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da_dacy_edge

Token Classification
spaCy
Danish
dacy
danish
morphological analysis
dependency parsing
named entity recognition
Eval Results (legacy)
Model card Files
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metadata 
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

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 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 (Johannsen, Anders; Martínez Alonso, Héctor; Plank, Barbara)
DaCoref (Buch-Kromann, Matthias)
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

Label Scheme

View label scheme (200 labels for 3 components)
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

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

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