are researchers and hobbyists in natural language processing, machine learning, and artificial intelligence.
## How to Get Started with the Model
- Command line interface: https://github.com/lm-sys/FastChat#vicuna-weights
- APIs (OpenAI API, Huggingface API): https://github.com/lm-sys/FastChat/tree/main#api
## Training Details
Vicuna v1.5 (16k) is fine-tuned from Llama 2 with supervised instruction fine-tuning and linear RoPE scaling.
The training data is around 125K conversations collected from ShareGPT.com. These conversations are packed into sequences that contain 16K tokens each.
See more details in the "Training Details of Vicuna Models" section in the appendix of this [paper](https://arxiv.org/pdf/2306.05685.pdf).
## Evaluation
Vicuna is evaluated with standard benchmarks, human preference, and LLM-as-a-judge. See more details in this [paper](https://arxiv.org/pdf/2306.05685.pdf) and [leaderboard](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard).
## Difference between different versions of Vicuna
See [vicuna_weights_version.md](https://github.com/lm-sys/FastChat/blob/main/docs/vicuna_weights_version.md)
|
pepoo20/whisper_lora_small_r16_2442
|
pepoo20
| 2023-10-10T05:21:49Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:openai/whisper-small",
"base_model:adapter:openai/whisper-small",
"region:us"
] | null | 2023-10-10T05:21:46Z |
---
library_name: peft
base_model: openai/whisper-small
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
FreedomIntelligence/GrammarGPT
|
FreedomIntelligence
| 2023-10-10T04:57:08Z | 19 | 6 |
transformers
|
[
"transformers",
"pytorch",
"bloom",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-31T04:58:45Z |
---
license: apache-2.0
---
Please see our project [https://github.com/FreedomIntelligence/GrammarGPT](https://github.com/FreedomIntelligence/GrammarGPT)
|
liujch1998/crystal-11b
|
liujch1998
| 2023-10-10T04:50:03Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"arxiv:2310.04921",
"license:mit",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-01T07:29:44Z |
---
license: mit
language:
- en
pipeline_tag: text2text-generation
arxiv: 2310.04921
model-index:
- name: crystal-11b
results:
- task:
type: question-answering
name: Commonsense Question Answering
dataset:
type: openbookqa
name: OpenBookQA
metrics:
- type: accuracy
value: 84.58
name: Accuracy
- task:
type: question-answering
name: Commonsense Question Answering
dataset:
type: ai2_arc
name: ARC (easy)
config: ARC-Easy
metrics:
- type: accuracy
value: 87.54
name: Accuracy
- task:
type: question-answering
name: Commonsense Question Answering
dataset:
type: ai2_arc
name: ARC (challenge)
config: ARC-Challenge
metrics:
- type: accuracy
value: 73.24
name: Accuracy
- task:
type: question-answering
name: Commonsense Question Answering
dataset:
type: commonsense_qa
name: CommonsenseQA
metrics:
- type: accuracy
value: 82.31
name: Accuracy
- task:
type: question-answering
name: Commonsense Question Answering
dataset:
type: qasc
name: QASC
metrics:
- type: accuracy
value: 81.97
name: Accuracy
- task:
type: question-answering
name: Commonsense Question Answering
dataset:
type: piqa
name: Physical IQA
metrics:
- type: accuracy
value: 88.08
name: Accuracy
- task:
type: question-answering
name: Commonsense Question Answering
dataset:
type: social_i_qa
name: Social IQA
metrics:
- type: accuracy
value: 82.24
name: Accuracy
- task:
type: question-answering
name: Commonsense Question Answering
dataset:
type: winogrande
name: Winogrande
config: winogrande_xl
metrics:
- type: accuracy
value: 90.77
name: Accuracy
---
# Model Card for Crystal
<!-- Provide a quick summary of what the model is/does. -->
Crystal is an introspective reasoning model commonsense QA. See our paper at: <https://arxiv.org/abs/2310.04921>.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Crystal can answer a given commonsense question by first generating a relevant knowledge statement, and then predict the final answer by referencing the generated knowledge.
We call this process "introspective reasoning", and it improves both the prediction accuracy and the interpretability of neural models at reasoning tasks.
- **Developed by:** Jiacheng Liu, Ramakanth Pasunuru, Hannaneh Hajishirzi, Yejin Choi, Asli Celikyilmaz
- **Shared by [optional]:** Jiacheng Liu
- **Model type:** Transformers
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** t5-11b
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** <https://github.com/liujch1998/crystal>
- **Paper [optional]:** <https://arxiv.org/abs/2310.04921>
- **Demo [optional]:** <https://huggingface.co/spaces/liujch1998/crystal>
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
Crystal is intended to answer commonsense questions via an "introspective reasoning" process.
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
Crystal is a research prototype and may give incorrect answers or reasoning process. Do not use for making critical decisions. It is intended to answer questions about commonsense, and may be unreliable when taking input out of this scope.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
See the **Limitations** section of our paper.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained('liujch1998/crystal-11b')
model = AutoModelForSeq2SeqLM.from_pretrained('liujch1998/crystal-11b')
model.eval()
max_question_len, max_knowledge_len, max_answer_len = 128, 32, 2
k = 1 # number of knowledge statements to generate
top_p = 0.0001
question = 'If the mass of an object gets bigger what will happen to the amount of matter contained within it? \\n (A) gets bigger (B) gets smaller'
choices = ['A', 'B']
choices_ids = tokenizer(choices, return_tensors='pt', padding='max_length', truncation='longest_first', max_length=max_answer_len).input_ids # (C, AL)
prompt = question + ' \\n Knowledge: '
prompt_tok = tokenizer(prompt, return_tensors='pt', padding='max_length', truncation='longest_first', max_length=max_question_len) # (1, QL)
knowledges_ids = self.model.generate(
input_ids=prompt_tok.input_ids,
attention_mask=prompt_tok.attention_mask,
max_length=max_knowledge_len + 1,
min_length=3,
do_sample=True,
num_return_sequences=k,
top_p=top_p,
) # (K, KL); begins with 0 ([BOS]); ends with 1 ([EOS])
knowledges_ids = knowledges_ids[:, 1:].contiguous() # no beginning; ends with 1 ([EOS])
knowledges = tokenizer.batch_decode(knowledges_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
prompts = [question + (f' \\n Knowledge: {knowledge} \\n Answer: ' if knowledge != '' else ' \\n Answer:') for knowledge in knowledges]
prompts_tok = self.tokenizer(prompts, return_tensors='pt', padding='max_length', truncation='longest_first', max_length=max_question_len + max_knowledge_len) # (K, QL+KL)
output = model(
input_ids=prompts_tok.input_ids,
attention_mask=prompts_tok.attention_mask,
labels=choices_ids[0].unsqueeze(0).repeat(len(knowledges), 1),
)
logitsss = output.logits # (K, AL, V)
logitss = logitsss[:, 0, :] # (K, V)
choice_ids = choices_ids[:, 0] # (C)
answer_logitss = logitss.gather(dim=1, index=choice_ids.unsqueeze(0).expand(len(knowledges), -1)) # (K, C)
answer_probss = answer_logitss.softmax(dim=1) # (K, C)
answer_probs = answer_probss.max(dim=0).values # (C)
pred = answer_probs.argmax(dim=0).item()
pred = choices[pred]
print(f'Question: {question}\nKnowledge: {knowledges[0]}\nAnswer: {pred}')
```
You may also refer to <https://huggingface.co/spaces/liujch1998/crystal/blob/main/app.py#L10-L86> for implementation.
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```
@article{Liu2023CrystalIR,
title={Crystal: Introspective Reasoners Reinforced with Self-Feedback},
author={Jiacheng Liu and Ramakanth Pasunuru and Hannaneh Hajishirzi and Yejin Choi and Asli Celikyilmaz},
journal={ArXiv},
year={2023},
volume={abs/2310.04921}
}
```
## Model Card Contact
Jiacheng Liu
|
PiggyJerry/roberta-large-peft-p-tuning
|
PiggyJerry
| 2023-10-10T04:42:40Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T04:38:42Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
ryatora/xlm-roberta-base-finetuned-panx-all
|
ryatora
| 2023-10-10T04:36:45Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-10T04:01:35Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-all
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. -->
# xlm-roberta-base-finetuned-panx-all
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1828
- F1: 0.8519
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2947 | 1.0 | 739 | 0.1879 | 0.8175 |
| 0.152 | 2.0 | 1478 | 0.1853 | 0.8385 |
| 0.0974 | 3.0 | 2217 | 0.1828 | 0.8519 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.14.1
|
neenax/vicuna_tuned_v1.0
|
neenax
| 2023-10-10T04:34:20Z | 2 | 0 |
peft
|
[
"peft",
"tensorboard",
"arxiv:1910.09700",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"base_model:adapter:vilsonrodrigues/falcon-7b-instruct-sharded",
"region:us"
] | null | 2023-10-10T03:22:45Z |
---
library_name: peft
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
|
princeton-nlp/SWE-Llama-7b-peft
|
princeton-nlp
| 2023-10-10T04:31:45Z | 289 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T04:31:43Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
liujch1998/crystal-large
|
liujch1998
| 2023-10-10T04:30:36Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-01T04:27:06Z |
---
license: mit
language:
- en
pipeline_tag: text2text-generation
---
See model card at <https://huggingface.co/liujch1998/crystal-11b>
|
LilyNgo/lora_Galaxiga_stone-trained-xl-colab
|
LilyNgo
| 2023-10-10T04:21:21Z | 3 | 0 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-10-10T03:44:23Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of Galaxiga stone
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - LilyNgo/lora_Galaxiga_stone-trained-xl-colab
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of Galaxiga stone using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
ryatora/xlm-roberta-base-finetuned-panx-it
|
ryatora
| 2023-10-10T04:16:28Z | 126 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-10T04:02:52Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8218390804597702
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2503
- F1: 0.8218
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8253 | 1.0 | 70 | 0.3503 | 0.7160 |
| 0.2781 | 2.0 | 140 | 0.2643 | 0.8148 |
| 0.1871 | 3.0 | 210 | 0.2503 | 0.8218 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.14.1
|
ryatora/xlm-roberta-base-finetuned-panx-fr
|
ryatora
| 2023-10-10T04:12:59Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-10T04:02:38Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8115649689023365
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3184
- F1: 0.8116
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7671 | 1.0 | 96 | 0.3643 | 0.7537 |
| 0.325 | 2.0 | 192 | 0.3360 | 0.7977 |
| 0.2209 | 3.0 | 288 | 0.3184 | 0.8116 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.14.1
|
kms-healthcare/llama-lora-medical-model
|
kms-healthcare
| 2023-10-10T04:06:51Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:medalpaca/medalpaca-7b",
"base_model:adapter:medalpaca/medalpaca-7b",
"region:us"
] | null | 2023-10-10T03:40:03Z |
---
library_name: peft
base_model: medalpaca/medalpaca-7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
latent-space-dreams/LS_Merus
|
latent-space-dreams
| 2023-10-10T03:33:09Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-10-10T02:25:51Z |
---
license: creativeml-openrail-m
---
An anime style model with a focus on cuteness.
I fully denounce the use of this model for any inappropriate or harmful content. You are solely responsible for how you use this model.
No commercial use, please.
|
ryatora/xlm-roberta-base-finetuned-panx-de
|
ryatora
| 2023-10-10T03:09:41Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-09T16:44:06Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8653353814644136
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- F1: 0.8653
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 |
| 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 |
| 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.0.1+cu118
- Datasets 1.16.1
- Tokenizers 0.14.1
|
cccastaneda/marian-finetuned-kde4-en-to-fr
|
cccastaneda
| 2023-10-10T03:09:25Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-10-09T20:16:50Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
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
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.92454808849736
---
<!-- 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: 0.8556
- Bleu: 52.9245
## 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
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
peterccn/llm_test
|
peterccn
| 2023-10-10T02:37:49Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-10-10T02:36:59Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
adenp/qlora-polyglot-12.8b-800step
|
adenp
| 2023-10-10T02:34:26Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T02:33:11Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
|
letdosomething/bloomz
|
letdosomething
| 2023-10-10T02:31:31Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T02:31:30Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
pigpeter/dummy
|
pigpeter
| 2023-10-10T02:25:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-10-10T01:42:38Z |
# My dummy model
Welcome to my model page!
Central definition, reproducibility tips, code samples.
|
Mattrfu/new_model
|
Mattrfu
| 2023-10-10T02:24:33Z | 223 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-10T01:54:49Z |
---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: new_model
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. -->
# new_model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) 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: 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: 3.0
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Kamal99919/MedicalFineTunedModel1
|
Kamal99919
| 2023-10-10T01:59:28Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:TinyPixel/Llama-2-7B-bf16-sharded",
"base_model:adapter:TinyPixel/Llama-2-7B-bf16-sharded",
"region:us"
] | null | 2023-10-10T01:59:23Z |
---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
alexisdpc/sentiment_analysis
|
alexisdpc
| 2023-10-10T01:52:20Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-09T12:22:22Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: sentiment_analysis
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93148
---
<!-- 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. -->
# sentiment_analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2220
- Accuracy: 0.9315
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2246 | 1.0 | 1563 | 0.2627 | 0.9101 |
| 0.1486 | 2.0 | 3126 | 0.2220 | 0.9315 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
digiplay/quincemix_v1
|
digiplay
| 2023-10-10T01:52:16Z | 427 | 4 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-21T00:00:35Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info:
https://civitai.com/models/24675?modelVersionId=29517
Sample images I made :
Original Author's DEMO image :
|
hyejungg/qlora-koalpaca-polyglot-12.8b-50step
|
hyejungg
| 2023-10-10T01:40:38Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T01:27:52Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
|
digiplay/realmixUnrealjourney_v1
|
digiplay
| 2023-10-10T01:13:20Z | 490 | 3 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-06-26T06:18:42Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Model info :
https://civitai.com/models/83214/realmixunrealjourney
Sample image I made :
Original Author's DEMO images :
|
minoosh/videomae-base-finetuned-IEMOCAP_1xx
|
minoosh
| 2023-10-10T01:13:11Z | 59 | 0 |
transformers
|
[
"transformers",
"pytorch",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-10-09T19:51:11Z |
---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-IEMOCAP_1xx
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. -->
# videomae-base-finetuned-IEMOCAP_1xx
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2253
- Accuracy: 0.3303
## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 4440
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2487 | 0.1 | 445 | 1.5638 | 0.3912 |
| 0.6787 | 1.1 | 890 | 1.1789 | 0.4877 |
| 0.7851 | 2.1 | 1335 | 0.9786 | 0.5811 |
| 0.3104 | 3.1 | 1780 | 1.1209 | 0.6273 |
| 0.5358 | 4.1 | 2225 | 0.8696 | 0.7084 |
| 0.3483 | 5.1 | 2670 | 1.0214 | 0.7084 |
| 0.3458 | 6.1 | 3115 | 1.0766 | 0.7125 |
| 0.2962 | 7.1 | 3560 | 1.2876 | 0.7351 |
| 0.0641 | 8.1 | 4005 | 1.3037 | 0.7382 |
| 0.0131 | 9.1 | 4440 | 1.3754 | 0.7474 |
### Framework versions
- Transformers 4.34.0
- Pytorch 1.12.0+cu116
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Karam142/vicuna-karam13b-qlora-100entry
|
Karam142
| 2023-10-10T00:48:54Z | 2 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:lmsys/vicuna-13b-v1.5",
"base_model:adapter:lmsys/vicuna-13b-v1.5",
"region:us"
] | null | 2023-10-10T00:48:45Z |
---
library_name: peft
base_model: lmsys/vicuna-13b-v1.5
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
judithrosell/biobert-ft-ner
|
judithrosell
| 2023-10-10T00:48:42Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:dmis-lab/biobert-v1.1",
"base_model:finetune:dmis-lab/biobert-v1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-10-09T23:25:48Z |
---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_keras_callback
model-index:
- name: judithrosell/biobert-ft-ner
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# judithrosell/biobert-ft-ner
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0737
- Validation Loss: 0.3557
- Epoch: 4
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 23335, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.4049 | 0.3010 | 0 |
| 0.1876 | 0.3061 | 1 |
| 0.1289 | 0.3322 | 2 |
| 0.0946 | 0.3444 | 3 |
| 0.0737 | 0.3557 | 4 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
ken0997/old-whisper-tiny-khmer2
|
ken0997
| 2023-10-10T00:47:34Z | 86 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-10-09T01:57:10Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-tiny-khmer
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. -->
# whisper-tiny-khmer
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2487
- Wer: 1.3242
## 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: 6.25e-06
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 80
- training_steps: 800
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.4242 | 0.51 | 200 | 1.4528 | 1.1595 |
| 1.2641 | 1.01 | 400 | 1.3215 | 1.3512 |
| 1.1987 | 1.52 | 600 | 1.2649 | 1.2247 |
| 1.1687 | 2.03 | 800 | 1.2487 | 1.3242 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.6.dev0
- Tokenizers 0.14.1
|
SeanWu25/Racoon7B-lora
|
SeanWu25
| 2023-10-10T00:47:10Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"region:us"
] | null | 2023-10-07T22:22:22Z |
---
library_name: peft
base_model: decapoda-research/llama-7b-hf
---
# Model Card for Model ID
 Raccoon: Improving Open-Sourced Language Models with Proprietary Data Imitation in Nephrology
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Sean Wu, Michael Koo, Lesley Blum, Andy Black, Fabien Scalzo, Ira Kurtz @Pepperdine University, Division of UCLA Nephrology
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Fine Tuned
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [llama-7b]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
nozagleh/IceBERT-QA-Is-finetune
|
nozagleh
| 2023-10-10T00:35:10Z | 130 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:icelandic-qa-n_qi_i",
"base_model:mideind/IceBERT",
"base_model:finetune:mideind/IceBERT",
"license:agpl-3.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-09T11:22:08Z |
---
license: agpl-3.0
base_model: mideind/IceBERT
tags:
- generated_from_trainer
datasets:
- icelandic-qa-n_qi_i
model-index:
- name: IceBERT-QA-Is-finetune
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. -->
# IceBERT-QA-Is-finetune
This model is a fine-tuned version of [mideind/IceBERT](https://huggingface.co/mideind/IceBERT) on the icelandic-qa-n_qi_i 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
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
cxllin/Mistral-7b-sharded-Econ
|
cxllin
| 2023-10-10T00:06:34Z | 0 | 0 |
transformers
|
[
"transformers",
"finance",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-09T18:44:22Z |
---
license: apache-2.0
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-generation
tags:
- finance
---
|
wasertech/assistant-llama2-7b-chat-qlora
|
wasertech
| 2023-10-10T00:05:39Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-10T00:04:08Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
flytech/togetherchat-dev-7b-v2
|
flytech
| 2023-10-09T23:45:45Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:togethercomputer/LLaMA-2-7B-32K",
"base_model:finetune:togethercomputer/LLaMA-2-7B-32K",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-09T19:52:16Z |
---
license: llama2
base_model: togethercomputer/LLaMA-2-7B-32K
tags:
- generated_from_trainer
model-index:
- name: togetherchat-dev-7b-v2
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. -->
# togetherchat-dev-7b-v2
This model is a fine-tuned version of [togethercomputer/LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) on 25000 entries for 3 epochs.
## Model description
Model can be used for text-to-code generation and for further fine-tuning,
Colab notebook example (on free T4 GPU) soon!
## Datasets used:
- evol-codealpaca-80k - 10000 entries
- codealpaca-20k - 10000 entries
- open-platypus - 5000 entries
## Intended uses & limitations
Please remember that model may (and will) produce inaccurate informations,
you need to fine-tune it for your specific task.
## Training and evaluation data
See 'Metrics'
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 10
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
liujch1998/rainier-large-value
|
liujch1998
| 2023-10-09T23:38:10Z | 34 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"en",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2023-07-19T00:19:39Z |
---
license: mit
language:
- en
---
This is the value model associated with the [Rainier policy model](https://huggingface.co/liujch1998/rainier-large).
Please see the model card in the policy model.
|
erkam/sg2im-256-bs-16x2-lr1e4-depth-snr-12k
|
erkam
| 2023-10-09T23:36:07Z | 2 | 0 |
diffusers
|
[
"diffusers",
"sg-to-image",
"scene-graph",
"stable-diffusion",
"stable-diffusion-diffusers",
"lora",
"base_model:stabilityai/stable-diffusion-2",
"base_model:adapter:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-10-06T15:26:12Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
tags:
- sg-to-image
- scene-graph
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - erkam/sg2im-256-bs-16x2-lr1e4-depth-snr-12k
These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the vg dataset. You can find some example images in the following.
|
jlpan/Mistral_ET_1
|
jlpan
| 2023-10-09T23:25:31Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-10-09T23:13:21Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral_ET_1
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. -->
# Mistral_ET_1
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7456
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5079 | 0.41 | 100 | 1.6764 |
| 1.3602 | 0.82 | 200 | 1.1685 |
| 1.0132 | 1.23 | 300 | 0.9737 |
| 0.8592 | 1.65 | 400 | 0.8837 |
| 0.7742 | 2.06 | 500 | 0.8322 |
| 0.6621 | 2.47 | 600 | 0.7871 |
| 0.6402 | 2.88 | 700 | 0.7554 |
| 0.5713 | 3.29 | 800 | 0.7530 |
| 0.5468 | 3.7 | 900 | 0.7472 |
| 0.5542 | 4.12 | 1000 | 0.7456 |
### Framework versions
- Transformers 4.34.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.14.0
|
thesephist/contra-bottleneck-t5-small-wikipedia
|
thesephist
| 2023-10-09T23:22:59Z | 242 | 11 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text-generation",
"custom_code",
"en",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-30T21:50:20Z |
---
license: mit
datasets:
- wikipedia
language:
- en
---
# Bottleneck T5 ⏳
The Bottleneck T5 model powers many of my experiments and demos exploring interfaces for inspecting and editing text in latent space. This model is an autoencoder for text; it's able to encode text up to 512 tokens into an embedding, then reconstruct the original text from the embedding. The structure of the embedding space produced by this model also allows for semantic edits to text through vector arithmetic in latent space.
## Model Details
Using embeddings produced by this model, we can semantically interpolate between pieces of text and edit sentences using their latent attributes like length, tone, structure, or topic.
All Bottleneck T5 models are trained on a filtered subset of the English Wikipedia, and performs best at encoding and decoding encyclopedic and other similar kinds of text. Text that's heavily technical, conversational, or otherwise unconventional may be out of distribution for the model, and the model may not perform as well on such inputs.
Bottleneck T5 embeddings are always normalized to length 1; the encoder produces embeddings of length 1, and any inputs to the decoder will be normalized to length 1.
- **Developed by:** [Linus Lee](https://thesephist.com/)
- **Model type:** T5-style encoder-decoder transformer with an attention pooled bottleneck and gated cross-attention
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** LM-adapted T5 v1.1
## Using the model
The model is currently in a prototype state implemented on top of the T5 language model, so we need a small wrapper class around it to use it for embedding and generating text:
```py
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
class BottleneckT5Autoencoder:
def __init__(self, model_path: str, device='cpu'):
self.device = device
self.tokenizer = AutoTokenizer.from_pretrained(model_path, model_max_length=512)
self.model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(self.device)
self.model.eval()
@torch.no_grad()
def embed(self, text: str) -> torch.FloatTensor:
inputs = self.tokenizer(text, return_tensors='pt').to(self.device)
decoder_inputs = self.tokenizer('', return_tensors='pt').to(self.device)
return self.model(
**inputs,
decoder_input_ids=decoder_inputs['input_ids'],
encode_only=True,
)[0]
@torch.no_grad()
def generate_from_latent(self, latent: torch.FloatTensor, max_length=512, temperature=1.0) -> str:
dummy_text = '.'
dummy = self.embed(dummy_text)
perturb_vector = latent - dummy
self.model.perturb_vector = perturb_vector
input_ids = self.tokenizer(dummy_text, return_tensors='pt').to(self.device).input_ids
output = self.model.generate(
input_ids=input_ids,
max_length=max_length,
do_sample=True,
temperature=temperature,
top_p=0.9,
num_return_sequences=1,
)
return self.tokenizer.decode(output[0], skip_special_tokens=True)
```
Then we can initialize this autoencoder class based on a model class.
```py
device = 'cuda' if torch.cuda.is_available() else 'cpu'
autoencoder = BottleneckT5Autoencoder(model_path='thesephist/contra-bottleneck-t5-large-wikipedia', device=device)
```
Embed and un-embed text with `.embed(text: str)` and `.generate_from_latent(embedding: torch.FloatTensor)`.
```py
texts = [
'The quick brown fox jumps over the lazy dog',
'Hi there! My name is Linus, and I spend a lot of my time thinking about latent spaces of neural network models.',
'Notion is a single space where you can think, write, and plan. Capture thoughts, manage projects, or even run an entire company — and do it exactly the way you want.',
]
for t in texts:
embedding = autoencoder.embed(t)
reconstruction = autoencoder.generate_from_latent(embedding)
print(reconstruction)
```
produces the text:
```
The quick brown fox jumps over the lazy dog
I'm named after Linus, and I spend a lot of my time thinking about neural networks of latent space models.
Notion is a single place where you can think, plan, and spend time. Capture ideas, manage projects, and even do your own writing — or plan it exactly the way you want.
```
For more examples on how to use the model to compute interpolations and semantic edits with Contra, see [this Google Colab notebook](https://linus.zone/contra-colab).
## Training Details
Contra was initialized from the [language modeling-adapted T5 v1.1 checkpoint](https://huggingface.co/models?other=t5-lm-adapt) and trained on a subset of the English [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset filtered for length, for a single epoch, as a denoising autoencoder with 30% of tokens randomly masked, using the Adafactor optimizer.
#### Model family and checkpoints
I recommend experimenting first with `thesephist/contra-bottleneck-t5-large-wikipedia`, which strikes a good balance between model size and output quality, but I've trained four variants ranging from 330M to 3B parameters:
- [thesephist/contra-bottleneck-t5-small-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-small-wikipedia): 60M params, 512 embedding dimensions
- [thesephist/contra-bottleneck-t5-base-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-base-wikipedia): 220M params, 768 embedding dimensions
- [thesephist/contra-bottleneck-t5-large-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-large-wikipedia): 770M params, 1024 embedding dimensions
- [thesephist/contra-bottleneck-t5-xl-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-xl-wikipedia): 3B params, 2048 embedding dimensions
|
thesephist/contra-bottleneck-t5-base-wikipedia
|
thesephist
| 2023-10-09T23:22:47Z | 154 | 3 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text-generation",
"custom_code",
"en",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-30T21:50:05Z |
---
license: mit
datasets:
- wikipedia
language:
- en
---
# Bottleneck T5 ⏳
The Bottleneck T5 model powers many of my experiments and demos exploring interfaces for inspecting and editing text in latent space. This model is an autoencoder for text; it's able to encode text up to 512 tokens into an embedding, then reconstruct the original text from the embedding. The structure of the embedding space produced by this model also allows for semantic edits to text through vector arithmetic in latent space.
## Model Details
Using embeddings produced by this model, we can semantically interpolate between pieces of text and edit sentences using their latent attributes like length, tone, structure, or topic.
All Bottleneck T5 models are trained on a filtered subset of the English Wikipedia, and performs best at encoding and decoding encyclopedic and other similar kinds of text. Text that's heavily technical, conversational, or otherwise unconventional may be out of distribution for the model, and the model may not perform as well on such inputs.
Bottleneck T5 embeddings are always normalized to length 1; the encoder produces embeddings of length 1, and any inputs to the decoder will be normalized to length 1.
- **Developed by:** [Linus Lee](https://thesephist.com/)
- **Model type:** T5-style encoder-decoder transformer with an attention pooled bottleneck and gated cross-attention
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** LM-adapted T5 v1.1
## Using the model
The model is currently in a prototype state implemented on top of the T5 language model, so we need a small wrapper class around it to use it for embedding and generating text:
```py
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
class BottleneckT5Autoencoder:
def __init__(self, model_path: str, device='cpu'):
self.device = device
self.tokenizer = AutoTokenizer.from_pretrained(model_path, model_max_length=512)
self.model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(self.device)
self.model.eval()
@torch.no_grad()
def embed(self, text: str) -> torch.FloatTensor:
inputs = self.tokenizer(text, return_tensors='pt').to(self.device)
decoder_inputs = self.tokenizer('', return_tensors='pt').to(self.device)
return self.model(
**inputs,
decoder_input_ids=decoder_inputs['input_ids'],
encode_only=True,
)[0]
@torch.no_grad()
def generate_from_latent(self, latent: torch.FloatTensor, max_length=512, temperature=1.0) -> str:
dummy_text = '.'
dummy = self.embed(dummy_text)
perturb_vector = latent - dummy
self.model.perturb_vector = perturb_vector
input_ids = self.tokenizer(dummy_text, return_tensors='pt').to(self.device).input_ids
output = self.model.generate(
input_ids=input_ids,
max_length=max_length,
do_sample=True,
temperature=temperature,
top_p=0.9,
num_return_sequences=1,
)
return self.tokenizer.decode(output[0], skip_special_tokens=True)
```
Then we can initialize this autoencoder class based on a model class.
```py
device = 'cuda' if torch.cuda.is_available() else 'cpu'
autoencoder = BottleneckT5Autoencoder(model_path='thesephist/contra-bottleneck-t5-large-wikipedia', device=device)
```
Embed and un-embed text with `.embed(text: str)` and `.generate_from_latent(embedding: torch.FloatTensor)`.
```py
texts = [
'The quick brown fox jumps over the lazy dog',
'Hi there! My name is Linus, and I spend a lot of my time thinking about latent spaces of neural network models.',
'Notion is a single space where you can think, write, and plan. Capture thoughts, manage projects, or even run an entire company — and do it exactly the way you want.',
]
for t in texts:
embedding = autoencoder.embed(t)
reconstruction = autoencoder.generate_from_latent(embedding)
print(reconstruction)
```
produces the text:
```
The quick brown fox jumps over the lazy dog
I'm named after Linus, and I spend a lot of my time thinking about neural networks of latent space models.
Notion is a single place where you can think, plan, and spend time. Capture ideas, manage projects, and even do your own writing — or plan it exactly the way you want.
```
For more examples on how to use the model to compute interpolations and semantic edits with Contra, see [this Google Colab notebook](https://linus.zone/contra-colab).
## Training Details
Contra was initialized from the [language modeling-adapted T5 v1.1 checkpoint](https://huggingface.co/models?other=t5-lm-adapt) and trained on a subset of the English [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset filtered for length, for a single epoch, as a denoising autoencoder with 30% of tokens randomly masked, using the Adafactor optimizer.
#### Model family and checkpoints
I recommend experimenting first with `thesephist/contra-bottleneck-t5-large-wikipedia`, which strikes a good balance between model size and output quality, but I've trained four variants ranging from 330M to 3B parameters:
- [thesephist/contra-bottleneck-t5-small-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-small-wikipedia): 60M params, 512 embedding dimensions
- [thesephist/contra-bottleneck-t5-base-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-base-wikipedia): 220M params, 768 embedding dimensions
- [thesephist/contra-bottleneck-t5-large-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-large-wikipedia): 770M params, 1024 embedding dimensions
- [thesephist/contra-bottleneck-t5-xl-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-xl-wikipedia): 3B params, 2048 embedding dimensions
|
thesephist/contra-bottleneck-t5-xl-wikipedia
|
thesephist
| 2023-10-09T23:22:00Z | 206 | 16 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text-generation",
"custom_code",
"en",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-09-30T21:48:13Z |
---
license: mit
datasets:
- wikipedia
language:
- en
---
# Bottleneck T5 ⏳
The Bottleneck T5 model powers many of my experiments and demos exploring interfaces for inspecting and editing text in latent space. This model is an autoencoder for text; it's able to encode text up to 512 tokens into an embedding, then reconstruct the original text from the embedding. The structure of the embedding space produced by this model also allows for semantic edits to text through vector arithmetic in latent space.
## Model Details
Using embeddings produced by this model, we can semantically interpolate between pieces of text and edit sentences using their latent attributes like length, tone, structure, or topic.
All Bottleneck T5 models are trained on a filtered subset of the English Wikipedia, and performs best at encoding and decoding encyclopedic and other similar kinds of text. Text that's heavily technical, conversational, or otherwise unconventional may be out of distribution for the model, and the model may not perform as well on such inputs.
Bottleneck T5 embeddings are always normalized to length 1; the encoder produces embeddings of length 1, and any inputs to the decoder will be normalized to length 1.
- **Developed by:** [Linus Lee](https://thesephist.com/)
- **Model type:** T5-style encoder-decoder transformer with an attention pooled bottleneck and gated cross-attention
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** LM-adapted T5 v1.1
## Using the model
The model is currently in a prototype state implemented on top of the T5 language model, so we need a small wrapper class around it to use it for embedding and generating text:
```py
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from transformers import AutoTokenizer, AutoModelForCausalLM
class BottleneckT5Autoencoder:
def __init__(self, model_path: str, device='cpu'):
self.device = device
self.tokenizer = AutoTokenizer.from_pretrained(model_path, model_max_length=512)
self.model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(self.device)
self.model.eval()
@torch.no_grad()
def embed(self, text: str) -> torch.FloatTensor:
inputs = self.tokenizer(text, return_tensors='pt').to(self.device)
decoder_inputs = self.tokenizer('', return_tensors='pt').to(self.device)
return self.model(
**inputs,
decoder_input_ids=decoder_inputs['input_ids'],
encode_only=True,
)[0]
@torch.no_grad()
def generate_from_latent(self, latent: torch.FloatTensor, max_length=512, temperature=1.0) -> str:
dummy_text = '.'
dummy = self.embed(dummy_text)
perturb_vector = latent - dummy
self.model.perturb_vector = perturb_vector
input_ids = self.tokenizer(dummy_text, return_tensors='pt').to(self.device).input_ids
output = self.model.generate(
input_ids=input_ids,
max_length=max_length,
do_sample=True,
temperature=temperature,
top_p=0.9,
num_return_sequences=1,
)
return self.tokenizer.decode(output[0], skip_special_tokens=True)
```
Then we can initialize this autoencoder class based on a model class.
```py
device = 'cuda' if torch.cuda.is_available() else 'cpu'
autoencoder = BottleneckT5Autoencoder(model_path='thesephist/contra-bottleneck-t5-large-wikipedia', device=device)
```
Embed and un-embed text with `.embed(text: str)` and `.generate_from_latent(embedding: torch.FloatTensor)`.
```py
texts = [
'The quick brown fox jumps over the lazy dog',
'Hi there! My name is Linus, and I spend a lot of my time thinking about latent spaces of neural network models.',
'Notion is a single space where you can think, write, and plan. Capture thoughts, manage projects, or even run an entire company — and do it exactly the way you want.',
]
for t in texts:
embedding = autoencoder.embed(t)
reconstruction = autoencoder.generate_from_latent(embedding)
print(reconstruction)
```
produces the text:
```
The quick brown fox jumps over the lazy dog
I'm named after Linus, and I spend a lot of my time thinking about neural networks of latent space models.
Notion is a single place where you can think, plan, and spend time. Capture ideas, manage projects, and even do your own writing — or plan it exactly the way you want.
```
For more examples on how to use the model to compute interpolations and semantic edits with Contra, see [this Google Colab notebook](https://linus.zone/contra-colab).
## Training Details
Contra was initialized from the [language modeling-adapted T5 v1.1 checkpoint](https://huggingface.co/models?other=t5-lm-adapt) and trained on a subset of the English [Wikipedia](https://huggingface.co/datasets/wikipedia) dataset filtered for length, for a single epoch, as a denoising autoencoder with 30% of tokens randomly masked, using the Adafactor optimizer.
#### Model family and checkpoints
I recommend experimenting first with `thesephist/contra-bottleneck-t5-large-wikipedia`, which strikes a good balance between model size and output quality, but I've trained four variants ranging from 330M to 3B parameters:
- [thesephist/contra-bottleneck-t5-small-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-small-wikipedia): 60M params, 512 embedding dimensions
- [thesephist/contra-bottleneck-t5-base-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-base-wikipedia): 220M params, 768 embedding dimensions
- [thesephist/contra-bottleneck-t5-large-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-large-wikipedia): 770M params, 1024 embedding dimensions
- [thesephist/contra-bottleneck-t5-xl-wikipedia](https://huggingface.co/thesephist/contra-bottleneck-t5-xl-wikipedia): 3B params, 2048 embedding dimensions
|
frivasplata/ALE-GPT-llama2-7B-1562-int8-lora256-constant-adamw8bit
|
frivasplata
| 2023-10-09T22:37:25Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-10-09T22:28:00Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [h2oai/h2ogpt-4096-llama2-7b](https://huggingface.co/h2oai/h2ogpt-4096-llama2-7b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.34.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="frivasplata/ALE-GPT-llama2-7B-1562-int8-lora256-constant-adamw8bit",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?</s><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"frivasplata/ALE-GPT-llama2-7B-1562-int8-lora256-constant-adamw8bit",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"frivasplata/ALE-GPT-llama2-7B-1562-int8-lora256-constant-adamw8bit",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "frivasplata/ALE-GPT-llama2-7B-1562-int8-lora256-constant-adamw8bit" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?</s><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.3),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 4096, padding_idx=0)
(layers): ModuleList(
(0-31): 32 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=4096, out_features=4096, bias=False)
(k_proj): Linear(in_features=4096, out_features=4096, bias=False)
(v_proj): Linear(in_features=4096, out_features=4096, bias=False)
(o_proj): Linear(in_features=4096, out_features=4096, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=4096, out_features=11008, bias=False)
(up_proj): Linear(in_features=4096, out_features=11008, bias=False)
(down_proj): Linear(in_features=11008, out_features=4096, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
blockplacer4/Hobby-Ki-V4
|
blockplacer4
| 2023-10-09T22:31:33Z | 7 | 0 |
peft
|
[
"peft",
"hobby",
"programming",
"text-generation",
"de",
"dataset:blockplacer4/hobby-dataset-v4",
"region:us"
] |
text-generation
| 2023-10-09T21:47:55Z |
---
datasets:
- blockplacer4/hobby-dataset-v4
language:
- de
library_name: peft
pipeline_tag: text-generation
tags:
- hobby
- programming
---
# Model Card for Model ID
- Dieses Modell wurde unter Verwendung eines maßgeschneiderten Datasets entwickelt, das Pro-Argumente für das Programmieren als Hobby enthält. Das Modell wurde trainiert, um menschenähnlichen Text basierend auf den gegebenen Argumenten zu generieren.
### Model Description
- Dieses Modell wurde speziell entwickelt, um überzeugende Texte mit Pro-Argumenten für das Programmieren als Hobby zu generieren. Es basiert auf einer maßgeschneiderten Trainingsgrundlage, die es ihm ermöglicht, auf menschenähnliche Weise auf Benutzereingaben zu reagieren und informative Argumente für das Hobby des Programmierens bereitzustellen. Dieses Modell ist darauf ausgerichtet, Texte zu generieren, die die Vorzüge und Vorteile des Programmierens als Freizeitbeschäftigung erläutern.
- **Developed by:** Janosch, Jannes, ...
- **Model type:** Textgenerierung
- **Language(s) (NLP):** German/Deutsch
### Model Sources [optional]
- **Repository:** https://github.com/blockplacer4/HobbyKI-GUI
## Uses
### Direct Use
- Dieses Modell kann verwendet werden, um menschenähnlichen Text zu generieren, der Pro-Argumente für das Programmieren als Hobby enthält. Dies kann für Textgenerierungsaufgaben oder zur Erstellung von informativem Inhalt verwendet werden.
### Out-of-Scope Use
- Die Verwendung dieses Modells für bösartige oder irreführende Zwecke oder in Situationen, in denen es nicht angemessen ist, wird nicht unterstützt.
## Bias, Risks, and Limitations
- Dieses Modell hat einige wichtige Einschränkungen und Risiken:
- **Bias:** Das Modell kann durch das verwendete Dataset und Trainingsdaten Bias aufweisen, der sich auf die generierten Texte auswirken kann.
- **Qualität:** Die Qualität der generierten Texte kann variieren, und es besteht die Möglichkeit von inkorrekten oder irreführenden Informationen.
- **Missbrauch:** Es besteht das Potenzial für Missbrauch, wenn der generierte Text für schädliche oder unethische Zwecke verwendet wird.
### Recommendations
- Nutzer sollten sich der Risiken, des möglichen Bias und der Einschränkungen dieses Modells bewusst sein. Bei der Verwendung des Modells ist es wichtig, die generierten Texte sorgfältig zu überprüfen und zu validieren, insbesondere wenn sie für wichtige Anwendungen oder Entscheidungsfindungen verwendet werden.
### Training Data
- Das Modell wurde mit einem maßgeschneiderten Dataset trainiert, das Pro-Argumente für das Programmieren als Hobby enthielt. Das Dataset wurde auf Grundlage von Benutzereingaben und Modellantworten erstellt
- https://huggingface.co/datasets/blockplacer4/hobby-dataset-v4
### Training Procedure
- **Vorverarbeitung:** Das Training umfasste die Vorverarbeitung von Textdaten, darunter Tokenisierung und Textbereinigung.
- **Trainingregime:** Das Modell wurde mit einem Fine-Tuning-Verfahren trainiert, um die Generierung von Pro-Argumenten zu optimieren.
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
- Die Testdaten bestanden aus einer Vielzahl von menschlichen Eingaben, die Pro-Argumente für das Programmieren als Hobby enthielten.
#### Factors
- Es wurden verschiedene Faktoren berücksichtigt, darunter die Qualität der generierten Texte, die Kohärenz und die Relevanz der Antworten.
#### Metrics
- Die Bewertung basierte auf menschlicher Überprüfung und Beurteilung der generierten Texte.
### Results
- Die Ergebnisse der Evaluierung zeigten, dass das Modell in der Lage ist, überzeugende Pro-Argumente für das Programmieren als Hobby zu generieren. Die Qualität der generierten Texte wurde als zufriedenstellend bewertet.
#### Summary
- Dieses Modell wurde auf der Grundlage eines maßgeschneiderten Datasets entwickelt, um Pro-Argumente für das Programmieren als Hobby zu generieren. Es kann für Textgenerierungsaufgaben und die Erstellung von informativem Inhalt verwendet werden, wobei jedoch auf mögliche Bias und Qualitätsunterschiede in den generierten Texten geachtet werden sollte. Es wird empfohlen, die generierten Texte sorgfältig zu überprüfen und zu validieren, insbesondere für wichtige Anwendungen.
## Model Card Authors
- blockyy aka. Janosch
|
LarryAIDraw/CocoliaV5-10
|
LarryAIDraw
| 2023-10-09T22:16:06Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-10-09T21:49:31Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/62870/cocolia-lora-honkai-star-rail
|
LarryAIDraw/witch-lora-nochekaiser
|
LarryAIDraw
| 2023-10-09T22:15:04Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-10-09T21:50:10Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/159191/witch-goblin-slayer-commission
|
LarryAIDraw/shion
|
LarryAIDraw
| 2023-10-09T22:14:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-10-09T21:48:22Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/158784/shion-kujo-sounan-desu-ka-are-you-lost
|
baebee/GPTNeoX-mychatgptconvos-adapter
|
baebee
| 2023-10-09T22:03:03Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:EleutherAI/gpt-neo-2.7B",
"base_model:adapter:EleutherAI/gpt-neo-2.7B",
"region:us"
] | null | 2023-10-09T22:02:54Z |
---
library_name: peft
base_model: EleutherAI/gpt-neo-2.7B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
ThuyNT03/t5-base-standardized-color
|
ThuyNT03
| 2023-10-09T21:52:22Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-09T21:37:43Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-base-standardized-color
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-standardized-color
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2702
- Rouge1: 58.8296
- Rouge2: 50.9332
- Rougel: 58.2604
- Rougelsum: 58.323
- Gen Len: 16.2521
## 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 236 | 0.3490 | 49.2479 | 40.2468 | 48.6246 | 48.5062 | 18.0148 |
| No log | 2.0 | 472 | 0.3080 | 52.8701 | 44.4405 | 52.3371 | 52.2684 | 17.1589 |
| 0.3775 | 3.0 | 708 | 0.2871 | 55.4404 | 46.9716 | 54.9257 | 54.8833 | 16.9004 |
| 0.3775 | 4.0 | 944 | 0.2792 | 61.4338 | 53.5456 | 60.9375 | 61.0613 | 15.0636 |
| 0.2834 | 5.0 | 1180 | 0.2789 | 56.7293 | 48.3876 | 56.1734 | 56.2194 | 16.6589 |
| 0.2834 | 6.0 | 1416 | 0.2742 | 53.2995 | 44.7666 | 52.7346 | 52.7591 | 17.3644 |
| 0.2553 | 7.0 | 1652 | 0.2757 | 57.3854 | 49.1456 | 56.6424 | 56.7503 | 16.5318 |
| 0.2553 | 8.0 | 1888 | 0.2717 | 56.9399 | 48.9799 | 56.405 | 56.4246 | 16.7055 |
| 0.2393 | 9.0 | 2124 | 0.2703 | 58.4279 | 50.4598 | 57.8832 | 57.9165 | 16.3856 |
| 0.2393 | 10.0 | 2360 | 0.2702 | 58.8296 | 50.9332 | 58.2604 | 58.323 | 16.2521 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
jwhedbee/lora-trained-xl-take-two
|
jwhedbee
| 2023-10-09T21:48:27Z | 5 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-10-09T18:43:05Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sks dog
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - jwhedbee/lora-trained-xl-take-two
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
abeiler/NumAndAlphaInstruct-75-25-100K
|
abeiler
| 2023-10-09T21:41:13Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-09T17:24:55Z |
---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: goatNumAndAlphaInstruct-75-25-100K-QLORA
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. -->
# goatNumAndAlphaInstruct-75-25-100K-QLORA
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 0.0001
- train_batch_size: 4
- 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
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
vedica1011/llama-2-7b-int4-python-code-20k
|
vedica1011
| 2023-10-09T21:33:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"text2text-generation",
"en",
"dataset:iamtarun/python_code_instructions_18k_alpaca",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-09T21:02:18Z |
---
license: apache-2.0
datasets:
- iamtarun/python_code_instructions_18k_alpaca
language:
- en
pipeline_tag: text2text-generation
tags:
- code
---
|
ayshi/undersampling_distil
|
ayshi
| 2023-10-09T21:18:11Z | 63 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-05T09:46:24Z |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_keras_callback
model-index:
- name: ayshi/undersampling_distil
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ayshi/undersampling_distil
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7701
- Validation Loss: 1.0288
- Train Accuracy: 0.5824
- Epoch: 8
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 130, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.7867 | 1.7551 | 0.4176 | 0 |
| 1.7228 | 1.6637 | 0.4835 | 1 |
| 1.5961 | 1.4869 | 0.5934 | 2 |
| 1.4148 | 1.3503 | 0.5934 | 3 |
| 1.2203 | 1.2274 | 0.6264 | 4 |
| 1.0720 | 1.1445 | 0.5934 | 5 |
| 0.9397 | 1.0827 | 0.5824 | 6 |
| 0.8296 | 1.0548 | 0.6044 | 7 |
| 0.7701 | 1.0288 | 0.5824 | 8 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
ThuyNT03/vit5-base-standardized-color
|
ThuyNT03
| 2023-10-09T21:03:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-base",
"base_model:finetune:VietAI/vit5-base",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-09T20:00:31Z |
---
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: vit5-base-standardized-color
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. -->
# vit5-base-standardized-color
This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9951
- Rouge1: 74.1102
- Rouge2: 67.9199
- Rougel: 73.686
- Rougelsum: 73.7568
- Gen Len: 7.0148
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 118 | 0.7373 | 74.1623 | 67.7624 | 73.6071 | 73.6764 | 7.3326 |
| No log | 2.0 | 236 | 0.7758 | 74.1167 | 67.7666 | 73.7039 | 73.8076 | 7.0869 |
| No log | 3.0 | 354 | 0.8174 | 73.8958 | 67.4854 | 73.3437 | 73.4362 | 7.1822 |
| No log | 4.0 | 472 | 0.8195 | 74.8085 | 68.4703 | 74.3389 | 74.4854 | 6.7903 |
| 0.2234 | 5.0 | 590 | 0.8848 | 74.1319 | 67.6899 | 73.5608 | 73.6273 | 7.2013 |
| 0.2234 | 6.0 | 708 | 0.9413 | 73.4933 | 67.0495 | 73.0176 | 73.0687 | 7.2839 |
| 0.2234 | 7.0 | 826 | 0.9167 | 74.1512 | 67.7638 | 73.7512 | 73.8058 | 6.9703 |
| 0.2234 | 8.0 | 944 | 0.9577 | 73.8412 | 67.3981 | 73.3697 | 73.4324 | 7.1525 |
| 0.1303 | 9.0 | 1062 | 0.9869 | 73.9929 | 67.64 | 73.4942 | 73.5355 | 7.2309 |
| 0.1303 | 10.0 | 1180 | 0.9951 | 74.1102 | 67.9199 | 73.686 | 73.7568 | 7.0148 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
FredZhang7/efficientnetv25_rw_s
|
FredZhang7
| 2023-10-09T21:03:00Z | 119 | 2 |
transformers
|
[
"transformers",
"pytorch",
"efficientnetv25",
"arxiv:2010.07611",
"arxiv:2104.00298",
"image-classification",
"custom_code",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-09-24T04:05:11Z |
---
pipeline_tag: image-classification
tags:
- arxiv:2010.07611
- arxiv:2104.00298
license: cc-by-nc-4.0
---
To be clear, this model is tailored to my image and video classification tasks, not to imagenet.
I built EfficientNetV2.5 s to outperform the existing EfficientNet b0 to b4, EfficientNet b1 to b4 pruned (I pruned b4), and EfficientNetV2 t to l models, whether trained using TensorFlow or PyTorch,
in terms of top-1 accuracy, efficiency, and robustness on my dataset and [CMAD benchmark](https://huggingface.co/datasets/aistrova/CMAD).
## Model Details
- **Model tasks:** Image classification / video classification / feature backbone
- **Model stats:**
- Params: 16.64 M
- Multiply-Add Operations: 5.32 G
- Image size: train = 299x299 / 304x304, test = 304x304
- Classification layer: defaults to 1,000 classes
- **Papers:**
- EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298
- Layer-adaptive sparsity for the Magnitude-based Pruning: https://arxiv.org/abs/2010.07611
- **Dataset:** ImageNet-1k
- **Pretrained:** Yes, but requires more pretraining
- **Original:** This model architecture is original
<br>
### Load PyTorch Jit Model with 1000 Classes
```python
from transformers import AutoModel
model = AutoModel.from_pretrained("FredZhang7/efficientnetv2.5_rw_s", trust_remote_code=True)
```
### Load Model with Custom Classes
To change the number of classes, replace the linear classification layer.
Here's an example of how to convert the architecture into a trainable model.
```bash
pip install ptflops timm
```
```python
from ptflops import get_model_complexity_info
import torch
import urllib.request
nclass = 3 # number of classes in your dataset
input_size = (3, 304, 304) # recommended image input size
print_layer_stats = True # prints the statistics for each layer of the model
verbose = True # prints additional info about the MAC calculation
# Download the model. Skip this step if already downloaded
base_model = "efficientnetv2.5_base_in1k"
url = f"https://huggingface.co/FredZhang7/efficientnetv2.5_rw_s/resolve/main/{base_model}.pth"
file_name = f"./{base_model}.pth"
urllib.request.urlretrieve(url, file_name)
shape = (2,) + input_size
example_inputs = torch.randn(shape)
example_inputs = (example_inputs - example_inputs.min()) / (example_inputs.max() - example_inputs.min())
model = torch.load(file_name)
model.classifier = torch.nn.Linear(in_features=1984, out_features=nclass, bias=True)
macs, nparams = get_model_complexity_info(model, input_size, as_strings=False, print_per_layer_stat=print_layer_stats, verbose=verbose)
traced_model = torch.jit.trace(model, example_inputs)
model_name = f'{base_model}_{"{:.2f}".format(nparams / 1e6)}M_{"{:.2f}".format(macs / 1e9)}G.pth'
traced_model.save(model_name)
# Load the trainable model
model = torch.load(model_name)
```
### Top-1 Accuracy Comparisons
I finetuned the existing models on either 299x299, 304x304, 320x320, or 384x384 resolution, depending on the input size used during pretraining and the VRAM usage.
`efficientnet_b3_pruned` achieved the second highest top-1 accuracy as well as the highest epoch-1 training accuracy on my task, out of EfficientNetV2.5 small and all existing EfficientNet models my 24 GB VRAM RTX 3090 could handle.
I will publish the detailed report in [this model repository](https://huggingface.co/aistrova/safesearch-v5.0).
This repository is only for the base model, pretrained a bit on ImageNet, not my task.
### Carbon Emissions
Comparing all models and testing my new architectures costed roughly 648 GPU hours, over a span of 35 days.
|
kporzycki/PyramidsRND
|
kporzycki
| 2023-10-09T20:34:44Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-10-09T20:34:38Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: kporzycki/PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
vedantjumle/bert
|
vedantjumle
| 2023-10-09T20:17:38Z | 65 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-09T19:56:20Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: vedantjumle/bert
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# vedantjumle/bert
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3131
- Validation Loss: 0.6052
- Train Accuracy: 0.8499
- Epoch: 4
## 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:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2325, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.3808 | 1.2434 | 0.7405 | 0 |
| 0.8940 | 0.7746 | 0.8247 | 1 |
| 0.5247 | 0.6507 | 0.8432 | 2 |
| 0.3778 | 0.6127 | 0.8499 | 3 |
| 0.3131 | 0.6052 | 0.8499 | 4 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Ziyad/ppo-LunarLander-v2
|
Ziyad
| 2023-10-09T20:01:31Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-09T20:01:11Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 254.21 +/- 36.04
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
stiven2323/fonetica
|
stiven2323
| 2023-10-09T19:56:55Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2023-10-09T19:56:55Z |
---
license: other
license_name: other
license_link: LICENSE
---
|
liliwululu/roberta-large-peft-p-tuning
|
liliwululu
| 2023-10-09T19:55:12Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T19:55:11Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
sarasarasara/bert-finetuned-sem_eval-english
|
sarasarasara
| 2023-10-09T19:45:50Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:sem_eval_2018_task_1",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-10-09T19:45:30Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- sem_eval_2018_task_1
metrics:
- f1
- accuracy
model-index:
- name: bert-finetuned-sem_eval-english
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sem_eval_2018_task_1
type: sem_eval_2018_task_1
config: subtask5.english
split: validation
args: subtask5.english
metrics:
- name: F1
type: f1
value: 0.6611215454789374
- name: Accuracy
type: accuracy
value: 0.24717832957110608
---
<!-- 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-sem_eval-english
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sem_eval_2018_task_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3263
- F1: 0.6611
- Roc Auc: 0.7630
- Accuracy: 0.2472
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.404 | 1.0 | 855 | 0.3263 | 0.6611 | 0.7630 | 0.2472 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
Asharma02/my_awesome_opus_books_model
|
Asharma02
| 2023-10-09T19:14:53Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-09T18:35:08Z |
---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: Asharma02/my_awesome_opus_books_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Asharma02/my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9077
- Validation Loss: 1.6091
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.9077 | 1.6091 | 0 |
### Framework versions
- Transformers 4.34.0
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
|
TheBloke/Ziya-Coding-34B-v1.0-GPTQ
|
TheBloke
| 2023-10-09T19:11:26Z | 28 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"zh",
"en",
"arxiv:2210.08590",
"base_model:IDEA-CCNL/Ziya-Coding-34B-v1.0",
"base_model:quantized:IDEA-CCNL/Ziya-Coding-34B-v1.0",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-10-09T10:31:53Z |
---
base_model: IDEA-CCNL/Ziya-Coding-34B-v1.0
inference: false
language:
- zh
- en
library_name: transformers
license: gpl-3.0
model_creator: Fengshenbang-LM
model_name: Ziya Coding 34B v1.0
model_type: llama
pipeline_tag: text-generation
prompt_template: "<human>: \nPlease Complete the given function below according to\
\ the docstring: \n{prompt}\n<bot>: \n"
quantized_by: TheBloke
---
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Ziya Coding 34B v1.0 - GPTQ
- Model creator: [Fengshenbang-LM](https://huggingface.co/IDEA-CCNL)
- Original model: [Ziya Coding 34B v1.0](https://huggingface.co/IDEA-CCNL/Ziya-Coding-34B-v1.0)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Fengshenbang-LM's Ziya Coding 34B v1.0](https://huggingface.co/IDEA-CCNL/Ziya-Coding-34B-v1.0).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GGUF)
* [Fengshenbang-LM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/IDEA-CCNL/Ziya-Coding-34B-v1.0)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Ziya
```
<human>:
Please Complete the given function below according to the docstring:
{prompt}
<bot>:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `gpl-3.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Fengshenbang-LM's Ziya Coding 34B v1.0](https://huggingface.co/IDEA-CCNL/Ziya-Coding-34B-v1.0).
<!-- licensing end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 17.69 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 18.33 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 20.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 14.14 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 34.30 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 8192 | 34.30 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Ziya-Coding-34B-v1.0-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Ziya-Coding-34B-v1.0-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Ziya-Coding-34B-v1.0-GPTQ`:
```shell
mkdir Ziya-Coding-34B-v1.0-GPTQ
huggingface-cli download TheBloke/Ziya-Coding-34B-v1.0-GPTQ --local-dir Ziya-Coding-34B-v1.0-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Ziya-Coding-34B-v1.0-GPTQ
huggingface-cli download TheBloke/Ziya-Coding-34B-v1.0-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Ziya-Coding-34B-v1.0-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir Ziya-Coding-34B-v1.0-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Ziya-Coding-34B-v1.0-GPTQ --local-dir Ziya-Coding-34B-v1.0-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/Ziya-Coding-34B-v1.0-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Ziya-Coding-34B-v1.0-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Ziya-Coding-34B-v1.0-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Ziya-Coding-34B-v1.0-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Ziya-Coding-34B-v1.0-GPTQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<human>:
Please Complete the given function below according to the docstring:
{prompt}
<bot>:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Ziya-Coding-34B-v1.0-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<human>:
Please Complete the given function below according to the docstring:
{prompt}
<bot>:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Fengshenbang-LM's Ziya Coding 34B v1.0
# Ziya-Coding-34B-v1.0
# 姜子牙系列模型
- [Ziya-LLaMA-13B-v1.1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1)
- [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1)
- [Ziya-LLaMA-7B-Reward](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward)
- [Ziya-LLaMA-13B-Pretrain-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1)
- [Ziya-BLIP2-14B-Visual-v1](https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1)
- [Ziya-Writing-LLaMa-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-Writing-LLaMa-13B-v1)
- [Ziya-Coding-15B-v1](https://huggingface.co/IDEA-CCNL/Ziya-Coding-15B-v1)
## 简介 Brief Introduction
使用自然语言生成高质量的代码是大模型落地中的高频需求。今天，IDEA研究院封神榜团队正式开源最新的代码大模型Ziya-Coding-34B-v1.0，我们在HumanEval Pass@1的评测上，取得了75.5的好成绩，超过了GPT-4（67.0）的得分，也成为目前已知开源模型新高。封神榜团队正在为社区提供先进的大模型技术和经验，帮助生产和定制更多优秀垂类模型，推进大模型生态发展。
Generating high-quality code using natural language is a high-frequency demand in the deployment of large models. Today, the IDEA Research Institute's Fengshenbang team officially open-sourced the latest code model, Ziya-Coding-34B-v1.0. We achieved a good score of 75.5 on the HumanEval Pass@1 evaluation, surpassing the score of GPT-4 (67.0) and setting a new high for known open-source models. The Fengshenbang team is providing the community with advanced large model technology and experience, helping to produce and customize more excellent vertical models, and promoting the development of the large model ecosystem.
更多细节可以参考我们的公众号文章：
[再创新高！姜子牙大模型开源代码大模型Ziya-Coding-34B-v1.0](https://mp.weixin.qq.com/s/Op4Wkiu2J9jwFr_Zj0YSZg)
[姜子牙大模型系列 | 代码模型ziya-coding发布！低成本微调即可学会在专有场景编程](https://mp.weixin.qq.com/s/tWaRF1wL3HM87ZDEawd2UA)
## 软件依赖
```
pip install torch==1.12.1 tokenizers==0.13.3 git+https://github.com/huggingface/transformers
```
## 模型信息 Model Information
在9月初，我们开源了基于StarCoder-15B的代码模型Ziya-Coding-15B-v1，我们将训练Ziya-Coding-15B-v1积累的训练经验迁移到了新版本的训练中。
我们收集并构造了约45万涵盖了几乎所有代码相关任务的指令数据进行第一阶段的微调，这其中包括约10万的中文指令和35万的英文指令，保证了数据的多样性，在构造数据时，我们充分利用了高质量的无指令代码数据，使用LLM生成对应的指令，扩充得到了更多高质量的代码指令数据。
同时实验过程中，我们注意到，代码指令的难度和正确性是训练代码模型成功的关键。因此，我们引入了第二阶段的精调。我们使用evol-instruct的方法生成了大量高难度多要求的代码指令数据，并利用代码编译器作为反馈，筛选出能够通过编译的代码。最后利用LLM生成单元测试进一步验证代码的正确性。我们最终筛选出了46k数据，在第一阶段模型的基础上，使用较低的学习率进行微调，最终得到了我们的Ziya-coding-34B-v1.0。
In early September, we open-sourced the code model Ziya-Coding-15B-v1 based on StarCoder-15B. The training experience accumulated in training Ziya-Coding-15B-v1 was transferred to the training of the new version.
We collected and constructed about 450,000 instruction data covering almost all code-related tasks for the first stage of fine-tuning. This includes about 100,000 Chinese instructions and 350,000 English instructions, ensuring data diversity. When constructing the data, we made full use of high-quality non-instructional code data, used LLM to generate corresponding instructions, and expanded to obtain more high-quality code instruction data.
During the experiment, we noticed that the difficulty and correctness of code instructions are key to the successful training of code models. Therefore, we introduced a second stage of fine-tuning. We used the evol-instruct method to generate a large amount of high-difficulty, multi-requirement code instruction data, and used a code compiler as feedback to filter out code that could pass compilation. Finally, we used LLM to generate unit tests to further verify the correctness of the code. We ultimately filtered out 46k data, and on the basis of the first-stage model, we fine-tuned it with a lower learning rate to finally obtain our Ziya-coding-34B-v1.0.
### 效果评估 Performance
| Model | HumanEval(pass@1) |
|:----------------------------|:-----------------:|
| **Ziya-Coding-34B-v1.0** | **75.5%** |
| CodeFuse-CodeLlama-34B | 74.4% |
| Phind-CodeLLaMa-34B-v2 | 73.8% |
| WizardCoder-Python-34B-V1.0 | 73.2% |
| GPT-4 | 67.0% |
| PanGu-Coder2 15B | 61.6% |
| WizardCoder-15B-V1.0 | 59.8% |
| CodeLlama-34b-Python | 53.7% |
| Ziya-Coding-15B-v1 | 50.1% |
| CodeLlama-34b | 48.8% |
| GPT-3.5 | 48.1% |
| StarCoder-15B | 33.6% |
其中，我们对微调数据集进行了去污处理，避免数据泄露，HumanEval的pass@1指标是贪婪生成的结果。
Prompt Format
```python3
"<human>: \nPlease Complete the given function below according to the docstring: \n{prompt}\n<bot>: \n"
```
In this process, we performed a decontamination process on the fine-tuning dataset to avoid data leakage. The pass@1 metric for HumanEval is based on the results of greedy generation.
## <span id="jump"> 使用 Usage </span>
```python3
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = torch.device("cuda")
prompt = "写一段快速排序"
model = AutoModelForCausalLM.from_pretrained("IDEA-CCNL/Ziya-Coding-34B-v1.0", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("IDEA-CCNL/Ziya-Coding-34B-v1.0", use_fast=False)
input = f"<human>: \n{prompt}\n<bot>: \n"
input_ids = tokenizer(input, return_tensors="pt").input_ids.to(device)
generate_ids = model.generate(
input_ids,
max_new_tokens = 512,
do_sample = True,
top_p = 0.85,
temperature = 1.0,
repetition_penalty = 1.0,
eos_token_id = tokenizer.eos_token_id,
pad_token_id = tokenizer.pad_token_id,
)
output = tokenizer.batch_decode(generate_ids)[0]
print(output)
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
欢迎引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
wcarr993/llama2-7B-ft-med
|
wcarr993
| 2023-10-09T19:04:44Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2023-09-04T07:21:58Z |
---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: llama2-7B-ft-med
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. -->
# llama2-7B-ft-med
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ziblets/wav2vec2-base-finetuned-gtzan
|
ziblets
| 2023-10-09T18:59:17Z | 159 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-10-07T20:57:37Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.88
---
<!-- 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-base-finetuned-gtzan
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5153
- Accuracy: 0.88
## 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.0258 | 1.0 | 113 | 1.7851 | 0.47 |
| 1.5568 | 2.0 | 226 | 1.4525 | 0.48 |
| 0.9996 | 3.0 | 339 | 1.3081 | 0.56 |
| 0.9843 | 4.0 | 452 | 0.8743 | 0.81 |
| 0.5038 | 5.0 | 565 | 0.7456 | 0.8 |
| 0.5988 | 6.0 | 678 | 0.6650 | 0.82 |
| 0.5372 | 7.0 | 791 | 0.7010 | 0.8 |
| 0.2489 | 8.0 | 904 | 0.5241 | 0.87 |
| 0.2511 | 9.0 | 1017 | 0.4765 | 0.89 |
| 0.0771 | 10.0 | 1130 | 0.5153 | 0.88 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.0
|
Aminrhmni/PersianAutomaticPunctuation
|
Aminrhmni
| 2023-10-09T18:59:14Z | 1,091 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mt5",
"text2text-generation",
"fa",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-09-16T08:29:33Z |
---
language:
- fa
license: mit
---
***This model is intended for non-commercial use only. If you wish to use it commercially, please refer to this LinkedIn address.
https://www.linkedin.com/in/amin-rahmani-41417b121/
"Viravirast" is an editor based on transformer algorithms. By visiting Viravirast.com, you can use a Persian semantic and structural text editor.
***Use this code in order to get the complete and correct sentence
from transformers import (
T5Tokenizer,
MT5ForConditionalGeneration,
Text2TextGenerationPipeline,
)
path = ""
pipe = Text2TextGenerationPipeline(
model=MT5ForConditionalGeneration.from_pretrained(path),
tokenizer=T5Tokenizer.from_pretrained(path),
)
sentence = "ویراویراست یک نرم افزار ویرایش متن ساختاری و معنایی زبان فارسی است چیزی شبیه به گرامرلی در زبان انگلیسی"
#res = pipe(sentence, max_length=100, num_beams=4
res = pipe(sentence, max_length=100)
print(res[0]['generated_text'])
n_epochs = 4
train_batch_size = 8
eval_batch_size = 4
lr = 5e-4
Training_Loss=0.00550
Validation_Loss=0.052046
|
SohamNale/Mistral7B_Finance
|
SohamNale
| 2023-10-09T18:58:40Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"region:us"
] | null | 2023-10-09T16:16:22Z |
---
library_name: peft
base_model: bn22/Mistral-7B-Instruct-v0.1-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
igorktech/hier-bert-i3-mlm-v0.1
|
igorktech
| 2023-10-09T18:49:55Z | 139 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hierarchical-bert",
"fill-mask",
"generated_from_trainer",
"custom_code",
"autotrain_compatible",
"region:us"
] |
fill-mask
| 2023-09-24T16:12:09Z |
---
base_model: /content/hier-bert-pytorch/data/hier-model
tags:
- generated_from_trainer
model-index:
- name: hier-bert-i3-mlm
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. -->
# hier-bert-i3-mlm
This model is a fine-tuned version of [/content/hier-bert-pytorch/data/hier-model](https://huggingface.co//content/hier-bert-pytorch/data/hier-model) 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: 0.001
- train_batch_size: 64
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.5
- training_steps: 5000
### Training results
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Rafaelrosendo1/gerador_de_poemas
|
Rafaelrosendo1
| 2023-10-09T18:49:36Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation",
"pt",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-18T11:52:54Z |
---
license: openrail
language:
- pt
library_name: transformers
pipeline_tag: text-generation
code: https://github.com/RafaelRosendof/test4/blob/main/generator.ipynb
---
|
AustinMcMike/Stevie_llama_v1
|
AustinMcMike
| 2023-10-09T18:46:38Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-10-09T18:36:28Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0
|
Amarjitkr/llama2-qlora-finetunined-medical_updated_part2
|
Amarjitkr
| 2023-10-09T18:44:30Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-10-09T18:44:12Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0
|
douglasgoodwin/sd-class-butterflies-32
|
douglasgoodwin
| 2023-10-09T18:28:37Z | 46 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-10-09T18:28:33Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('douglasgoodwin/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
Mtc2/rl_course_vizdoom_health_gathering_supreme
|
Mtc2
| 2023-10-09T18:28:27Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-10-09T18:28:19Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 11.14 +/- 5.11
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r Mtc2/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
naphatmanu/sdxl-lora-index-contemporary-1
|
naphatmanu
| 2023-10-09T18:21:12Z | 4 | 1 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2023-10-09T17:29:56Z |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Index Contemporary
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - naphatmanu/sdxl-lora-index-contemporary-1
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on Index Contemporary using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
Gayathri142214002/Pegasus_paraphraser_ComQG_2
|
Gayathri142214002
| 2023-10-09T18:13:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-10-09T16:17:40Z |
---
tags:
- generated_from_trainer
model-index:
- name: Pegasus_paraphraser_ComQG_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. -->
# Pegasus_paraphraser_ComQG_2
This model is a fine-tuned version of [Gayathri142214002/Pegasus_paraphraser_1](https://huggingface.co/Gayathri142214002/Pegasus_paraphraser_1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2872
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2379 | 0.49 | 1000 | 0.2476 |
| 0.2658 | 0.97 | 2000 | 0.2449 |
| 0.2192 | 1.46 | 3000 | 0.2616 |
| 0.2301 | 1.95 | 4000 | 0.2632 |
| 0.1943 | 2.44 | 5000 | 0.2761 |
| 0.2043 | 2.92 | 6000 | 0.2769 |
| 0.1752 | 3.41 | 7000 | 0.2871 |
| 0.1738 | 3.9 | 8000 | 0.2872 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
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