readme: ---
  tags:
  - spacy
  - token-classification
  language:
  - fr
  model-index:
  - name: fr_ner_ingredients
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.8990228013
      - name: NER Recall
        type: recall
        value: 0.9019607843
      - name: NER F Score
        type: f_score
        value: 0.9004893964
  ---
  | Feature | Description |
  | --- | --- |
  | **Name** | `fr_ner_ingredients` |
  | **Version** | `0.0.0` |
  | **spaCy** | `>=3.2.1,<3.3.0` |
  | **Default Pipeline** | `tok2vec`, `ner` |
  | **Components** | `tok2vec`, `ner` |
  | **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
  | **Sources** | n/a |
  | **License** | n/a |
  | **Author** | [n/a]() |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (5 labels for 1 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`ner`** | `BRAND`, `FOOD PRODUCT`, `INGREDIENT`, `MEASURE`, `QUANTITY` |
  
  </details>
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `ENTS_F` | 90.05 |
  | `ENTS_P` | 89.90 |
  | `ENTS_R` | 90.20 |
  | `TOK2VEC_LOSS` | 65769.53 |
  | `NER_LOSS` | 7865.95 |