README.md

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: El proyecto lo financia el Ministerio de Industria y Competitividad.
model-index:
  - name: es_spacy_ner_cds
    results:
      - task:
          name: NER
          type: token-classification
        metrics:
          - name: NER Precision
            type: precision
            value: 0.9690286251
          - name: NER Recall
            type: recall
            value: 0.9683470106
          - name: NER F Score
            type: f_score
            value: 0.9686876979

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	es_spacy_ner_cds_trf
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("es_spacy_ner_cds_trf")
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

Accuracy

Type	Score
ENTS_F	97.27
ENTS_P	97.21
ENTS_R	97.32
TRANSFORMER_LOSS	399.1012151603
NER_LOSS	92.456780956