metadata
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.
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 |