Token Classification
spaCy
Danish
dacy
danish
morphological analysis
dependency parsing
named entity recognition
Eval Results (legacy)
Instructions to use chcaa/da_dacy_edge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- spaCy
How to use chcaa/da_dacy_edge with spaCy:
!pip install https://huggingface.co/chcaa/da_dacy_edge/resolve/main/da_dacy_edge-any-py3-none-any.whl # Using spacy.load(). import spacy nlp = spacy.load("da_dacy_edge") # Importing as module. import da_dacy_edge nlp = da_dacy_edge.load() - Notebooks
- Google Colab
- Kaggle
File size: 13,524 Bytes
377db24 3db72d4 982d8cc 3db72d4 8c80bce 3db72d4 eb15cde 3db72d4 377db24 982d8cc 377db24 3db72d4 377db24 3db72d4 eb15cde | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 | ---
tags:
- spacy
- dacy
- danish
- token-classification
- morphological analysis
- dependency parsing
- named entity recognition
language:
- da
license: apache-2.0
model-index:
- name: da_dacy_edge-0.3.0
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.744
- name: NER Recall
type: recall
value: 0.7081218274
- name: NER F Score
type: f_score
value: 0.7256176853
dataset:
name: DaNE
split: test
type: dane
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9452654945
dataset:
name: UD Danish DDT
split: test
type: universal_dependencies
config: da_ddt
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9337539432
dataset:
name: UD Danish DDT
split: test
type: universal_dependencies
config: da_ddt
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.9426952509
dataset:
name: UD Danish DDT
split: test
type: universal_dependencies
config: da_ddt
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.8100649351
dataset:
name: UD Danish DDT
split: test
type: universal_dependencies
config: da_ddt
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.7605519481
dataset:
name: UD Danish DDT
split: test
type: universal_dependencies
config: da_ddt
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9456159822
dataset:
name: UD Danish DDT
split: test
type: universal_dependencies
config: da_ddt
library_name: spacy
datasets:
- universal-dependencies/universal_dependencies
- alexandrainst/dane
- alexandrainst/dacoref
- chcaa/dacy-data
metrics:
- accuracy
---
<a href="https://github.com/centre-for-humanities-computing/Dacy"><img src="https://centre-for-humanities-computing.github.io/DaCy/_static/icon.png" width="175" height="175" align="right" /></a>
# DaCy edge
DaCy is a Danish language processing framework with state-of-the-art pipelines as well as functionality for analysing Danish pipelines (Enevoldsen et al., 2021).
This model is the **edge Dacy pipeline**, trained for Danish linguistic
annotation and downstream NLP tasks with efficient CPU inference.
To read more check out the [DaCy repository](https://github.com/centre-for-humanities-computing/DaCy) for material on how to use DaCy and reproduce the results.
DaCy also contains guides on usage of the package as well as behavioural test for biases and robustness of Danish NLP pipelines.
| Feature | Description |
| --- | --- |
| **Name** | `da_dacy_edge` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.8.14,<3.9.0` |
| **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 Danish DDT v2.18](https://github.com/UniversalDependencies/UD_Danish-DDT) (Johannsen, Anders; Martínez Alonso, Héctor; Plank, Barbara)<br>[DaCoref](https://huggingface.co/datasets/alexandrainst/dacoref) (Buch-Kromann, Matthias)<br>[DaNE](https://huggingface.co/datasets/alexandrainst/dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard) |
| **License** | `Apache-2.0` |
| **Author** | [Johana Mayerova, Mikkel Krøjer Svendsen, Kenneth Enevoldsen](https://chc.au.dk/)|
### Label Scheme
<details>
<summary>View label scheme (200 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `AdpType=Prep\|POS=ADP`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=AUX\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=PROPN`, `Definite=Ind\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Act`, `POS=ADV`, `Number=Plur\|POS=DET\|PronType=Dem`, `Degree=Pos\|Number=Plur\|POS=ADJ`, `Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `POS=PUNCT`, `NumType=Ord\|POS=ADJ`, `POS=CCONJ`, `Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `POS=VERB\|VerbForm=Inf\|Voice=Act`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Sup\|POS=ADV`, `Degree=Pos\|POS=ADV`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Number=Plur\|POS=DET\|PronType=Ind`, `POS=ADP`, `POS=ADV\|PartType=Inf`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|POS=AUX\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Act`, `POS=ADP\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `NumType=Card\|POS=NUM`, `Degree=Pos\|POS=ADJ`, `Definite=Ind\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `POS=PART\|PartType=Inf`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Number[psor]=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `POS=VERB\|Tense=Pres\|VerbForm=Part`, `Case=Nom\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Sing\|POS=NOUN`, `Definite=Def\|Degree=Sup\|Number=Plur\|POS=ADJ`, `Case=Acc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `POS=AUX\|VerbForm=Inf\|Voice=Act`, `Definite=Ind\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Definite=Ind\|Degree=Cmp\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADJ`, `POS=PRON\|PartType=Inf`, `Definite=Ind\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Com\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=PRON\|PronType=Ind`, `POS=INTJ`, `Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `Case=Gen\|Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Ind\|POS=VERB\|Tense=Pres\|VerbForm=Fin\|Voice=Pass`, `Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Degree=Cmp\|POS=ADV`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Gen\|POS=PROPN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Ind`, `Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Definite=Def\|Degree=Sup\|POS=ADJ`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=DET\|PronType=Dem`, `Definite=Def\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `POS=PRON\|PronType=Dem`, `Degree=Pos\|Gender=Com\|Number=Sing\|POS=ADJ`, `Number=Plur\|POS=NUM`, `POS=VERB\|VerbForm=Inf\|Voice=Pass`, `Definite=Def\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=PRON`, `Definite=Ind\|Number=Sing\|POS=NOUN`, `Definite=Ind\|Number=Sing\|POS=NUM`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Sing\|POS=NOUN`, `Foreign=Yes\|POS=ADV`, `POS=NOUN`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Gender=Com\|Number=Plur\|POS=NOUN`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Ind`, `Case=Gen\|Definite=Ind\|Gender=Com\|Number=Plur\|POS=NOUN`, `Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Degree=Sup\|POS=ADJ`, `Degree=Pos\|Number=Sing\|POS=ADJ`, `Mood=Imp\|POS=VERB`, `Case=Nom\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Case=Acc\|Gender=Com\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `POS=X`, `Case=Gen\|Definite=Def\|Gender=Com\|Number=Plur\|POS=NOUN`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Number=Plur\|POS=PRON\|PronType=Int,Rel`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Case=Nom\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Acc\|Gender=Com\|Number=Sing\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Com\|POS=PRON\|PronType=Int,Rel`, `Case=Gen\|Degree=Pos\|Number=Plur\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `POS=VERB\|VerbForm=Ger`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Gen\|POS=PRON\|PronType=Int,Rel`, `Mood=Ind\|POS=VERB\|Tense=Past\|VerbForm=Fin\|Voice=Pass`, `Abbr=Yes\|POS=X`, `Case=Gen\|Definite=Ind\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Number=Plur\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Number=Plur\|POS=PRON\|PronType=Rcp`, `Case=Nom\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Case=Gen\|Degree=Cmp\|POS=ADJ`, `Case=Gen\|Definite=Def\|Gender=Neut\|Number=Plur\|POS=NOUN`, `Case=Acc\|Gender=Com\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Gender=Neut\|Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Gender=Neut\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs\|Style=Form`, `Number=Plur\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=PRON\|PronType=Rcp`, `POS=DET\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `POS=SYM`, `POS=DET\|PronType=Dem`, `Gender=Com\|Number=Sing\|POS=NUM`, `Number[psor]=Plur\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Case=Gen\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Degree=Abs\|POS=ADJ`, `POS=VERB\|Tense=Pres`, `Definite=Ind\|Gender=Neut\|Number=Sing\|POS=NUM`, `Degree=Abs\|POS=ADV`, `Case=Gen\|Definite=Def\|Degree=Pos\|Number=Sing\|POS=ADJ`, `Gender=Com\|Number=Sing\|POS=PRON\|PronType=Int,Rel`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Ind\|Degree=Sup\|Number=Sing\|POS=ADJ`, `Gender=Neut\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number[psor]=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|POS=NOUN`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Ind`, `Definite=Ind\|Gender=Com\|Number=Sing\|POS=NUM`, `Definite=Def\|Number=Plur\|POS=NOUN`, `Case=Gen\|POS=NOUN`, `POS=AUX\|Tense=Pres\|VerbForm=Part`, `Number=Plur\|POS=DET\|PronType=Ind\|Style=Arch`, `Case=Gen\|Gender=Com\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=PRON\|Person=2\|Polite=Form\|Poss=Yes\|PronType=Prs`, `Gender=Com\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs\|Reflex=Yes`, `Degree=Sup\|Number=Plur\|POS=ADJ`, `Definite=Def\|Gender=Com\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part` |
| **`parser`** | `ROOT`, `acl:relcl`, `advcl`, `advmod`, `advmod:lmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `expl`, `fixed`, `flat`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:lmod`, `obl:tmod`, `punct`, `xcomp` |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `POS_ACC` | 94.53 |
| `MORPH_ACC` | 93.38 |
| `DEP_UAS` | 81.01 |
| `DEP_LAS` | 76.06 |
| `SENTS_P` | 94.25 |
| `SENTS_R` | 94.88 |
| `SENTS_F` | 94.56 |
| `LEMMA_ACC` | 94.27 |
| `ENTS_F` | 72.56 |
| `ENTS_P` | 74.40 |
| `ENTS_R` | 70.81 |
| `TOK2VEC_LOSS` | 17929810.36 |
| `MORPHOLOGIZER_LOSS` | 1975672.57 |
| `PARSER_LOSS` | 18907453.12 |
| `LEMMATIZER_LOSS` | 1065818.42 |
| `NER_LOSS` | 110246.07 |
### Training
This model was trained using [spaCy](https://spacy.io)
### References
Enevoldsen, K., Hansen, L., & Nielbo, K. L. (2021). DaCy: A Unified Framework for Danish NLP. https://ceur-ws.org/Vol-2989/short_paper24.pdf |