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 |
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Evaluation results
- NER Precisionself-reported0.972
- NER Recallself-reported0.973
- NER F Scoreself-reported0.973