es_spacy_ner_cds

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).

Label Scheme

<details> <summary>View label scheme (4 labels for 1 components)</summary> </details>

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 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