---
language: es
license: gpl-3.0
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: []
---

# Introduction

spaCy NER model trained 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).

## 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")
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 Competitiv
idad."
ner_pipe = nlp(example)

print(ner_pipe.ents)
for token in merge_entities(ner_pipe):
    print(token.text, token.ent_type_)
```

## Dataset

ToDo

## Model performance

entity|precision|recall|f1
-|-|-|-
PER|0.942|0.890|0.915
ORG|0.869|0.688|0.768
LOC|0.975|0.987|0.981
MISC|0.854|0.757|0.803
micro avg|0.963|0.958|0.961
macro avg|0.910|0.831|0.867