readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - de
  model-index:
  - name: de_pipeline
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.8379052369
      - name: NER Recall
        type: recall
        value: 0.8704663212
      - name: NER F Score
        type: f_score
        value: 0.8538754765
  ---
  ### Introduction
  German Named Entity Recognition model for recognizing Bavarian landmarks. Fine-tuned "bert-base-german-cased" with 6450 annotated sentences of which 1467 contained landmarks, from subtitles of videos from Bayerischer Rundfunk.
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `de_pipeline` |
  | **Version** | `0.1.0` |
  | **spaCy** | `>=3.3.0,<3.4.0` |
  | **Default Pipeline** | `transformer`, `ner` |
  | **Components** | `transformer`, `ner` |
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
  | **Sources** | n/a |
  | **License** | n/a |
  | **Author** | Constantin Förster |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (1 labels for 1 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`ner`** | `LM` |
  
  </details>
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `ENTS_F` | 85.39 |
  | `ENTS_P` | 83.79 |
  | `ENTS_R` | 87.05 |
  | `TRANSFORMER_LOSS` | 4216.96 |
  | `NER_LOSS` | 78511.31 |