sdocio/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).

Feature	Description
Name	es_spacy_ner_cds
Version	0.0.1a
spaCy	>=3.4.3,<3.5.0
Default Pipeline	tok2vec, ner
Components	tok2vec, ner

Label Scheme

View label scheme (4 labels for 1 components)

Component	Labels
ner	LOC, MISC, ORG, PER

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

Type	Score
ENTS_F	96.26
ENTS_P	96.49
ENTS_R	96.04
TOK2VEC_LOSS	62780.17
NER_LOSS	34006.41