metadata

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: Si te metes en el Franco desde la Alameda, vas hacia la Catedral.
  - text: Y allí precisamente es Santiago el patrón del pueblo.
model-index:
  - name: bne-spacy-corgale-ner-es
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.9721311475
          - name: NER Recall
            type: recall
            value: 0.9732708089
          - name: NER F Score
            type: f_score
            value: 0.9727006444

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). It was fine-tuned using PlanTL-GOB-ES/roberta-base-bne.

Feature	Description
Name	`bne-spacy-corgale-ner-es`
Version	`0.0.2`
spaCy	`>=3.5.2,<3.6.0`
Default Pipeline	`transformer`, `ner`
Components	`transformer`, `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("bne-spacy-corgale-ner-es")
nlp.add_pipe('sentencizer')

example = "Fue antes de llegar a Sigüeiro, en el Camino de Santiago. Si te metes en el Franco desde la Alameda, vas hacia la Catedral. Y allí precisamente es Santiago el patrón del pueblo."
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
LOC	0.985	0.987	0.986
MISC	0.862	0.865	0.863
ORG	0.938	0.779	0.851
PER	0.921	0.941	0.931
micro avg	0.971	0.972	0.971
macro avg	0.926	0.893	0.908
weighted avg	0.971	0.972	0.971