readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - de
  license: mit
  model-index:
  - name: de_core_news_md
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.8439391945
      - name: NER Recall
        type: recall
        value: 0.8342856523
      - name: NER F Score
        type: f_score
        value: 0.8390846587
    - task:
        name: TAG
        type: token-classification
      metrics:
      - name: TAG (XPOS) Accuracy
        type: accuracy
        value: 0.9781087492
    - task:
        name: POS
        type: token-classification
      metrics:
      - name: POS (UPOS) Accuracy
        type: accuracy
        value: 0.9829388521
    - task:
        name: MORPH
        type: token-classification
      metrics:
      - name: Morph (UFeats) Accuracy
        type: accuracy
        value: 0.9150510777
    - task:
        name: LEMMA
        type: token-classification
      metrics:
      - name: Lemma Accuracy
        type: accuracy
        value: 0.9769605349
    - task:
        name: UNLABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Unlabeled Attachment Score (UAS)
        type: f_score
        value: 0.9254139496
    - task:
        name: LABELED_DEPENDENCIES
        type: token-classification
      metrics:
      - name: Labeled Attachment Score (LAS)
        type: f_score
        value: 0.9056705833
    - task:
        name: SENTS
        type: token-classification
      metrics:
      - name: Sentences F-Score
        type: f_score
        value: 0.9507828582
  ---
  ### Details: https://spacy.io/models/de#de_core_news_md
  
  German pipeline optimized for CPU. Components: tok2vec, tagger, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner.
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `de_core_news_md` |
  | **Version** | `3.5.0` |
  | **spaCy** | `>=3.5.0,<3.6.0` |
  | **Default Pipeline** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` |
  | **Components** | `tok2vec`, `tagger`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` |
  | **Vectors** | 500000 keys, 20000 unique vectors (300 dimensions) |
  | **Sources** | [TIGER Corpus](https://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/tiger.html) (Brants, Sabine, Stefanie Dipper, Peter Eisenberg, Silvia Hansen, Esther König, Wolfgang Lezius, Christian Rohrer, George Smith, and Hans Uszkoreit)<br />[Tiger2Dep](https://www.ims.uni-stuttgart.de/forschung/ressourcen/werkzeuge/tiger2dep/) (Wolfgang Seeker)<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)<br />[Explosion fastText Vectors (cbow, OSCAR Common Crawl + Wikipedia)](https://spacy.io) (Explosion) |
  | **License** | `MIT` |
  | **Author** | [Explosion](https://explosion.ai) |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (772 labels for 4 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`tagger`** | `$(`, `$,`, `$.`, `ADJA`, `ADJD`, `ADV`, `APPO`, `APPR`, `APPRART`, `APZR`, `ART`, `CARD`, `FM`, `ITJ`, `KOKOM`, `KON`, `KOUI`, `KOUS`, `NE`, `NN`, `NNE`, `PDAT`, `PDS`, `PIAT`, `PIS`, `PPER`, `PPOSAT`, `PPOSS`, `PRELAT`, `PRELS`, `PRF`, `PROAV`, `PTKA`, `PTKANT`, `PTKNEG`, `PTKVZ`, `PTKZU`, `PWAT`, `PWAV`, `PWS`, `TRUNC`, `VAFIN`, `VAIMP`, `VAINF`, `VAPP`, `VMFIN`, `VMINF`, `VMPP`, `VVFIN`, `VVIMP`, `VVINF`, `VVIZU`, `VVPP`, `XY`, `_SP` |
  | **`morphologizer`** | `POS=PUNCT`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `POS=ADV`, `Case=Nom\|Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Nom\|Degree=Pos\|Gender=Masc\|Number=Sing\|POS=ADJ`, `Case=Nom\|Gender=Masc\|Number=Sing\|POS=NOUN`, `Case=Nom\|Gender=Masc\|Number=Plur\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=NOUN`, `POS=ADP`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Degree=Pos\|Gender=Fem\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=NOUN`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=DET\|Poss=Yes\|PronType=Prs`, `Case=Nom\|Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Definite=Def\|Gender=Neut\|Number=Sing\|POS=DET\|PronType=Art`, `Case=Acc\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=PROPN`, `POS=VERB\|VerbForm=Part`, `Case=Dat\|Gender=Fem\|Number=Plur\|POS=NOUN`, `Foreign=Yes\|POS=X`, `Degree=Pos\|POS=ADV`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=ADP`, `Case=Dat\|Gender=Neut\|Number=Sing\|POS=NOUN`, `Case=Dat\|Degree=Pos\|Gender=Neut\|Number=Sing\|POS=ADJ`, `Case=Dat\|Definite=Def\