readme: ---
  license: gpl-3.0
  language:
  - es
  library_name: spacy
  pipeline_tag: token-classification
  tags:
  - spacy
  - token-classification
  widget:
  - text: "Fue antes de llegar a Sigüeiro, en el Camino de Santiago."
  - text: "El proyecto lo financia el Ministerio de Industria y Competitividad."
  model-index:
  - name: es_spacy_ner_cds
    results:
    - task:
        name: NER
        type: token-classification
      metrics:
      - name: NER Precision
        type: precision
        value: 0.9690286251
      - name: NER Recall
        type: recall
        value: 0.9683470106
      - name: NER F Score
        type: f_score
        value: 0.9686876979
  ---
  
  # Introduction
  
  spaCy NER model for Spanish trained with interviews in the domain of tourism related to the Way of Saint Jacques. It recognizes four types of entities: location (LOC), organizations (ORG), person (PER) and miscellaneous (MISC).
  
  | Feature | Description |
  | --- | --- |
  | **Name** | `es_spacy_ner_cds_trf` |
  | **Version** | `0.0.1a` |
  | **spaCy** | `>=3.4.4,<3.5.0` |
  | **Default Pipeline** | `transformer`, `ner` |
  | **Components** | `transformer`, `ner` |
  
  ### Label Scheme
  
  <details>
  
  <summary>View label scheme (4 labels for 1 components)</summary>
  
  | Component | Labels |
  | --- | --- |
  | **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
  
  </details>
  
  ## Usage
  
  You can use this model with the spaCy *pipeline* for NER.
  
  ```python
  import spacy
  from spacy.pipeline import merge_entities
  
  
  nlp = spacy.load("es_spacy_ner_cds_trf")
  nlp.add_pipe('sentencizer')
  
  example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. El proyecto lo financia el Ministerio de Industria y Competitividad."
  ner_pipe = nlp(example)
  
  print(ner_pipe.ents)
  for token in merge_entities(ner_pipe):
      print(token.text, token.ent_type_)
  ```
  
  ## Dataset
  
  ToDo
  
  ### Accuracy
  
  | Type | Score |
  | --- | --- |
  | `ENTS_F` | 96.87 |
  | `ENTS_P` | 96.90 |
  | `ENTS_R` | 96.83 |
  | `TRANSFORMER_LOSS` | 7662.71 |
  | `NER_LOSS` | 7673.80 |
  